Learning accurate rigid registration for longitudinal brain MRI from synthetic data
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915654368493568 |
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| author | Fu, Jingru Dalca, Adrian V. Fischl, Bruce Moreno, Rodrigo Hoffmann, Malte |
| author_facet | Fu, Jingru Dalca, Adrian V. Fischl, Bruce Moreno, Rodrigo Hoffmann, Malte |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning accurate rigid registration for longitudinal brain MRI from synthetic data Fu, Jingru Dalca, Adrian V. Fischl, Bruce Moreno, Rodrigo Hoffmann, Malte Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Learning accurate rigid registration for longitudinal brain MRI from synthetic data |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.13010 |