Conditional Denoising Diffusion Probabilistic Model (DDPM) for Cardiac Perfusion Image Reconstruction

Abstract

Motivation: Cardiac perfusion imposes challenges in reconstruction due to intrinsic low SNR, and large signal intensity change. Recently proposed Conditional Denoising Diffusion Probabilistic Models (DDPM) achieves exceptional performance in a broad range of inverse problems. Goal(s): To reconstruct undersampled cardiac perfusion datasets with conditional DDPM. Approach: We conduct the Langevin diffusion process on unacquired k-space data. Conditioning on the acquired data is explicitly embedded in the network structure, instead of utilizing Bayes rule to decouple learned unconditional DDPM prior information of perfusion images and MRI sensing model. Results: Our experimental results validate the good performance of conditional DDPM reconstruction for R=4 accelerated perfusion imaging. Impact: Our proposed work can help the challenging perfusion reconstruction for higher acceleration rate and benefit clinical diagnosis.