Auría A, Daducci A, Thiran J-P, Wiaux Y
Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland.
Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland; University Hospital Center (CHUV) and University of Lausanne (UNIL), Switzerland.
Neuroimage. 2015 Jul 15;115:245-55. doi: 10.1016/j.neuroimage.2015.04.049. Epub 2015 May 2.
We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.
我们提出了一种新颖的公式，用于从扩散磁共振成像（MRI）数据解决大脑白质每个体素中纤维取向分布函数（FOD）的体素内重建问题。该领域的大多数先进方法在逐个体素的层面上进行重建，以促进取向分布的稀疏性。最近的方法提出在重建之前使用空间信息对扩散数据进行全局去噪，而其他方法则通过在每个q空间点的扩散图像上附加经验先验来促进空间正则化。我们的方法协调了体素级稀疏性和空间正则化，并定义了一种空间结构化的FOD稀疏先验，其中该结构源自相邻体素之间纤维取向的空间相干性。通过模拟数据和真实数据均表明，该方法能够从比现有技术少得多的q空间样本中实现准确的FOD重建，通常为15个样本，即使在相当不利的噪声条件下也是如此。
