ARDMR: Adaptive recursive inference and representation disentanglement for multimodal large deformation registration

Deformable registration of multimodal medical images constitutes a fundamental task in many medical image analysis applications, particularly in the diagnosis and treatment of liver cancer where different modality images are frequently employed. Despite the emergence of various learning-based registration models, multimodal registration remains a challenging task due to pronounced intensity differences and significant tissue deformations. To address these challenges, we approach the problem from two perspectives-mitigating modality discrepancies and enhancing the model's capacity to handle large deformations. We propose a multimodal deformable registration model based on adaptive recursive inference and representation disentanglement (ARDMR). Specifically, to enhance the model's ability to learn features from different modalities, we introduce a modality representation disentanglement method, incorporating Multi-layer Contrastive Loss (MCL) to enforce the learning of modality-invariant features. To address the challenge of large complex deformations, we propose a Multi-Scale Feature Registration module (MSFR), which integrates features and deformation fields from different scales during the registration process. Finally, to further exploit the registration potential of the trained model, we propose an adaptive recursive inference strategy. This strategy automatically determines the optimal recursive registration scale and the number of iterations based on the image's real-time registration performance. We conducted experiments on multimodal datasets collected from multiple medical centers and compared our method with several state-of-the-art registration models. The results indicate that our proposed ARDMR model outperforms others in both qualitative and quantitative evaluations. Specifically, compared with the baseline VoxelMorph model, ARDMR improves the DSC metric by 2.5%-5% across different datasets. Furthermore, tests on distribution-shifted data demonstrate that our model exhibits the best robustness and generalization. Code is available at https://github.com/ybby2020/ARDMR_MIA.