Comparing Two Bootstrapped Regions in Images: The D-Test

Abstract

ObjectivesMany molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available. Material and methodsAfter presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance. ResultsThe results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test. ConclusionThis paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.