Synthesis of coronary 4D CT Image by denoising diffusion probabilistic model

Abstract

Purpose: Fluctuations in the pressure drop during the cardiac cycle can provide prognostic information for coronary artery disease (CAD). However, 4D computed tomography (CT) is required for time-variant flow analysis, which results in high doses of radiation exposure. In this study, we propose a novel diffusion-based framework for synthesizing physiologically consistent 4D CT images and performing 4D CT flow analysis. Methods: A denoising diffusion probabilistic model (DDPM) integrated with a deformation module was used for precise anatomical reconstruction. Subsequently, a computational fluid dynamics (CFD) model coupled with quasi-steady fluid-structure interaction (FSI) was utilized to calculate the 4D hemodynamic flow field. Results: The model achieved a peak signal-to-noise ratio of 32.01 and a structural similarity index measure of 0.937. After 3D construction and segmentation, the average Dice coefficient was 0.973. Furthermore, the computational fluid analysis was also performed with a fractional flow reserve (FFR) accuracy of 90.5%, demonstrating its efficacy in reducing radiation exposure without compromising diagnostic quality. Conclusion: Our results demonstrate that this synthesized 4D CT-based hemodynamic approach provides timevariant information for CAD diagnosis. This method offers valuable guidance for clinical decision-making as well as the possibility of prognostic information based on dynamic lumen evaluation.