Diffusion boost: Leveraging diffusion model for groupwise registration in myocardial T1 mapping

BACKGROUND: As a quantitative magnetic resonance imaging (MRI) technique, myocardial T1 mapping plays a crucial role in the diagnosis and treatment of cardiovascular diseases. In practice, involuntary cardiac and respiratory motion often results in reduced accuracy and precision in T1 estimation. Therefore, image registration remains crucial for accurate and precise myocardial T1 mapping. Compared with pairwise registration that warps each baseline image to a predefined template, groupwise registration aligns all images from one sequence simultaneously without the need for a template. However, a persistent challenge is the difficulty of extracting the structural representation of T1 mapping data that contains vastly varying contrast, which severely undermines the performance of image registration. PURPOSE: The purpose of this study is to incorporate the learning capabilities of the diffusion model to tackle the main challenge encountered in the registration of myocardial T1 mapping. Our goal is to align all images within an image series simultaneously in a groupwise manner. METHODS: In this article, we propose a novel template-free groupwise registration framework that can align one T1-weighted image series through a single forward propagation. Notably, we introduce the diffusion process to effectively boost the structural information extraction under the drastic contrast changes for reliable image registration. Furthermore, we design a Hybrid Attention Feature Fusion (HAFF) module to promote the multi-scale feature fusion from diffusion to registration. To evaluate the registration performance of the proposed model, experiments are conducted on a publicly available myocardial T1 mapping dataset comprising 210 consecutive patients, using an independent test set for comparison experiments and ablation studies. RESULTS: Experimental results demonstrated the great superiority of our proposed method in the registration of myocardial T1 mapping. Quantitatively, the proposed method resulted in a Dice score of 0.839, groupwise Dice score of 0.601, Hausdorff distance of 10.389 mm, and T1 mapping error of 11.372 ms, surpassing the current state-of-the-art approaches. CONCLUSIONS: Our proposed framework realizes robust groupwise registration for myocardial T1 mapping by leveraging the state-of-the-art diffusion model, demonstrating its strong feature extraction capacity for image registration, beyond image generation.