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Tibrewala R, Dutt T, Tong A, Ginocchio L, Lattanzi R, Keerthivasan MB, Baete SH, Chopra S, Lui YW, Sodickson DK, Chandarana H, Johnson PM. FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging. Sci Data 2024; 11:404. [PMID: 38643291 PMCID: PMC11032332 DOI: 10.1038/s41597-024-03252-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/11/2024] [Indexed: 04/22/2024] Open
Abstract
Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.
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Affiliation(s)
- Radhika Tibrewala
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA.
| | - Tarun Dutt
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Angela Tong
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Luke Ginocchio
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Riccardo Lattanzi
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
| | - Mahesh B Keerthivasan
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Siemens Medical Solutions USA, New York, NY, USA
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
| | - Sumit Chopra
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Patricia M Johnson
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
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Chung S, Bacon T, Rath JF, Alivar A, Coelho S, Amorapanth P, Fieremans E, Novikov DS, Flanagan SR, Bacon JH, Lui YW. Callosal Interhemispheric Communication in Mild Traumatic Brain Injury: A Mediation Analysis on WM Microstructure Effects. AJNR Am J Neuroradiol 2024:ajnr.A8213. [PMID: 38637026 DOI: 10.3174/ajnr.a8213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 01/27/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND PURPOSE Because the corpus callosum connects the left and right hemispheres and a variety of WM bundles across the brain in complex ways, damage to the neighboring WM microstructure may specifically disrupt interhemispheric communication through the corpus callosum following mild traumatic brain injury. Here we use a mediation framework to investigate how callosal interhemispheric communication is affected by WM microstructure in mild traumatic brain injury. MATERIALS AND METHODS Multishell diffusion MR imaging was performed on 23 patients with mild traumatic brain injury within 1 month of injury and 17 healthy controls, deriving 11 diffusion metrics, including DTI, diffusional kurtosis imaging, and compartment-specific standard model parameters. Interhemispheric processing speed was assessed using the interhemispheric speed of processing task (IHSPT) by measuring the latency between word presentation to the 2 hemivisual fields and oral word articulation. Mediation analysis was performed to assess the indirect effect of neighboring WM microstructures on the relationship between the corpus callosum and IHSPT performance. In addition, we conducted a univariate correlation analysis to investigate the direct association between callosal microstructures and IHSPT performance as well as a multivariate regression analysis to jointly evaluate both callosal and neighboring WM microstructures in association with IHSPT scores for each group. RESULTS Several significant mediators in the relationships between callosal microstructure and IHSPT performance were found in healthy controls. However, patients with mild traumatic brain injury appeared to lose such normal associations when microstructural changes occurred compared with healthy controls. CONCLUSIONS This study investigates the effects of neighboring WM microstructure on callosal interhemispheric communication in healthy controls and patients with mild traumatic brain injury, highlighting that neighboring noncallosal WM microstructures are involved in callosal interhemispheric communication and information transfer. Further longitudinal studies may provide insight into the temporal dynamics of interhemispheric recovery following mild traumatic brain injury.
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Affiliation(s)
- Sohae Chung
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Tamar Bacon
- Department of Neurology (T.B., J.H.B.), NY University Grossman School of Medicine, New York, New York
| | - Joseph F Rath
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University Grossman School of Medicine, New York, New York
| | - Alaleh Alivar
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Santiago Coelho
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Prin Amorapanth
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University Grossman School of Medicine, New York, New York
| | - Els Fieremans
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Dmitry S Novikov
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
| | - Steven R Flanagan
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University Grossman School of Medicine, New York, New York
| | - Joshua H Bacon
- Department of Neurology (T.B., J.H.B.), NY University Grossman School of Medicine, New York, New York
| | - Yvonne W Lui
- From the Department of Radiology (S. Chung, A.A., S. Coelho, E.F., D.S.N., Y.W.L.), Center for Advanced Imaging Innovation and Research, NY University Grossman School of Medicine, New York, New York
- Department of Radiology (S. Chung, A.A., S. Coehlo, E.F., D.S.N., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, NY University Grossman School of Medicine, New York, New York
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Dogra S, Arabshahi S, Wei J, Saidenberg L, Kang SK, Chung S, Laine A, Lui YW. Functional Connectivity Changes on Resting-State fMRI after Mild Traumatic Brain Injury: A Systematic Review. AJNR Am J Neuroradiol 2024:ajnr.A8204. [PMID: 38637022 DOI: 10.3174/ajnr.a8204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/22/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Mild traumatic brain injury is theorized to cause widespread functional changes to the brain. Resting-state fMRI may be able to measure functional connectivity changes after traumatic brain injury, but resting-state fMRI studies are heterogeneous, using numerous techniques to study ROIs across various resting-state networks. PURPOSE We systematically reviewed the literature to ascertain whether adult patients who have experienced mild traumatic brain injury show consistent functional connectivity changes on resting-state -fMRI, compared with healthy patients. DATA SOURCES We used 5 databases (PubMed, EMBASE, Cochrane Central, Scopus, Web of Science). STUDY SELECTION Five databases (PubMed, EMBASE, Cochrane Central, Scopus, and Web of Science) were searched for research published since 2010. Search strategies used keywords of "functional MR imaging" and "mild traumatic brain injury" as well as related terms. All results were screened at the abstract and title levels by 4 reviewers according to predefined inclusion and exclusion criteria. For full-text inclusion, each study was evaluated independently by 2 reviewers, with discordant screening settled by consensus. DATA ANALYSIS Data regarding article characteristics, cohort demographics, fMRI scan parameters, data analysis processing software, atlas used, data characteristics, and statistical analysis information were extracted. DATA SYNTHESIS Across 66 studies, 80 areas were analyzed 239 times for at least 1 time point, most commonly using independent component analysis. The most analyzed areas and networks were the whole brain, the default mode network, and the salience network. Reported functional connectivity changes varied, though there may be a slight trend toward decreased whole-brain functional connectivity within 1 month of traumatic brain injury and there may be differences based on the time since injury. LIMITATIONS Studies of military, sports-related traumatic brain injury, and pediatric patients were excluded. Due to the high number of relevant studies and data heterogeneity, we could not be as granular in the analysis as we would have liked. CONCLUSIONS Reported functional connectivity changes varied, even within the same region and network, at least partially reflecting differences in technical parameters, preprocessing software, and analysis methods as well as probable differences in individual injury. There is a need for novel rs-fMRI techniques that better capture subject-specific functional connectivity changes.
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Affiliation(s)
- Siddhant Dogra
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Soroush Arabshahi
- Department of Biomedical Engineering (S.A., A.L.), Department of Radiology, Columbia University, New York, New York
| | - Jason Wei
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Lucia Saidenberg
- Department of Neurology (L.S.), Department of Radiology. New York University Grossman School of Medicine, New York, New York
| | - Stella K Kang
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Sohae Chung
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Andrew Laine
- Department of Biomedical Engineering (S.A., A.L.), Department of Radiology, Columbia University, New York, New York
| | - Yvonne W Lui
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
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Arabshahi S, Chung S, Alivar A, Amorapanth PX, Flanagan SR, Foo FYA, Laine AF, Lui YW. A Comprehensive and Broad Approach to Resting-State Functional Connectivity in Adult Patients with Mild Traumatic Brain Injury. AJNR Am J Neuroradiol 2024:ajnr.A8193. [PMID: 38604737 DOI: 10.3174/ajnr.a8193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/12/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND PURPOSE Several recent works using resting-state fMRI suggest possible alterations of resting-state functional connectivity after mild traumatic brain injury. However, the literature is plagued by various analysis approaches and small study cohorts, resulting in an inconsistent array of reported findings. In this study, we aimed to investigate differences in whole-brain resting-state functional connectivity between adult patients with mild traumatic brain injury within 1 month of injury and healthy control subjects using several comprehensive resting-state functional connectivity measurement methods and analyses. MATERIALS AND METHODS A total of 123 subjects (72 patients with mild traumatic brain injury and 51 healthy controls) were included. A standard fMRI preprocessing pipeline was used. ROI/seed-based analyses were conducted using 4 standard brain parcellation methods, and the independent component analysis method was applied to measure resting-state functional connectivity. The fractional amplitude of low-frequency fluctuations was also measured. Group comparisons were performed on all measurements with appropriate whole-brain multilevel statistical analysis and correction. RESULTS There were no significant differences in age, sex, education, and hand preference between groups as well as no significant correlation between all measurements and these potential confounders. We found that each resting-state functional connectivity measurement revealed various regions or connections that were different between groups. However, after we corrected for multiple comparisons, the results showed no statistically significant differences between groups in terms of resting-state functional connectivity across methods and analyses. CONCLUSIONS Although previous studies point to multiple regions and networks as possible mild traumatic brain injury biomarkers, this study shows that the effect of mild injury on brain resting-state functional connectivity has not survived after rigorous statistical correction. A further study using subject-level connectivity analyses may be necessary due to both subtle and variable effects of mild traumatic brain injury on brain functional connectivity across individuals.
