Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad, Eva Alonso-Ortiz, Stephanie Alley, Maria Marcella Lagana, Francesca Baglio, Signe Johanna Vannesjo, Haleh Karbasforoushan, Maryam Seif, Alan C. Seifert, Junqian Xu, Joo-Won Kim, René Labounek, Lubomír Vojtíšek, Marek Dostál, Jan Valošek, Rebecca S. Samson, Francesco Grussu, Marco Battiston, Claudia A. M. Gandini Wheeler-Kingshott, Marios C. Yiannakas, Guillaume Gilbert, Torben Schneider, Brian Johnson, Ferran Prados
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.