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Tourell M, Jin J, Bachrata B, Stewart A, Ropele S, Enzinger C, Bollmann S, Bollmann S, Robinson SD, O'Brien K, Barth M. Three-dimensional EPI with shot-selective CAIPIRIHANA for rapid high-resolution quantitative susceptibility mapping at 3 T. Magn Reson Med 2024; 92:997-1010. [PMID: 38778631 DOI: 10.1002/mrm.30101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/14/2024] [Accepted: 03/16/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging (3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring. METHODS To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. RESULTS The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. CONCLUSION We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.
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Affiliation(s)
- Monique Tourell
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthineers Pty Ltd, Bowen Hills, Queensland, Australia
| | - Beata Bachrata
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
- Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria
| | - Ashley Stewart
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | | | - Saskia Bollmann
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Simon Daniel Robinson
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
- High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Neurology, Medical University of Graz, Graz, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, Department for Biomedical Imaging and Image-Guided Therapy, University of Vienna, Vienna, Austria
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthineers Pty Ltd, Bowen Hills, Queensland, Australia
| | - Markus Barth
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
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Chen X, Wu W, Chiew M. Motion compensated structured low-rank reconstruction for 3D multi-shot EPI. Magn Reson Med 2024; 91:2443-2458. [PMID: 38361309 DOI: 10.1002/mrm.30019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/08/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE The 3D multi-shot EPI imaging offers several benefits including higher SNR and high isotropic resolution compared to 2D single shot EPI. However, it suffers from shot-to-shot inconsistencies arising from physiologically induced phase variations and bulk motion. This work proposed a motion compensated structured low-rank (mcSLR) reconstruction method to address both issues for 3D multi-shot EPI. METHODS Structured low-rank reconstruction has been successfully used in previous work to deal with inter-shot phase variations for 3D multi-shot EPI imaging. It circumvents the estimation of phase variations by reconstructing an individual image for each phase state which are then sum-of-squares combined, exploiting their linear interdependency encoded in structured low-rank constraints. However, structured low-rank constraints become less effective in the presence of inter-shot motion, which corrupts image magnitude consistency and invalidates the linear relationship between shots. Thus, this work jointly models inter-shot phase variations and motion corruptions by incorporating rigid motion compensation for structured low-rank reconstruction, where motion estimates are obtained in a fully data-driven way without relying on external hardware or imaging navigators. RESULTS Simulation and in vivo experiments at 7T have demonstrated that the mcSLR method can effectively reduce image artifacts and improve the robustness of 3D multi-shot EPI, outperforming existing methods which only address inter-shot phase variations or motion, but not both. CONCLUSION The proposed mcSLR reconstruction compensates for rigid motion, and thus improves the validity of structured low-rank constraints, resulting in improved robustness of 3D multi-shot EPI to both inter-shot motion and phase variations.
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Affiliation(s)
- Xi Chen
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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Haacke EM, Xu Q, Kokeny P, Gharabaghi S, Chen Y, Wu B, Liu Y, He N, Yan F. Strategically Acquired Gradient Echo (STAGE) Imaging, part IV: Constrained Reconstruction of White Noise (CROWN) Processing as a Means to Improve Signal-to-Noise in STAGE Imaging at 3 Tesla. Magn Reson Imaging 2024; 107:55-68. [PMID: 38181834 DOI: 10.1016/j.mri.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/30/2023] [Accepted: 01/01/2024] [Indexed: 01/07/2024]
Abstract
Increasing the signal-to-noise ratio (SNR) has always been of critical importance for magnetic resonance imaging. Although increasing field strength provides a linear increase in SNR, it is more and more costly as field strength increases. Therefore, there is a major effort today to use signal processing methods to improve SNR since it is more efficient and economical. There are a variety of methods to improve SNR such as averaging the data at the expense of imaging time, or collecting the data with a lower resolution, all of these methods, including imaging processing methods, usually come at the expense of loss of image detail or image blurring. Therefore, we developed a new mathematical approach called CROWN (Constrained Reconstruction of White Noise) to enhance SNR without loss of structural detail and without affecting scanning time. In this study, we introduced and tested the concept behind CROWN specifically for STAGE (strategically acquired gradient echo) imaging. The concept itself is presented first, followed by simulations to demonstrate its theoretical effectiveness. Then the SNR improvement on proton spin density (PSD) and R2⁎ maps was investigated using brain STAGE data acquired from 10 healthy controls (HCs) and 10 patients with Parkinson's disease (PD). For the PSD and R2* maps, the SNR and CNR between white matter and gray matter were improved by a factor of 1.87 ± 0.50 and 1.72 ± 0.88, respectively. The white matter hyperintensity lesions in PD patients were more clearly defined after CROWN processing. Using these improved maps, simulated images for any repeat time, echo time or flip angle can be created with improved SNR. The potential applications of this technology are to trade off the increased SNR for higher resolution images and/or faster imaging.
