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Abstract #4187

Dynamic Cardiac Late Gadolinium Enhancement Imaging Using Deep Equilibrium Models

Yuanyuan Liu1, Yuanbiao Yang1, Jing Cheng1, Zhuoxu Cui2, Qingyong Zhu2, Yining Wang3, Weixiong Fan4, Dong Liang2, and Yanjie Zhu1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 3Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China, 4Department of Magnetic Resonance,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou People's Hospital, Meizhou, China

Synopsis

Keywords: Myocardium, Machine Learning/Artificial Intelligence

Motivation: Long scan time of phase sensitive inversion recovery (PSIR) for cardiac late gadolinium enhancement imaging significantly hinders the widespread applications of time-sensitive clinical scenarios.

Goal(s): This study aims to accelerate PSIR acquisition and enhance reconstruction performance using deep equilibrium models.

Approach: To reduce cardiac motion-induced blurring, segmented undersampled PSIR k-space data was divided into multiple shots instead of a single dataset. Using a deep learning approach with deep equilibrium models, dynamic inversion recovery and proton density reference images were reconstructed from each shot's highly undersampled k-space data.

Results: This approach enables accurate dynamic PSIR image reconstruction with acceleration rates up to 12.5.

Impact: The proposed method could greatly reduce the scan time of PSIR data acquisition and achieve high quality images, therefore has a broad spectrum of potential clinical applications.

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