Contrast Transformation-Guided Multimodal Medical Image Registration

Multi-modal medical image registration, especially MRI/CT, is challenging due to inconsistent appearance and large nonlinear deformations. Methods based on hand-crafted metrics are noise-sensitive and require expensive iterative optimization, while deep models still struggle to separate modality-induced intensity differences from true misalignment. Recent translation-based methods normalize appearance before registration, but weak coupling between synthesis and alignment and possible geometric distortion can introduce spurious deformations. We propose an end-to-end framework with a contrast transformation branch that aligns gray-level distributions, mapping CT to MR-like contrast without changing anatomy, directly guided by registration loss. On two public CT-MR datasets, our approach achieves state-of-the-art performance, surpassing existing methods by 7.34% and 4.29%. The proposed contrast transformation network improves seven established models, with DSC gains up to 11.9% and 8.0%. These results demonstrate state-of-the-art accuracy, strong generalization, and easy integration into dual-stream registration frameworks.