Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota Medical School Minneapolis, MN, USA.
Front Neurosci. 2013 Apr 4;7:41. doi: 10.3389/fnins.2013.00041. eCollection 2013.
Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis.
注册弥散加权磁共振图像（DW-MRI）是人群研究或构建大脑图谱等重要任务的关键步骤。鉴于数据的高维性，注册通常通过依赖于标量代表图像（如分数各向异性（FA）和非弥散加权（b0）图像）来完成，从而忽略了 DW-MR 数据集本身传达的许多方向信息。或者，已经提出了基于模型的配准算法来利用每个体素的优选纤维方向（s）的信息。为此目的，已经使用了诸如扩散张量或方向分布函数（ODF）之类的模型。基于张量的配准方法依赖于不完全捕获 DW-MRI 中包含的信息的模型，并且在很大程度上取决于张量的准确估计。基于 ODF 的方法更新且计算上具有挑战性，但也更好地描述了复杂的纤维结构，从而有可能提高 DW-MRI 配准的准确性。提出了一种新的基于扩散加权体积的角度插值的仿射配准算法，并且不依赖于任何特定的局部扩散模型。在这项工作中，我们首先广泛比较了基于（i）角度插值，（ii）非弥散加权标量体积（b0）和（iii）扩散张量图像（DTI）的配准算法的性能。此外，我们将角度插值（AI）的概念推广到非线性图像配准，并在 FMRIB 软件库（FSL）中实现了它。我们证明，AI 配准 DW-MRI 是体积和张量方法的强大替代方法。特别是，我们表明 AI 在许多情况下可以提高配准精度，超过现有的最先进算法，同时提供可用于任何后续分析的配准原始 DW-MRI 数据。
