Enhancing CT-MR Image Feature Matching and Rigid Registration Accuracy Using Modality Translation
Wei Teng, Yajun Wang, Ping Jiang, Yaoqin Xie · 2025
In medical multimodal image registration, images from different modalities exhibit significant differences in intensity distributions. These differences hinder traditional methods from detecting robust feature points and computing accurate descriptors. This study employs modality translation to address these challenges, transforming multimodal registration into a unimodal task and enabling robust feature detection across modalities. Specifically, CycleGAN is utilized to convert CT images to pseudo-MRI, reducing inter-modality discrepancies. The significance of this approach lies in extending feature-pointbased methods, such as R2D2, from unimodal to multimodal scenarios, with experiments showing enhanced feature matching and rigid registration accuracy on converted images.