$\texttt{DisMorph}$: learning to disentangle technical distortions from true biological change
Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease. Deformable image registration is a key tool for estimating such change by computing a dense deformation that captures geometric differences between longitudinal scans. However, MRI scanners introduce geometric distortions that vary across acquisition systems and protocols, such as gradient non-linearity (GNL) distortion. Existing registration methods estimate a single field that conflates biological and technical effects, potentially biasing downstream morphometric measurements if distortions remain (partially) uncorrected. We propose $\texttt{DisMorph}$, a registrat...
| Authors | Jingru Fu, Kathleen E. Larson, Douglas N. Greve et al. |
|---|---|
| Primary category | eess.IV |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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