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Affiliation(s)
- E Mark Haacke
- SpinTech MRI, Bingham Farms, MI 48025, United States of America; Wayne State University, Department of Neurology, Detroit, MI 48201, United States of America; Wayne State University, Department of Radiology, Detroit, MI 48201, United States of America; Zhuyan Limited, Shanghai, China.
| | - Qiuyun Xu
- SpinTech MRI, Bingham Farms, MI 48025, United States of America
| | - Paul Kokeny
- SpinTech MRI, Bingham Farms, MI 48025, United States of America
| | - Sara Gharabaghi
- SpinTech MRI, Bingham Farms, MI 48025, United States of America
| | - Yongsheng Chen
- Wayne State University, Department of Neurology, Detroit, MI 48201, United States of America
| | - Bo Wu
- Zhuyan Limited, Shanghai, China
| | - Yu Liu
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Department of Radiology, Shanghai, China
| | - Naying He
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Department of Radiology, Shanghai, China
| | - Fuhua Yan
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Department of Radiology, Shanghai, China
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Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. Neuroimage 2024; 289:120547. [PMID: 38373677 DOI: 10.1016/j.neuroimage.2024.120547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Scott Ayton
- Florey Institute, The University of Melbourne, Australia
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Auenbruggerplatz 22, Prague 8036, Czechia
| | | | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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Ekanayake M, Pawar K, Harandi M, Egan G, Chen Z. McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction. Comput Biol Med 2024; 168:107775. [PMID: 38061154 DOI: 10.1016/j.compbiomed.2023.107775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy.
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Affiliation(s)
- Mevan Ekanayake
- Monash Biomedical Imaging, Monash University, Australia; Department of Electrical and Computer Systems Engineering, Monash University, Australia.
| | - Kamlesh Pawar
- Monash Biomedical Imaging, Monash University, Australia
| | - Mehrtash Harandi
- Department of Electrical and Computer Systems Engineering, Monash University, Australia
| | - Gary Egan
- Monash Biomedical Imaging, Monash University, Australia; School of Psychological Sciences, Monash University, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Australia; Department of Data Science and AI, Monash University, Australia
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Sun K, Chen Z, Dan G, Luo Q, Yan L, Liu F, Zhou XJ. Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion. Magn Reson Med 2023; 90:2375-2387. [PMID: 37667533 PMCID: PMC10903279 DOI: 10.1002/mrm.29828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/08/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE EPI with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly fMRI. This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot. METHODS A 3D esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0 -field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets. RESULTS The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images. CONCLUSION The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications.
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Affiliation(s)
- Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Zhifeng Chen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Qingfei Luo
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Lirong Yan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States
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Cheng J, Song M, Xu Z, Zheng Q, Zhu L, Chen W, Feng Y, Bao J, Cheng J. A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping. Front Neurosci 2023; 17:1287788. [PMID: 38033538 PMCID: PMC10684715 DOI: 10.3389/fnins.2023.1287788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Background Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. Methods In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. Results Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. Conclusion The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.
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Affiliation(s)
- Junying Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Manli Song
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhongbiao Xu
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Science, Guangzhou, China
| | - Qian Zheng
- College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Li Zhu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wufan Chen
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jianfeng Bao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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