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Tang X, Gao J, Aburas A, Wu D, Chen Z, Chen H, Hu C. Accelerated multi-b-value multi-shot diffusion-weighted imaging based on EPI with keyhole and a low-rank tensor constraint. Magn Reson Imaging 2024; 110:138-148. [PMID: 38641211 DOI: 10.1016/j.mri.2024.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/21/2024]
Abstract
PURPOSE Multi-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. METHODS We firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. RESULTS Through theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). CONCLUSION The SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI.
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Affiliation(s)
- Xin Tang
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; United Imaging Healthcare Co. Ltd, Shanghai, China
| | - Juan Gao
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ahmed Aburas
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhuo Chen
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hao Chen
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chenxi Hu
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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Consagra W, Ning L, Rathi Y. Neural orientation distribution fields for estimation and uncertainty quantification in diffusion MRI. Med Image Anal 2024; 93:103105. [PMID: 38377728 DOI: 10.1016/j.media.2024.103105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/13/2023] [Accepted: 02/05/2024] [Indexed: 02/22/2024]
Abstract
Inferring brain connectivity and structure in-vivo requires accurate estimation of the orientation distribution function (ODF), which encodes key local tissue properties. However, estimating the ODF from diffusion MRI (dMRI) signals is a challenging inverse problem due to obstacles such as significant noise, high-dimensional parameter spaces, and sparse angular measurements. In this paper, we address these challenges by proposing a novel deep-learning based methodology for continuous estimation and uncertainty quantification of the spatially varying ODF field. We use a neural field (NF) to parameterize a random series representation of the latent ODFs, implicitly modeling the often ignored but valuable spatial correlation structures in the data, and thereby improving efficiency in sparse and noisy regimes. An analytic approximation to the posterior predictive distribution is derived which can be used to quantify the uncertainty in the ODF estimate at any spatial location, avoiding the need for expensive resampling-based approaches that are typically employed for this purpose. We present empirical evaluations on both synthetic and real in-vivo diffusion data, demonstrating the advantages of our method over existing approaches.
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Affiliation(s)
- William Consagra
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, 399 Revolution Drive, Boston, 02215, MA, United States.
| | - Lipeng Ning
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, 399 Revolution Drive, Boston, 02215, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, 399 Revolution Drive, Boston, 02215, MA, United States
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair DA, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush‐Goebel S, Shinohara RT, Shou H, Tisdall MD, Vettel JM, Grafton ST, Cieslak M, Satterthwaite TD. A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging. Hum Brain Mapp 2024; 45:e26580. [PMID: 38520359 PMCID: PMC10960521 DOI: 10.1002/hbm.26580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Bioengineering, School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip A. Cook
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Damien A. Fair
- Masonic Institute for the Developing BrainUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Barry Giesbrecht
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sage Rush‐Goebel
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jean M. Vettel
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
- U.S. Army Research LaboratoryAberdeen Proving GroundAberdeenMarylandUSA
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Becker D, Scherer M, Neher P, Jungk C, Jesser J, Pflüger I, Bendszus M, Maier-Hein K, Unterberg A. Q-ball high-resolution fiber tractography of language associated tracts: quantitative evaluation of applicability for glioma resections. J Neurosurg Sci 2024; 68:1-12. [PMID: 31680507 DOI: 10.23736/s0390-5616.19.04782-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND To date, fiber tractography (FT) is predominantly based on diffusion tensor imaging (DTI). High angular resolution diffusion imaging (HARDI)-based reconstructions have become a focus of interest, enabling the resolution of intravoxel fiber crossing. However, experience with high resolution tractography (HRFT) for neurosurgical applications is still limited to a few reports. This prospectively designed feasibility study shares our initial experience using an analytical q-ball approach (QBI) for FT of language-associated pathways in comparison with DTI-FT, focussing on a quantitative analysis and evaluation of its applicability in clinical routine. METHODS Probabilistic QBI-, and DTI-FT were performed for the major components of the language-associated fiber bundles (superior longitudinal fasciculus, inferior fronto-occipital fasciculus, medial/inferior longitudinal faciculus) in 11 patients with eloquent gliomas. The data was derived from a routine DWI sequence (b=1000s/mm2, 64 gradient directions). Quantitative analysis evaluated tract volume (TV), tract length (TL) and tract density (TD). Results were correlated to tumor and edema size. RESULTS Quantitative analysis showed larger TV and TL of the overall fiber object using QBI-FT compared with DTI-FT (TV: 16.45±1.85 vs. 10.07±1.15cm3; P<0.0001; TL: 81.95±6.14 vs. 72.06±6.92 mm; P=0.0011). Regarding overall TD, DTI delivered significantly higher values (40.57±6.59 vs. 60.98±15.94 points/voxel; P=0.0118). Bland-Altman analysis illustrated a systematic advantage to yield lager TV and TL via QBI compared with DTI for all reconstructed pathways. The results were independent of tumor or edema volume. CONCLUSIONS QBI proved to be suitable for an application in the neurosurgical setting without additional expense for the patient. Quantitative analysis of FT reveals larger overall TV, longer TL with lower TD using QBI compared with DTI, suggesting the better depiction of marginal and terminal fibers according to neuroanatomical knowledge. This emphasizes the known limitation of DTI to underestimate the dimensions of a pathway. Rather than relying on DTI, sophisticated HRFT techniques should be considered for preoperative planning and intraoperative guidance in selected cases of eloquent glioma surgery.
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Affiliation(s)
- Daniela Becker
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany -
| | - Moritz Scherer
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Christine Jungk
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany
| | - Jessica Jesser
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany
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Chen Z, Peng C, Li Y, Zeng Q, Feng Y. Super-resolved q-space learning of diffusion MRI. Med Phys 2023; 50:7700-7713. [PMID: 37219814 DOI: 10.1002/mp.16478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/07/2023] [Accepted: 04/08/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Diffusion magnetic resonance imaging (dMRI) provides a powerful tool to non-invasively investigate neural structures in the living human brain. Nevertheless, its reconstruction performance on neural structures relies on the number of diffusion gradients in the q-space. High-angular (HA) dMRI requires a long scan time, limiting its use in clinical practice, whereas directly reducing the number of diffusion gradients would lead to the underestimation of neural structures. PURPOSE We propose a deep compressive sensing-based q-space learning (DCS-qL) approach to estimate HA dMRI from low-angular dMRI. METHODS In DCS-qL, we design the deep network architecture by unfolding the proximal gradient descent procedure that addresses the compressive sense problem. In addition, we exploit a lifting scheme to design a network structure with reversible transform properties. For implementation, we apply a self-supervised regression to enhance the signal-to-noise ratio of diffusion data. Then, we utilize a semantic information-guided patch-based mapping strategy for feature extraction, which introduces multiple network branches to handle patches with different tissue labels. RESULTS Experimental results show that the proposed approach can yield a promising performance on the tasks of reconstructed HA dMRI images, microstructural indices of neurite orientation dispersion and density imaging, fiber orientation distribution, and fiber bundle estimation. CONCLUSIONS The proposed method achieves more accurate neural structures than competing approaches.
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Affiliation(s)
- Zan Chen
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Chenxu Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yongqiang Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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Esopenko C, Sollmann N, Bonke EM, Wiegand TLT, Heinen F, de Souza NL, Breedlove KM, Shenton ME, Lin AP, Koerte IK. Current and Emerging Techniques in Neuroimaging of Sport-Related Concussion. J Clin Neurophysiol 2023; 40:398-407. [PMID: 36930218 PMCID: PMC10329721 DOI: 10.1097/wnp.0000000000000864] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
SUMMARY Sport-related concussion (SRC) affects an estimated 1.6 to 3.8 million Americans each year. Sport-related concussion results from biomechanical forces to the head or neck that lead to a broad range of neurologic symptoms and impaired cognitive function. Although most individuals recover within weeks, some develop chronic symptoms. The heterogeneity of both the clinical presentation and the underlying brain injury profile make SRC a challenging condition. Adding to this challenge, there is also a lack of objective and reliable biomarkers to support diagnosis, to inform clinical decision making, and to monitor recovery after SRC. In this review, the authors provide an overview of advanced neuroimaging techniques that provide the sensitivity needed to capture subtle changes in brain structure, metabolism, function, and perfusion after SRC. This is followed by a discussion of emerging neuroimaging techniques, as well as current efforts of international research consortia committed to the study of SRC. Finally, the authors emphasize the need for advanced multimodal neuroimaging to develop objective biomarkers that will inform targeted treatment strategies after SRC.
