Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter.

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.