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

摘要：

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.

作者: Thomas Schultz Carl-Fredrik Westin Gordon Kindlmann

作者单位: Computer Science Department and Computation Institute,University of Chicago, Chicago IL, USA 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, USA

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 674-681

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）