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高阶张量扩散数据的梯度编码方向比较

Comparison of gradient encoding directions for higher order tensor diffusion data.

作者信息

Mang Sarah C, Gembris Daniel, Grodd Wolfgang, Klose Uwe

机构信息

Section Experimental MR of CNS, Diagnostic and Interventional Neuroradiology, University Hospital Tuebingen, Tuebingen, Germany.

出版信息

Magn Reson Med. 2009 Feb;61(2):335-43. doi: 10.1002/mrm.21797.

Abstract

Recently, higher order tensors were proposed for a more advanced representation of non-Gaussian diffusion. These advanced diffusion models have new requirements for the gradient encoding schemes used in the diffusion weighted image acquisition. The influence of the gradient encoding schemes on the estimated standard second order diffusion tensor was previously investigated. Here, we focus on the suitability of different encoding scheme types for higher order tensor models. Two quality measures for the gradient encoding schemes, the condition number of the estimation matrix and a new measure that evaluates the signal deviation on simulated data, are used to determine which gradient encoding is suited best for higher order tensor estimations. Six different gradient encoding scheme types were investigated. A certain force-minimizing scheme type gave the best results in the evaluations presented here.

摘要

最近,有人提出使用高阶张量来更高级地表示非高斯扩散。这些先进的扩散模型对扩散加权图像采集所使用的梯度编码方案有新的要求。之前已经研究了梯度编码方案对估计的标准二阶扩散张量的影响。在这里,我们关注不同编码方案类型对高阶张量模型的适用性。使用梯度编码方案的两种质量度量,即估计矩阵的条件数和一种评估模拟数据上信号偏差的新度量,来确定哪种梯度编码最适合高阶张量估计。研究了六种不同的梯度编码方案类型。在此呈现的评估中,某种力最小化方案类型给出了最佳结果。

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