title: Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework.
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.
authors.affiliation: Computer Science Department and Computation Institute, University of Chicago, Chicago IL, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Schultz
authors.forename: Carl-Fredrik
authors.initials: CF
authors.lastname: Westin
authors.forename: Gordon
authors.initials: G
authors.lastname: Kindlmann
mesh_terms: D000465:Algorithms; D001369:Axons / Q000648:ultrastructure*; D001921:Brain / Q000648:ultrastructure*; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008959:Models, Neurological; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016008:Statistical Distributions