Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework

Authors:
Thomas Schultz;Carl-Fredrik Westin;Gordon Kindlmann
Affiliations:
Computer Science Department and Computation Institute, University of Chicago, Chicago, IL;Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA;Computer Science Department and Computation Institute, University of Chicago, Chicago, IL
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

In analyzing diffusion magnetic resonance imaging, multitensor models address the limitations of the single diffusion tensor in situations of partial voluming and fiber crossings. However, selection of a suitable number of fibers and numerical difficulties in model fitting have limited their practical use. This paper addresses both problems by making spherical deconvolution part of the fitting process: We demonstrate that with an appropriate kernel, the deconvolution provides a reliable approximative fit that is efficiently refined by a subsequent descent-type optimization. Moreover, deciding on the number of fibers based on the orientation distribution function produces favorable results when compared to the traditional F-Test. Our work demonstrates the benefits of unifying previously divergent lines of work in diffusion image analysis.