Assemlal Haz-Edine, Campbell Jennifer, Pike Bruce, Siddiqi Kaleem
Centre for Intelligent Machines, McGill University, 3480 University Street, Montréal, QC, Canada H3A 2A7.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):157-65. doi: 10.1007/978-3-642-23629-7_20.
The vast majority of High Angular Resolution Diffusion Imaging (HARDI) modeling methods recover networks of neuronal fibres, using a heuristic extraction of their local orientation. In this paper, we present a method for computing the apparent intravoxel Fibre Population Dispersion (FPD), which conveys the manner in which distinct fibre populations are partitioned within the same voxel. We provide a statistical analysis, without any prior assumptions on the number or size of these fibre populations, using an analytical formulation of the diffusion signal autocorrelation function in the spherical harmonics basis. We also propose to extract features of the FPD obtained in the group of rotations, using several metrics based on unit quaternions. We show results on simulated data and on physical phantoms, that demonstrate the effectiveness of the FPD to reveal regions with crossing tracts, in contrast to the standard anisotropy measures.
绝大多数高角分辨率扩散成像（HARDI）建模方法通过启发式提取局部方向来恢复神经元纤维网络。在本文中，我们提出了一种计算表观体素内纤维群离散度（FPD）的方法，该方法传达了不同纤维群在同一体素内的划分方式。我们提供了一种统计分析方法，在不对这些纤维群的数量或大小做任何先验假设的情况下，利用球谐函数基中扩散信号自相关函数的解析公式进行分析。我们还建议使用基于单位四元数的多个指标来提取在旋转组中获得的FPD特征。我们展示了在模拟数据和物理模型上的结果，这些结果表明与标准各向异性测量相比，FPD在揭示交叉纤维束区域方面的有效性。
