Enhanced Deformation Vector Field Generation With Diffusion Models and Mamba-Based Network for Registration Performance Enhancement

Abstract

Recent advancements in deformable image registration (DIR) have seen the emergence of supervised and unsupervised deep learning techniques. However, supervised methods are limited by the quality of deformation vector fields (DVFs), while unsupervised approaches often yield suboptimal results due to their reliance on indirect dissimilarity metrics. Moreover, both methods struggle to effectively model long-range dependencies. This study proposes a novel DIR method that integrates the advantages of supervised and unsupervised learning and tackle issues related to long-range dependencies, thereby improving registration results. Specifically, we propose a DVF generation diffusion model to enhance DVFs diversity, which could be used to facilitate the integration of supervised and unsupervised learning approaches. This fusion allows the method to leverage the benefits of both paradigms. Furthermore, a multi-scale frequency-weighted denoising module is integrated to enhance DVFs generation quality and improve the registration accuracy. Additionally, we propose a novel MambaReg network that adeptly manages long-range dependencies, further optimizing registration outcomes. Experimental evaluation of four public data sets demonstrates that our method outperforms several state-of-the-art techniques based on either supervised or unsupervised learning. Qualitative and quantitative comparisons highlight the superior performance of our approach.