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Abstract #3472

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

Xinqi Li1,2, Yuheng Huang1,3,4, Archana Malagi1, Xingming Guan4, Yun Shang5, Chia-Chi Yang1, Li-Ting Huang1, Ziyang Long1,3,5, Jeremy Zepeda1, Xinheng Zhang1, Ghazal Yoosefian4, Xiaoming Bi6, Chang Gao6, Hsu-Lei Lee1, Debiao Li1, Rohan Dharmakumar4, Hui Han5, and Hsin-Jung Yang1
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, 3Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, 4Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, IN, United States, 5Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States, 6MR R&D Collaborations, Siemens Medical Solutions USA, Los Angeles, CA, United States

Synopsis

Keywords: Shims, Cardiovascular

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.

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Abstract

Keywords