March 14, 2024, 4:46 a.m. | Shihan Qiu, Shaoyan Pan, Yikang Liu, Lin Zhao, Jian Xu, Qi Liu, Terrence Chen, Eric Z. Chen, Xiao Chen, Shanhui Sun

cs.CV updates on

arXiv:2403.08758v1 Announce Type: cross
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 …

abstract aim arxiv current deep learning diffusion diffusion model eess.iv image mri sampling spatial temporal type undersampling

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