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DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease

1School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
2Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA.
3Department of Computer Science, University College London, London, UK.
4Program for Neurodevelopmental Function in Rare Disorders, Clinical Center for the Study of Development and Learning, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
5Scientific Computing and Imaging Institute, School of Computing, University of Utah, Salt Lake City, UT, USA.
6Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA.
7Parietal team, INRIA Saclay - Ile-de-France, France.
Elsevier Science
Publication Date:
Volume Number:
Issue Number:
Neuroimage. 2011 Apr 15;55(4):1577-86.
PubMed ID:
Diffusion Tensor Imaging, Registration, Krabbe disease, Fiber Tracts, MRI, Evaluation metrics, Projects:DiffusionMRI
Appears in Collections:
P30 HD003110/HD/NICHD NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
R01 NS061965/NS/NINDS NIH HHS/United States
R03 EB009321/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wang Y., Gupta A., Liu Z., Zhang H., Escolar M.L., Gilmore J.H., Gouttard S., Fillard P., Maltbie E., Gerig G., Styner M. DTI Registration in Atlas Based Fiber Analysis of Infantile Krabbe Disease. Neuroimage. 2011 Apr 15;55(4):1577-86. PMID: 21256236. PMCID: PMC3062693.
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In recent years, diffusion tensor imaging (DTI) has become the modality of choice to investigate white matter pathology in the developing brain. To study neonate Krabbe disease with DTI, we evaluate the performance of linear and non-linear DTI registration algorithms for atlas based fiber tract analysis. The DTI scans of 10 age-matched neonates with infantile Krabbe disease are mapped into an atlas for the analysis of major fiber tracts - the genu and splenium of the corpus callosum, the internal capsules tracts and the uncinate fasciculi. The neonate atlas is based on 377 healthy control subjects, generated using an unbiased diffeomorphic atlas building method. To evaluate the performance of one linear and seven nonlinear commonly used registration algorithms for DTI we propose the use of two novel evaluation metrics: a regional matching quality criterion incorporating the local tensor orientation similarity, and a fiber property profile based metric using normative correlation. Our experimental results indicate that the whole tensor based registration method within the DTI-ToolKit (DTI-TK) shows the best performance for our application.

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