Apparent Diffusion Coefficient Approximation and Diffusion Anisotropy Characterization in DWI
Apparent Diffusion Coefficient Approximation and Diffusion Anisotropy Characterization in DWI
复制标题
DWI 中的表观扩散系数近似和扩散各向异性表征
DOI：
--
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Yijun Liu
中科院分区：
文献类型：
--
作者：
Yunmei Chen;Weihong Guo;Q. Zeng;Xiaolu Yan;M. Rao;Yijun Liu
我们提出了一种新的近似非高斯水扩散的表观扩散系数（ADC），在一个体素内最多有两个纤维取向。该模型通过高角分辨率扩散加权（HARD） MRI数据中两个高达2阶的球谐级数（SHS）的乘积来近似ADC曲线。通过求解约束最小化问题，同时估计和正则化了SHS的系数。为了降低计算复杂度和提高计算效率，本文还提供了一种等效但不受约束的方法。此外，我们使用累积残差熵（CRE）作为表征扩散各向异性的测量。利用CRE可以在两个阈值下得到合理的结果，而现有的方法要么只能用于表征高斯扩散，要么需要更多的测量值和阈值来对两种光纤取向的各向异性扩散进行分类。在HARD MRI人脑数据上的实验表明了该方法在ADC剖面恢复中的有效性。基于所提出方法的扩散表征显示了我们的结果与已知神经解剖学之间的一致性。
We present a new approximation for the apparent diffusion coefficient (ADC) of non-Gaussian water diffusion with at most two fiber orientations within a voxel. The proposed model approximates ADC profiles by product of two spherical harmonic series (SHS) up to order 2 from High Angular Resolution Diffusion-weighted (HARD) MRI data. The coefficients of SHS are estimated and regularized simultaneously by solving a constrained minimization problem. An equivalent but non-constrained version of the approach is also provided to reduce the complexity and increase the efficiency in computation. Moreover we use the Cumulative Residual Entropy (CRE) as a measurement to characterize diffusion anisotropy. By using CRE we can get reasonable results with two thresholds, while the existing methods either can only be used to characterize Gaussian diffusion or need more measurements and thresholds to classify anisotropic diffusion with two fiber orientations. The experiments on HARD MRI human brain data indicate the effectiveness of the method in the recovery of ADC profiles. The characterization of diffusion based on the proposed method shows a consistency between our results and known neuroanatomy.