Abstract
Lesion detection by MR imaging depends on the contrast-to-noise ratio of the voxels containing the lesion relative to those containing the background. When the lesion voxels are less than completely filled, the inherent contrast between lesion and background is modified by the filling factor. Lesion detection thus depends on lesion size, slice thickness, lesion position relative to slice, thickness of gap between slices, and inherent contrast between lesion and background. Using computer simulation, the effect of variation in the slice thickness and the interslice gap on lesion detection is modeled as a function of lesion size, filling factor, and inherent contrast. Detection of small, low-contrast lesions is shown to be most sensitive to partial volume effects and to be greatest with thin slices. Detection of high-contrast lesions is shown to be limited primarily by the presence of a gap between slices. For patients with diffusely distributed disease-e.g., the small, low-contrast lesions of multiple sclerosis-lesion detection is greater for thin slices, even with a gap, than for thick, contiguous slices.
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