Schultz Thomas, Westin Carl-Fredrik, Kindlmann Gordon
Computer Science Department and Computation Institute, University of Chicago, Chicago IL, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):674-81. doi: 10.1007/978-3-642-15705-9_82.
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.
在分析扩散磁共振成像时，多张量模型解决了单扩散张量在部分容积和纤维交叉情况下的局限性。然而，合适纤维数量的选择以及模型拟合中的数值困难限制了它们的实际应用。本文通过使球形反卷积成为拟合过程的一部分来解决这两个问题：我们证明，使用适当的核函数，反卷积可提供可靠的近似拟合，随后通过下降型优化有效地对其进行细化。此外，与传统的F检验相比，基于方向分布函数确定纤维数量会产生更好的结果。我们的工作证明了统一扩散图像分析中先前不同工作思路的好处。
