Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images

• URL: http://arxiv.org/abs/2601.11689v1
• Date: Fri, 16 Jan 2026 13:22:21 GMT
• Title: Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images
• Authors: Xiaofan Wang, Junyi Wang, Yuqian Chen, Lauren J. O' Donnell, Fan Zhang,
• Abstract summary: This paper proposes an unsupervised registration framework based on a generative registration network.<n>It transforms the original multimodal registration problem between b0 and T1w images into a unimodal registration task between a generated image and the real T1w image.<n> Experiments conducted on two independent datasets demonstrate that the proposed method outperforms several state-of-the-art approaches in multimodal registration tasks.
• Score: 8.802336586929613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal image registration between diffusion MRI (dMRI) and T1-weighted (T1w) MRI images is a critical step for aligning diffusion-weighted imaging (DWI) data with structural anatomical space. Traditional registration methods often struggle to ensure accuracy due to the large intensity differences between diffusion data and high-resolution anatomical structures. This paper proposes an unsupervised registration framework based on a generative registration network, which transforms the original multimodal registration problem between b0 and T1w images into a unimodal registration task between a generated image and the real T1w image. This effectively reduces the complexity of cross-modal registration. The framework first employs an image synthesis model to generate images with T1w-like contrast, and then learns a deformation field from the generated image to the fixed T1w image. The registration network jointly optimizes local structural similarity and cross-modal statistical dependency to improve deformation estimation accuracy. Experiments conducted on two independent datasets demonstrate that the proposed method outperforms several state-of-the-art approaches in multimodal registration tasks.

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