Within the PAM50-normalized-metrics project, we built a database of normative spinal cord morphometrics (such as CSA, CR, etc) computed from T2w iso images. The database was built using a method based on liner interpolation as illustrated in this figure.
It would also be great to have a normative database of quantitative metrics such as fractional anisotropy (FA), magnetization transfer ratio (MTR), etc, computed from DWI and MT scans. Again, spine-generic healthy subjects (n=203) could be used to build such a database. The quantitative metrics could be computed from individual WM tracts, e.g., dorsal columns, lateral CST, etc.:
NOTE: The current method (sct_process_segmentation.py -normalize-PAM50) interpolates the morphometrics to 0.5mm iso PAM50 resolution, which might be overkill for DWI and MT data with 0.9 x 0.9 x 5 mm resolution.
Background
Within the PAM50-normalized-metrics project, we built a database of normative spinal cord morphometrics (such as CSA, CR, etc) computed from T2w iso images. The database was built using a method based on liner interpolation as illustrated in this figure.
It would also be great to have a normative database of quantitative metrics such as fractional anisotropy (FA), magnetization transfer ratio (MTR), etc, computed from DWI and MT scans. Again, spine-generic healthy subjects (n=203) could be used to build such a database. The quantitative metrics could be computed from individual WM tracts, e.g., dorsal columns, lateral CST, etc.:
NOTE: The current method (sct_process_segmentation.py -normalize-PAM50) interpolates the morphometrics to 0.5mm iso PAM50 resolution, which might be overkill for DWI and MT data with 0.9 x 0.9 x 5 mm resolution.