Learning accurate rigid registration for longitudinal brain MRI from synthetic data

2025/01/22 by Jingru Fu, Fu, Jingru, Adrian V. Dalca +7 · 2 citations
Computer Science · Medicine · #Medical Image Segmentation Techniques #Advanced Neuroimaging Techniques and Applications #Advanced MRI Techniques and Applications

paper · pdf · doi:10.48550/arxiv.2501.13010

Abstract

Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-art for linear and deformable registration across subjects, they have demonstrated limitations when applied to longitudinal (within-subject) registration, where achieving precise alignment is critical. Building on an existing framework for anatomy-aware, acquisition-agnostic affine registration, we propose a model optimized for longitudinal, rigid brain registration. By training the model with synthetic within-subject pairs augmented with rigid and subtle nonlinear transforms, the model estimates more accurate rigid transforms than previous cross-subject networks and performs robustly on longitudinal registration pairs within and across magnetic resonance imaging (MRI) contrasts.

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• Deep infant brain segmentation from multi-contrast MRI
• Domain-randomized deep learning for neuroimage analysis

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