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Affiliation(s)
- Carrie Esopenko
- Department of Rehabilitation and Movement Sciences, Rutgers Biomedical and Health Sciences, Newark, NJ, USA
| | - Nico Sollmann
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena M. Bonke
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
| | - Tim L. T. Wiegand
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Felicitas Heinen
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Nicola L. de Souza
- School of Graduate Studies, Biomedical Sciences, Rutgers Biomedical and Health Sciences, Newark, NJ, USA
| | - Katherine M. Breedlove
- Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - Alexander P. Lin
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Clinical Spectroscopy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K. Koerte
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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de Souza DAR, Mathieu H, Deloulme JC, Barbier EL. Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging. Front Neurosci 2023; 17:1172830. [PMID: 37332879 PMCID: PMC10272537 DOI: 10.3389/fnins.2023.1172830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/28/2023] [Indexed: 06/20/2023] Open
Abstract
Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift.
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Affiliation(s)
| | - Hervé Mathieu
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
- Université Grenoble Alpes, INSERM, US17, CNRS, UAR 3552, CHU Grenoble Alpes, Grenoble, France
| | | | - Emmanuel L. Barbier
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
- Université Grenoble Alpes, INSERM, US17, CNRS, UAR 3552, CHU Grenoble Alpes, Grenoble, France
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Pal A, Ning L, Rathi Y. A domain-agnostic MR reconstruction framework using a randomly weighted neural network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533764. [PMID: 36993372 PMCID: PMC10055311 DOI: 10.1101/2023.03.22.533764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
PURPOSE To design a randomly-weighted neural network that performs domain-agnostic MR image reconstruction from undersampled k-space data without the need for ground truth or extensive in-vivo training datasets. The network performance must be similar to the current state-of-the-art algorithms that require large training datasets. METHODS We propose a Weight Agnostic randomly weighted Network method for MRI reconstruction (termed WAN-MRI) which does not require updating the weights of the neural network but rather chooses the most appropriate connections of the network to reconstruct the data from undersampled k-space measurements. The network architecture has three components, i.e. (1) Dimensionality Reduction Layers comprising of 3d convolutions, ReLu, and batch norm; (2) Reshaping Layer is Fully Connected layer; and (3) Upsampling Layers that resembles the ConvDecoder architecture. The proposed methodology is validated on fastMRI knee and brain datasets. RESULTS The proposed method provides a significant boost in performance for structural similarity index measure (SSIM) and root mean squared error (RMSE) scores on fastMRI knee and brain datasets at an undersampling factor of R=4 and R=8 while trained on fractal and natural images, and fine-tuned with only 20 samples from the fastMRI training k-space dataset. Qualitatively, we see that classical methods such as GRAPPA and SENSE fail to capture the subtle details that are clinically relevant. We either outperform or show comparable performance with several existing deep learning techniques (that require extensive training) like GrappaNET, VariationNET, J-MoDL, and RAKI. CONCLUSION The proposed algorithm (WAN-MRI) is agnostic to reconstructing images of different body organs or MRI modalities and provides excellent scores in terms of SSIM, PSNR, and RMSE metrics and generalizes better to out-of-distribution examples. The methodology does not require ground truth data and can be trained using very few undersampled multi-coil k-space training samples.
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair D, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush-Goebel S, Shinohara R, Shou H, Tisdall MD, Vettel J, Grafton S, Cieslak M, Satterthwaite T. Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529546. [PMID: 36865219 PMCID: PMC9980087 DOI: 10.1101/2023.02.22.529546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip A. Cook
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sage Rush-Goebel
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jean Vettel
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Scott Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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10
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Weiss Lucas C, Faymonville AM, Loução R, Schroeter C, Nettekoven C, Oros-Peusquens AM, Langen KJ, Shah NJ, Stoffels G, Neuschmelting V, Blau T, Neuschmelting H, Hellmich M, Kocher M, Grefkes C, Goldbrunner R. Surgery of Motor Eloquent Glioblastoma Guided by TMS-Informed Tractography: Driving Resection Completeness Towards Prolonged Survival. Front Oncol 2022; 12:874631. [PMID: 35692752 PMCID: PMC9186060 DOI: 10.3389/fonc.2022.874631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 03/21/2022] [Indexed: 12/13/2022] Open
Abstract
Background Surgical treatment of patients with glioblastoma affecting motor eloquent brain regions remains critically discussed given the risk–benefit dilemma of prolonging survival at the cost of motor-functional damage. Tractography informed by navigated transcranial magnetic stimulation (nTMS-informed tractography, TIT) provides a rather robust estimate of the individual location of the corticospinal tract (CST), a highly vulnerable structure with poor functional reorganisation potential. We hypothesised that by a more comprehensive, individualised surgical decision-making using TIT, tumours in close relationship to the CST can be resected with at least equal probability of gross total resection (GTR) than less eloquently located tumours without causing significantly more gross motor function harm. Moreover, we explored whether the completeness of TIT-aided resection translates to longer survival. Methods A total of 61 patients (median age 63 years, m = 34) with primary glioblastoma neighbouring or involving the CST were operated on between 2010 and 2015. TIT was performed to inform surgical planning in 35 of the patients (group T; vs. 26 control patients). To achieve largely unconfounded group comparisons for each co-primary outcome (i.e., gross-motor functional worsening, GTR, survival), (i) uni- and multivariate regression analyses were performed to identify features of optimal outcome prediction; (ii), optimal propensity score matching (PSM) was applied to balance those features pairwise across groups, followed by (iii) pairwise group comparison. Results Patients in group T featured a significantly higher lesion-CST overlap compared to controls (8.7 ± 10.7% vs. 3.8 ± 5.7%; p = 0.022). The frequency of gross motor worsening was higher in group T, albeit non-significant (n = 5/35 vs. n = 0/26; p = 0.108). PSM-based paired-sample comparison, controlling for the confounders of preoperative tumour volume and vicinity to the delicate vasculature of the insula, showed higher GTR rates in group T (77% vs. 69%; p = 0.025), particularly in patients with a priori intended GTR (87% vs. 78%; p = 0.003). This translates into a prolonged PFS in the same PSM subgroup (8.9 vs. 5.8 months; p = 0.03), with GTR representing the strongest predictor of PFS (p = 0.001) and OS (p = 0.0003) overall. Conclusion The benefit of TIT-aided GTR appears to overcome the drawbacks of potentially elevated motor functional risk in motor eloquent tumour localisation, leading to prolonged survival of patients with primary glioblastoma close to the CST.
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Affiliation(s)
- Carolin Weiss Lucas
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andrea Maria Faymonville
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Department of Neurosurgery, University Hospital Mannheim, Mannheim, Germany
| | - Ricardo Loução
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Department of Stereotaxy and Functional Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - Catharina Schroeter
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Charlotte Nettekoven
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | - Karl Josef Langen
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - Volker Neuschmelting
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Tobias Blau
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Hannah Neuschmelting
- Institute of Pathology and Neuropathology, University Hospital Essen, Essen, Germany
| | - Martin Hellmich
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Martin Kocher
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Department of Stereotaxy and Functional Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - Christian Grefkes
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany.,Institute for Medical Statistics and Computational Biology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Roland Goldbrunner
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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11
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Jha RR, Pathak SK, Nath V, Schneider W, Kumar BVR, Bhavsar A, Nigam A. VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI. Magn Reson Imaging 2022; 90:1-16. [PMID: 35341904 DOI: 10.1016/j.mri.2022.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 02/19/2022] [Accepted: 03/19/2022] [Indexed: 10/18/2022]
Abstract
Diffusion MRI (dMRI) is one of the most popular techniques for studying the brain structure, mainly the white matter region. Among several sampling methods in dMRI, the high angular resolution diffusion imaging (HARDI) technique has attracted researchers due to its more accurate fiber orientation estimation. However, the current single-shell HARDI makes the intravoxel structure challenging to estimate accurately. While multi-shell acquisition can address this problem, it takes a longer scanning time, restricting its use in clinical applications. In addition, most existing dMRI scanners with low gradient-strengths often acquire single-shell up to b=1000s/mm2 because of signal-to-noise ratio issues and severe image artefacts. Hence, we propose a novel generative adversarial network, VRfRNet, for the reconstruction of multi-shell multi-tissue fiber orientation distribution function from single-shell HARDI volumes. Such a transformation learning is performed in the spherical harmonics (SH) space, as raw input HARDI volume is transformed to SH coefficients to soften gradient directions. The proposed VRfRNet consists of several modules, such as multi-context feature enrichment module, feature level attention, and softmax level attention. In addition, three loss functions have been used to optimize network learning, including L1, adversarial, and total variation. The network is trained and tested using standard qualitative and quantitative performance metrics on the publicly available HCP data-set.