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Affiliation(s)
- Soroush Arabshahi
- From Biomedical Engineering Department (S.A., A.F.L.), Columbia University, New York, New York
| | - Sohae Chung
- Departments of Radiology (S.C., A.A., Y.W.L.), NYU Grossman School of Medicine, New York, New York
| | - Alaleh Alivar
- Departments of Radiology (S.C., A.A., Y.W.L.), NYU Grossman School of Medicine, New York, New York
| | - Prin X Amorapanth
- Rehabilitation Medicine (P.X.A., S.R.F.), NYU Grossman School of Medicine, New York, New York
| | - Steven R Flanagan
- Rehabilitation Medicine (P.X.A., S.R.F.), NYU Grossman School of Medicine, New York, New York
| | - Farng-Yang A Foo
- Department of Neurology (F.-Y.A.F.), NYU Grossman School of Medicine, New York, New York
| | - Andrew F Laine
- From Biomedical Engineering Department (S.A., A.F.L.), Columbia University, New York, New York
| | - Yvonne W Lui
- Departments of Radiology (S.C., A.A., Y.W.L.), NYU Grossman School of Medicine, New York, New York
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Schramm G, Filipovic M, Qian Y, Alivar A, Lui YW, Nuyts J, Boada F. Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction. Magn Reson Med 2024; 91:1404-1418. [PMID: 38044789 PMCID: PMC10916150 DOI: 10.1002/mrm.29936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE Sodium MRI is challenging because of the low tissue concentration of the 23 Na nucleus and its extremely fast biexponential transverse relaxation rate. In this article, we present an iterative reconstruction framework using dual-echo 23 Na data and exploiting anatomical prior information (AGR) from high-resolution, low-noise, 1 H MR images. This framework enables the estimation and modeling of the spatially varying signal decay due to transverse relaxation during readout (AGRdm), which leads to images of better resolution and reduced noise resulting in improved quantification of the reconstructed 23 Na images. METHODS The proposed framework was evaluated using reconstructions of 30 noise realizations of realistic simulations of dual echo twisted projection imaging (TPI) 23 Na data. Moreover, three dual echo 23 Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION AGR and AGRdm generate 23 Na images with high resolution, high levels of anatomical detail, and low levels of noise, potentially enabling high-quality 23 Na MR imaging at 3T.
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Affiliation(s)
- Georg Schramm
- Radiological Sciences Laboratory, School of Medicine, Stanford University, Stanford, California, USA
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Yongxian Qian
- Center for Biomedical Imaging, Department of Radiology, Grossman School of Medicine, New York University (NYU), New York, New York, USA
| | - Alaleh Alivar
- Center for Biomedical Imaging, Department of Radiology, Grossman School of Medicine, New York University (NYU), New York, New York, USA
| | - Yvonne W. Lui
- Center for Biomedical Imaging, Department of Radiology, Grossman School of Medicine, New York University (NYU), New York, New York, USA
| | - Johan Nuyts
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Fernando Boada
- Radiological Sciences Laboratory, School of Medicine, Stanford University, Stanford, California, USA
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Kadom N, Lasiecka ZM, Nemeth AJ, Rykken JB, Lui YW, Seidenwurm D. Patient Engagement in Neuroradiology: A Narrative Review and Case Studies. AJNR Am J Neuroradiol 2024; 45:250-255. [PMID: 38216301 DOI: 10.3174/ajnr.a8077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 01/14/2024]
Abstract
The field of patient engagement in radiology is evolving and offers ample opportunities for neuroradiologists to become involved. The patient journey can serve as a model that inspires patient engagement initiatives. The patient journey in radiology may be viewed in 5 stages: 1) awareness that an imaging test is needed, 2) considering having a specific imaging test, 3) access to imaging, 4) imaging service delivery, and 5) ongoing care. Here, we describe patient engagement opportunities based on literature review and paired with case studies by practicing neuroradiologists.
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Affiliation(s)
- Nadja Kadom
- From the Emory University School of Medicine (N.K.), Children's Healthcare of Atlanta, Atlanta, Georgia
| | | | - Alexander J Nemeth
- Northwestern University, Feinberg School of Medicine, Northwestern Memorial Hospital (A.J.N.), Chicago, Illinois
| | | | - Yvonne W Lui
- New York University, Grossman School of Medicine (Y.W.L.), New York, New York
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Coelho S, Liao Y, Szczepankiewicz F, Veraart J, Chung S, Lui YW, Novikov DS, Fieremans E. Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry. ArXiv 2024:arXiv:2402.17175v1. [PMID: 38463511 PMCID: PMC10925389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T 2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.
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Affiliation(s)
- Santiago Coelho
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Ying Liao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Sohae Chung
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Liao Y, Coelho S, Chen J, Ades-Aron B, Pang M, Osorio R, Shepherd T, Lui YW, Novikov DS, Fieremans E. Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI. ArXiv 2023:arXiv:2307.16386v2. [PMID: 37576119 PMCID: PMC10418545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.
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Chen J, Chung S, Li T, Fieremans E, Novikov DS, Wang Y, Lui YW. Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes. Neuroradiol J 2023; 36:693-701. [PMID: 37212469 PMCID: PMC10649530 DOI: 10.1177/19714009231177396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2023] Open
Abstract
PURPOSE Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI. METHODS 36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics. RESULTS Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81). CONCLUSION Feature selection and classification identifies diffusion metrics that characterize subconcussive RHI. Linear classifiers yield the best performance and mean diffusion, tissue microstructure complexity, and radial extra-axonal compartment diffusion (MD, MK, De,⊥) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.
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Affiliation(s)
- Junbo Chen
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Sohae Chung
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Tianhao Li
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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10
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Chung S, Fieremans E, Novikov DS, Lui YW. Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction. Res Sq 2023:rs.3.rs-3645723. [PMID: 38045398 PMCID: PMC10690318 DOI: 10.21203/rs.3.rs-3645723/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.
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Affiliation(s)
- Sohae Chung
- New York University Grossman School of Medicine
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11
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Johnson PM, Lui YW. The deep route to low-field MRI with high potential. Nature 2023; 623:700-701. [PMID: 37964114 DOI: 10.1038/d41586-023-03531-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
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12
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Li YT, Kuo DP, Tseng P, Chen YC, Cheng SJ, Wu CW, Hsieh LC, Chiang YH, Chung HW, Lui YW, Chen CY. Thalamocortical Coherence Predicts Persistent Postconcussive Symptoms. Prog Neurobiol 2023; 226:102464. [PMID: 37169275 DOI: 10.1016/j.pneurobio.2023.102464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 04/10/2023] [Accepted: 05/07/2023] [Indexed: 05/13/2023]
Abstract
The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.
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Affiliation(s)
- Yi-Tien Li
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Neuroscience Research Center, Taipei Medical University, Taipei 11031, Taiwan
| | - Duen-Pang Kuo
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Philip Tseng
- Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University, Taipei 11031, Taiwan; Brain and Consciousness Research Center, Shuang-Ho Hospital, Taipei Medical University, New Taipei 23561, Taiwan; Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan
| | - Yung-Chieh Chen
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Sho-Jen Cheng
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Changwei W Wu
- Graduate Institute of Mind, Brain, and Consciousness, Taipei Medical University, Taipei 11031, Taiwan; Brain and Consciousness Research Center, Shuang-Ho Hospital, Taipei Medical University, New Taipei 23561, Taiwan
| | - Li-Chun Hsieh
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Yung-Hsiao Chiang
- Neuroscience Research Center, Taipei Medical University, Taipei 11031, Taiwan; Center for Neurotrauma and Neuroregeneration, Taipei Medical University, Taipei 11031, Taiwan; Department of Neurosurgery, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Hsiao-Wen Chung
- Graduate Institute of Biomedical Electrics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Yvonne W Lui
- Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY, 10016, USA; Department of Radiology, NYU Grossman School of Medicine, New York University, New York, NY, 10016, USA
| | - Cheng-Yu Chen
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 11031, Taiwan.
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13
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Tibrewala R, Dutt T, Tong A, Ginocchio L, Keerthivasan MB, Baete SH, Chopra S, Lui YW, Sodickson DK, Chandarana H, Johnson PM. FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging. ArXiv 2023:arXiv:2304.09254v1. [PMID: 37131871 PMCID: PMC10153282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.
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14
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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15
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Kasmanoff N, Lee MD, Razavian N, Lui YW. Deep multi-task learning and random forest for series classification by pulse sequence type and orientation. Neuroradiology 2023; 65:77-87. [PMID: 35906437 PMCID: PMC9361920 DOI: 10.1007/s00234-022-03023-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 07/19/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content would aid post-processing and PACS viewing. Recent deep/machine learning efforts classify 5-8 basic brain MR sequences. We present an ensemble model combining a convolutional neural network and a random forest classifier to differentiate 25 brain sequences and image orientation. METHODS Series were grouped by descriptions into 25 sequences and 4 orientations. Dataset A, obtained from our institution, was divided into training (16,828 studies; 48,512 series; 112,028 images), validation (4746 studies; 16,612 series; 26,222 images) and test sets (6348 studies; 58,705 series; 3,314,018 images). Dataset B, obtained from a separate hospital, was used for out-of-domain external validation (1252 studies; 2150 series; 234,944 images). We developed an ensemble model combining a 2D convolutional neural network with a custom multi-task learning architecture and random forest classifier trained on DICOM metadata to classify sequence and orientation by series. RESULTS The neural network, random forest, and ensemble achieved 95%, 97%, and 98% overall sequence accuracy on dataset A, and 98%, 99%, and 99% accuracy on dataset B, respectively. All models achieved > 99% orientation accuracy on both datasets. CONCLUSION The ensemble model for series identification accommodates the complexity of brain MRI studies in state-of-the-art clinical practice. Expanding on previous work demonstrating proof-of-concept, our approach is more comprehensive with greater sequence diversity and orientation classification.
