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

Citation:

Thomas Schultz, Carl-Fredrik Westin, and Gordon Kindlmann. 2010. “Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework.” Med Image Comput Comput Assist Interv, 13, Pt 1, Pp. 674-81.

Abstract:

In analyzing diffusion magnetic resonance imaging, multi-tensor 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.
Last updated on 01/24/2017