Fiber tracking, based on diffusion tensor imaging (DTI), is the only
approach available to non-invasively study the three-dimensional
structure of white matter tracts. Two major obstacles to this technique
are partial volume artifacts and tracking errors caused by image
noise. In this paper, a novel fiber tracking algorithm called Guided
Tensor Restore Anatomical Connectivity Tractography (GTRACT) is
presented. This algorithm utilizes a multi-pass approach to fiber
tracking. In the first pass, a 3D graph search algorithm is utilized.
The second pass incorporates anatomical connectivity information
generated in the first pass to guide the tracking in this stage.
This approach improves the ability to reconstruct complex fiber
paths as well as the tracking accuracy. Validation and reliability
studies using this algorithm were performed on both synthetic phantom
data and clinical human brain data. A method is also proposed for
the evaluating reliability of fiber tract generation based both
on the position of the fiber tracts, as well the anisotropy values
along the path. The results demonstrate that the GTRACT algorithm
is less sensitive to image noise and more capable of handling areas
of complex fiber crossing, compared to conventional streamline methods.
%0 Journal Article
%1 Cheng2006
%A Cheng, Peng
%A Magnotta, Vincent A
%A Wu, Dee
%A Nopoulos, Peg
%A Moser, David J
%A Paulsen, Jane
%A Jorge, Ricardo
%A Andreasen, Nancy C
%D 2006
%J Neuroimage
%K Diffusion, Tensor DTI Imaging, Diffusion
%N 3
%P 1075--1085
%R 10.1016/j.neuroimage.2006.01.028
%T Evaluation of the GTRACT diffusion tensor tractography algorithm:
a validation and reliability study.
%U http://dx.doi.org/10.1016/j.neuroimage.2006.01.028
%V 31
%X Fiber tracking, based on diffusion tensor imaging (DTI), is the only
approach available to non-invasively study the three-dimensional
structure of white matter tracts. Two major obstacles to this technique
are partial volume artifacts and tracking errors caused by image
noise. In this paper, a novel fiber tracking algorithm called Guided
Tensor Restore Anatomical Connectivity Tractography (GTRACT) is
presented. This algorithm utilizes a multi-pass approach to fiber
tracking. In the first pass, a 3D graph search algorithm is utilized.
The second pass incorporates anatomical connectivity information
generated in the first pass to guide the tracking in this stage.
This approach improves the ability to reconstruct complex fiber
paths as well as the tracking accuracy. Validation and reliability
studies using this algorithm were performed on both synthetic phantom
data and clinical human brain data. A method is also proposed for
the evaluating reliability of fiber tract generation based both
on the position of the fiber tracts, as well the anisotropy values
along the path. The results demonstrate that the GTRACT algorithm
is less sensitive to image noise and more capable of handling areas
of complex fiber crossing, compared to conventional streamline methods.
@article{Cheng2006,
abstract = {Fiber tracking, based on diffusion tensor imaging (DTI), is the only
approach available to non-invasively study the three-dimensional
structure of white matter tracts. Two major obstacles to this technique
are partial volume artifacts and tracking errors caused by image
noise. In this paper, a novel fiber tracking algorithm called Guided
Tensor Restore Anatomical Connectivity Tractography (GTRACT) is
presented. This algorithm utilizes a multi-pass approach to fiber
tracking. In the first pass, a 3D graph search algorithm is utilized.
The second pass incorporates anatomical connectivity information
generated in the first pass to guide the tracking in this stage.
This approach improves the ability to reconstruct complex fiber
paths as well as the tracking accuracy. Validation and reliability
studies using this algorithm were performed on both synthetic phantom
data and clinical human brain data. A method is also proposed for
the evaluating reliability of fiber tract generation based both
on the position of the fiber tracts, as well the anisotropy values
along the path. The results demonstrate that the GTRACT algorithm
is less sensitive to image noise and more capable of handling areas
of complex fiber crossing, compared to conventional streamline methods.},
added-at = {2007-01-10T11:43:56.000+0100},
author = {Cheng, Peng and Magnotta, Vincent A and Wu, Dee and Nopoulos, Peg and Moser, David J and Paulsen, Jane and Jorge, Ricardo and Andreasen, Nancy C},
biburl = {https://www.bibsonomy.org/bibtex/286da4af19b4262a969ac6e94ec9c5618/bmeyer},
description = {Diffusion Tensor Imaging (DTI)},
doi = {10.1016/j.neuroimage.2006.01.028},
interhash = {a4a9f7ea4798690f4825f77b2aebd4d6},
intrahash = {86da4af19b4262a969ac6e94ec9c5618},
journal = {Neuroimage},
keywords = {Diffusion, Tensor DTI Imaging, Diffusion},
month = Jul,
number = 3,
owner = {bzfbmeye},
pages = {1075--1085},
pii = {S1053-8119(06)00086-3},
pmid = {16631385},
timestamp = {2007-01-10T11:43:56.000+0100},
title = {Evaluation of the GTRACT diffusion tensor tractography algorithm:
a validation and reliability study.},
url = {http://dx.doi.org/10.1016/j.neuroimage.2006.01.028},
volume = 31,
year = 2006
}