Quantitative ¹H spectroscopic imaging of human brain at 4.1 T using image segmentation

Abstract

Metabolic differences in the content of N-acetylaspartate (NAA), creatinine (CR), and choline (CH) in cerebral gray and white matter can complicate the interpretation of 1H spectroscopic images. To account for these variations, the gray- and white-matter content of each voxel must be known. To provide these data, a T1-based image segmentation scheme was implemented at 4.1 T. The tissue composition of each voxel was determined using the point-spread function of the spectroscopic imaging acquisition and the segmented anatomical image. Pure gray- and white-matter values for CR/NAA and CH/NAA, and the content of CR, CH, and NAA, were determined using a linear-regression analysis of 984 voxels acquired from 10 subjects using white-matter CR as an internal standard. This information was used to establish means and confidence intervals for CR/NAA and CH/NAA from a voxel of arbitrary tissue composition. Using a single-tailed t test, the extent and locations of the metabolic abnormalities (P < 0.05) in a patient with multiple sclerosis were identified.