Tournier J-Donald, Calamante Fernando, Gadian David G, Connelly Alan
Radiology and Physics Unit, Institute of Child Health, University College London, and Great Ormond Street Hospital for Children NHS Trust, London, WC1N 1EH, UK.
Neuroimage. 2004 Nov;23(3):1176-85. doi: 10.1016/j.neuroimage.2004.07.037.
Diffusion-weighted magnetic resonance imaging can provide information related to the arrangement of white matter fibers. The diffusion tensor is the model most commonly used to derive the orientation of the fibers within a voxel. However, this model has been shown to fail in regions containing several fiber populations with distinct orientations. A number of alternative models have been suggested, such as multiple tensor fitting, q-space, and Q-ball imaging. However, each of these has inherent limitations. In this study, we propose a novel method for estimating the fiber orientation distribution directly from high angular resolution diffusion-weighted MR data without the need for prior assumptions regarding the number of fiber populations present. We assume that all white matter fiber bundles in the brain share identical diffusion characteristics, thus implicitly assigning any differences in diffusion anisotropy to partial volume effects. The diffusion-weighted signal attenuation measured over the surface of a sphere can then be expressed as the convolution over the sphere of a response function (the diffusion-weighted attenuation profile for a typical fiber bundle) with the fiber orientation density function (ODF). The fiber ODF (the distribution of fiber orientations within the voxel) can therefore be obtained using spherical deconvolution. The properties of the technique are demonstrated using simulations and on data acquired from a volunteer using a standard 1.5-T clinical scanner. The technique can recover the fiber ODF in regions of multiple fiber crossing and holds promise for applications such as tractography.
扩散加权磁共振成像可以提供与白质纤维排列相关的信息。扩散张量是最常用于推导体素内纤维方向的模型。然而，已证明该模型在包含多个具有不同方向的纤维群的区域中会失效。已经提出了许多替代模型，例如多张量拟合、q空间和Q球成像。然而，这些模型中的每一个都有其固有的局限性。在本研究中，我们提出了一种新颖的方法，可直接从高角分辨率扩散加权MR数据估计纤维方向分布，而无需对存在的纤维群数量进行先验假设。我们假设大脑中的所有白质纤维束具有相同的扩散特征，从而将扩散各向异性的任何差异隐含地归因于部分容积效应。然后，在球体表面测量的扩散加权信号衰减可以表示为响应函数（典型纤维束的扩散加权衰减剖面）与纤维方向密度函数（ODF）在球体上的卷积。因此，可以使用球面反卷积获得纤维ODF（体素内纤维方向的分布）。使用模拟以及从一名志愿者使用标准1.5-T临床扫描仪采集的数据展示了该技术的特性。该技术可以在多个纤维交叉区域恢复纤维ODF，并有望用于诸如纤维束成像等应用。
