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Abstract #2627

Fast Cardiac T1 Mapping in Three Heartbeats with A Transformer Integrated Network

Hongyu Li1, Qiao Liu2, Hanrui Shi3, Hongyi Gu1, Yiyun Dong3, Chengcheng Zhu3, Bochao Li4, Zheng Zhong1, Guangyu Dan1, Zihao Chen1, Yanning Liu1, Qi Liu1, Yongquan Ye1, Jian Xu1, Jue Hou1, Feng Fang1, Yanjie Zhu2, Shengping Liu5, Haifeng Wang2, Dong Liang2, and Yihang Zhou2
1UIH America Inc, Houston, TX, United States, 2Shenzhen Institute of Advanced Technology, Shenzhen, China, 3University of Washington, Seattle, WA, United States, 4University of Southern California, Los Angeles, CA, United States, 5Chongqing University of Technology, Chongqing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Cardiomyopathy

Motivation: Current cardiac T1 mapping technique suffers from long acquisition time and its sensitivity to noise or motion artifacts.

Goal(s): To reduce the acquisition time and to improve the robustness against motion or noise artifacts in cardiac T1 mapping.

Approach: A deep learning framework integrated with Trans-Unet and a fully connected network was developed to realize T1 mapping with T1 weighted images acquired within three cardiac cycles.

Results: The method combined the advantages of current deep learning methods and achieved comparable accuracy to MOLLI, with shortened image acquisitions and enhanced robustness.

Impact: The approach achieves cardiac T1 mapping within three cardiac cycles, clinically reducing the breath-hold time and causes less discomfort to patients. Meanwhile, the integrated network takes advantages of current deep learning methods and significantly improves the reconstruction quality.

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Keywords