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Affiliation(s)
- Ranjeet Ranjan Jha
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India.
| | - Sudhir K Pathak
- Learning Research and Development Center, University of Pittsburgh, USA
| | - Vishwesh Nath
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
| | - Walter Schneider
- Learning Research and Development Center, University of Pittsburgh, USA
| | - B V Rathish Kumar
- Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, India
| | - Arnav Bhavsar
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
| | - Aditya Nigam
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
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12
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Pal A, Rathi Y. A review and experimental evaluation of deep learning methods for MRI reconstruction. THE JOURNAL OF MACHINE LEARNING FOR BIOMEDICAL IMAGING 2022; 1:001. [PMID: 35722657 PMCID: PMC9202830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with plenty of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.
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13
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Serai SD. Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging. Pediatr Radiol 2022; 52:217-227. [PMID: 33856502 DOI: 10.1007/s00247-021-05042-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/18/2021] [Accepted: 03/01/2021] [Indexed: 01/27/2023]
Abstract
Magnetic resonance imaging is widely available and accepted as the imaging method of choice for many pediatric body imaging applications. Traditionally, it has been used in a qualitative way, where the images are reported non-numerically by radiologists. But now MRI machines have built-in post-processing software connected to the scanner and the database of MR images. This setting enables and encourages simple quantitative analysis of MR images. In this paper, the author reviews the fundamentals of MRI and discusses the most common quantitative MRI techniques for body imaging: T1, T2, T2*, T1rho and diffusion-weighted imaging (DWI). For each quantitative imaging method, this article reviews the technique, its measurement mechanism, and selected clinical applications to body imaging.
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Affiliation(s)
- Suraj D Serai
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA. .,Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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14
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Pesce M, Repetti A, Auría A, Daducci A, Thiran JP, Wiaux Y. Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors. J Imaging 2021; 7:jimaging7110226. [PMID: 34821857 PMCID: PMC8618762 DOI: 10.3390/jimaging7110226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/09/2021] [Accepted: 10/10/2021] [Indexed: 11/18/2022] Open
Abstract
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for the reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxel-wise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter, and cerebrospinal fluid is also leveraged. A minimization problem is formulated and solved via a stochastic forward–backward algorithm. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.
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Affiliation(s)
- Marica Pesce
- Institute of Sensors Signals and Systems (ISSS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Correspondence: (M.P.); (A.R.); (Y.W.)
| | - Audrey Repetti
- Institute of Sensors Signals and Systems (ISSS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Department of Actuarial Mathematics & Statistics (AMS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Correspondence: (M.P.); (A.R.); (Y.W.)
| | - Anna Auría
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; (A.A.); (J.-P.T.)
| | - Alessandro Daducci
- Department of Computer Science, University of Verona, 37134 Verona, Italy;
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; (A.A.); (J.-P.T.)
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne, CH-1011 Lausanne, Switzerland
| | - Yves Wiaux
- Institute of Sensors Signals and Systems (ISSS), Heriot-Watt University, Edinburgh EH14 4AS, UK
- Correspondence: (M.P.); (A.R.); (Y.W.)
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15
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Chen Z, Guo W, Feng Y, Li Y, Zhao C, Ren Y, Shao L. Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:7112-7126. [PMID: 34138708 DOI: 10.1109/tip.2021.3088611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Deep learning has recently been intensively studied in the context of image compressive sensing (CS) to discover and represent complicated image structures. These approaches, however, either suffer from nonflexibility for an arbitrary sampling ratio or lack an explicit deep-learned regularization term. This paper aims to solve the CS reconstruction problem by combining the deep-learned regularization term and proximal operator. We first introduce a regularization term using a carefully designed residual-regressive net, which can measure the distance between a corrupted image and a clean image set and accurately identify to which subspace the corrupted image belongs. We then address a proximal operator with a tailored dilated residual channel attention net, which enables the learned proximal operator to map the distorted image into the clean image set. We adopt an adaptive proximal selection strategy to embed the network into the loop of the CS image reconstruction algorithm. Moreover, a self-ensemble strategy is presented to improve CS recovery performance. We further utilize state evolution to analyze the effectiveness of the designed networks. Extensive experiments also demonstrate that our method can yield superior accurate reconstruction (PSNR gain over 1 dB) compared to other competing approaches while achieving the current state-of-the-art image CS reconstruction performance. The test code is available at https://github.com/zjut-gwl/CSDRCANet.
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16
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Mani M, Magnotta VA, Jacob M. qModeL: A plug-and-play model-based reconstruction for highly accelerated multi-shot diffusion MRI using learned priors. Magn Reson Med 2021; 86:835-851. [PMID: 33759240 PMCID: PMC8076086 DOI: 10.1002/mrm.28756] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 01/19/2023]
Abstract
PURPOSE To introduce a joint reconstruction method for highly undersampled multi-shot diffusion weighted (msDW) scans. METHODS Multi-shot EPI methods enable higher spatial resolution for diffusion MRI, but at the expense of long scan-time. Highly accelerated msDW scans are needed to enable their utilization in advanced microstructure studies, which require high q-space coverage. Previously, joint k-q undersampling methods coupled with compressed sensing were shown to enable very high acceleration factors. However, the reconstruction of this data using sparsity priors is challenging and is not suited for multi-shell data. We propose a new reconstruction that recovers images from the combined k-q data jointly. The proposed qModeL reconstruction brings together the advantages of model-based iterative reconstruction and machine learning, extending the idea of plug-and-play algorithms. Specifically, qModeL works by prelearning the signal manifold corresponding to the diffusion measurement space using deep learning. The prelearned manifold prior is incorporated into a model-based reconstruction to provide a voxel-wise regularization along the q-dimension during the joint recovery. Notably, the learning does not require in vivo training data and is derived exclusively from biophysical modeling. Additionally, a plug-and-play total variation denoising provides regularization along the spatial dimension. The proposed framework is tested on k-q undersampled single-shell and multi-shell msDW acquisition at various acceleration factors. RESULTS The qModeL joint reconstruction is shown to recover DWIs from 8-fold accelerated msDW acquisitions with error less than 5% for both single-shell and multi-shell data. Advanced microstructural analysis performed using the undersampled reconstruction also report reasonable accuracy. CONCLUSION qModeL enables the joint recovery of highly accelerated multi-shot dMRI utilizing learning-based priors. The bio-physically driven approach enables the use of accelerated multi-shot imaging for multi-shell sampling and advanced microstructure studies.
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Affiliation(s)
- Merry Mani
- Department of Radiology, University of Iowa, Iowa City, Iowa
| | | | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
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17
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Li H, Liang Z, Zhang C, Liu R, Li J, Zhang W, Liang D, Shen B, Zhang X, Ge Y, Zhang J, Ying L. SuperDTI: Ultrafast DTI and fiber tractography with deep learning. Magn Reson Med 2021; 86:3334-3347. [PMID: 34309073 DOI: 10.1002/mrm.28937] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/04/2021] [Accepted: 07/04/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography. METHODS SuperDTI was developed to learn the nonlinear relationship between DWIs and the corresponding diffusion tensor parameter maps. It bypasses the tensor fitting procedure, which is highly susceptible to noises and motions in DWIs. The network was trained and tested using data sets from the Human Connectome Project and patients with ischemic stroke. Results from SuperDTI were compared against widely used methods for tensor parameter estimation and fiber tracking. RESULTS Using training and testing data acquired using the same protocol and scanner, SuperDTI was shown to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six raw DWIs, with a quantification error of less than 5% in all white-matter and gray-matter regions of interest. It was robust to noises and motions in the testing data. Furthermore, the network trained using healthy volunteer data showed no apparent reduction in lesion detectability when directly applied to stroke patient data. CONCLUSIONS Our results demonstrate the feasibility of superfast DTI and fiber tractography using deep learning with as few as six DWIs directly, bypassing tensor fitting. Such a significant reduction in scan time may allow the inclusion of DTI into the clinical routine for many potential applications.
