Richard McKinley

Brain morphometry is usually based on non‐enhanced (pre‐contrast) T1‐weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast‐enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)‐based morphometry methods can extract morphometric measures also from contrast‐enhanced MRI. We have extended DL+DiReCT to cope with contrast‐enhanced MRI. Training data for our DL‐based model were enriched with non‐enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non‐enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non‐enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non‐enhanced and CE images were significantly larger with DL+DiReCT ( r = 0.92) than with FreeSurfer ( r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non‐enhanced ( d = −0.304) and CE images ( d = −0.169) than for FreeSurfer (non‐enhanced d = −0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast‐enhanced MRI using DL‐based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.