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Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI

Institution:
Max Planck Institute for Intelligent Systems, Tübingen, Germany.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2012
Publication Date:
Oct-2012
Journal:
Med Image Comput Comput Assist Interv
Volume Number:
15
Issue Number:
Pt 3
Pages:
493-500
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 3):493-500.
PubMed ID:
23286167
Appears in Collections:
NA-MIC
Sponsors:
R01 NS054893/NS/NINDS NIH HHS/United States
R01 NS040068/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Schultz T. Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI. Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 3):493-500. PMID: 23286167.
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Having to determine an adequate number of fiber directions is a fundamental limitation of multi-compartment models in diffusion MRI. This paper proposes a novel strategy to approach this problem, based on simulating data that closely follows the characteristics of the measured data. This provides the ground truth required to determine the number of directions that optimizes a formal measure of accuracy, while allowing us to transfer the result to real data by support vector regression. The method is shown to result in plausible and reproducible decisions on three repeated scans of the same subject. When combined with the ball-and-stick model, it produces directional estimates comparable to constrained spherical deconvolution, but with significantly smaller variance between re-scans, and at a reduced computational cost.

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