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Affiliation(s)
- Hongyu Li
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Zifei Liang
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, USA
| | - Chaoyi Zhang
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Ruiying Liu
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Jing Li
- Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Weihong Zhang
- Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Medical AI Research Center, SIAT, CAS, Shenzhen, China
| | - Bowen Shen
- Computer Science, Virginia Tech, Blacksburg, Virginia, USA
| | - Xiaoliang Zhang
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA
| | - Yulin Ge
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, USA
| | - Jiangyang Zhang
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, USA
| | - Leslie Ying
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA.,Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, New York, USA
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18
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Sollmann N, Krieg SM, Säisänen L, Julkunen P. Mapping of Motor Function with Neuronavigated Transcranial Magnetic Stimulation: A Review on Clinical Application in Brain Tumors and Methods for Ensuring Feasible Accuracy. Brain Sci 2021; 11:brainsci11070897. [PMID: 34356131 PMCID: PMC8305823 DOI: 10.3390/brainsci11070897] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/29/2021] [Accepted: 07/02/2021] [Indexed: 12/15/2022] Open
Abstract
Navigated transcranial magnetic stimulation (nTMS) has developed into a reliable non-invasive clinical and scientific tool over the past decade. Specifically, it has undergone several validating clinical trials that demonstrated high agreement with intraoperative direct electrical stimulation (DES), which paved the way for increasing application for the purpose of motor mapping in patients harboring motor-eloquent intracranial neoplasms. Based on this clinical use case of the technique, in this article we review the evidence for the feasibility of motor mapping and derived models (risk stratification and prediction, nTMS-based fiber tracking, improvement of clinical outcome, and assessment of functional plasticity), and provide collected sets of evidence for the applicability of quantitative mapping with nTMS. In addition, we provide evidence-based demonstrations on factors that ensure methodological feasibility and accuracy of the motor mapping procedure. We demonstrate that selection of the stimulation intensity (SI) for nTMS and spatial density of stimuli are crucial factors for applying motor mapping accurately, while also demonstrating the effect on the motor maps. We conclude that while the application of nTMS motor mapping has been impressively spread over the past decade, there are still variations in the applied protocols and parameters, which could be optimized for the purpose of reliable quantitative mapping.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany;
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, San Francisco, CA 94143, USA
- Correspondence:
| | - Sandro M. Krieg
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany;
- Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Laura Säisänen
- Department of Clinical Neurophysiology, Kuopio University Hospital, 70029 Kuopio, Finland; (L.S.); (P.J.)
- Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
| | - Petro Julkunen
- Department of Clinical Neurophysiology, Kuopio University Hospital, 70029 Kuopio, Finland; (L.S.); (P.J.)
- Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
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19
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Bopp MHA, Emde J, Carl B, Nimsky C, Saß B. Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery. Brain Sci 2021; 11:brainsci11030381. [PMID: 33802710 PMCID: PMC8002557 DOI: 10.3390/brainsci11030381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/08/2021] [Accepted: 03/14/2021] [Indexed: 01/31/2023] Open
Abstract
Diffusion tensor imaging (DTI)-based fiber tractography is routinely used in clinical applications to visualize major white matter tracts, such as the corticospinal tract (CST), optic radiation (OR), and arcuate fascicle (AF). Nevertheless, DTI is limited due to its capability of resolving intra-voxel multi-fiber populations. Sophisticated models often require long acquisition times not applicable in clinical practice. Diffusion kurtosis imaging (DKI), as an extension of DTI, combines sophisticated modeling of the diffusion process with short acquisition times but has rarely been investigated in fiber tractography. In this study, DTI- and DKI-based fiber tractography of the CST, OR, and AF was investigated in healthy volunteers and glioma patients. For the CST, significantly larger tract volumes were seen in DKI-based fiber tractography. Similar results were obtained for the OR, except for the right OR in patients. In the case of the AF, results of both models were comparable with DTI-based fiber tractography showing even significantly larger tract volumes in patients. In the case of the CST and OR, DKI-based fiber tractography contributes to advanced visualization under clinical time constraints, whereas for the AF, other models should be considered.
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Affiliation(s)
- Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
- Correspondence:
| | - Julia Emde
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
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20
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Naeyaert M, Aelterman J, Van Audekerke J, Golkov V, Cremers D, Pižurica A, Sijbers J, Verhoye M. Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing. Magn Reson Med 2020; 85:1397-1413. [PMID: 33009866 DOI: 10.1002/mrm.28520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/22/2020] [Accepted: 08/24/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE Echo planar imaging (EPI) is commonly used to acquire the many volumes needed for high angular resolution diffusion Imaging (HARDI), posing a higher risk for artifacts, such as distortion and deformation. An alternative to EPI is fast spin echo (FSE) imaging, which has fewer artifacts but is inherently slower. The aim is to accelerate FSE such that a HARDI data set can be acquired in a time comparable to EPI using compressed sensing. METHODS Compressed sensing was applied in either q-space or simultaneously in k-space and q-space, by undersampling the k-space in the phase-encoding direction or retrospectively eliminating diffusion directions for different degrees of undersampling. To test the replicability of the acquisition and reconstruction, brain data were acquired from six mice, and a numerical phantom experiment was performed. All HARDI data were analyzed individually using constrained spherical deconvolution, and the apparent fiber density and complexity metric were evaluated, together with whole-brain tractography. RESULTS The apparent fiber density and complexity metric showed relatively minor differences when only q-space undersampling was used, but deteriorate when k-space undersampling was applied. Likewise, the tract density weighted image showed good results when only q-space undersampling was applied using 15 directions or more, but information was lost when fewer volumes or k-space undersampling were used. CONCLUSION It was found that acquiring 15 to 20 diffusion directions with a full k-space and reconstructed using compressed sensing could suffice for a replicable measurement of quantitative measures in mice, where areas near the sinuses and ear cavities are untainted by signal loss.
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Affiliation(s)
| | - Jan Aelterman
- Imec-IPI, Department of Telecommunications and Information Processing, Ghent University, Ghent, Belgium
| | | | - Vladimir Golkov
- Department of Computer Science, Technical University of Munich, Garching, Germany
| | - Daniel Cremers
- Department of Computer Science, Technical University of Munich, Garching, Germany
| | - Aleksandra Pižurica
- Imec-IPI, Department of Telecommunications and Information Processing, Ghent University, Ghent, Belgium
| | - Jan Sijbers
- Imec-Vision Lab, University of Antwerp, Antwerp, Belgium
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21
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Ramos-Llordén G, Ning L, Liao C, Mukhometzianov R, Michailovich O, Setsompop K, Rathi Y. High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR). Magn Reson Med 2020; 84:1781-1795. [PMID: 32125020 PMCID: PMC9149785 DOI: 10.1002/mrm.28232] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/06/2020] [Accepted: 02/07/2020] [Indexed: 01/26/2023]
Abstract
PURPOSE To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in vivo scan on a clinical scanner. METHODS We extend the ultra-high resolution diffusion MRI acquisition technique, gSlider, by allowing undersampling in q-space and radiofrequency (RF)-encoding space, thereby dramatically reducing the total acquisition time of conventional gSlider. The novel method, termed gSlider-SR, compensates for the lack of acquired information by exploiting redundancy in the dMRI data using a basis of spherical ridgelets (SR), while simultaneously enhancing the signal-to-noise ratio. Using Monte Carlo simulation with realistic noise levels and several acquisitions of in vivo human brain dMRI data (acquired on a Siemens Prisma 3T scanner), we demonstrate the efficacy of our method using several quantitative metrics. RESULTS For high-resolution dMRI data with realistic noise levels (synthetically added), we show that gSlider-SR can reconstruct high-quality dMRI data at different acceleration factors preserving both signal and angular information. With in vivo data, we demonstrate that gSlider-SR can accurately reconstruct 860 μm diffusion MRI data (64 diffusion directions at b = 2000 s / mm 2 ), at comparable quality as that obtained with conventional gSlider with four averages, thereby providing an eight-fold reduction in scan time (from 1 hour 20 to 10 minutes). CONCLUSIONS gSlider-SR enables whole-brain high angular resolution dMRI at a submillimeter spatial resolution with a dramatically reduced acquisition time, making it feasible to use the proposed scheme on existing clinical scanners.
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Affiliation(s)
- Gabriel Ramos-Llordén
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Lipeng Ning
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Rinat Mukhometzianov
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Oleg Michailovich
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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22
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Moeller S, Pisharady Kumar P, Andersson J, Akcakaya M, Harel N, Ma RE, Wu X, Yacoub E, Lenglet C, Ugurbil K. Diffusion Imaging in the Post HCP Era. J Magn Reson Imaging 2020; 54:36-57. [PMID: 32562456 DOI: 10.1002/jmri.27247] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/11/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
Diffusion imaging is a critical component in the pursuit of developing a better understanding of the human brain. Recent technical advances promise enabling the advancement in the quality of data that can be obtained. In this review the context for different approaches relative to the Human Connectome Project are compared. Significant new gains are anticipated from the use of high-performance head gradients. These gains can be particularly large when the high-performance gradients are employed together with ultrahigh magnetic fields. Transmit array designs are critical in realizing high accelerations in diffusion-weighted (d)MRI acquisitions, while maintaining large field of view (FOV) coverage, and several techniques for optimal signal-encoding are now available. Reconstruction and processing pipelines that precisely disentangle the acquired neuroanatomical information are established and provide the foundation for the application of deep learning in the advancement of dMRI for complex tissues. Level of Evidence: 3 Technical Efficacy Stage: Stage 3.
