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With the increasing use of three-dimensional MRI techniques it is becoming necessary to explore automated techniques for locating pathology in the volume images. The suitability of a specific technique to locate and identify healthy tissues of the brain was examined as a first step toward eventually identifying pathology in images. This technique, called multispectral image segmentation, is based on the classification of tissue types in an image according to their characteristics in various spectral regions. The spectral regions chosen for this study were the hydrogen spin-lattice relaxation time T1, spin-spin relaxation time T2, and spin density, rho. Single-echo, spin-echo magnetic resonance images of axial slices through the brain at the level of the lateral ventricles were recorded on a 1.5 Tesla imager from 20 volunteers ranging in age from 17 to 72 years. These images were used to calculate the T1, T2, and rho images used for the classification. Tissue classification was performed by locating clusters of pixels in a three-dimensional T1(-1)-T2(-1)-rho histogram. Gray matter, white matter, cerebrospinal fluid, meninges, muscle, and adipose tissues were readily classified in magnetic resonance images of the volunteers with a single set of T1, T2, and rho values. Cluster characteristics, such as size, shape, and location, provided information on the imaging procedure and tissue characteristics.

To determine if the quality of spin-echo magnetic resonance (MR) images of the temporomandibular joint (TMJ) could be improved by reducing section thickness, coronal and sagittal 3.0- and 1.5-mm MR images of the same joints were evaluated. Depiction of the disk, trabecular pattern, and cortex of the condyle was better on coronal 1.5-mm images than on 3.0-mm images (P less than .01), and 1.5-mm sagittal images were better for depiction of the trabecular pattern of the condyle than were 3.0-mm images (P less than .05). The ability of MR imaging with thinner sections to reveal more anatomic details should result in improved diagnostic accuracy.

Thirty-four carotid artery bifurcations were examined using both magnetic resonance angiography (MRA) and digital subtraction arch aortography to determine their accuracy when compared to selective carotid angiography. The sensitivity of MRA was 73% and its specificity was 91% when compared with selective carotid angiography. The sensitivity of arch aortography was 27% and its specificity was 100%.