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

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.

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.

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.

Our results demonstrate that this synthesized 4D CT-based hemodynamic approach provides time-variant 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.