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Clarke WT, Ligneul C, Cottaar M, Ip IB, Jbabdi S. Universal dynamic fitting of magnetic resonance spectroscopy. Magn Reson Med 2024; 91:2229-2246. [PMID: 38265152 DOI: 10.1002/mrm.30001] [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: 09/29/2023] [Revised: 11/27/2023] [Accepted: 12/17/2023] [Indexed: 01/25/2024]
Abstract
PURPOSE Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type. METHODS A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting. RESULTS Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data. CONCLUSION A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.
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Affiliation(s)
- William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - I Betina Ip
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Morelli M, Dudzikowska K, Deelchand DK, Quinn AJ, Mullins PG, Apps MAJ, Wilson M. Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594270. [PMID: 38798416 PMCID: PMC11118477 DOI: 10.1101/2024.05.15.594270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background Functional MRS (fMRS) is a technique used to measure metabolic changes in response to increased neuronal activity, providing unique insights into neurotransmitter dynamics and neuroenergetics. In this study we investigate the response of lactate and glutamate levels in the motor cortex during a sustained motor task using conventional spectral fitting and explore the use of a novel analysis approach based on the application of linear modelling directly to the spectro-temporal fMRS data. Methods fMRS data were acquired at a field strength of 3 Tesla from 23 healthy participants using a short echo-time (28ms) semi-LASER sequence. The functional task involved rhythmic hand clenching over a duration of 8 minutes and standard MRS preprocessing steps, including frequency and phase alignment, were employed. Both conventional spectral fitting and direct linear modelling were applied, and results from participant-averaged spectra and metabolite-averaged individual analyses were compared. Results We observed a 20% increase in lactate in response to the motor task, consistent with findings at higher magnetic field strengths. However, statistical testing showed some variability between the two averaging schemes and fitting algorithms. While lactate changes were supported by the direct spectral modelling approach, smaller increases in glutamate (2%) were inconsistent. Exploratory spectral modelling identified a 4% decrease in aspartate, aligning with conventional fitting and observations from prolonged visual stimulation. Conclusion We demonstrate that lactate dynamics in response to a prolonged motor task are observed using short-echo time semi-LASER at 3 Tesla, and that direct linear modelling of fMRS data is a useful complement to conventional analysis. Future work includes mitigating spectral confounds, such as scalp lipid contamination and lineshape drift, and further validation of our novel direct linear modelling approach through experimental and simulated datasets.
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Affiliation(s)
- Maria Morelli
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Katarzyna Dudzikowska
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Dinesh K. Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Andrew J. Quinn
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | | | - Matthew A. J. Apps
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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Zöllner HJ, Davies-Jenkins C, Simicic D, Tal A, Sulam J, Oeltzschner G. Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation. Magn Reson Med 2024. [PMID: 38649977 DOI: 10.1002/mrm.30110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/05/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. METHODS Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs). RESULTS Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. CONCLUSION Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.
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Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Christopher Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland, USA
- Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Hui SC, Murali-Manohar S, Zöllner HJ, Hupfeld KE, Davies-Jenkins CW, Gudmundson AT, Song Y, Yedavalli V, Wisnowski JL, Gagoski B, Oeltzschner G, Edden RA. Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580516. [PMID: 38659947 PMCID: PMC11042202 DOI: 10.1101/2024.02.15.580516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Background To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. Methods ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based the default white matter and gray matter T2 reference values in Osprey; 2) shorter WM and GM T2 values from recent literature; and 3) reduced CSF fractions. Results No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio. Conclusions ISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error.
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Affiliation(s)
- Steve C.N. Hui
- Developing Brain Institute, Children’s National Hospital, Washington, D.C. USA
- Departments of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C. USA
- Departments of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, D.C. USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
- Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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Shamaei A, Starcukova J, Rizzo R, Starcuk Z. Water removal in MR spectroscopic imaging with Casorati singular value decomposition. Magn Reson Med 2024; 91:1694-1706. [PMID: 38181180 DOI: 10.1002/mrm.29959] [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: 08/10/2023] [Revised: 11/04/2023] [Accepted: 11/17/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE Water removal is one of the computational bottlenecks in the processing of high-resolution MRSI data. The purpose of this work is to propose an approach to reduce the computing time required for water removal in large MRS data. METHODS In this work, we describe a singular value decomposition-based approach that uses the partial position-time separability and the time-domain linear predictability of MRSI data to reduce the computational time required for water removal. Our approach arranges MRS signals in a Casorati matrix form, applies low-rank approximations utilizing singular value decomposition, removes residual water from the most prominent left-singular vectors, and finally reconstructs the water-free matrix using the processed left-singular vectors. RESULTS We have demonstrated the effectiveness of our proposed algorithm for water removal using both simulated and in vivo data. The proposed algorithm encompasses a pip-installable tool ( https://pypi.org/project/CSVD/), available on GitHub ( https://github.com/amirshamaei/CSVD), empowering researchers to use it in future studies. Additionally, to further promote transparency and reproducibility, we provide comprehensive code for result replication. CONCLUSIONS The findings of this study suggest that the proposed method is a promising alternative to existing water removal methods due to its low processing time and good performance in removing water signals.
