Characterization of normal brain tissue using seven calculated MRI parameters and a statistical analysis system

A statistical analysis system for classifying normal brain tissue has been applied to the analysis of MRI scans on 45 volunteers. The Bayes Maximum Likelihood method was used to achieve a discrimination accuracy of 84% for 13 tissue types among three age group sets, with classification accuracies for individual regions ranging from 50 to 100%. In order to attain this level of discrimination a set of seven derived relaxation‐type parameters was used to categorize the tissue types. Values for these experimentally estimated parameters were derived from the MRI intensities of eight images in the following pulse sequences: (1) a Carr‐Purcell‐Meiboom‐Gill (CPMG) four‐echo train, (2)a single‐echo inversion recovery, and (3) three single‐echo sequences with varying repetition times, TR, and echo delays, TE. The T2 values derived from ratios of single‐echo intensities showed better discrimination power than those from the four‐echo CPMG train. The general precision of the seven estimated parameters was excellent, with percentage stan‐ dard deviations ranging from 4 to 18% for the various regions studied. The tissue discrimination achieved by use of just three relaxation parameters, T1, T2, and proton density, calculated from intensities of images from a four‐echo sequence, an inversion recovery sequence, and a short TR single‐echo sequence, was not as good, being only 55%. © 1989 Academic Press. Inc.