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Software-based Diffusion MR Human Brain Phantom for Evaluating Fiber-tracking Algorithms

Institution:
1Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
2Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA.
Publication Date:
Mar-2013
Journal:
Proc Soc Photo Opt Instrum Eng
Volume Number:
8669
Citation:
Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669.
PubMed ID:
24357914
PMCID:
PMC3865235
Keywords:
Diffusion Weighted MRI, Atlas, Fiber Tracking, Tractography, Phantom, Validation
Appears in Collections:
NA-MIC, SLICER
Sponsors:
P30 HD003110/HD/NICHD NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Shi Y., Roger G., Vachet C., Budin F., Maltbie E., Verde A., Hoogstoel M., Berger J-B., Styner M. Software-based Diffusion MR Human Brain Phantom for Evaluating Fiber-tracking Algorithms. Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669. PMID: 24357914. PMCID: PMC3865235.
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Fiber tracking provides insights into the brain white matter network and has become more and more popular in diffusion MR imaging. Hardware or software phantom provides an essential platform to investigate, validate and compare various tractography algorithms towards a “gold standard”. Software phantoms excel due to their flexibility in varying imaging parameters, such as tissue composition, SNR, as well as potential to model various anatomies and pathologies. This paper describes a novel method in generating diffusion MR images with various imaging parameters from realistically appearing, individually varying brain anatomy based on predefined fiber tracts within a high-resolution human brain atlas. Specifically, joint, high resolution DWI and structural MRI brain atlases were constructed with images acquired from 6 healthy subjects (age 22–26) for the DWI data and 56 healthy subject (age 18–59) for the structural MRI data. Full brain fiber tracking was performed with filtered, two-tensor tractography in atlas space. A deformation field based principal component model from the structural MRI as well as unbiased atlas building was then employed to generate synthetic structural brain MR images that are individually varying. Atlas fiber tracts were accordingly warped into each synthetic brain anatomy. Diffusion MR images were finally computed from these warped tracts via a composite hindered and restricted model of diffusion with various imaging parameters for gradient directions, image resolution and SNR. Furthermore, an open-source program was developed to evaluate the fiber tracking results both qualitatively and quantitatively based on various similarity measures.

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