Multispectral magnetic resonance image analysis.

Abstract

Multiecho magnetic resonance (MR) scanning produces tomographic images with approximately equal morphologic information but varying gray scales at the same anatomic level. Multispectral image classification techniques, originally developed for satellite imaging, have recently been applied to MR tissue characterization. Statistical assessment of multispectral tissue classification techniques has been used to select the most promising of several alternative methods. MR examinations of the head and body, obtained with a 0.35, 0.5, or 1.5T imager, comprised data sets with at least two pulse sequences yielding three images at each anatomical level: (1) TR = 0.3 sec, TE = 30 msec, (2) TR = 1.5, TE = 30, (3) TR = 1.5, TE = 120. Normal and pathological images have been analyzed using multispectral analysis and image classification. MR image data are first subjected to radiometric and geometric corrections to reduce error resulting from (1) instrumental variations in data acquisition, (2) image noise, and (3) misregistration. Training regions of interest (ROI) are outlined in areas of normal (gray and white matter, CSF) and pathological tissue. Statistics are extracted from these ROIs and classification maps generated using table lookup, minimum distance to means, maximum likelihood, and cluster analysis. These synthetic maps are then compared pixel by pixel with manually prepared classification maps of the same MR images. Using these methods, the authors have found that: (1) both supervised and unsupervised classification techniques yielded theme maps (class maps) which demonstrated tissue characteristic signatures and (2) tissue classification errors found in computer-generated theme maps were due to subtle gray scale changes present in the original MR data sets arising from radiometric inhomogeneity and spatial nonuniformity.