Julien Cohen-Adad1,2,3, Eva Alonso-Ortiz1, Stephanie Alley1, Maria Marcella Lagana4, Francesca Baglio4, Signe Johanna Vannesjo5,6, Haleh Karbasforoushan7,8, Maryam Seif9,10, Alan C Seifert11, Junqian Xu11, Joo-Won Kim11, René Labounek12,13, Lubomír Vojtíšek14, Marek Dostál15, Jan Valošek12, Rebecca S Samson16, Francesco Grussu16,17, Marco Battiston16, Claudia A M Gandini Wheeler-Kingshott16,18,19, Marios C Yiannakas16, Guillaume Gilbert20, Torben Schneider21, Brian Johnson22, Ferran Prados16,23,24. 1. NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada. 2. Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Canada. 3. Mila - Quebec AI Institute, Montreal, Canada. 4. IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy. 5. Wellcome Center for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Oxford, UK. 6. Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway. 7. Interdepartmental Neuroscience Program, Northwestern University School of Medicine, Chicago, IL, USA. 8. Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA. 9. Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland. 10. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. 11. Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 12. Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic. 13. Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA. 14. Central European Institute of Technology, Masaryk University, Brno, Czech Republic. 15. Department of Radiology and Nuclear Medicine, University Hospital Brno, Brno, Czech Republic. 16. Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. 17. Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain. 18. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. 19. Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy. 20. MR Clinical Science, Philips Canada, Mississauga, Canada. 21. MR Clinical Science, Philips UK, Surrey, UK. 22. MR Clinical Development, Philips North America, Gainesville, FL, USA. 23. e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain. 24. Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, University College London, London, UK.
Abstract
PURPOSE: Spinal cord gray-matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter. METHODS: In vivo data of the cervical spinal cord were collected from nine different imaging centers. Data processing consisted of automatically segmenting the spinal cord and its gray matter and co-registering back-to-back scans. We computed the SNR using two methods (SNR_single using a single scan and SNR_diff using the difference between back-to-back scans) and the white/gray matter contrast-to-noise ratio per unit time. Synthetic phantom data were generated to evaluate the metrics performance. Experienced radiologists qualitatively scored the images. We ran the same processing on an open-access multicenter data set of the spinal cord MRI (N = 267 participants). RESULTS: Qualitative assessments indicated comparable image quality for 3T and 7T scans. Spatial resolution was higher at higher field strength, and image quality at 1.5 T was found to be moderate to low. The proposed quantitative metrics were found to be robust to underlying changes to the SNR and contrast; however, the SNR_single method lacked accuracy when there were excessive partial-volume effects. CONCLUSION: We propose quality assessment criteria and metrics for gray-matter visualization and apply them to different protocols. The proposed criteria and metrics, the analyzed protocols, and our open-source code can serve as a benchmark for future optimization of spinal cord gray-matter imaging protocols.
PURPOSE: Spinal cord gray-matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter. METHODS: In vivo data of the cervical spinal cord were collected from nine different imaging centers. Data processing consisted of automatically segmenting the spinal cord and its gray matter and co-registering back-to-back scans. We computed the SNR using two methods (SNR_single using a single scan and SNR_diff using the difference between back-to-back scans) and the white/gray matter contrast-to-noise ratio per unit time. Synthetic phantom data were generated to evaluate the metrics performance. Experienced radiologists qualitatively scored the images. We ran the same processing on an open-access multicenter data set of the spinal cord MRI (N = 267 participants). RESULTS: Qualitative assessments indicated comparable image quality for 3T and 7T scans. Spatial resolution was higher at higher field strength, and image quality at 1.5 T was found to be moderate to low. The proposed quantitative metrics were found to be robust to underlying changes to the SNR and contrast; however, the SNR_single method lacked accuracy when there were excessive partial-volume effects. CONCLUSION: We propose quality assessment criteria and metrics for gray-matter visualization and apply them to different protocols. The proposed criteria and metrics, the analyzed protocols, and our open-source code can serve as a benchmark for future optimization of spinal cord gray-matter imaging protocols.
Authors: Silvan Büeler; Marios C Yiannakas; Zdravko Damjanovski; Patrick Freund; Martina D Liechti; Gergely David Journal: Sci Rep Date: 2022-10-03 Impact factor: 4.996