Probabilistic ODF estimation from reduced HARDI data with sparse regularization.
Probabilistic ODF estimation from reduced HARDI data with sparse regularization.
复制标题
DOI：
10.1007/978-3-642-23629-7_23
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Westin CF
中科院分区：
文献类型：
--
作者：
Tristán-Vega A;Westin CF
高角度分辨率弥散成像（HARDI）与弥散张量成像（DTI）相比，需要更多的数据测量，限制了其在实践中的使用。我们建议表示的概率方向分布函数（ODF）的球面波（SW），它是高度稀疏的框架。从一个减少的测量子集（近四倍小于HARDI的标准），我们提出的估计与稀疏正则化的逆问题。这允许从14-16个样本快速计算正的、单位质量的、概率ODF，如我们用合成扩散信号和具有典型参数的真实的HARDI数据所示。
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.