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Affiliation(s)
- Noah Kasmanoff
- Center for Data Science, New York University, New York, NY USA
| | - Matthew D. Lee
- Department of Radiology, NYU Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - Narges Razavian
- Center for Data Science, New York University, New York, NY USA ,Department of Radiology, NYU Grossman School of Medicine, New York University, New York, NY 10016 USA ,Department of Population Health, NYU Grossman School of Medicine, New York University, New York, NY USA
| | - Yvonne W. Lui
- Department of Radiology, NYU Grossman School of Medicine, New York University, New York, NY 10016 USA
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16
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Radmanesh A, Muckley MJ, Murrell T, Lindsey E, Sriram A, Knoll F, Sodickson DK, Lui YW. Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRI. Radiol Artif Intell 2022; 4:e210313. [PMID: 36523647 PMCID: PMC9745443 DOI: 10.1148/ryai.210313] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 10/05/2022] [Accepted: 10/18/2022] [Indexed: 06/03/2023]
Abstract
Purpose To explore the limits of deep learning-based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. Materials and Methods In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold along a single phase-encoded direction for two-dimensional [2D] sections) on the fastMRI test set collected at New York University, consisting of 558 image volumes. In a sample of 69 volumes, reconstructions were classified by radiologists for identification of two clinical thresholds: (a) general-purpose diagnostic imaging and (b) potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method. Results Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model's ability to produce images substantially distinct from the training set, even at 100× acceleration. Conclusion For 2D brain images using deep learning-based reconstruction, maximum acceleration for potential screening was three to four times higher than that for diagnostic general-purpose imaging.Keywords: MRI Reconstruction, High Acceleration, Deep Learning, Screening, Out of Distribution Supplemental material is available for this article. © RSNA, 2022.
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18
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Munawar K, Raz E, Dehkharghani S, Fatterpekar GM, Block TK, Lui YW. Radial spoiled gradient T1 weighted imaging of the internal auditory canal: Is Scarpa's ganglion now an expected finding and source of fundal enhancement? Neuroradiol J 2022; 35:563-565. [PMID: 35015577 PMCID: PMC9513923 DOI: 10.1177/19714009211067407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
StarVIBE is a 3D gradient-echo sequence with a radial, stack-of-stars acquisition having spatial resolution and tissue contrast. With newer sequences, it is important to be familiar with sequence tissue contrasts and appearance of anatomical variants. We evaluated 450 patients utilizing this sequence; 35 patients demonstrated fluffy "cotton wool" enhancement at the internal auditory canal fundus without clear pathology. We favor this represents anatomic neurovascular enhancement that StarVIBE is sensitive to and is a touch-me-not finding.
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Affiliation(s)
- Kamran Munawar
- Department of Radiology, New York University Medical Center, New York, NY, USA
| | - Eytan Raz
- Department of Radiology, New York University Medical Center, New York, NY, USA
| | - Seena Dehkharghani
- Department of Radiology, New York University Medical Center, New York, NY, USA
| | | | - Tobias K Block
- Department of Radiology, New York University Medical Center, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, New York University Medical Center, New York, NY, USA
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19
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Chung S, Chen J, Li T, Wang Y, Lui YW. Investigating Brain White Matter in Football Players with and without Concussion Using a Biophysical Model from Multishell Diffusion MRI. AJNR Am J Neuroradiol 2022; 43:823-828. [PMID: 35589140 DOI: 10.3174/ajnr.a7522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/04/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE There have been growing concerns around potential risks related to sports-related concussion and contact sport exposure to repetitive head impacts in young athletes. Here we investigate WM microstructural differences between collegiate football players with and without sports-related concussion. MATERIALS AND METHODS The study included 78 collegiate athletes (24 football players with sports-related concussion, 26 football players with repetitive head impacts, and 28 non-contact-sport control athletes), available through the Federal Interagency Traumatic Brain Injury Research registry. Diffusion metrics of diffusion tensor/kurtosis imaging and WM tract integrity were calculated. Tract-Based Spatial Statistics and post hoc ROI analyses were performed to test group differences. RESULTS Significantly increased axial kurtosis in those with sports-related concussion compared with controls was observed diffusely across the whole-brain WM, and some focal areas demonstrated significantly higher mean kurtosis and extra-axonal axial diffusivity in sports-related concussion. The extent of significantly different WM regions decreased across time points and remained present primarily in the corpus callosum. Similar differences in axial kurtosis were found between the repetitive head impact and control groups. Other significant differences were seen at unrestricted return-to-play with lower radial kurtosis and intra-axonal diffusivity in those with sports-related concussion compared with the controls, mainly restricted to the posterior callosum. CONCLUSIONS This study highlights the fact that there are differences in diffusion microstructure measures that are present not only between football players with sports-related injuries and controls, but that there are also measurable differences between football players with repetitive head impacts and controls. This work reinforces previous work showing that the corpus callosum is specifically implicated in sports-related concussion and also suggests this to be true for repetitive head impacts.
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Affiliation(s)
- S Chung
- From the Department of Radiology (S.C., Y.W.L.), Center for Advanced Imaging Innovation and Research .,Department of Radiology (S.C., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, New York
| | - J Chen
- Department of Electrical and Computer Engineering (J.C., T.L., Y.W.), New York University Tandon School of Engineering, Brooklyn, New York
| | - T Li
- Department of Electrical and Computer Engineering (J.C., T.L., Y.W.), New York University Tandon School of Engineering, Brooklyn, New York
| | - Y Wang
- Department of Electrical and Computer Engineering (J.C., T.L., Y.W.), New York University Tandon School of Engineering, Brooklyn, New York
| | - Y W Lui
- From the Department of Radiology (S.C., Y.W.L.), Center for Advanced Imaging Innovation and Research.,Department of Radiology (S.C., Y.W.L.), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, New York
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20
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Gibson E, Georgescu B, Ceccaldi P, Trigan PH, Yoo Y, Das J, Re TJ, Rs V, Balachandran A, Eibenberger E, Chekkoury A, Brehm B, Bodanapally UK, Nicolaou S, Sanelli PC, Schroeppel TJ, Flohr T, Comaniciu D, Lui YW. Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans. Radiol Artif Intell 2022; 4:e210115. [PMID: 35652116 DOI: 10.1148/ryai.210115] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 03/01/2022] [Accepted: 04/01/2022] [Indexed: 11/11/2022]
Abstract
Purpose To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools. Materials and Methods This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; n = 25 946) and evaluation (n = 2947) and three "external" centers for calibration (n = 400) and evaluation (n = 16 764). Internal centers contributed developmental data, whereas external centers did not. Deep neural networks predicted the presence of ICH and subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and/or epidural hemorrhage) and segmentations per case. Two ICH confidence scores are discussed: a calibrated classifier entropy score and a Dempster-Shafer score. Evaluation was completed by using receiver operating characteristic curve analysis and report turnaround time (RTAT) modeling on the evaluation set and on confidence score-defined subsets using bootstrapping. Results The areas under the receiver operating characteristic curve for ICH were 0.97 (0.97, 0.98) and 0.95 (0.94, 0.95) on internal and external center data, respectively. On 80% of the data stratified by calibrated classifier and Dempster-Shafer scores, the system improved the Youden indexes, increasing them from 0.84 to 0.93 (calibrated classifier) and from 0.84 to 0.92 (Dempster-Shafer) for internal centers and increasing them from 0.78 to 0.88 (calibrated classifier) and from 0.78 to 0.89 (Dempster-Shafer) for external centers (P < .001). Models estimated shorter RTAT for AI-prioritized worklists with confidence measures than for AI-prioritized worklists without confidence measures, shortening RTAT by 27% (calibrated classifier) and 27% (Dempster-Shafer) for internal centers and shortening RTAT by 25% (calibrated classifier) and 27% (Dempster-Shafer) for external centers (P < .001). Conclusion AI that provided statistical confidence measures for ICH detection on NCCT scans reliably detected and subtyped hemorrhages, identified high-confidence predictions, and improved worklist prioritization in simulation.Keywords: CT, Head/Neck, Hemorrhage, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.
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Affiliation(s)
- Eli Gibson
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Bogdan Georgescu
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Pascal Ceccaldi
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Pierre-Hugo Trigan
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Youngjin Yoo
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Jyotipriya Das
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Thomas J Re
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Vishwanath Rs
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Abishek Balachandran
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Eva Eibenberger
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Andrei Chekkoury
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Barbara Brehm
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Uttam K Bodanapally
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Savvas Nicolaou
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Pina C Sanelli
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Thomas J Schroeppel
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Thomas Flohr
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Dorin Comaniciu
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
| | - Yvonne W Lui
- Department of Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (E.G., B.G., P.C., P.H.T., Y.Y., J.D., T.J.R., D.C.); Department of Digital Technology and Innovation, Siemens Healthineers, Bangalore, India (V.R.S., A.B.); Department of Computed Tomography, Siemens Healthineers, Forchheim, Germany (E.E., A.C., B.B., T.F.); Department of Radiology, University of Maryland Medical Center, Baltimore, Md (U.K.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Northwell Health, New York, NY (P.C.S.); Department of Surgery, UCHealth Memorial Hospital, Colorado Springs, Colo (T.J.S.); and Department of Radiology, NYU Langone Health, New York University School of Medicine, New York, NY (Y.W.L.)