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Affiliation(s)
- Amirmohammad Shamaei
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
- Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Jana Starcukova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Rudy Rizzo
- MR Methodology, Department of Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Department of Biomedical Research, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute of Translational Entrepreneurial Medicine, Bern, Switzerland
| | - Zenon Starcuk
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
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Susnjar A, Kaiser A, Simicic D, Nossa G, Lin A, Oeltzschner G, Gudmundson A. Reproducibility Made Easy: A Tool for Methodological Transparency and Efficient Standardized Reporting based on the proposed MRSinMRS Consensus. ARXIV 2024:arXiv:2403.19594v1. [PMID: 38584615 PMCID: PMC10996772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Purpose Recent expert consensus publications have highlighted the issue of poor reproducibility in magnetic resonance spectroscopy (MRS) studies, mainly due to the lack of standardized reporting criteria, which affects their clinical applicability. To combat this, guidelines for minimum reporting standards (MRSinMRS) were introduced to aid journal editors and reviewers in ensuring the comprehensive documentation of essential MRS study parameters. Despite these efforts, the implementation of MRSinMRS standards has been slow, attributed to the diverse nomenclature used by different vendors, the variety of raw MRS data formats, and the absence of appropriate software tools for identifying and reporting necessary parameters. To overcome this obstacle, we have developed the REproducibility Made Easy (REMY) standalone toolbox. Methods REMY software supports a range of MRS data formats from major vendors like GE (p. file), Siemens (.twix, .rda, .dcm), Philips (.spar/.sdat), and Bruker (.method), facilitating easy data import and export through a user-friendly interface. REMY employs external libraries such as spec2nii and pymapVBVD to accurately read and process these diverse data formats, ensuring compatibility and ease of use for researchers in generating reproducible MRS research outputs. Users can select and import datasets, choose the appropriate vendor and data format, and then generate an MRSinMRS table, log file, and methodological documents in both Latex and PDF formats. Results REMY effectively populated key sections of the MRSinMRS table with data from all supported file types. In the hardware section, it successfully read and filled in fields for Field Strength [T], Manufacturer Name, and Software Version, covering three of the five required hardware fields. However, it could not input data for RF coil and additional hardware information due to their absence in the files. For the acquisition section, REMY accurately read and populated fields for the pulse sequence name, nominal voxel size, repetition time, echo time, number of acquisitions/excitations/shots, spectral width [Hz], and number of spectral points, significantly contributing to the completion of the Acquisition fields of the table. Furthermore, REMY generates a boilerplate methods text section for manuscripts. Conclusion This approach reduces effort and obstacles associated with writing and reporting acquisition parameters and should lead to the widespread adoption of MRSinMRS within the MRS community.
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Affiliation(s)
- Antonia Susnjar
- Athinoula A. Martinos Center for Biomedical Imaging, Institute for Innovation in Imaging, Department of Radiology Massachusetts General Hospital and Harvard Medical School, Boston MA
| | - Antonia Kaiser
- CIBM Center for Biomedical Imaging, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Gianna Nossa
- School of Health Sciences, Purdue University, West Lafayette, IN, USA
| | - Alexander Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Aaron Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore MD
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Elhadad A, Jamjoom M, Abulkasim H. Reduction of NIFTI files storage and compression to facilitate telemedicine services based on quantization hiding of downsampling approach. Sci Rep 2024; 14:5168. [PMID: 38431641 PMCID: PMC10908832 DOI: 10.1038/s41598-024-54820-4] [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: 09/06/2023] [Accepted: 02/16/2024] [Indexed: 03/05/2024] Open
Abstract
Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality.
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Affiliation(s)
- Ahmed Elhadad
- Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt
| | - Mona Jamjoom
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Hussein Abulkasim
- Department of Mathematics and Computer Science, Faculty of Science, New Valley University, El-Kharja, Egypt.
- College of Engineering and Technology, University of Science and Technology of Fujairah, Fujairah, United Arab Emirates.
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Zöllner HJ, Davies-Jenkins C, Simicic D, Tal A, Sulam J, Oeltzschner G. Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565164. [PMID: 38260650 PMCID: PMC10802456 DOI: 10.1101/2023.11.01.565164] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Purpose The interest in applying and modeling dynamic MRS has recently grown. 2D modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to 1D modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. Methods Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multi-transient LCM with 1D-LCM of the average. 2,500 datasets per condition with different noise representations of a 64-transient MRS experiment at 6 signal-to-noise levels for two separate spin systems (scyllo-inositol and GABA) were analyzed. Additional datasets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by standard deviations and Cramér-Rao Lower Bounds (CRLB). Results Amplitude estimates for 1D- and 2D-LCM agreed well and showed similar level of bias compared to the ground truth. Estimated CRLBs agreed well between both models and with ground truth CRLBs. For correlated noise the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. Conclusion Our results indicate that the model performance of 2D multi-transient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as necessary basis for further applications of 2D modeling.
