Optimized Cardiac B0 Shimming at 3T with Deep-Learning-Enabled Motion-Adapted Shim

Motivation: Achieving consistent B0 homogeneity in cardiac MRI at 3T remains challenging due to the impact of respiratory motion. Current clinical shimming protocols lack adequate motion compensation and require manual intervention. Goal(s): To develop a motion-adapted B0 shimming pipeline that autonomously adjusts for motion-induced field variations, thereby improving field homogeneity. Approach: A motion-resolved multi-echo GRE sequence was implemented and integrated with deep learning-based segmentation for automated shimming volume delineation. Results: Our approach demonstrated a 28% improvement in B0 field homogeneity compared to conventional scanner manual selected shimming, with a significant 15% reduction in T2* mapping inhomogeneity. Impact: This study develops a motion-adapted shimming technique for reliable, operator-independent CMR shimming at 3T. This approach holds particular promise for patients with compromised breath-hold capacity, providing the potential for more consistent image quality and accurate clinical CMR assessments.

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/10.58530/2025/3472
Primary Topic
Cardiac and Coronary Surgery Techniques
Type
article
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article

Optimized Cardiac B0 Shimming at 3T with Deep-Learning-Enabled Motion-Adapted Shim

Chia-Chi Yang, Ghazal Yoosefian, Hsu‐Lei Lee, Xiaowen Guan 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
Cardiac and Coronary Surgery Techniques
article

Optimized Cardiac B0 Shimming at 3T with Deep-Learning-Enabled Motion-Adapted Shim

Chia-Chi Yang, Ghazal Yoosefian, Hsu‐Lei Lee, Xiaowen Guan, Xinqi Li, Chang Gao, Hui Han, Jeremy Zepeda, Xiaoming Bi, Yun Shang, Xinheng Zhang, Ziyang Long, Rohan Dharmakumar, Archana Malagi, Yuheng Huang, Hsin‐Jung Yang, Li‐Ting Huang, Debiao Li
article en

Abstract

Motivation: Achieving consistent B0 homogeneity in cardiac MRI at 3T remains challenging due to the impact of respiratory motion. Current clinical shimming protocols lack adequate motion compensation and require manual intervention. Goal(s): To develop a motion-adapted B0 shimming pipeline that autonomously adjusts for motion-induced field variations, thereby improving field homogeneity. Approach: A motion-resolved multi-echo GRE sequence was implemented and integrated with deep learning-based segmentation for automated shimming volume delineation. Results: Our approach demonstrated a 28% improvement in B0 field homogeneity compared to conventional scanner manual selected shimming, with a significant 15% reduction in T2* mapping inhomogeneity. Impact: This study develops a motion-adapted shimming technique for reliable, operator-independent CMR shimming at 3T. This approach holds particular promise for patients with compromised breath-hold capacity, providing the potential for more consistent image quality and accurate clinical CMR assessments.

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 60%
Cardiac and Coronary Surgery Techniques
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