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21
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Zhao R, Yaman B, Zhang Y, Stewart R, Dixon A, Knoll F, Huang Z, Lui YW, Hansen MS, Lungren MP. fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data. Sci Data 2022; 9:152. [PMID: 35383186 PMCID: PMC8983757 DOI: 10.1038/s41597-022-01255-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/11/2022] [Indexed: 12/02/2022] Open
Abstract
Improving speed and image quality of Magnetic Resonance Imaging (MRI) using deep learning reconstruction is an active area of research. The fastMRI dataset contains large volumes of raw MRI data, which has enabled significant advances in this field. While the impact of the fastMRI dataset is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks and exploring important questions regarding rendering of specific pathology using such novel approaches. This work introduces fastMRI+, which consists of 16154 subspecialist expert bounding box annotations and 13 study-level labels for 22 different pathology categories on the fastMRI knee dataset, and 7570 subspecialist expert bounding box annotations and 643 study-level labels for 30 different pathology categories for the fastMRI brain dataset. The fastMRI+ dataset is open access and aims to support further research and advancement of medical imaging in MRI reconstruction and beyond. Measurement(s) | Pathotology annotations in knee and brain MRI images | Technology Type(s) | Expert delineation |
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Affiliation(s)
- Ruiyang Zhao
- Microsoft Research, Redmond, USA.,University of Wisconsin-Madison, Department of Radiology, Madison, USA.,University of Wisconsin-Madison, Department of Medical Physics, Madison, USA
| | - Burhaneddin Yaman
- Microsoft Research, Redmond, USA.,University of Minnesota, Department of Electrical and Computer Engineering, Minneapolis, USA
| | - Yuxin Zhang
- Microsoft Research, Redmond, USA.,University of Wisconsin-Madison, Department of Radiology, Madison, USA.,University of Wisconsin-Madison, Department of Medical Physics, Madison, USA
| | - Russell Stewart
- Microsoft Research, Redmond, USA.,Stanford University, School of Medicine, Stanford, USA
| | - Austin Dixon
- Microsoft Research, Redmond, USA.,Duke University, School of Medicine, Durham, USA
| | - Florian Knoll
- New York University, School of Medicine, New York, USA
| | | | - Yvonne W Lui
- New York University, School of Medicine, New York, USA
| | | | - Matthew P Lungren
- Microsoft Research, Redmond, USA.,Stanford University, School of Medicine, Stanford, USA
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22
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Lee MD, Elsayed M, Chopra S, Lui YW. A No-Math Primer on the Principles of Machine Learning for Radiologists. Semin Ultrasound CT MR 2022; 43:133-141. [PMID: 35339253 DOI: 10.1053/j.sult.2022.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Machine learning is becoming increasingly important in both research and clinical applications in radiology due to recent technological developments, particularly in deep learning. As these technologies are translated toward clinical practice, there is a need for radiologists and radiology trainees to understand the basic principles behind them. This primer provides an accessible introduction to the vocabulary and concepts that are central to machine learning and relevant to the radiologist.
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Affiliation(s)
- Matthew D Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Mohammed Elsayed
- Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Sumit Chopra
- Department of Radiology, NYU Grossman School of Medicine, New York, NY; Courant Institute of Mathematical Sciences, New York University, New York, NY
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY.
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23
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Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging 2021; 40:2306-2317. [PMID: 33929957 PMCID: PMC8428775 DOI: 10.1109/tmi.2021.3075856] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.
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24
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Hoch MJ, Bruno M, Pacione D, Lui YW, Fieremans E, Shepherd TM. Simultaneous Multislice for Accelerating Diffusion MRI in Clinical Neuroradiology Protocols. AJNR Am J Neuroradiol 2021; 42:1437-1443. [PMID: 33985946 DOI: 10.3174/ajnr.a7140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 03/04/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND AND PURPOSE Diffusion MR imaging sequences essential for clinical neuroradiology imaging protocols may be accelerated with simultaneous multislice acquisitions. We tested whether simultaneous multislice-accelerated diffusion data were clinically equivalent to standard acquisitions. MATERIALS AND METHODS In this retrospective study, clinical diffusion sequences obtained before and after implementation of 2-slice simultaneous multislice acceleration and an altered diffusion gradient sampling scheme using the same 3T MRI scanner and 20-channel coil (n = 25 per group) were independently and blindly evaluated by 2 neuroradiologists for perceived quality, artifacts, and overall diagnostic utility. Diffusion tractography was performed in 13 patients both with and without 2-slice simultaneous multislice acceleration (b = 0, 1000, 2000 s/mm2; 60 directions). The corticospinal tract and arcuate fasciculus ipsilateral to the lesion were generated using the same ROIs and then blindly assessed by a neurosurgeon for anatomic fidelity, perceived quality, and impact on surgical management. Tract volumes were compared for spatial overlap. RESULTS Two-slice simultaneous multislice diffusion reduced acquisition times from 141 to 45 seconds for routine diffusion and from 7.5 to 5.9 minutes for diffusion tractography using 3T MR imaging. The simultaneous multislice-accelerated diffusion sequence was rated equivalent for diagnostic utility despite reductions to perceived image quality. Simultaneous multislice-accelerated diffusion tractography was rated clinically equivalent. Dice similarity coefficients between routine and simultaneous multislice-generated corticospinal tract and arcuate fasciculus tract volumes were 0.78 (SD, 0.03) and 0.71 (SD, 0.05), respectively. CONCLUSIONS Two-slice simultaneous multislice diffusion appeared clinically equivalent for standard acquisitions and diffusion tractography. Simultaneous multislice makes it feasible to acquire higher angular and q-space-resolution diffusion acquisitions required for translating advanced diffusion models into clinical practice.
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Affiliation(s)
- M J Hoch
- From the Department of Radiology (M.J.H.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - M Bruno
- Department of Radiology (M.B., Y.W.L., E.F., T.M.S.), New York University Langone School of Medicine, New York, New York
| | - D Pacione
- Department of Neurosurgery (D.P.), New York University Langone School of Medicine, New York, New York
| | - Y W Lui
- Department of Radiology (M.B., Y.W.L., E.F., T.M.S.), New York University Langone School of Medicine, New York, New York
| | - E Fieremans
- Department of Radiology (M.B., Y.W.L., E.F., T.M.S.), New York University Langone School of Medicine, New York, New York.,Center for Advanced Imaging Innovation and Research (E.F.), New York, New York
| | - T M Shepherd
- Department of Radiology (M.B., Y.W.L., E.F., T.M.S.), New York University Langone School of Medicine, New York, New York
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25
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Shamout FE, Shen Y, Wu N, Kaku A, Park J, Makino T, Jastrzębski S, Witowski J, Wang D, Zhang B, Dogra S, Cao M, Razavian N, Kudlowitz D, Azour L, Moore W, Lui YW, Aphinyanaphongs Y, Fernandez-Granda C, Geras KJ. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit Med 2021; 4:80. [PMID: 33980980 PMCID: PMC8115328 DOI: 10.1038/s41746-021-00453-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/19/2021] [Indexed: 12/23/2022] Open
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
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Affiliation(s)
| | - Yiqiu Shen
- Center for Data Science, New York University, New York, NY, USA
| | - Nan Wu
- Center for Data Science, New York University, New York, NY, USA
| | - Aakash Kaku
- Center for Data Science, New York University, New York, NY, USA
| | - Jungkyu Park
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA
| | - Taro Makino
- Center for Data Science, New York University, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Stanisław Jastrzębski
- Center for Data Science, New York University, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
| | - Jan Witowski
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
| | - Duo Wang
- Department of Population Health, NYU Langone Health, New York, NY, USA
| | - Ben Zhang
- Department of Population Health, NYU Langone Health, New York, NY, USA
| | - Siddhant Dogra
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Meng Cao
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Narges Razavian
- Center for Data Science, New York University, New York, NY, USA
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Department of Population Health, NYU Langone Health, New York, NY, USA
| | - David Kudlowitz
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Lea Azour
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - William Moore
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Langone Health, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
| | | | - Carlos Fernandez-Granda
- Center for Data Science, New York University, New York, NY, USA
- Department of Mathematics, Courant Institute, New York University, New York, NY, USA
| | - Krzysztof J Geras
- Center for Data Science, New York University, New York, NY, USA.
- Department of Radiology, NYU Langone Health, New York, NY, USA.
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA.
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26
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Dogra S, Borja MJ, Lui YW. Impact of Kidney Function on CNS Gadolinium Deposition in Patients Receiving Repeated Doses of Gadobutrol. AJNR Am J Neuroradiol 2021; 42:824-830. [PMID: 33632738 DOI: 10.3174/ajnr.a7031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/24/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Studies associate repeat gadolinium-based contrast agent administration with T1 shortening in the dentate nucleus and globus pallidus, indicating CNS gadolinium deposition, most strongly with linear agents but also reportedly with macrocyclics. Renal impairment effects on long-term CNS gadolinium deposition remain underexplored. We investigated the relationship between signal intensity changes and renal function in patients who received ≥10 administrations of the macrocyclic agent gadobutrol. MATERIALS AND METHODS Patients who underwent ≥10 brain MR imaging examinations with administration of intravenous gadobutrol between February 1, 2014, and January 1, 2018, were included in this retrospective study. Dentate nucleus-to-pons and globus pallidus-to-thalamus signal intensity ratios were calculated, and correlations were calculated between the estimated glomerular filtration rate (minimum and mean) and the percentage change in signal intensity ratios from the first to last scan. Partial correlations were calculated to control for potential confounders. RESULTS One hundred thirty-one patients (73 women; mean age at last scan, 55.9 years) showed a mean percentage change of the dentate nucleus-to-pons of 0.31%, a mean percentage change of the globus pallidus-to-thalamus of 0.15%, a mean minimum estimated glomerular filtration rate of 69.65 (range, 10.16-132.26), and a mean average estimated glomerular filtration rate at 89.48 (range, 38.24-145.93). No significant association was found between the estimated glomerular filtration rate and percentage change of the dentate nucleus-to-pons (minimum estimated glomerular filtration rate, r = -0.09, P = .28; average estimated glomerular filtration rate, r = -0.09, P = .30,) or percentage change of the globus pallidus-to-thalamus (r = 0.07, P = .43; r = 0.07, P = .40). When we controlled for age, sex, number of scans, and total dose, there were no significant associations between the estimated glomerular filtration rate and the percentage change of the dentate nucleus-to-pons (r = 0.16, P = .07; r = 0.15, P = .08) or percentage change of the globus pallidus-to-thalamus (r = -0.14, P = .12; r = -0.15, P = .09). CONCLUSIONS In patients receiving an average of 12 intravenous gadobutrol administrations, no correlation was found between renal function and signal intensity ratio changes, even in those with mild or moderate renal impairment.