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Affiliation(s)
- Steen Moeller
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pramod Pisharady Kumar
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.,Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Noam Harel
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ruoyun Emily Ma
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Xiaoping Wu
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
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23
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Mani MP, Aggarwal HK, Ghosh S, Jacob M. Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2020; 2020:913-916. [PMID: 33574989 DOI: 10.1109/isbi45749.2020.9098593] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high resolution imaging. The proposed reconstruction jointly recovers all the diffusion weighted images in a single step from a joint k-q under-sampled acquisition in a parallel MRI setting. We propose the novel use of a pre-trained denoiser as a regularizer in a model-based reconstruction for the recovery of highly under-sampled data. Specifically, we designed the denoiser based on a general diffusion MRI tissue microstructure model for multi-compartmental modeling. By using a wide range of biologically plausible parameter values for the multi-compartmental microstructure model, we simulated diffusion signal that spans the entire microstructure parameter space. A neural network was trained in an unsupervised manner using an autoencoder to learn the diffusion MRI signal subspace. We employed the autoencoder in a model-based reconstruction and show that the autoencoder provides a strong denoising prior to recover the q-space signal. We show reconstruction results on a simulated brain dataset that shows high acceleration capabilities of the proposed method.
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24
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Zhang C, Arefin TM, Nakarmi U, Lee CH, Li H, Liang D, Zhang J, Ying L. Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method. Neuroimage 2020; 210:116584. [PMID: 32004717 DOI: 10.1016/j.neuroimage.2020.116584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly under-sampled k-space data, which significantly reduced the scan time. Using full k-space 3D dMRI data of post-mortem mouse brains, we retrospectively compared the performance of the so-called kernel low rank (KLR) method with a conventional compressed sensing (CS) method in terms of image quality and ability to resolve complex fiber orientations and connectivity. The results demonstrated that the KLR-CS method outperformed the conventional CS method for acceleration factors up to 8 and was likely to enhance our ability to investigate brain microstructure and connectivity using high-resolution 3D dMRI.
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Affiliation(s)
- Chaoyi Zhang
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Tanzil Mahmud Arefin
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Ukash Nakarmi
- Radiology, Stanford University, Stanford, CA, United States
| | - Choong Heon Lee
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Hongyu Li
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China
| | - Jiangyang Zhang
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Leslie Ying
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States; Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, NY, United States.
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25
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Pizzolato M, Palombo M, Bonet-Carne E, Tax CMW, Grussu F, Ianus A, Bogusz F, Pieciak T, Ning L, Larochelle H, Descoteaux M, Chamberland M, Blumberg SB, Mertzanidou T, Alexander DC, Afzali M, Aja-Fernández S, Jones DK, Westin CF, Rathi Y, Baete SH, Cordero-Grande L, Ladner T, Slator PJ, Hajnal JV, Thiran JP, Price AN, Sepehrband F, Zhang F, Hutter J. Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. COMPUTATIONAL DIFFUSION MRI 2020. [DOI: 10.1007/978-3-030-52893-5_17] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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26
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Hong Y, Kim J, Chen G, Lin W, Yap PT, Shen D. Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2717-2725. [PMID: 30990424 PMCID: PMC6935161 DOI: 10.1109/tmi.2019.2911203] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.
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Affiliation(s)
- Yoonmi Hong
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
| | - Jaeil Kim
- School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea
| | - Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
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27
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Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI. Med Image Anal 2019; 54:122-137. [DOI: 10.1016/j.media.2019.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 11/22/2018] [Accepted: 02/26/2019] [Indexed: 11/22/2022]
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28
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Bopp MH, Pietruk PM, Nimsky C, Carl B. Fiber tractography of the optic radiations: impact of diffusion model, voxel shape and orientation. J Neurosurg Sci 2019; 65:494-502. [PMID: 30724054 DOI: 10.23736/s0390-5616.19.04622-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Reliable visualization of the optic radiations (OR) is of major importance in tumor surgery close to the OR to prevent permanent visual field deficits. Diffusion tensor imaging (DTI) based fiber tractography (FT) has become a standard tool to visualize major white matter tracts and to support the prevention of postoperative deficits. Nevertheless, FT of the OR is notoriously challenging due to its high neuroanatomical complexity. METHODS To improve FT of the OR we analyzed the effect of a more complex diffusion model and the effect of different voxel shapes and orientations. MRI data of 21 healthy subjects was acquired using isometric and anisometric voxel sizes and standard and adapted slice angulation. FT was performed using the DTI based approach and an orientation distribution function (ODF) based approach. Results were visually inspected, and fiber tract volumes were compared. RESULTS DTI based FT led to poor results, failing to reconstruct plausible tracts at all in up to 26.11 % of all cases. The ODF based approach resulted in more compound and solid tracts showing also significantly larger tract volumes. Voxel shape or orientation did not influence DTI but ODF based FT. Isometric or anisometric voxels with standard slice orientation revealed highest tract volumes. Adapted orientation in combination with anisometric voxels led to significantly smaller tract volumes. CONCLUSIONS Plausible tractography of the OR can be achieved using ODF based fiber tracking within a clinically feasible timeframe. Voxel shape and orientation seem to be of minor importance and might be kept to isometric voxel for flexible application of FT.
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Affiliation(s)
- Miriam H Bopp
- Department of Neurosurgery, University of Marburg, Marburg, Germany - .,Marburg Center for Mind, Brain and Behavior, Marburg, Germany -
| | - Peter M Pietruk
- Department of Neurosurgery, University of Marburg, Marburg, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Marburg, Germany.,Marburg Center for Mind, Brain and Behavior, Marburg, Germany
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Marburg, Germany.,Marburg Center for Mind, Brain and Behavior, Marburg, Germany
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29
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Pasternak O, Kelly S, Sydnor VJ, Shenton ME. Advances in microstructural diffusion neuroimaging for psychiatric disorders. Neuroimage 2018; 182:259-282. [PMID: 29729390 PMCID: PMC6420686 DOI: 10.1016/j.neuroimage.2018.04.051] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 04/18/2018] [Accepted: 04/23/2018] [Indexed: 12/18/2022] Open
Abstract
Understanding the neuropathological underpinnings of mental disorders such as schizophrenia, major depression, and bipolar disorder is an essential step towards the development of targeted treatments. Diffusion MRI studies utilizing the diffusion tensor imaging (DTI) model have been extremely successful to date in identifying microstructural brain abnormalities in individuals suffering from mental illness, especially in regions of white matter, although identified abnormalities have been biologically non-specific. Building on DTI's success, in recent years more advanced diffusion MRI methods have been developed and applied to the study of psychiatric populations, with the aim of offering increased sensitivity to subtle neurological abnormalities, as well as improved specificity to candidate pathologies such as demyelination and neuroinflammation. These advanced methods, however, usually come at the cost of prolonged imaging sequences or reduced signal to noise, and they are more difficult to evaluate compared with the more simplified approach taken by the now common DTI model. To date, a limited number of advanced diffusion MRI methods have been employed to study schizophrenia, major depression and bipolar disorder populations. In this review we survey these studies, compare findings across diverse methods, discuss the main benefits and limitations of the different methods, and assess the extent to which the application of more advanced diffusion imaging approaches has led to novel and transformative information with regards to our ability to better understand the etiology and pathology of mental disorders.
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Affiliation(s)
- Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Sinead Kelly
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Valerie J Sydnor
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Veteran Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
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30
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Tobisch A, Stirnberg R, Harms RL, Schultz T, Roebroeck A, Breteler MMB, Stöcker T. Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging. Front Neurosci 2018; 12:650. [PMID: 30319336 PMCID: PMC6165908 DOI: 10.3389/fnins.2018.00650] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 08/30/2018] [Indexed: 11/23/2022] Open
Abstract
Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.
