Fig. 1.

Schematic of workflow to obtain localization errors (above), and registration errors (below). In summary, 5 raters placed 32 anatomical fiducials (AFIDs) on each clinical image (blue). The mean location was calculated for each AFID (green), and the Euclidean distance from each rater’s placement was calculated (termed the localization error). Each rater independently placed AFIDs on the MNI images, and the mean location was calculated (purple). Rater placed AFIDs were transformed to MNI space. The Euclidean distance between each rater’s transformed AFID to the mean location of that AFID placed in MNI space was calculated and termed real-world registration error. Each mean AFID placement on the clinical images was transformed to MNI space, its Euclidean distance to that AFID placed in MNI space was calculated and termed consensus registration error