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Affiliation(s)
- S Dogra
- From the Department of Radiology, New York University Langone Health, New York, New York
| | - M J Borja
- From the Department of Radiology, New York University Langone Health, New York, New York
| | - Y W Lui
- From the Department of Radiology, New York University Langone Health, New York, New York
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27
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Muckley MJ, Ades-Aron B, Papaioannou A, Lemberskiy G, Solomon E, Lui YW, Sodickson DK, Fieremans E, Novikov DS, Knoll F. Training a neural network for Gibbs and noise removal in diffusion MRI. Magn Reson Med 2021; 85:413-428. [PMID: 32662910 PMCID: PMC7722184 DOI: 10.1002/mrm.28395] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 05/29/2020] [Accepted: 06/03/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. RESULTS Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. CONCLUSIONS The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
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Affiliation(s)
- Matthew J. Muckley
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Benjamin Ades-Aron
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Antonios Papaioannou
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Gregory Lemberskiy
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Eddy Solomon
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Yvonne W. Lui
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S. Novikov
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Florian Knoll
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
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28
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Shamout FE, Shen Y, Wu N, Kaku A, Park J, Makino T, Jastrzębski S, Witowski J, Wang D, Zhang B, Dogra S, Cao M, Razavian N, Kudlowitz D, Azour L, Moore W, Lui YW, Aphinyanaphongs Y, Fernandez-Granda C, Geras KJ. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. ArXiv 2020:arXiv:2008.01774v2. [PMID: 32793769 PMCID: PMC7418753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
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29
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Mowry EM, Bermel RA, Williams JR, Benzinger TLS, de Moor C, Fisher E, Hersh CM, Hyland MH, Izbudak I, Jones SE, Kieseier BC, Kitzler HH, Krupp L, Lui YW, Montalban X, Naismith RT, Nicholas JA, Pellegrini F, Rovira A, Schulze M, Tackenberg B, Tintore M, Tivarus ME, Ziemssen T, Rudick RA. Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS). Front Neurol 2020; 11:632. [PMID: 32849170 PMCID: PMC7426489 DOI: 10.3389/fneur.2020.00632] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 05/28/2020] [Indexed: 12/31/2022] Open
Abstract
Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). This paper describes the initial implementation of MS PATHS and initial patient characteristics. Methods: MS PATHS is an ongoing initiative conducted in 10 healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500, at least one Siemens 3T magnetic resonance imaging scanner, and willingness to standardize patient assessments, share standardized data for research, and offer universal enrolment to capture a representative sample. The eligible participants have diagnosis of MS, including clinically isolated syndrome, and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history, patient-reported outcomes, and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, the patients contribute DNA, RNA, and serum for future research. The clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research. Results: As of August 5, 2019, MS PATHS enrolment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. The average patient contributed 15.6 person-months of follow-up (95% CI: 15.5–15.8); overall, 166,158 person-months of follow-up have been accumulated. Those with relapsing–remitting MS demonstrated more demographic heterogeneity than the participants in six randomized phase 3 MS treatment trials. Across sites, a significant variation was observed in the follow-up frequency and the patterns of disease-modifying therapy use. Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, and interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.
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Affiliation(s)
- Ellen M Mowry
- Johns Hopkins University, Baltimore, MD, United States
| | | | | | | | | | | | - Carrie M Hersh
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Megan H Hyland
- University of Rochester Medical Center, Rochester, NY, United States
| | - Izlem Izbudak
- Johns Hopkins University, Baltimore, MD, United States
| | | | | | - Hagen H Kitzler
- Center of Clinical Neuroscience, University Clinic Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Lauren Krupp
- New York University, New York, NY, United States
| | - Yvonne W Lui
- New York University, New York, NY, United States
| | | | | | | | | | - Alex Rovira
- Vall d'Hebron University Hospital, Barcelona, Spain
| | | | | | - Mar Tintore
- Vall d'Hebron University Hospital, Barcelona, Spain
| | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, University Clinic Carl Gustav Carus, TU Dresden, Dresden, Germany
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30
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Shamout FE, Shen Y, Wu N, Kaku A, Park J, Makino T, Jastrzębski S, Witowski J, Wang D, Zhang B, Dogra S, Cao M, Razavian N, Kudlowitz D, Azour L, Moore W, Lui YW, Aphinyanaphongs Y, Fernandez-Granda C, Geras KJ. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. ArXiv 2020:2008.01774. [PMID: 32793769 DOI: 10.48550/arxiv.2005.11475] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Revised: 11/04/2020] [Indexed: 05/27/2023]
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
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Lui YW, Chang PD, Zaharchuk G, Barboriak DP, Flanders AE, Wintermark M, Hess CP, Filippi CG. Artificial Intelligence in Neuroradiology: Current Status and Future Directions. AJNR Am J Neuroradiol 2020; 41:E52-E59. [PMID: 32732276 PMCID: PMC7658873 DOI: 10.3174/ajnr.a6681] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.
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Affiliation(s)
- Y W Lui
- From the Department of Radiology (Y.W.L.), New York University Langone Medical Center, New York, New York
| | - P D Chang
- Department of Radiology (P.D.C.), University of California Irvine Health Medical Center, Orange, California
| | - G Zaharchuk
- Department of Neuroradiology (G.Z., M.W.), Stanford University, Stanford, California
| | - D P Barboriak
- Department of Radiology (D.P.B.), Duke University Medical Center, Durham, North Carolina
| | - A E Flanders
- Department of Radiology (A.E.F.), Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | - M Wintermark
- Department of Neuroradiology (G.Z., M.W.), Stanford University, Stanford, California
| | - C P Hess
- Department of Radiology and Biomedical Imaging (C.P.H.), University of California, San Francisco, San Francisco, California
| | - C G Filippi
- Department of Radiology (C.G.F.), Northwell Health, New York, New York.
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Chung S, Storey P, Shepherd TM, Lui YW. MR Susceptibility Imaging with a Short TE (MR-SISET): A Clinically Feasible Technique to Resolve Thalamic Nuclei. AJNR Am J Neuroradiol 2020; 41:1629-1631. [PMID: 32675340 DOI: 10.3174/ajnr.a6683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 05/11/2020] [Indexed: 11/07/2022]
Abstract
The thalamus consists of several functionally distinct nuclei, some of which serve as targets for functional neurosurgery. Visualization of such nuclei is a major challenge due to their low signal contrast on conventional imaging. We introduce MR susceptibility imaging with a short TE, leveraging susceptibility differences among thalamic nuclei, to automatically delineate 15 thalamic subregions. The technique has the potential to enable direct targeting of thalamic nuclei for functional neurosurgical guidance.
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Affiliation(s)
- S Chung
- From the Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York.
| | - P Storey
- From the Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York
| | - T M Shepherd
- From the Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York
| | - Y W Lui
- From the Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York
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Radmanesh A, Derman A, Lui YW, Raz E, Loh JP, Hagiwara M, Borja MJ, Zan E, Fatterpekar GM. COVID-19-associated Diffuse Leukoencephalopathy and Microhemorrhages. Radiology 2020; 297:E223-E227. [PMID: 32437314 PMCID: PMC7507998 DOI: 10.1148/radiol.2020202040] [Citation(s) in RCA: 192] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Diffuse leukoencephalopathy and juxtacortical and/or callosal microhemorrhages were brain imaging features in critically ill patients with coronavirus disease 2019. Coronavirus disease 2019 (COVID-19) has been reported in association with a variety of brain imaging findings such as ischemic infarct, hemorrhage, and acute hemorrhagic necrotizing encephalopathy. Herein, the authors report brain imaging features in 11 critically ill patients with COVID-19 with persistently diminished mental status who underwent MRI between April 5 and April 25, 2020. These imaging features include (a) confluent T2 hyperintensity and mild restricted diffusion in bilateral supratentorial deep and subcortical white matter (in 10 of 11 patients) and (b) multiple punctate microhemorrhages in juxtacortical and callosal white matter (in seven of 11 patients). The authors also discuss potential pathogeneses. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Alireza Radmanesh
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Anna Derman
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Yvonne W Lui
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Eytan Raz
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - John P Loh
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Mari Hagiwara
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Maria J Borja
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Elcin Zan
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Girish M Fatterpekar
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
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Lotan E, Tschider C, Sodickson DK, Caplan AL, Bruno M, Zhang B, Lui YW. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future. J Am Coll Radiol 2020; 17:1159-1162. [PMID: 32360449 DOI: 10.1016/j.jacr.2020.04.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/02/2020] [Accepted: 04/07/2020] [Indexed: 10/24/2022]
Affiliation(s)
- Eyal Lotan
- Department of Radiology, New York University School of Medicine, New York, New York
| | - Charlotte Tschider
- Beazley Institute for Health Law & Policy, Loyola University Chicago School of Law, Chicago, Illinois; Owner/ Principal for Cybersimple Security, Minneapolis, Minnesota
| | - Daniel K Sodickson
- Vice Chair for Research, Department of Radiology, Department of Radiology, New York University School of Medicine, New York, New York; Director of the Bernard and Irene Schwartz Center for Biomedical Imaging, New York, New York; Co-Director of Tech4Health New York University, New York, New York; Vice Chair for Research, Department of Radiology, Department of Radiology, New York University School of Medicine, New York, New York; Director of the Bernard and Irene Schwartz Center for Biomedical Imaging, New York, New York; Co-Director of Tech4Health New York University, New York, New York
| | - Arthur L Caplan
- Director of the Division of Medical Ethics, New York University, New York, New York
| | - Mary Bruno
- Advanced Practice Specialist, New York University School of Medicine, New York, New York
| | - Ben Zhang
- Associate Chair of Artificial Intelligence, Department of Radiology, New York University School Langone Health, New York, New York
| | - Yvonne W Lui
- Associate Chair of Artificial Intelligence, Department of Radiology, New York University School Langone Health, New York, New York.
