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Probabilistic tractography using Lasso bootstrap
Dec 16, 2016Author:
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Title: Probabilistic tractography using Lasso bootstrap
Authors: Ye, CY; Prince, JL
Author Full Names: Ye, Chuyang; Prince, Jerry L.
Source: MEDICAL IMAGE ANALYSIS, 35 544-553; 10.1016/j.media.2016.08.013 JAN 2017
Language: English
Abstract: 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.
ISSN: 1361-8415
eISSN: 1361-8423
IDS Number: EC6LT
Unique ID: WOS:000388248300039
PubMed ID: 27662597
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