Freidlin Raisa Z, Ozarslan Evren, Assaf Yaniv, Komlosh Michal E, Basser Peter J
Biomedical Imaging and Visualization Section, Computational Bioscience and Engineering Laboratory, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland 20892, USA.
NMR Biomed. 2009 Aug;22(7):716-29. doi: 10.1002/nbm.1383.
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.
这项工作的主要目的是提出并研究一种用于扩散张量磁共振成像（DTI）数据的新型无监督组织聚类和分类算法的有效性。所提出的算法利用了体素内扩散张量分布的均匀程度信息。我们采用了Hext和Snedecor提出的框架，其中通过F检验评估属于同一分布的扩散张量的零假设。根据四种可能的扩散模型之一对组织类型进行分类，其分配由基于施瓦茨准则的简约模型选择框架确定。数值模型以及从切除的大鼠和猪脊髓获得的扩散加权成像（DWI）数据都用于测试和验证这些组织聚类和分类方法。这种无监督聚类方法有效地识别了模型和实际实验DTI数据中不同的感兴趣区域（ROI）。
