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Li X, Huang Y, Malagi A, Yang CC, Yoosefian G, Huang LT, Tang E, Gao C, Han F, Bi X, Ku MC, Yang HJ, Han H. Reliable Off-Resonance Correction in High-Field Cardiac MRI Using Autonomous Cardiac B 0 Segmentation with Dual-Modality Deep Neural Networks. Bioengineering (Basel) 2024; 11:210. [PMID: 38534485 DOI: 10.3390/bioengineering11030210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/18/2024] [Indexed: 03/28/2024] Open
Abstract
B0 field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous B0 fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. B0 shimming is the most straightforward way to improve the B0 homogeneity. However, today's standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large B0 deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for B0 shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the B0 field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved B0 shim compared to the standard method (SD(B0Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines.
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Affiliation(s)
- Xinqi Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Yuheng Huang
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Archana Malagi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Chia-Chi Yang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ghazal Yoosefian
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li-Ting Huang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Eric Tang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Chang Gao
- MR R&D Collaborations, Siemens Medical Solutions Inc., Los Angeles, CA 90048, USA
| | - Fei Han
- MR R&D Collaborations, Siemens Medical Solutions Inc., Los Angeles, CA 90048, USA
| | - Xiaoming Bi
- MR R&D Collaborations, Siemens Medical Solutions Inc., Los Angeles, CA 90048, USA
| | - Min-Chi Ku
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Hsin-Jung Yang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Hui Han
- Department of Radiology, Weill Medical College of Cornell University, New York, NY 10065, USA
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