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Affiliation(s)
- Alexandra Tobisch
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Robbert L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Bonn, Germany.,Bonn-Aachen International Center for Information Technology, University of Bonn, Bonn, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Monique M B Breteler
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Faculty of Medicine, Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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31
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Lee MB, Kim YH, Jahng GH, Kwon OI. Angular resolution enhancement technique for diffusion-weighted imaging (DWI) using predicted diffusion gradient directions. Neuroimage 2018; 183:836-846. [PMID: 30193975 DOI: 10.1016/j.neuroimage.2018.08.072] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 08/02/2018] [Accepted: 08/29/2018] [Indexed: 11/27/2022] Open
Abstract
Anisotropic diffusion MRI techniques using single-shell or multi-shell acquisitions have been proposed as a means to overcome some limitations imposed by diffusion tensor imaging (DTI), especially in complex models of fibre orientation distribution in voxels. A long acquisition time for the angular resolution of diffusion MRI is a major obstacle to practical clinical implementations. In this paper, we propose a novel method to improve angular resolution of diffusion MRI acquisition using given diffusion gradient (DG) directions. First, we define a local diffusion pattern map of diffusion MR signals on a single shell in given DG directions. Using the local diffusion pattern map, we design a prediction scheme to determine the best DG direction to be synthesized within a nearest neighborhood DG directions group. Second, the local diffusion pattern map and the spherical distance on the shell are combined to determine a synthesized diffusion signal in the new DG direction. Using the synthesized and measured diffusion signals on a single sphere, we estimate a spin orientation distribution function (SDF) with human brain data. Although the proposed method is applied to SDF, a basic idea is to increase the angular resolution using the measured diffusion signals in various DG directions. The method can be applicable to different acquired multi-shell data or diffusion spectroscopic imaging (DSI) data. We validate the proposed method by comparing the recovered SDFs using the angular resolution enhanced diffusion signals with the recovered SDF using the measured diffusion data. The developed method provides an enhanced SDF resolution and improved multiple fiber structure by incorporating synthesized signals. The proposed method was also applied neurite orientation dispersion and density imaging (NODDI) using multi-shell acquisitions.
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Affiliation(s)
- Mun Bae Lee
- Department of Mathematics, Konkuk University, Neungdong-ro, Gwangjin-gu, Seoul, 05029, Republic of Korea
| | - Yeon Hyang Kim
- Department of Mathematics, Central Michigan University, Mount Pleasant, MI, 48859, USA
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Dongnam-ro, Gangdong-gu, Seoul, 05278, Republic of Korea
| | - Oh-In Kwon
- Department of Mathematics, Konkuk University, Neungdong-ro, Gwangjin-gu, Seoul, 05029, Republic of Korea.
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32
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Schwab E, Vidal R, Charon N. Joint spatial-angular sparse coding for dMRI with separable dictionaries. Med Image Anal 2018; 48:25-42. [PMID: 29803921 DOI: 10.1016/j.media.2018.05.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 05/01/2018] [Accepted: 05/07/2018] [Indexed: 01/28/2023]
Abstract
Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, in vivo, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times. To accelerate dMRI, compressed sensing (CS) has been utilized by exploiting a sparse dictionary representation of the data, discovered through sparse coding. The sparser the representation, the fewer samples are needed to reconstruct a high resolution signal with limited information loss, and so an important area of research has focused on finding the sparsest possible representation of dMRI. Current reconstruction methods however, rely on an angular representation per voxel with added spatial regularization, and so, for non-zero signals, one is required to have at least one non-zero coefficient per voxel. This means that the global level of sparsity must be greater than the number of voxels. In contrast, we propose a joint spatial-angular representation of dMRI that will allow us to achieve levels of global sparsity that are below the number of voxels. A major challenge, however, is the computational complexity of solving a global sparse coding problem over large-scale dMRI. In this work, we present novel adaptations of popular sparse coding algorithms that become better suited for solving large-scale problems by exploiting spatial-angular separability. Our experiments show that our method achieves significantly sparser representations of HARDI than is possible by the state of the art.
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Affiliation(s)
- Evan Schwab
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
| | - René Vidal
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Nicolas Charon
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
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33
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Hong Y, O'Donnell LJ, Savadjiev P, Zhang F, Wassermann D, Pasternak O, Johnson H, Paulsen J, Vonsattel JP, Makris N, Westin CF, Rathi Y. Genetic load determines atrophy in hand cortico-striatal pathways in presymptomatic Huntington's disease. Hum Brain Mapp 2018; 39:3871-3883. [PMID: 29797744 DOI: 10.1002/hbm.24217] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 04/30/2018] [Accepted: 05/06/2018] [Indexed: 12/31/2022] Open
Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.
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Affiliation(s)
- Yi Hong
- Department of Computer Science, University of Georgia, Athens, Georgia
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter Savadjiev
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Ofer Pasternak
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Hans Johnson
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
| | - Jane Paulsen
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
| | - Jean-Paul Vonsattel
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Nikos Makris
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Carl F Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Yan H, Carmichael O, Paul D, Peng J. Estimating fiber orientation distribution from diffusion MRI with spherical needlets. Med Image Anal 2018; 46:57-72. [PMID: 29502033 PMCID: PMC5910185 DOI: 10.1016/j.media.2018.01.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 12/22/2017] [Accepted: 01/16/2018] [Indexed: 11/25/2022]
Abstract
We present a novel method for estimation of the fiber orientation distribution (FOD) function based on diffusion-weighted magnetic resonance imaging (D-MRI) data. We formulate the problem of FOD estimation as a regression problem through spherical deconvolution and a sparse representation of the FOD by a spherical needlets basis that forms a multi-resolution tight frame for spherical functions. This sparse representation allows us to estimate the FOD by ℓ1-penalized regression under a non-negativity constraint on the estimated FOD. The resulting convex optimization problem is solved by an alternating direction method of multipliers (ADMM) algorithm. The proposed method leads to a reconstruction of the FOD that is accurate, has low variability and preserves sharp features. Through extensive experiments, we demonstrate the effectiveness and favorable performance of the proposed method compared to three existing methods. Specifically, we demonstrate that the proposed method is able to successfully resolve fiber crossings at small angles and automatically identify isotropic diffusion. We also apply the proposed method to real 3T D-MRI data sets of healthy individuals. The results show realistic depictions of crossing fibers that are more accurate, less noisy, and lead to superior tractography results compared to competing methods.
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Affiliation(s)
- Hao Yan
- Department of Statistics, University of California, One Shields Ave., Davis, CA 95616, United States
| | - Owen Carmichael
- Pennington Biomedical Research Center, Louisiana State University, 6400 Perkins Road, Baton Rouge, LA 70808, United States
| | - Debashis Paul
- Department of Statistics, University of California, One Shields Ave., Davis, CA 95616, United States
| | - Jie Peng
- Department of Statistics, University of California, One Shields Ave., Davis, CA 95616, United States.
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Yin S, You X, Yang X, Peng Q, Zhu Z, Jing XY. A joint space-angle regularization approach for single 4D diffusion image super-resolution. Magn Reson Med 2018; 80:2173-2187. [PMID: 29672917 DOI: 10.1002/mrm.27184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 02/28/2018] [Accepted: 02/28/2018] [Indexed: 11/08/2022]
Abstract
PURPOSE Low signal-to-noise-ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space-angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down-sampled in both 3D-space and q-space. METHODS Different from the existing works which combine multiple 4D LR diffusion images acquired using specific acquisition protocols, the proposed method reconstructs HSAR dMRI from only a single 4D dMRI by exploring and integrating two key priors, that is, the nonlocal self-similarity in the spatial domain as a prior to increase spatial resolution and ridgelet approximations in the diffusion domain as another prior to increase the angular resolution of dMRI. To more effectively capture nonlocal self-similarity in the spatial domain, a novel 3D block-based nonlocal means filter is imposed as the 3D image space regularization term which is accurate in measuring the similarity and fast for 3D reconstruction. To reduce computational complexity, we use the L2 -norm instead of sparsity constraint on the representation coefficients. RESULTS Experimental results demonstrate that the proposed method can obtain the HSAR dMRI efficiently with approximately 2% per-voxel root-mean-square error between the actual and reconstructed HSAR dMRI. CONCLUSION The proposed approach can effectively increase the spatial and angular resolution of the dMRI which is independent of the acquisition protocol, thus overcomes the inherent resolution limitation of imaging systems.
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Affiliation(s)
- Shi Yin
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xinge You
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Qinmu Peng
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ziqi Zhu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
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Ye C, Prince JL. Dictionary-based fiber orientation estimation with improved spatial consistency. Med Image Anal 2018; 44:41-53. [PMID: 29190575 PMCID: PMC5771867 DOI: 10.1016/j.media.2017.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 11/19/2017] [Accepted: 11/22/2017] [Indexed: 10/18/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) has enabled in vivo investigation of white matter tracts. Fiber orientation (FO) estimation is a key step in tract reconstruction and has been a popular research topic in dMRI analysis. In particular, the sparsity assumption has been used in conjunction with a dictionary-based framework to achieve reliable FO estimation with a reduced number of gradient directions. Because image noise can have a deleterious effect on the accuracy of FO estimation, previous works have incorporated spatial consistency of FOs in the dictionary-based framework to improve the estimation. However, because FOs are only indirectly determined from the mixture fractions of dictionary atoms and not modeled as variables in the objective function, these methods do not incorporate FO smoothness directly, and their ability to produce smooth FOs could be limited. In this work, we propose an improvement to Fiber Orientation Reconstruction using Neighborhood Information (FORNI), which we call FORNI+; this method estimates FOs in a dictionary-based framework where FO smoothness is better enforced than in FORNI alone. We describe an objective function that explicitly models the actual FOs and the mixture fractions of dictionary atoms. Specifically, it consists of data fidelity between the observed signals and the signals represented by the dictionary, pairwise FO dissimilarity that encourages FO smoothness, and weighted ℓ1-norm terms that ensure the consistency between the actual FOs and the FO configuration suggested by the dictionary representation. The FOs and mixture fractions are then jointly estimated by minimizing the objective function using an iterative alternating optimization strategy. FORNI+ was evaluated on a simulation phantom, a physical phantom, and real brain dMRI data. In particular, in the real brain dMRI experiment, we have qualitatively and quantitatively evaluated the reproducibility of the proposed method. Results demonstrate that FORNI+ produces FOs with better quality compared with competing methods.
