Nakai Toshiharu, Muraki Shigeru, Bagarinao Epifanio, Miki Yukio, Takehara Yasuo, Matsuo Kayako, Kato Chikako, Sakahara Harumi, Isoda Haruo
Medical Vision Laboratory, Life Electronics Research Center, National Institute of Advanced Industrial Science and Technology, 563-8577, Osaka, Japan.
Neuroimage. 2004 Jan;21(1):251-60. doi: 10.1016/j.neuroimage.2003.08.036.
An application of independent component analysis (ICA) was attempted to develop a method of processing magnetic resonance (MR) images to extract physiologically independent components representing tissue relaxation times and achieve improved visualization of normal and pathologic structures. Anatomical T1-weighted, T2-weighted and proton density images were obtained from 10 normal subjects, 3 patients with brain tumors and 1 patient with multiple sclerosis. The data sets were analyzed using ICA based on the learning rule of Bell and Sejnowski after prewhitening operations. The three independent components obtained from the three original data sets corresponded to (1) short T1 components representing myelin of white matter and lipids, (2) relatively short T1 components representing gray matter and (3) long T2 components representing free water. The involvement of gray or white matter in brain tumor cases and the demyelination in the case of multiple sclerosis were enhanced and visualized in independent component images. ICA can potentially achieve separation of tissues with different relaxation characteristics and generate new contrast images of gray and white matter. With the proper choice of contrast for the original images, ICA may be useful not only for extracting subtle or hidden changes but also for preprocessing transformation before clustering and segmenting the structure of the human brain.
尝试应用独立成分分析（ICA）来开发一种处理磁共振（MR）图像的方法，以提取代表组织弛豫时间的生理独立成分，并改善正常和病理结构的可视化效果。从10名正常受试者、3名脑肿瘤患者和1名多发性硬化症患者获取了解剖学T1加权、T2加权和质子密度图像。在进行白化预处理操作后，基于贝尔和塞乔夫斯基的学习规则，使用ICA对数据集进行分析。从三个原始数据集中获得的三个独立成分分别对应于：（1）代表白质髓磷脂和脂质的短T1成分；（2）代表灰质的相对短T1成分；（3）代表自由水的长T2成分。在独立成分图像中，脑肿瘤病例中灰质或白质的受累情况以及多发性硬化症病例中的脱髓鞘情况得到了增强和可视化。ICA有可能实现具有不同弛豫特性的组织分离，并生成新的灰质和白质对比图像。通过对原始图像进行适当的对比度选择，ICA不仅可用于提取细微或隐藏的变化，还可用于在对人脑结构进行聚类和分割之前进行预处理变换。
