Basic study for constructing the statistical patient analysis system by using multi-modality of MRI

MRI多模态构建统计患者分析系统的基础研究

基本信息

批准号：
21500442
负责人：
SAKAI Koji
金额：
$ 2.91万
依托单位：
Kyoto University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2009
资助国家：
日本
起止时间：
2009 至 2011
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21500442/
关键词：
磁気共鳴像脳白質統計的病態解析マルチモダリティ

项目摘要

The voxel-based group analysis(VBA) is one of the most effective examination methods of the entire white matter(WM) of brain. However, the VBA often suffers from low statistical power(high false discovery rate), which caused by embedded noise in voxels. To ameliorate this problem, the combination of the VBA with a pre-segmented WM has already been proposed. On the other hand, 3D whole brain WM atlas(ABA : atlas-based analysis) was proposed to gain statistical power on the examination of the WM analysis. In this report, we attempted to further extend the ABA to obtain statistically stronger detection power than the VBA. We proposed a sub-atlas-based analysis(SBA), which uses 3D plane made from the fitting curves to the WM atlas. From the results of outlier test, the detection power of SBA was clearly affected by both abnormal area and its value. On the other hand, the detection power of VBA was not affected abnormal area and it was only affected by abnormal values. In the case of cluster abnormality, the detection power of SBA was affected by both abnormal area and value. The affection of abnormal area was opposite from random abnormality due to the difference of their arrangement. The detection power of VBA was also affected by both abnormal area and value. From the results, these two methods showed better detection power on the cluster abnormal arrangement than the random abnormal arrangement. This means that the distribution of abnormal values affects the detection power of VBM-style examination methods. We proposed new method"SBA"to analyse abnormality within the white matter in brain. We compared our proposed method with conventional ABA. The SBA had a larger abnormality detection power than the ABA in the case of obsessive-compulsive disorder(OCD) patients, even if the method is naturally including false discovery rate.

基于体素的小组分析（VBA）是整个大脑整个白质（WM）中最有效的检查方法之一。但是，VBA通常患有低统计功率（高的发现率），这是由于体素中嵌入的噪声引起的。为了改善这个问题，已经提出了VBA与预分段的WM的组合。另一方面，提出了3D全脑WM地图集（基于ABA：基于ATLAS的分析），以在检查WM分析时获得统计能力。在本报告中，我们试图进一步扩展ABA，以获得比VBA更强大的检测能力。我们提出了一个基于子ATLA的分析（SBA），该分析使用了从拟合曲线制成的3D平面到WM地图集。从异常测试的结果来看，SBA的检测能力显然受异常面积及其价值的影响。另一方面，VBA的检测能力不影响异常面积，仅受异常值的影响。在群集异常的情况下，SBA的检测能力受异常面积和价值的影响。由于其排列的差异，异常区域的感情与随机异常相反。 VBA的检测能力也受异常面积和价值的影响。从结果来看，这两种方法比随机异常排列在簇异常排列上显示出更好的检测能力。这意味着异常值的分布会影响VBM式检查方法的检测能力。我们提出了新方法“ SBA”，以分析大脑白质中的异常。我们将我们提出的方法与常规ABA进行了比较。在强迫症（OCD）患者的情况下，SBA的异常检测能力比ABA更大，即使该方法自然包括假发现率。