Probabilistic tractography using Lasso bootstrap

doi:10.1016/j.media.2016.08.013

	Probabilistic tractography using Lasso bootstrap
	Ye, Chuyang1 ; Prince, Jerry L.2
刊名	MEDICAL IMAGE ANALYSIS
	2017
卷号	35 期号:35 页码:544-553
关键词	Diffusion Magnetic Resonance Imaging Probabilistic Tractography Lasso Bootstrap
DOI	10.1016/j.media.2016.08.013
文献子类	Article
英文摘要	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.
WOS关键词	DIFFUSION TENSOR MRI ; FIBER ORIENTATION ; SPHERICAL DECONVOLUTION ; RESIDUAL BOOTSTRAP ; WILD BOOTSTRAP ; WEIGHTED MRI ; UNCERTAINTY ; ESTIMATORS ; BRAIN ; RECONSTRUCTION
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
WOS记录号	WOS:000388248300039
资助机构	NIH/NINDS(5R01NS056307) ; NSFC(61601461) ; "100 Talents Program" of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12094]
专题	自动化研究所_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
推荐引用方式 GB/T 7714	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.