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Yao J, Morrison MA, Jakary A, Avadiappan S, Chen Y, Luitjens J, Glueck J, Driscoll T, Geschwind MD, Nelson AB, Villanueva-Meyer JE, Hess CP, Lupo JM. Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease. Neuroimage 2023; 265:119788. [PMID: 36476567 DOI: 10.1016/j.neuroimage.2022.119788] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/08/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD. We compared an iterative least-squares-based method (iLSQR), iterative methods that use regularization, single-step approaches, and deep learning-based techniques. Their performance was evaluated by comparing: (1) deviations from a multiple-orientation QSM reference; (2) visual appearance of QSM maps and the presence of artifacts; (3) susceptibility in subcortical brain regions with age; (4) regional brain susceptibility with published postmortem brain iron quantification; and (5) susceptibility in HD-affected basal ganglia regions between HD subjects and healthy controls. We found that single-step QSM methods with either total variation or total generalized variation constraints (SSTV/SSTGV) and the single-step deep learning method iQSM generally provided the best performance in terms of correlation with iron deposition and were better at differentiating between healthy controls and premanifest HD individuals, while deep learning QSM methods trained with multiple-orientation susceptibility data created QSM maps that were most similar to the multiple orientation reference and with the best visual scores.
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Affiliation(s)
- Jingwen Yao
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Melanie A Morrison
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Angela Jakary
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Sivakami Avadiappan
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Yicheng Chen
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Program in Bioengineering, San Francisco & Berkeley, CA, USA; Meta Platforms, Inc., Mountain View, CA, USA
| | - Johanna Luitjens
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Julia Glueck
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Theresa Driscoll
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Michael D Geschwind
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Alexandra B Nelson
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Program in Bioengineering, San Francisco & Berkeley, CA, USA.
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Shin HG, Lee J, Yun YH, Yoo SH, Jang J, Oh SH, Nam Y, Jung S, Kim S, Fukunaga M, Kim W, Choi HJ, Lee J. χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. Neuroimage 2021; 240:118371. [PMID: 34242783 DOI: 10.1016/j.neuroimage.2021.118371] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/04/2021] [Accepted: 07/05/2021] [Indexed: 11/26/2022] Open
Abstract
Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.
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Affiliation(s)
- Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jingu Lee
- AIRS Medical Inc., Seoul, Republic of Korea
| | - Young Hyun Yun
- Department of Medicine, Anatomy and Cell Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seong Ho Yoo
- Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jinhee Jang
- Department of Radiology, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Se-Hong Oh
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Sehoon Jung
- Research Institute of Industrial Science and Technology, Pohang, Republic of Korea
| | - Sunhye Kim
- Research Institute of Industrial Science and Technology, Pohang, Republic of Korea
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan
| | - Woojun Kim
- Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyung Jin Choi
- Department of Biomedical Sciences, Anatomy and Cell Biology, Neuroscience Research Institute, Wide River Institute of Immunology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
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