Cardiac Physiology Knowledge-Driven Diffusion Model for Contrast-Free Synthesis Myocardial Infarction Enhancement

Ronghui Qi, Xiaohu Li, Lei Xu, Jie Zhang, Yanping Zhang, Chenchu Xu

Abstract: Contrast-free AI myocardial infarction enhancement (MIE) synthesis technology has a significant impact on clinics due to its ability to eliminate contrast agents (CAs) administration in the current MI diagnosis. In this paper, we propose a novel cardiac physiology knowledge-driven diffusion model (CPKDM) that, for the first time, integrates cardiac physiology knowledge into cardiac MR data to guide the synthesis of high-quality MIE, thereby enhancing the generalization performance of MIE synthesis. The combining helps the model understand the principles behind the data mapping between non-enhanced image inputs and enhanced image outputs, informing the model on how and why to synthesize MIE. CPKDM leverages cardiac mechanics knowledge and MR imaging atlas knowledge to respectively guide the learning of kinematic features in CINE sequences and morphological features in T1 sequences. Moreover, CPKDM proposes a kinematics-morphology diffusion integration model to progressively fuse kinematic and morphological features for precise MIE synthesis. Evaluation on 195 patients including chronic MI and normal controls, CPKDM significantly improves performance (SSIM by at least 4%) when comparing with the five most recent state-of-the-art methods. These results demonstrate that our CPKDM exhibits superiority and offers a promising alternative for clinical diagnostics.