A multivariate hypothesis testing framework for tissue clustering and classification of DTI data
A multivariate hypothesis testing framework for tissue clustering and classification of DTI data
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DOI：
10.1002/nbm.1383
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发表时间：
2009-08-01
影响因子：
2.9
通讯作者：
Basser, Peter J.
中科院分区：
文献类型：
--
作者：
Freidlin, Raisa Z.;Oezarslan, Evren;Basser, Peter J.
这项工作的主要目的是提出并研究一种新型无监督组织聚类和扩散张量 MRI (DTI) 数据分类算法的有效性。所提出的算法利用有关体素内扩散张量分布的均匀程度的信息。我们采用 Hext 和 Snedecor 提出的框架，其中属于同一分布的扩散张量的零假设通过 F 检验进行评估。组织类型根据四种可能的扩散模型之一进行分类，其分配由基于施瓦茨准则的简约模型选择框架确定。从切除的大鼠和猪脊髓获得的数值模型和扩散加权成像（DWI）数据用于测试和验证这些组织聚类和分类方法。无监督聚类方法可有效识别体模和真实实验 DTI 数据中的不同感兴趣区域 (ROI)。版权所有 (C) 2009 John Wiley & Sons, Ltd.
The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors belonging to the same distribution is assessed by an F-test. Tissue type is classified according to one of the four possible diffusion models, the assignment of which is determined by a parsimonious model selection framework based on Schwarz Criterion. Both numerical phantoms and diffusion-weighted imaging (DWI) data obtained from excised rat and pig spinal cords are used to test and validate these tissue clustering and classification approaches. The unsupervised clustering method effectively identifies distinct regions of interest (ROIs) in phantoms and real experimental DTI data. Copyright (C) 2009 John Wiley & Sons, Ltd.