Multi-Diffusion-Tensor Fitting via Spherical Deconvolution: A Unifying Framework [chapter]

Thomas Schultz, Carl-Fredrik Westin, Gordon Kindlmann
2010 Lecture Notes in Computer Science  
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
more » ... ative 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.
doi:10.1007/978-3-642-15705-9_82 fatcat:wqumevnsvfge7fdbgc7pp53im4