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Affiliation(s)
- Chuyang Ye
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
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Abascal JFPJ, Desco M, Parra-Robles J. Incorporation of Prior Knowledge of Signal Behavior Into the Reconstruction to Accelerate the Acquisition of Diffusion MRI Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:547-556. [PMID: 29408783 DOI: 10.1109/tmi.2017.2765281] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Diffusion MRI data are generally acquired using hyperpolarized gases during patient breath-hold, which yields a compromise between achievable image resolution, lung coverage, and number of -values. In this paper, we propose a novel method that accelerates the acquisition of diffusion MRI data by undersampling in both the spatial and -value dimensions and incorporating knowledge about signal decay into the reconstruction (SIDER). SIDER is compared with total variation (TV) reconstruction by assessing its effect on both the recovery of ventilation images and the estimated mean alveolar dimensions (MADs). Both methods are assessed by retrospectively undersampling diffusion data sets ( =8) of healthy volunteers and patients with Chronic Obstructive Pulmonary Disease (COPD) for acceleration factors between x2 and x10. TV led to large errors and artifacts for acceleration factors equal to or larger than x5. SIDER improved TV, with a lower solution error and MAD histograms closer to those obtained from fully sampled data for acceleration factors up to x10. SIDER preserved image quality at all acceleration factors, although images were slightly smoothed and some details were lost at x10. In conclusion, we developed and validated a novel compressed sensing method for lung MRI imaging and achieved high acceleration factors, which can be used to increase the amount of data acquired during breath-hold. This methodology is expected to improve the accuracy of estimated lung microstructure dimensions and provide more options in the study of lung diseases with MRI.
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38
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Cheng J, Basser PJ. Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in Diffusion MRI. Med Image Anal 2018; 43:112-128. [DOI: 10.1016/j.media.2017.10.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 09/27/2017] [Accepted: 10/05/2017] [Indexed: 11/15/2022]
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Shaffer JJ, Ghayoor A, Long JD, Kim RE, Lourens S, O'Donnell LJ, Westin C, Rathi Y, Magnotta V, Paulsen JS, Johnson HJ. Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration. Hum Brain Mapp 2017; 38:1460-1477. [PMID: 28045213 PMCID: PMC5400101 DOI: 10.1002/hbm.23465] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 10/11/2016] [Accepted: 11/03/2016] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Huntington's disease (HD) is a genetic neurodegenerative disorder that primarily affects striatal neurons. Striatal volume loss is present years before clinical diagnosis; however, white matter degradation may also occur prior to diagnosis. Diffusion-weighted imaging (DWI) can measure microstructural changes associated with degeneration that precede macrostructural changes. DWI derived measures enhance understanding of degeneration in prodromal HD (pre-HD). METHODS As part of the PREDICT-HD study, N = 191 pre-HD individuals and 70 healthy controls underwent two or more (baseline and 1-5 year follow-up) DWI, with n = 649 total sessions. Images were processed using cutting-edge DWI analysis methods for large multicenter studies. Diffusion tensor imaging (DTI) metrics were computed in selected tracts connecting the primary motor, primary somato-sensory, and premotor areas of the cortex with the subcortical caudate and putamen. Pre-HD participants were divided into three CAG-Age Product (CAP) score groups reflecting clinical diagnosis probability (low, medium, or high probabilities). Baseline and longitudinal group differences were examined using linear mixed models. RESULTS Cross-sectional and longitudinal differences in DTI measures were present in all three CAP groups compared with controls. The high CAP group was most affected. CONCLUSIONS This is the largest longitudinal DWI study of pre-HD to date. Findings showed DTI differences, consistent with white matter degeneration, were present up to a decade before predicted HD diagnosis. Our findings indicate a unique role for disrupted connectivity between the premotor area and the putamen, which may be closely tied to the onset of motor symptoms in HD. Hum Brain Mapp 38:1460-1477, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
| | - Ali Ghayoor
- Department of Electrical and Computer EngineeringUniversity of IowaIowa CityIowa
| | - Jeffrey D. Long
- Department of PsychiatryUniversity of IowaIowa CityIowa
- Department of BiostatisticsUniversity of IowaIowa CityIowa
| | | | - Spencer Lourens
- Department of BiostatisticsIndiana University School of MedicineIndianapolisIndiana
| | - Lauren J. O'Donnell
- Department of RadiologyBrigham and Women's Hospital Harvard Medical SchoolBostonMassachusetts
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women's Hospital Harvard Medical SchoolBostonMassachusetts
| | - Yogesh Rathi
- Department of RadiologyBrigham and Women's Hospital Harvard Medical SchoolBostonMassachusetts
| | - Vincent Magnotta
- Department of RadiologyUniversity of IowaIowa CityIowa
- Department of PsychiatryUniversity of IowaIowa CityIowa
- Biomedical EngineeringUniversity of IowaIowa CityIowa
| | - Jane S. Paulsen
- Department of PsychiatryUniversity of IowaIowa CityIowa
- Department of Neurology, Carver College of MedicineUniversity of IowaIowa CityIowa
- Neuroscience, Carver College of MedicineUniversity of IowaIowa CityIowa
| | - Hans J. Johnson
- Department of Electrical and Computer EngineeringUniversity of IowaIowa CityIowa
- Department of PsychiatryUniversity of IowaIowa CityIowa
- Biomedical EngineeringUniversity of IowaIowa CityIowa
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40
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Ye C, Prince JL. Probabilistic tractography using Lasso bootstrap. Med Image Anal 2017; 35:544-553. [PMID: 27662597 PMCID: PMC5099091 DOI: 10.1016/j.media.2016.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 08/16/2016] [Accepted: 08/29/2016] [Indexed: 10/21/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography. However, recently developed models have incorporated sparsity regularization to reduce the required number of gradient directions to resolve crossing FOs, and the residual bootstrap used in previous methods is not applicable to these models. In this work, we propose a probabilistic tractography algorithm named Lasso bootstrap tractography (LBT) for the models that incorporate sparsity. Using a fixed tensor basis and a sparsity assumption, diffusion signals are modeled using a Lasso formulation. With the residuals from the Lasso model, a distribution of diffusion signals is obtained according to a modified Lasso bootstrap strategy. FOs are then estimated from the synthesized diffusion signals by an algorithm that improves FO estimation by enforcing spatial consistency of FOs. Finally, streamlining fiber tracking is performed with the computed FOs. The LBT algorithm was evaluated on simulated and real dMRI data both qualitatively and quantitatively. Results demonstrate that LBT outperforms state-of-the-art algorithms.
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Affiliation(s)
- Chuyang Ye
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
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41
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Ye C, Prince JL. A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation. Comput Med Imaging Graph 2016; 54:35-47. [PMID: 27671948 DOI: 10.1016/j.compmedimag.2016.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 09/01/2016] [Accepted: 09/16/2016] [Indexed: 11/18/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) provides information about the microstructure of white matter in the human brain. From dMRI, streamlining tractography is often used to reconstruct computational representations of white matter tracts from which differences in structural connectivity can be explored. In the fiber tracking process, anatomical information can help reduce tracking errors caused by crossing fibers and image noise. In this paper, we propose a Bayesian method for estimating fiber orientations (FOs) guided by anatomical tract information using diffusion tensor imaging (DTI), which is a standard clinical and research dMRI protocol. The proposed method is named Fiber Orientation Reconstruction guided by Tract Segmentation (FORTS). A first step segments and labels the white matter tracts volumetrically, including explicit representations of crossing regions. A second step estimates the FOs using the diffusion information and the anatomical knowledge from segmented white matter tracts. A single FO is estimated in the noncrossing regions while two FOs are estimated in the crossing regions. A third step carries out streamlining tractography that uses information from both the segmented tracts and the estimated FOs. Experiments performed on a digital crossing phantom, a physical phantom, and brain DTI of 18 healthy subjects show that FORTS is able to use the anatomical information to produce FOs with better accuracy and to reduce anatomically incorrect streamlines. In particular, on the brain DTI data, we studied the connectivity of anatomically defined tracts to cortical areas, which is not straightforwardly achievable using only volumetric tract segmentation. These connectivity results demonstrate the potential application of FORTS to scientific studies.
