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Abstract #0051

Single-shot High-resolution Free-breathing Cardiac Dark-blood Imaging Based on Model-Incorporated Transfer Learning

Zhihao Xue1, Yiwen Gong2, Junpu Hu3, Zhongjie Zhou2, Wei Jin2, Sha Hua2, and Chenxi Hu1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China, Shanghai, China, 3United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: Myocardium, Tissue Characterization, Data Acquisition, Image Reconstruction, Preclinical

Motivation: Routine multi-shot T2-weighted dark-blood fast spin echo (T2W DB-FSE) is hindered by susceptibility to respiratory motion and the requirement for breath-holding.

Goal(s): To develop a single-shot cardiac T2W-DB imaging technique with a short echo train to eliminate breath-holding.

Approach: We designed a single-shot DB-FSE sequence with a deep learning-based reconstruction model. Transfer learning was employed to pre-train the model on large cardiac MR datasets and fine-tune it on clinical DB-FSE data. Validation was performed with single-shot imaging in 20 healthy volunteers.

Results: The transfer learning strategy enhanced model performance with limited data. Single-shot T2W DB-FSE showed image quality comparable to conventional multi-shot imaging.

Impact: The proposed single-shot imaging overcome the challenge of conventional cardiac T2W DB-FSE. It has the potential for clinical application in the assessment of myocardium edema on patients having trouble with long breath-holding.

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Keywords