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Affiliation(s)
- Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Dunja Simicic
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States
- Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, MD, United States
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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Dell'Orco A, Riemann LT, Ellison SLR, Aydin S, Göschel L, Tietze A, Scheel M, Fillmer A. Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567383. [PMID: 38014000 PMCID: PMC10680753 DOI: 10.1101/2023.11.16.567383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Purpose To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Methods Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated. Results The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Conclusion Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.
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Cai LY, Del Tufo SN, Barquero L, D'Archangel M, Sachs L, Cutting LE, Glaser N, Ghetti S, Jaser SS, Anderson AW, Jordan LC, Landman BA. Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.07.556701. [PMID: 37745381 PMCID: PMC10515761 DOI: 10.1101/2023.09.07.556701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.
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Affiliation(s)
- Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Stephanie N Del Tufo
- College of Education and Human Development, University of Delaware, Newark, DE, USA
| | - Laura Barquero
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Micah D'Archangel
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lanier Sachs
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laurie E Cutting
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Nicole Glaser
- Department of Pediatrics, UC Davis Health, UC Davis School of Medicine, Sacramento, CA, USA
| | - Simona Ghetti
- Department of Psychology, University of California, Davis, Davis, CA, USA
| | - Sarah S Jaser
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori C Jordan
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
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11
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Zöllner HJ, Davies-Jenkins CW, Lee EG, Hendrickson TJ, Clarke WT, Edden RAE, Wisnowski JL, Gudmundson AT, Oeltzschner G. Continuous Automated Analysis Workflow for MRS Studies. J Med Syst 2023; 47:69. [PMID: 37418036 PMCID: PMC10947169 DOI: 10.1007/s10916-023-01969-6] [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: 02/22/2023] [Accepted: 07/02/2023] [Indexed: 07/08/2023]
Abstract
Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.
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Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Erik G Lee
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, Department of Clinical Neurosciences, FMRIB, University of Oxford, Oxford, Nuffield, UK
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Keck School of Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, USA
- Fetal and Neonatal Institute, CHLA Division of Neonatology, Department of Pediatrics, Keck School of Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, USA
| | - Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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12
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Vella O, Bagshaw AP, Wilson M. SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data. Neuroimage 2023:120235. [PMID: 37331644 DOI: 10.1016/j.neuroimage.2023.120235] [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: 04/07/2023] [Accepted: 06/15/2023] [Indexed: 06/20/2023] Open
Abstract
1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals - in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.
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Affiliation(s)
- Olivia Vella
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Andrew P Bagshaw
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.
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13
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Soher BJ, Semanchuk P, Todd D, Ji X, Deelchand D, Joers J, Oz G, Young K. Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis. Magn Reson Med 2023. [PMID: 37183778 DOI: 10.1002/mrm.29686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. THEORY AND METHODS Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. RESULTS The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. CONCLUSION Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.
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Affiliation(s)
- Brian J Soher
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Philip Semanchuk
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - David Todd
- Center for Imaging of Neurodegenerative Disorders, University of California, San Francisco, CA, USA
| | - Xiao Ji
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Dinesh Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Gulin Oz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Karl Young
- Department of Radiology, University of California, San Francisco, CA, USA
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14
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Shamaei AM, Starcukova J, Starcuk Z. Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data. Comput Biol Med 2023; 158:106837. [PMID: 37044049 DOI: 10.1016/j.compbiomed.2023.106837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/06/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
PURPOSE While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. METHOD We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. RESULT The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSION The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.
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15
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Harris AD, Amiri H, Bento M, Cohen R, Ching CRK, Cudalbu C, Dennis EL, Doose A, Ehrlich S, Kirov II, Mekle R, Oeltzschner G, Porges E, Souza R, Tam FI, Taylor B, Thompson PM, Quidé Y, Wilde EA, Williamson J, Lin AP, Bartnik-Olson B. Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations. Front Neurol 2023; 13:1045678. [PMID: 36686533 PMCID: PMC9845632 DOI: 10.3389/fneur.2022.1045678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.
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Affiliation(s)
- Ashley D. Harris
- Department of Radiology, University of Calgary, Calgary, AB, Canada,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Houshang Amiri
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Mariana Bento
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Ronald Cohen
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, CA, United States
| | - Christina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - Arne Doose
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ivan I. Kirov
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY, United States
| | - Ralf Mekle
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Porges
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Roberto Souza
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Friederike I. Tam
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Brian Taylor
- Division of Diagnostic Imaging, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, CA, United States
| | - Yann Quidé
- School of Psychology, University of New South Wales (UNSW), Sydney, NSW, Australia
| | - Elisabeth A. Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - John Williamson
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Alexander P. Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States,*Correspondence: Brenda Bartnik-Olson ✉
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