Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
In clinical practice, imaging modalities with functional characteristics,
such as positron emission tomography (PET) and fractional anisotropy (FA), are
often aligned with a structural reference (e.g., MRI, CT) for accurate
interpretation or group analysis, necessitating multi-modal deformable image
registration (DIR). However, due to the extreme heterogeneity of these
modalities compared to standard structural scans, conventional unsupervised DIR
methods struggle to learn reliable spatial mappings and often distort images.
We find that the similarity metrics guiding these models fail to capture
alignment between highly disparate modalities. To address this, we propose
M2M-Reg (Multi-to-Mono Registration), a novel framework that trains multi-modal
DIR models using only mono-modal similarity while preserving the established
architectural paradigm for seamless integration into existing models. We also
introduce GradCyCon, a regularizer that leverages M2M-Reg's cyclic training
scheme to promote diffeomorphism. Furthermore, our framework naturally extends
to a semi-supervised setting, integrating pre-aligned and unaligned pairs only,
without requiring ground-truth transformations or segmentation masks.
Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset
demonstrate that M2M-Reg achieves up to 2x higher DSC than prior methods for
PET-MRI and FA-MRI registration, highlighting its effectiveness in handling
highly heterogeneous multi-modal DIR. Our code is available at
https://github.com/MICV-yonsei/M2M-Reg.
