A MS-lesion pattern discrimination plot based on geostatistics

Abstract

Introduction: A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented. Methods: A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill. Results: Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot. Conclusions: The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis. A geostatistics-based MS-lesion pattern description is developed. The resulting graphical representations are considered efficient exploratory data analysis tools to accompany cross-sectional, follow-up, and medication impact analysis.