Using virtual monoenergetic images in Karhunen–Loève domain to differentiate lesion pathology

Abstract

Dual-energy computed tomography (DECT) enables to generate a series of virtual monoenergetic images (VMIs). Using VMIs of a desired energy level (5 – 45 keV) can enhance the lesion-to-background and voxel-to-voxel within lesion contrast, because that the lesion material composition may vary from voxel to voxel. However, there are also strong correlation of the voxel values among different energy channels. This correlation may result in redundant information for the VMIs based lesion pathology differentiation. Therefore, we transformed the VMIs in the Karhunen–Loève domain to reduce the correlation. In the new domain, the leading three principal components accounts for more than 99% information and then were used to form a new descriptor for the differentiation task. Two pathological proven datasets were used for the evaluation. Experimental results showed that the VMIs can improved the AUC (area under the receiver operating characteristic curve) value from 0.862 and 0.647 to 0.912 and 0.830 comparing to using the conventional CT.