Abstract #0381

Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRI

Shihan Qiu^1,2,3, Shaoyan Pan^1,4,5, Yikang Liu¹, Lin Zhao¹, Jian Xu⁶, Qi Liu⁶, Terrence Chen¹, Eric Z. Chen¹, Xiao Chen¹, and Shanhui Sun¹

¹United Imaging Intelligence, Burlington, MA, United States, ²Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ³Department of Bioengineering, UCLA, Los Angeles, CA, United States, ⁴Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States, ⁵Department of Biomedical Informatics, Emory University, Atlanta, GA, United States, ⁶UIH America, Inc., Houston, TX, United States

Synopsis

Keywords: AI Diffusion Models, Image Reconstruction, Heart

Motivation: Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring.

Goal(s): To improve image sharpness and motion delineation for cine MRI under high undersampling rates.

Approach: A combined non-generative reconstruction and diffusion enhancement model along with a novel paired sampling strategy was developed.

Results: The proposed combined method 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.

Impact: The approach has the potential to improve reconstruction quality in highly accelerated cardiac cine imaging. The novel paired sampling for diffusion generation may be applied to other conditional tasks to reduce the artificial noises stemming from noisy training data.

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