Knowledge Commons of Institute of Automation,CAS
Probabilistic tractography using Lasso bootstrap | |
Ye, Chuyang1; Prince, Jerry L.2 | |
发表期刊 | MEDICAL IMAGE ANALYSIS |
2017 | |
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 |
DOI | 10.1016/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12094 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
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. |
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