Tristán-Vega Antonio, Westin Carl-Fredrik
Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Boston, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):182-90. doi: 10.1007/978-3-642-23629-7_23.
High Angular Resolution Diffusion Imaging (HARDI) demands a higher amount of data measurements compared to Diffusion Tensor Imaging (DTI), restricting its use in practice. We propose to represent the probabilistic Orientation Distribution Function (ODF) in the frame of Spherical Wavelets (SW), where it is highly sparse. From a reduced subset of measurements (nearly four times less than the standard for HARDI), we pose the estimation as an inverse problem with sparsity regularization. This allows the fast computation of a positive, unit-mass, probabilistic ODF from 14-16 samples, as we show with both synthetic diffusion signals and real HARDI data with typical parameters.
与扩散张量成像（DTI）相比，高角分辨率扩散成像（HARDI）需要更多的数据测量，这限制了它在实际中的应用。我们建议在球面小波（SW）框架中表示概率性取向分布函数（ODF），在该框架中它是高度稀疏的。从减少的测量子集中（比HARDI标准少近四倍），我们将估计问题构建为一个具有稀疏正则化的逆问题。这使得我们能够从14 - 16个样本中快速计算出一个正的、单位质量的概率性ODF，我们在合成扩散信号和具有典型参数的真实HARDI数据中都证明了这一点。
