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Probabilistic tractography using Lasso bootstrap
Ye, Chuyang1; Prince, Jerry L.2
2017
发表期刊MEDICAL IMAGE ANALYSIS
卷号35期号:35页码:544-553
文章类型Article
摘要Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography. However, recently developed models have incorporated sparsity regularization to reduce the required number of gradient directions to resolve crossing FOs, and the residual bootstrap used in previous methods is not applicable to these models. In this work, we propose a probabilistic tractography algorithm named Lasso bootstrap tractography (LBT) for the models that incorporate sparsity. Using a fixed tensor basis and a sparsity assumption, diffusion signals are modeled using a Lasso formulation. With the residuals from the Lasso model, a distribution of diffusion signals is obtained according to a modified Lasso bootstrap strategy. FOs are then estimated from the synthesized diffusion signals by an algorithm that improves FO estimation by enforcing spatial consistency of FOs. Finally, streamlining fiber tracking is performed with the computed FOs. The LBT algorithm was evaluated on simulated and real dMRI data both qualitatively and quantitatively. Results demonstrate that LBT outperforms state-of-the-art algorithms. (C) 2016 Elsevier B.V. All rights reserved.
关键词Diffusion Magnetic Resonance Imaging Probabilistic Tractography Lasso Bootstrap
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.media.2016.08.013
关键词[WOS]DIFFUSION TENSOR MRI ; FIBER ORIENTATION ; SPHERICAL DECONVOLUTION ; RESIDUAL BOOTSTRAP ; WILD BOOTSTRAP ; WEIGHTED MRI ; UNCERTAINTY ; ESTIMATORS ; BRAIN ; RECONSTRUCTION
收录类别SCI
语种英语
项目资助者NIH/NINDS(5R01NS056307) ; NSFC(61601461) ; "100 Talents Program" of the Chinese Academy of Sciences
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000388248300039
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12094
专题脑网络组研究中心
通讯作者Ye, Chuyang
作者单位1.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China
2.Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA
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Ye, Chuyang,Prince, Jerry L.. Probabilistic tractography using Lasso bootstrap[J]. MEDICAL IMAGE ANALYSIS,2017,35(35):544-553.
APA Ye, Chuyang,&Prince, Jerry L..(2017).Probabilistic tractography using Lasso bootstrap.MEDICAL IMAGE ANALYSIS,35(35),544-553.
MLA Ye, Chuyang,et al."Probabilistic tractography using Lasso bootstrap".MEDICAL IMAGE ANALYSIS 35.35(2017):544-553.
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