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35
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Lin DJ, Johnson PM, Knoll F, Lui YW. Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians. J Magn Reson Imaging 2020; 53:1015-1028. [PMID: 32048372 DOI: 10.1002/jmri.27078] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 01/15/2020] [Accepted: 01/17/2020] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
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Affiliation(s)
- Dana J Lin
- Department of Radiology, NYU School of Medicine/NYU Langone Health, New York, New York, USA
| | - Patricia M Johnson
- Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Florian Knoll
- Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Yvonne W Lui
- Department of Radiology, NYU School of Medicine/NYU Langone Health, New York, New York, USA
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Knoll F, Zbontar J, Sriram A, Muckley MJ, Bruno M, Defazio A, Parente M, Geras KJ, Katsnelson J, Chandarana H, Zhang Z, Drozdzalv M, Romero A, Rabbat M, Vincent P, Pinkerton J, Wang D, Yakubova N, Owens E, Zitnick CL, Recht MP, Sodickson DK, Lui YW. fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. Radiol Artif Intell 2020; 2:e190007. [PMID: 32076662 DOI: 10.1148/ryai.2020190007] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/24/2019] [Accepted: 08/29/2019] [Indexed: 11/11/2022]
Abstract
A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
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Affiliation(s)
- Florian Knoll
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Jure Zbontar
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Anuroop Sriram
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Matthew J Muckley
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Mary Bruno
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Aaron Defazio
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Marc Parente
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Krzysztof J Geras
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Joe Katsnelson
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Hersh Chandarana
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Zizhao Zhang
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Michal Drozdzalv
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Adriana Romero
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Michael Rabbat
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Pascal Vincent
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - James Pinkerton
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Duo Wang
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Nafissa Yakubova
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Erich Owens
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - C Lawrence Zitnick
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Michael P Recht
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Daniel K Sodickson
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
| | - Yvonne W Lui
- Department of Radiology, NYU School of Medicine, 650 First Ave, New York, NY 10016 (F.K., M.J.M., M.B., M.P., K.J.G., J.K., H.C., D.W., N.Y., M.P.R., D.K.S., Y.W.L.); Department of Artificial Intelligence Research, Facebook, Menlo Park, Calif (J.Z., A.S., J.P., E.O., C.L.Z.); Department of Artificial Intelligence Research, Facebook, New York, NY (A.D.); Center for Data Science, New York University, New York, NY (K.J.G.); Department of Artificial Intelligence Research, Facebook, Montreal, Canada (M.D., A.R., M.R., P.V.); and Department of Computer Science, University of Florida, Gainesville, Fla (Z.Z.)
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Davitz MS, Gonen O, Tal A, Babb JS, Lui YW, Kirov II. Quantitative multivoxel proton MR spectroscopy for the identification of white matter abnormalities in mild traumatic brain injury: Comparison between regional and global analysis. J Magn Reson Imaging 2019; 50:1424-1432. [PMID: 30868703 PMCID: PMC6744359 DOI: 10.1002/jmri.26718] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/26/2019] [Accepted: 02/27/2019] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND 3D brain proton MR spectroscopic imaging (1 H MRSI) facilitates simultaneous metabolic profiling of multiple loci, at higher, sub-1 cm3 , spatial resolution than single-voxel 1 H MRS with the ability to separate tissue-type partial volume contribution(s). PURPOSE To determine if: 1) white matter (WM) damage in mild traumatic brain injury (mTBI) is homogeneously diffuse, or if specific regions are more affected; 2) partial-volume-corrected, structure-specific 1 H MRSI voxel averaging is sensitive to regional WM metabolic abnormalities. STUDY TYPE Retrospective cross-sectional cohort study. POPULATION Twenty-seven subjects: 15 symptomatic mTBI patients, 12 matched controls. FIELD STRENGTH/SEQUENCE 3T using 3D 1 H MRSI over a 360-cm3 volume of interest (VOI) centered over the corpus callosum, partitioned into 480 voxels, each 0.75 cm3 . ASSESSMENT N-acetyl-aspartate (NAA), creatine, choline, and myo-inositol concentrations estimated in predominantly WM regions: body, genu, and splenium of the corpus callosum, corona radiata, frontal, and occipital WM. STATISTICAL TESTS Analysis of covariance (ANCOVA) to compare patients with controls in terms of regional concentrations. The effect sizes (Cohen's d) of the mean differences were compared across regions and with previously published global data obtained with linear regression of the WM over the entire VOI in the same dataset. RESULTS Despite patients' global VOI WM NAA being significantly lower than the controls', no regional differences were observed for any metabolite. Regional NAA comparisons, however, were all unidirectional (patients' NAA concentrations < controls') within a narrow range: 0.3 ≤ Cohen's d ≤ 0.6. DATA CONCLUSION Since the patient group was symptomatic and exhibiting global WM NAA deficits, these findings suggest: 1) diffuse axonal mTBI damage; that is 2) below the 1 H MRSI detection threshold in small regions. Therefore, larger, ie, more sensitive, single-voxel 1 H MRS, placed anywhere in WM regions, may be well suited for mTBI 1 H MRS studies, given that these results are confirmed in other cohorts. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1424-1432.
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Affiliation(s)
- Matthew S. Davitz
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, USA
| | - Oded Gonen
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, USA
| | - Assaf Tal
- Department of Chemical Physics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - James S. Babb
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, USA
| | - Yvonne W. Lui
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, USA
| | - Ivan I. Kirov
- Center for Advanced Imaging Innovation and Research (CAIR), Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, Department of Radiology, 660 1 Avenue, New York, NY 10016, USA
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Minaee S, Wang Y, Aygar A, Chung S, Wang X, Lui YW, Fieremans E, Flanagan S, Rath J. MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features. IEEE Trans Med Imaging 2019; 38:2545-2555. [PMID: 30892204 PMCID: PMC6751027 DOI: 10.1109/tmi.2019.2905917] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.
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Minaee S, Wang Y, Choromanska A, Chung S, Wang X, Fieremans E, Flanagan S, Rath J, Lui YW. A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2018:1267-1270. [PMID: 30440621 DOI: 10.1109/embc.2018.8512556] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.
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Wang Y, Wang Y, Lui YW. Biophysically interpretable recurrent neural network for functional magnetic resonance imaging analysis and sparsity based causal architecture discovery. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2018:275-278. [PMID: 30440391 DOI: 10.1109/embc.2018.8512200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Recent efforts use state-of-the-art Recurrent Neural Networks (RNN) to gain insight into neuroscience. A limitation of these works is that the used generic RNNs lack biophysical meaning, making the interpretation of the results in a neuroscience context difficult. In this paper, we propose a biophysically interpretable RNN built on the Dynamic Causal Modelling (DCM). DCM is an advanced nonlinear generative model typically used to test hypotheses of brain causal architectures and associated effective connectivities. We show that DCM can be cast faithfully as a special form of a new generalized RNN. In the resulting DCM-RNN, the hidden states are neural activity, blood flow, blood volume, and deoxyhemoglobin content and the parameters are biological quantities such as effective connectivity, oxygen extraction fraction and vessel wall elasticity. DCM-RNN is a versatile tool for neuroscience with great potential especially when combined with deep learning networks. In this study, we explore sparsity- based causal architecture discovery with DCM-RNN. In the experiments, we demonstrate that DCM-RNN equipped with $l_{1}$ connectivity regulation is more robust to noise and more powerful at discovering sparse architectures than classic DCM with $l_{2}$ connectivity regulation.
