Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI

2024/03/13 by Shihan Qiu, Shaoyan Pan, Qiu, Shihan +16
Medicine · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #MRI in cancer diagnosis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2403.08758

openalex publication_date 2024/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring. We aim to improve image sharpness and motion delineation for cine MRI under high undersampling rates. A spatiotemporal diffusion enhancement model conditional on an existing deep learning reconstruction along with a novel paired sampling strategy was developed. The diffusion model provided sharper tissue boundaries and clearer motion than the original reconstruction in experts evaluation on clinical data. The innovative paired sampling strategy substantially reduced artificial noises in the generative results.

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