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Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction | |
2019-11-15 | |
发表期刊 | NEUROIMAGE |
ISSN | 1053-8119 |
EISSN | 1095-9572 |
卷号 | 202 |
发表状态 | 已发表 |
DOI | 10.1016/j.neuroimage.2019.116064 |
摘要 | Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g., in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications. |
关键词 | MRI Magnetic resonance imaging QSM Quantitative susceptibility mapping Deep learning Neural network |
URL | 查看原文 |
收录类别 | SCI ; SCIE |
资助项目 | National Institutes of Health[NIMH R01MH096979] ; National Institutes of Health[U01EB025162] |
WOS研究方向 | Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000491861000063 |
出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
WOS关键词 | ENABLED DIPOLE INVERSION ; ZERO-ECHO-TIME ; WHITE-MATTER ; IN-VIVO ; NEURAL-NETWORKS ; MRI ; IMAGE ; MULTIPLE ; CONTRAST ; FIELD |
原始文献类型 | Article |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/80512 |
专题 | 信息科学与技术学院_PI研究组_张玉瑶组 生物医学工程学院 |
通讯作者 | Wei, Hongjiang; Liu, Chunlei |
作者单位 | 1.Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai, Peoples R China 2.Univ Calif Berkeley, Dept Elect Engn & Comp Sci, 505 Cory Hall, Berkeley, CA 94720 USA 3.ShanghaiTech Univ, Sch Informat & Sci & Technol, Shanghai, Peoples R China 4.Zhejiang Univ, Affiliated Hosp 2, Dept Radiol, Sch Med, Hangzhou, Zhejiang, Peoples R China 5.Shanghai Jiao Tong Univ, Rui Jin Hosp, Sch Med, Dept Radiol, Shanghai, Peoples R China 6.Stanford Univ, Lucile Packard Childrens Hosp, Dept Radiol, Palo Alto, CA USA 7.Univ Calif Berkeley, Helen Wills Neurosci Inst, 505 Cory Hall, Berkeley, CA 94720 USA |
推荐引用方式 GB/T 7714 | Wei, Hongjiang,Cao, Steven,Zhang, Yuyao,et al. Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction[J]. NEUROIMAGE,2019,202. |
APA | Wei, Hongjiang.,Cao, Steven.,Zhang, Yuyao.,Guan, Xiaojun.,Yan, Fuhua.,...&Liu, Chunlei.(2019).Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.NEUROIMAGE,202. |
MLA | Wei, Hongjiang,et al."Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction".NEUROIMAGE 202(2019). |
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