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Chung S, Fieremans E, Wang X, Kucukboyaci NE, Morton CJ, Babb J, Amorapanth P, Foo FYA, Novikov DS, Flanagan SR, Rath JF, Lui YW. White Matter Tract Integrity: An Indicator of Axonal Pathology after Mild Traumatic Brain Injury. J Neurotrauma 2019; 35:1015-1020. [PMID: 29239261 DOI: 10.1089/neu.2017.5320] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (mTBI) using multi-shell diffusion magnetic resonance imaging, deriving compartment-specific white matter tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of white matter (WM) changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with mTBI within 30 days of injury and 21 age- and sex-matched controls were imaged on a 3 Tesla magnetic resonance scanner. Multi-shell diffusion acquisition was performed with five b-values (250-2500 sec/mm2) along 6-60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrated focally lower intra-axonal diffusivity (Daxon) in mTBI patients in the splenium of the corpus callosum (sCC; p < 0.05, FWE-corrected). The area under the curve value for Daxon was 0.76 with a low sensitivity of 46.9% but 100% specificity. These results indicate that Daxon may be a useful imaging biomarker highly specific for mTBI-related WM injury. The observed decrease in Daxon suggests restriction of the diffusion along the axons occurring shortly after injury.
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Affiliation(s)
- Sohae Chung
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Els Fieremans
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Xiuyuan Wang
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Nuri E Kucukboyaci
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Charles J Morton
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - James Babb
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Prin Amorapanth
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Farng-Yang A Foo
- 4 Department of Neurology, New York University Langone Health , New York, New York
| | - Dmitry S Novikov
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
| | - Steven R Flanagan
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Joseph F Rath
- 3 Department of Rehabilitation Medicine, New York University School of Medicine , New York, New York
| | - Yvonne W Lui
- 1 Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University School of Medicine , New York, New York.,2 Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine , New York, New York
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Chung S, Wang X, Fieremans E, Rath JF, Amorapanth P, Foo FYA, Morton CJ, Novikov DS, Flanagan SR, Lui YW. Altered Relationship between Working Memory and Brain Microstructure after Mild Traumatic Brain Injury. AJNR Am J Neuroradiol 2019; 40:1438-1444. [PMID: 31371359 DOI: 10.3174/ajnr.a6146] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 06/19/2019] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND PURPOSE Working memory impairment is one of the most troubling and persistent symptoms after mild traumatic brain injury (MTBI). Here we investigate how working memory deficits relate to detectable WM microstructural injuries to discover robust biomarkers that allow early identification of patients with MTBI at the highest risk of working memory impairment. MATERIALS AND METHODS Multi-shell diffusion MR imaging was performed on a 3T scanner with 5 b-values. Diffusion metrics of fractional anisotropy, diffusivity and kurtosis (mean, radial, axial), and WM tract integrity were calculated. Auditory-verbal working memory was assessed using the Wechsler Adult Intelligence Scale, 4th ed, subtests: 1) Digit Span including Forward, Backward, and Sequencing; and 2) Letter-Number Sequencing. We studied 19 patients with MTBI within 4 weeks of injury and 20 healthy controls. Tract-Based Spatial Statistics and ROI analyses were performed to reveal possible correlations between diffusion metrics and working memory performance, with age and sex as covariates. RESULTS ROI analysis found a significant positive correlation between axial kurtosis and Digit Span Backward in MTBI (Pearson r = 0.69, corrected P = .04), mainly present in the right superior longitudinal fasciculus, which was not observed in healthy controls. Patients with MTBI also appeared to lose the normal associations typically seen in fractional anisotropy and axonal water fraction with Letter-Number Sequencing. Tract-Based Spatial Statistics results also support our findings. CONCLUSIONS Differences between patients with MTBI and healthy controls with regard to the relationship between microstructure measures and working memory performance may relate to known axonal perturbations occurring after injury.
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Affiliation(s)
- S Chung
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - X Wang
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - E Fieremans
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - J F Rath
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University School of Medicine, New York, New York
| | - P Amorapanth
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University School of Medicine, New York, New York
| | - F-Y A Foo
- Department of Neurology (F.-Y.A.F.), New York University Langone Health, New York, New York
| | - C J Morton
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - D S Novikov
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
| | - S R Flanagan
- Department of Rehabilitation Medicine (J.F.R., P.A., S.R.F.), New York University School of Medicine, New York, New York
| | - Y W Lui
- From the Center for Advanced Imaging Innovation and Research & Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology (S.C., X.W., E.F., C.J.M., D.S.N., Y.W.L.)
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Prabhu V, Plana NM, Hagiwara M, Diaz-Siso JR, Lui YW, Davis AJ, Sliker CW, Shapiro M, Moin AS, Rodriguez ED. Preoperative Imaging for Facial Transplant: A Guide for Radiologists. Radiographics 2019; 39:1098-1107. [PMID: 31125293 DOI: 10.1148/rg.2019180167] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Facial transplant (FT) is a viable option for patients with severe craniomaxillofacial deformities. Transplant imaging requires coordination between radiologists and surgeons and an understanding of the merits and limitations of imaging modalities. Digital subtraction angiography and CT angiography are critical to mapping vascular anatomy, while volume-rendered CT allows evaluation of osseous defects and landmarks used for surgical cutting guides. This article highlights the components of successful FT imaging at two institutions and in two index cases. A deliberate stepwise approach to performance and interpretation of preoperative FT imaging, which consists of the modalities and protocols described here, is essential to seamless integration of the multidisciplinary FT team. ©RSNA, 2019 See discussion on this article by Lincoln .
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Affiliation(s)
- Vinay Prabhu
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Natalie M Plana
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Mari Hagiwara
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - J Rodrigo Diaz-Siso
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Yvonne W Lui
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Adam J Davis
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Clint W Sliker
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Maksim Shapiro
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Adnaan S Moin
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
| | - Eduardo D Rodriguez
- From the Department of Radiology (V.P., M.H., Y.W.L., A.J.D., M.S.) and Hanjörg Wyss Department of Plastic Surgery (N.M.P., J.R.D.S., E.D.R.), New York University Langone Health, 550 First Ave, New York, NY 10016; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (C.W.S., A.S.M.); and Department of Radiology, MedStar Health, Baltimore, Md (A.S.M.)
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Kao YCJ, Lui YW, Lu CF, Chen HL, Hsieh BY, Chen CY. Behavioral and Structural Effects of Single and Repeat Closed-Head Injury. AJNR Am J Neuroradiol 2019; 40:601-608. [PMID: 30923084 DOI: 10.3174/ajnr.a6014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 02/16/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The effects of multiple head impacts, even without detectable primary injury, on subsequent behavioral impairment and structural abnormality is yet well explored. Our aim was to uncover the dynamic changes and long-term effects of single and repetitive head injury without focal contusion on tissue microstructure and macrostructure. MATERIALS AND METHODS We introduced a repetitive closed-head injury rodent model (n = 70) without parenchymal lesions. We performed a longitudinal MR imaging study during a 50-day study period (T2-weighted imaging, susceptibility-weighted imaging, and diffusion tensor imaging) as well as sequential behavioral assessment. Immunohistochemical staining for astrogliosis was examined in a subgroup of animals. Paired and independent t tests were used to evaluate the outcome change after injury and the cumulative effects of impact load, respectively. RESULTS There was no gross morphologic evidence for head injury such as skull fracture, contusion, or hemorrhage on micro-CT and MR imaging. A significant decrease of white matter fractional anisotropy from day 21 on and an increase of gray matter fractional anisotropy from day 35 on were observed. Smaller mean cortical volume in the double-injury group was shown at day 50 compared with sham and single injury (P < .05). Behavioral deficits (P < .05) in neurologic outcome, balance, and locomotor activity were also aggravated after double injury. Histologic analysis showed astrogliosis 24 hours after injury, which persisted throughout the study period. CONCLUSIONS There are measurable and dynamic changes in microstructure, cortical volume, behavior, and histopathology after both single and double injury, with more severe effects seen after double injury. This work bridges cross-sectional evidence from human subject and pathologic studies using animal models with a multi-time point, longitudinal research paradigm.
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Affiliation(s)
- Y-C J Kao
- From the Neuroscience Research Center (Y.-C.J.K., C.-Y.C.).,Translational Imaging Research Center (Y.-C.J.K., C.-Y.C.), Taipei Medical University, Taipei, Taiwan.,Department of Radiology (Y.-C.J.K., C.-Y.C.), School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Radiogenomic Research Center (Y.-C.J.K., C.-Y.C.), Taipei Medical University Hospital, Taipei, Taiwan
| | - Y W Lui
- Department of Radiology (Y.W.L.), NYU School of Medicine/NYU Langone Health, New York, New York
| | - C-F Lu
- Department of Biomedical Imaging and Radiological Sciences (C.-F.L.), National Yang-Ming University, Taipei, Taiwan
| | - H-L Chen
- Departments of Medical Research (H.-L.C.)
| | - B-Y Hsieh
- Department of Biomedical Imaging and Radiological Science (B.-Y.H.), China Medical University, Taichung, Taiwan
| | - C-Y Chen
- From the Neuroscience Research Center (Y.-C.J.K., C.-Y.C.) .,Translational Imaging Research Center (Y.-C.J.K., C.-Y.C.), Taipei Medical University, Taipei, Taiwan.,Department of Radiology (Y.-C.J.K., C.-Y.C.), School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Medical Imaging (C.-Y.C.).,Radiogenomic Research Center (Y.-C.J.K., C.-Y.C.), Taipei Medical University Hospital, Taipei, Taiwan
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45
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Won E, Palma JA, Kaufmann H, Milla SS, Cohen B, Norcliffe-Kaufmann L, Babb JS, Lui YW. Quantitative magnetic resonance evaluation of the trigeminal nerve in familial dysautonomia. Clin Auton Res 2019; 29:469-473. [PMID: 30783821 DOI: 10.1007/s10286-019-00593-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 01/21/2019] [Indexed: 11/29/2022]
Abstract
PURPOSE Familial dysautonomia (FD) is a rare autosomal recessive disease that affects the development of sensory and autonomic neurons, including those in the cranial nerves. We aimed to determine whether conventional brain magnetic resonance imaging (MRI) could detect morphologic changes in the trigeminal nerves of these patients. METHODS Cross-sectional analysis of brain MRI of patients with genetically confirmed FD and age- and sex-matched controls. High-resolution 3D gradient-echo T1-weighted sequences were used to obtain measurements of the cisternal segment of the trigeminal nerves. Measurements were obtained using a two-reader consensus. RESULTS Twenty pairs of trigeminal nerves were assessed in ten patients with FD and ten matched controls. The median (interquartile range) cross-sectional area of the trigeminal nerves in patients with FD was 3.5 (2.1) mm2, compared to 5.9 (2.0) mm2 in controls (P < 0.001). No association between trigeminal nerve area and age was found in patients or controls. CONCLUSIONS Using conventional MRI, the caliber of the trigeminal nerves was significantly reduced bilaterally in patients with FD compared to controls, a finding that appears to be highly characteristic of this disorder. The lack of correlation between age and trigeminal nerve size supports arrested neuronal development rather than progressive atrophy.
