Characterization of normal brain tissue using seven calculated MRI parameters and a statistical analysis system
✍ Scribed by Timothy J. Hyman; George C. Levy; Robert J. Kurland; Jon D. Shoop
- Book ID
- 102954453
- Publisher
- John Wiley and Sons
- Year
- 1989
- Tongue
- English
- Weight
- 682 KB
- Volume
- 11
- Category
- Article
- ISSN
- 0740-3194
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✦ Synopsis
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 standard deviations ranging from 4 to 18% for the various regions studied. The tissue discrimination achieved by use of just three relaxation parameters, TI, 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%. o 1989 Academic Press, Inc.