弥合模态：联合合成与配准框架用于对齐扩散磁共振成像与T1加权图像
Bridging Modalities: Joint Synthesis and Registration Framework for Aligning Diffusion MRI with T1-Weighted Images
AI总结：
本文提出一种基于生成配准网络的无监督框架，通过图像合成和变形场学习，提升扩散MRI与T1加权图像的配准精度。
摘要：
多模态图像配准之间扩散磁共振成像（dMRI）和T1加权（T1w）MRI图像是一个关键步骤，用于将扩散加权成像（DWI）数据与结构性解剖空间对齐。传统配准方法往往难以确保准确性，因为扩散数据与高分辨率解剖结构之间的强度差异较大。本文提出了一种基于生成配准网络的无监督配准框架，将原始多模态配准问题（b0与T1w图像之间）转换为单模态配准任务（生成图像与真实T1w图像之间）。这有效降低了跨模态配准的复杂性。该框架首先使用图像合成模型生成具有T1w对比度的图像，然后从生成图像学习变形场到固定T1w图像。配准网络联合优化局部结构相似性和跨模态统计依赖性以提高变形估计准确性。在两个独立数据集上进行的实验表明，所提出的方法在多模态配准任务中优于几种最先进的方法。
英文摘要：
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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