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Two-tensor model-based bootstrapping on classified tensor morphologies: estimation of uncertainty in fiber orientation and probabilistic tractography.

Abstract：

:In this manuscript, fast and clinically feasible model-based bootstrapping algorithms using a geometrically constrained two-tensor diffusion model are employed for estimating uncertainty in fiber orientation. A Monte-Carlo-based tensor morphology voxel classification algorithm is initially applied using single-tensor bootstrap samples before the use of a two-tensor model-based bootstrapping algorithm. Classification of tensor morphologies allows the tensor morphology to be considered when selecting the most appropriate bootstrap procedure. A constrained two-tensor model approach can greatly reduce data acquisition and computational times for whole bootstrap data volume generation compared to other multifiber model techniques, facilitating widespread clinical use. For comparison, we propose a new repetition-bootstrap algorithm based on classified voxels and the constrained two-tensor model. Tractography with these bootstrapping algorithms is also developed to estimate the connection probabilities between brain regions, especially regions with complex fiber configurations. Experimental results on synthetic data, a hardware phantom and human brain data demonstrate the superior performance of our algorithms compared to conventional approaches.

journal_name

Magn Reson Imaging

journal_title

Magnetic resonance imaging

authors

Ratnarajah N,Simmons A,Bertoni M,Hojjatoleslami A

doi

10.1016/j.mri.2012.07.004

subject

Has Abstract

pub_date

2013-02-01 00:00:00

pages

296-312

issue

2

eissn

0730-725X

issn

1873-5894

pii

S0730-725X(12)00280-9

journal_volume

31

pub_type

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