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

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/2627
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Yongquan Ye, Guangyu Dan, Zheng Zhong, Dong Liang et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced MRI Techniques and Applications
article

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

Yongquan Ye, Guangyu Dan, Zheng Zhong, Dong Liang, Yanning Liu, Fang Fěng, Haiyang Shi, Bochao Li, Yanjie Zhu, Zihao Chen, Qi Liu, Hongyu Li, Liu Qiao, Chengcheng Zhu, Yiyun Dong, Hongyi Gu, Jian Xu, Shengping Liu, Yihang Zhou, Jiale Hou, Haifeng Wang
article en

Abstract

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Advanced MRI Techniques and Applications
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