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Affiliation(s)
- Eugene Won
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Jose-Alberto Palma
- Department of Neurology, Dysautonomia Center, New York University School of Medicine, New York, NY, 10016, USA
| | - Horacio Kaufmann
- Department of Neurology, Dysautonomia Center, New York University School of Medicine, New York, NY, 10016, USA.
| | - Sarah S Milla
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA.,Department of Radiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Benjamin Cohen
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
| | | | - James S Babb
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Yvonne W Lui
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA.
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Grover H, Qian Y, Boada FE, Lakshmanan K, Flanagan S, Lui YW. MRI Evidence of Altered Callosal Sodium in Mild Traumatic Brain Injury. AJNR Am J Neuroradiol 2018; 39:2200-2204. [PMID: 30498019 DOI: 10.3174/ajnr.a5903] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Accepted: 09/27/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND AND PURPOSE Mild traumatic brain injury is a leading cause of death and disability worldwide with 42 million cases reported annually, increasing the need to understand the underlying pathophysiology because this could help guide the development of targeted therapy. White matter, particularly the corpus callosum, is susceptible to injury. Animal models suggest stretch-induced mechanoporation of the axonal membrane resulting in ionic shifts and altered sodium ion distribution. The purpose of this study was to compare the distribution of total sodium concentration in the corpus callosum between patients with mild traumatic brain injury and controls using sodium (23Na) MR imaging. MATERIALS AND METHODS Eleven patients with a history of mild traumatic brain injury and 10 age- and sex-matched controls underwent sodium (23Na) MR imaging using a 3T scanner. Total sodium concentration was measured in the genu, body, and splenium of the corpus callosum with 5-mm ROIs; total sodium concentration of the genu-to-splenium ratio was calculated and compared between patients and controls. RESULTS Higher total sodium concentration in the genu (49.28 versus 43.29 mmol/L, P = .01) and lower total sodium concentration in the splenium (which was not statistically significant; 38.35 versus 44.06 mmol/L, P = .08) was seen in patients with mild traumatic brain injury compared with controls. The ratio of genu total sodium concentration to splenium total sodium concentration was also higher in patients with mild traumatic brain injury (1.3 versus 1.01, P = .001). CONCLUSIONS Complex differences are seen in callosal total sodium concentration in symptomatic patients with mild traumatic brain injury, supporting the notion of ionic dysfunction in the pathogenesis of mild traumatic brain injury. The total sodium concentration appears to be altered beyond the immediate postinjury phase, and further work is needed to understand the relationship to persistent symptoms and outcome.
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Affiliation(s)
- H Grover
- From the New York University Langone Medical Center, New York, New York
| | - Y Qian
- From the New York University Langone Medical Center, New York, New York
| | - F E Boada
- From the New York University Langone Medical Center, New York, New York
| | - K Lakshmanan
- From the New York University Langone Medical Center, New York, New York
| | - S Flanagan
- From the New York University Langone Medical Center, New York, New York
| | - Y W Lui
- From the New York University Langone Medical Center, New York, New York.
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Affiliation(s)
- Rajan Jain
- From the Department of Radiology and Neurosurgery, NYU School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
| | - Yvonne W Lui
- From the Department of Radiology and Neurosurgery, NYU School of Medicine, 660 First Ave, 2nd Floor, New York, NY 10016
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48
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Lotan E, Morley C, Newman J, Qian M, Abu-Amara D, Marmar C, Lui YW. Prevalence of Cerebral Microhemorrhage following Chronic Blast-Related Mild Traumatic Brain Injury in Military Service Members Using Susceptibility-Weighted MRI. AJNR Am J Neuroradiol 2018; 39:1222-1225. [PMID: 29794235 PMCID: PMC7655437 DOI: 10.3174/ajnr.a5688] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Accepted: 04/04/2018] [Indexed: 01/14/2023]
Abstract
BACKGROUND AND PURPOSE Cerebral microhemorrhages are a known marker of mild traumatic brain injury. Blast-related mild traumatic brain injury relates to a propagating pressure wave, and there is evidence that the mechanism of injury in blast-related mild traumatic brain injury may be different from that in blunt head trauma. Two recent reports in mixed cohorts of blunt and blast-related traumatic brain injury in military personnel suggest that the prevalence of cerebral microhemorrhages is lower than in civilian head injury. In this study, we aimed to characterize the prevalence of cerebral microhemorrhages in military service members specifically with chronic blast-related mild traumatic brain injury. MATERIALS AND METHODS Participants were prospectively recruited and underwent 3T MR imaging. Susceptibility-weighted images were assessed by 2 neuroradiologists independently for the presence of cerebral microhemorrhages. RESULTS Our cohort included 146 veterans (132 men) who experienced remote blast-related mild traumatic brain injury (mean, 9.4 years; median, 9 years after injury). Twenty-one (14.4%) reported loss of consciousness for <30 minutes. Seventy-seven subjects (52.7%) had 1 episode of blast-related mild traumatic brain injury; 41 (28.1%) had 2 episodes; and 28 (19.2%) had >2 episodes. No cerebral microhemorrhages were identified in any subject, as opposed to the frequency of SWI-detectable cerebral microhemorrhages following blunt-related mild traumatic brain injury in the civilian population, which has been reported to be as high as 28% in the acute and subacute stages. CONCLUSIONS Our results may reflect differences in pathophysiology and the mechanism of injury between blast- and blunt-related mild traumatic brain injury. Additionally, the chronicity of injury may play a role in the detection of cerebral microhemorrhages.
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Affiliation(s)
- E Lotan
- From the Departments of Radiology (E.L., C.M., Y.W.L.)
- Sackler Faculty of Medicine (E.L.), Tel Aviv University, Tel Aviv, Israel
| | - C Morley
- From the Departments of Radiology (E.L., C.M., Y.W.L.)
- Psychiatry (J.N., M.Q., D.A.-A., C.M.), Steven and Alexandra Cohen Veterans Center for Posttraumatic Stress and Traumatic Brain Injury, New York University Langone Medical Center, New York, New York
| | - J Newman
- Psychiatry (J.N., M.Q., D.A.-A., C.M.), Steven and Alexandra Cohen Veterans Center for Posttraumatic Stress and Traumatic Brain Injury, New York University Langone Medical Center, New York, New York
| | - M Qian
- Psychiatry (J.N., M.Q., D.A.-A., C.M.), Steven and Alexandra Cohen Veterans Center for Posttraumatic Stress and Traumatic Brain Injury, New York University Langone Medical Center, New York, New York
| | - D Abu-Amara
- Psychiatry (J.N., M.Q., D.A.-A., C.M.), Steven and Alexandra Cohen Veterans Center for Posttraumatic Stress and Traumatic Brain Injury, New York University Langone Medical Center, New York, New York
| | - C Marmar
- From the Departments of Radiology (E.L., C.M., Y.W.L.)
| | - Y W Lui
- From the Departments of Radiology (E.L., C.M., Y.W.L.)
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49
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Wang Y, Wang Y, Lui YW. Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis. Neuroimage 2018; 178:385-402. [PMID: 29782993 DOI: 10.1016/j.neuroimage.2018.05.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 04/23/2018] [Accepted: 05/17/2018] [Indexed: 11/16/2022] Open
Abstract
Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.
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Affiliation(s)
- Yuan Wang
- NYU WIRELESS, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA.
| | - Yao Wang
- NYU WIRELESS, Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA
| | - Yvonne W Lui
- Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA
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50
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Chung S, Fieremans E, Kucukboyaci NE, Wang X, Morton CJ, Novikov DS, Rath JF, Lui YW. Working Memory And Brain Tissue Microstructure: White Matter Tract Integrity Based On Multi-Shell Diffusion MRI. Sci Rep 2018; 8:3175. [PMID: 29453439 PMCID: PMC5816650 DOI: 10.1038/s41598-018-21428-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 02/05/2018] [Indexed: 11/30/2022] Open
Abstract
Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.
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Affiliation(s)
- Sohae Chung
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Els Fieremans
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | | | - Xiuyuan Wang
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Charles J Morton
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Dmitry S Novikov
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Joseph F Rath
- Department of Rehabilitation Medicine, New York University School of Medicine, New York, NY, 10016, USA
| | - Yvonne W Lui
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, 10016, USA.
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA.
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