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一种基于自适应自旋玻璃模型的新型全局纤维束成像算法。

A novel global tractography algorithm based on an adaptive spin glass model.

作者信息

Fillard Pierre, Poupon Cyril, Mangin Jean-François

机构信息

LNAO - CEA/DSV/I2BM/Neurospin, Paris, France.

出版信息

Med Image Comput Comput Assist Interv. 2009;12(Pt 1):927-34. doi: 10.1007/978-3-642-04268-3_114.

Abstract

This paper introduces a novel framework for global diffusion MRI tractography inspired from a spin glass model. The entire white matter fascicle map is parameterized by pieces of fibers called spins. Spins are encouraged to move and rotate to align with the main fiber directions, and to assemble into longer chains of low curvature. Moreover, they have the ability to adapt their quantity in regions where the spin concentration is not sufficient to correctly model the data. The optimal spin glass configuration is retrieved by an iterative minimization procedure, where chains are finally assimilated to fibers. As a result, all brain fibers appear as growing simultaneously until they merge with other fibers or reach the domain boundaries. In case of an ambiguity within a region like a crossing, the contribution of all neighboring fibers is used determine the correct neural pathway. This framework is tested on a MR phantom representing a 45 degrees crossing and a real brain dataset. Notably, the framework was able to retrieve the triple crossing between the callosal fibers, the corticospinal tract and the arcuate fasciculus.

摘要

本文介绍了一种受自旋玻璃模型启发的用于全局扩散磁共振成像纤维束成像的新型框架。整个白质纤维束图谱由称为自旋的纤维片段进行参数化。自旋被促使移动和旋转以与主要纤维方向对齐,并组装成低曲率的更长链。此外,它们有能力在自旋浓度不足以正确建模数据的区域调整其数量。通过迭代最小化过程检索最优的自旋玻璃配置,最终将链同化为纤维。结果,所有脑纤维似乎同时生长,直到它们与其他纤维合并或到达区域边界。在诸如交叉区域存在模糊性的情况下,所有相邻纤维的贡献被用于确定正确的神经通路。该框架在一个代表45度交叉的磁共振体模和一个真实脑数据集上进行了测试。值得注意的是,该框架能够检索到胼胝体纤维、皮质脊髓束和弓状束之间的三重交叉。

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