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Affiliation(s)
- Chuyang Ye
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
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42
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Multi-Tissue Decomposition of Diffusion MRI Signals via Sparse-Group Estimation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:4340-4353. [PMID: 27392357 PMCID: PMC5219847 DOI: 10.1109/tip.2016.2588328] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Sparse estimation techniques are widely utilized in diffusion magnetic resonance imaging (DMRI). In this paper, we present an algorithm for solving the ℓ0 sparse-group estimation problem and apply it to the tissue signal separation problem in DMRI. Our algorithm solves the ℓ0 problem directly, unlike existing approaches that often seek to solve its relaxed approximations. We include the mathematical proofs showing that the algorithm will converge to a solution satisfying the firstorder optimality condition within a finite number of iterations. We apply this algorithm to DMRI data to tease apart signal contributions from white matter, gray matter, and cerebrospinal fluid with the aim of improving the estimation of the fiber orientation distribution function (FODF). Unlike spherical deconvolution approaches that assume an invariant fiber response function (RF), our approach utilizes an RF group to span the signal subspace of each tissue type, allowing greater flexibility in accounting for possible variations of the RF throughout space and within each voxel. Our ℓ0 algorithm allows for the natural groupings of the RFs to be considered during signal decomposition. Experimental results confirm that our method yields estimates of FODFs and volume fractions of tissue compartments with improved robustness and accuracy. Our ℓ0 algorithm is general and can be applied to sparse estimation problems beyond the scope of this paper.
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43
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Ye C, Zhuo J, Gullapalli RP, Prince JL. Estimation of fiber orientations using neighborhood information. Med Image Anal 2016; 32:243-56. [PMID: 27209007 PMCID: PMC4903913 DOI: 10.1016/j.media.2016.05.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Revised: 05/09/2016] [Accepted: 05/14/2016] [Indexed: 10/21/2022]
Abstract
Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of fiber orientations (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted ℓ1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.
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Affiliation(s)
- Chuyang Ye
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Jiachen Zhuo
- Department of Radiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rao P Gullapalli
- Department of Radiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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Li J, Shi Y, Toga AW. Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain. IEEE SIGNAL PROCESSING MAGAZINE 2016; 33:36-51. [PMID: 27212872 PMCID: PMC4869891 DOI: 10.1109/msp.2015.2510024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Junning Li
- Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California
| | - Yonggang Shi
- Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California
| | - Arthur W Toga
- Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California
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46
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Liao C, Chen Y, Cao X, Chen S, He H, Mani M, Jacob M, Magnotta V, Zhong J. Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint. Magn Reson Med 2016; 77:1359-1366. [PMID: 26968846 DOI: 10.1002/mrm.26199] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 01/25/2016] [Accepted: 02/12/2016] [Indexed: 01/18/2023]
Abstract
PURPOSE To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging. THEORY AND METHODS The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets. RESULTS It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions. CONCLUSION Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Congyu Liao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ying Chen
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Song Chen
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Merry Mani
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Vincent Magnotta
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China.,Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang, China
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47
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Assessment of the Characteristics of Orientation Distribution Functions in HARDI Using Morphological Metrics. PLoS One 2016; 11:e0150161. [PMID: 26919477 PMCID: PMC4769219 DOI: 10.1371/journal.pone.0150161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 02/10/2016] [Indexed: 12/24/2022] Open
Abstract
Orientation distribution functions (ODFs) are widely used to resolve fiber crossing problems in high angular resolution diffusion imaging (HARDI). The characteristics of the ODFs are often assessed using a visual criterion, although the use of objective criteria is also reported, which are directly borrowed from classic signal and image processing theory because they are intuitive and simple to compute. However, they are not always pertinent for the characterization of ODFs. We propose a more general paradigm for assessing the characteristics of ODFs. The idea consists in regarding an ODF as a three-dimensional (3D) point cloud, projecting the 3D point cloud onto an angle-distance map, constructing an angle-distance matrix, and calculating metrics such as length ratio, separability, and uncertainty. The results from both simulated and real data show that the proposed metrics allow for the assessment of the characteristics of ODFs in a quantitative and relatively complete manner.
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48
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Ning L, Setsompop K, Michailovich O, Makris N, Shenton ME, Westin CF, Rathi Y. A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. Neuroimage 2016; 125:386-400. [PMID: 26505296 PMCID: PMC4691422 DOI: 10.1016/j.neuroimage.2015.10.061] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 09/11/2015] [Accepted: 10/20/2015] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.
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Affiliation(s)
- Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Kawin Setsompop
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Nikos Makris
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Martha E Shenton
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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49
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Ghosh A, Deriche R. A survey of current trends in diffusion MRI for structural brain connectivity. J Neural Eng 2015; 13:011001. [PMID: 26695367 DOI: 10.1088/1741-2560/13/1/011001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In this paper, we review the state of the art in diffusion magnetic resonance imaging (dMRI) and we present current trends in modelling the brain's tissue microstructure and the human connectome. dMRI is today the only tool that can probe the brain's axonal architecture in vivo and non-invasively, and has grown in leaps and bounds in the last two decades since its conception. A plethora of models with increasing complexity and better accuracy have been proposed to characterise the integrity of the cerebral tissue, to understand its microstructure and to infer its connectivity. Here, we discuss a wide range of the most popular, important and well-established local microstructure models and biomarkers that have been proposed from these models. Finally, we briefly present the state of the art in tractography techniques that allow us to understand the architecture of the brain's connectivity.
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Affiliation(s)
- Aurobrata Ghosh
- Project Team Athena, INRIA Sophia Antipolis-Méditerranée, 2004 Route des Lucioles-BP 93, 06902 Sophia Antipolis Cedex, France
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50
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Canales-Rodríguez EJ, Daducci A, Sotiropoulos SN, Caruyer E, Aja-Fernández S, Radua J, Yurramendi Mendizabal JM, Iturria-Medina Y, Melie-García L, Alemán-Gómez Y, Thiran JP, Sarró S, Pomarol-Clotet E, Salvador R. Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. PLoS One 2015; 10:e0138910. [PMID: 26470024 PMCID: PMC4607500 DOI: 10.1371/journal.pone.0138910] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 09/06/2015] [Indexed: 11/28/2022] Open
Abstract
Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.
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Affiliation(s)
- Erick J. Canales-Rodríguez
- FIDMAG Germanes Hospitalàries, C/ Dr. Antoni Pujadas, 38, 08830, Sant Boi de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, C/Dr Esquerdo, 46, 28007, Madrid, Spain
- * E-mail:
| | - Alessandro Daducci
- Signal Processing Lab (LTS5), École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
- University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Stamatios N. Sotiropoulos
- Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, John Radcliffe Hospital, Oxford, OX39DU, United Kingdom
| | - Emmanuel Caruyer
- CNRS—IRISA (UMR 6074), Inria, VisAGeS Project-Team, INSERM, VisAGeS U746, Université de Rennes 1, Campus de Beaulieu, 35042, Rennes Cedex, France
| | - Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Joaquim Radua
- FIDMAG Germanes Hospitalàries, C/ Dr. Antoni Pujadas, 38, 08830, Sant Boi de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, C/Dr Esquerdo, 46, 28007, Madrid, Spain
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jesús M. Yurramendi Mendizabal
- Departamento de Ciencia de la Computación e Inteligencial Artificial, Universidad del País Vasco, Euskal Herriko Unibertsitatea, Spain
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Lester Melie-García
- The Neuroimaging Research Laboratory, Laboratoire de Recherche en Neuroimagerie: LREN, Department of Clinical Neurosciences, University Hospital Center (CHUV), Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, C/Dr Esquerdo, 46, 28007, Madrid, Spain
- Departamento de Bioingeniería e Ingeniería Aeroespacial. Universidad Carlos III de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
- University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries, C/ Dr. Antoni Pujadas, 38, 08830, Sant Boi de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, C/Dr Esquerdo, 46, 28007, Madrid, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries, C/ Dr. Antoni Pujadas, 38, 08830, Sant Boi de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, C/Dr Esquerdo, 46, 28007, Madrid, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries, C/ Dr. Antoni Pujadas, 38, 08830, Sant Boi de Llobregat, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, C/Dr Esquerdo, 46, 28007, Madrid, Spain
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