Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional
Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions
due to similar composition as well as other factors including beam hardening and patient motion. This problem
is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions,
multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam
are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone
Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the
opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI
can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images
come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning
method for discriminating between cysts and hypodense liver metastases using these monochromatic images.
Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and
nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute
relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion.
We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided
lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single
projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification
using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp
acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating
benign liver cysts and metastases, especially for small lesions.