Conditional-likelihood approach to material decomposition in spectral absorption-based or phase-contrast CT

Abstract

Material decomposition in absorption-based X-ray CT imaging suffers certain inefficiencies when differentiating among soft tissue materials. To address this problem, decomposition techniques turn to spectral CT, which has gained popularity over the last few years. Although proven to be more effective, such techniques are primarily limited to the identification of contrast agents and soft and bone-like materials. In this work, we introduce a novel conditional likelihood, material-decomposition method capable of identifying any type of material objects scanned by spectral CT. The method takes advantage of the statistical independence of spectral data to assign likelihood values to each of the materials on a pixel-by-pixel basis. It results in likelihood images for each material, which can be further processed by setting certain conditions or thresholds, to yield a final material-diagnostic image. The method can also utilize phase-contrast CT (PCI) data, where measured absorption and phase-shift information can be treated as statistically independent datasets. In this method, the following cases were simulated: (i) single-scan PCI CT, (ii) spectral PCI CT, (iii) absorption-based spectral CT, and (iv) single-scan PCI CT with an added tumor mass. All cases were analyzed using a digital breast phantom; although, any other objects or materials could be used instead. As a result, all materials were identified, as expected, according to their assignment in the digital phantom. Materials with similar attenuation or phase-shift values (e.g., glandular tissue, skin, and tumor masses) were especially successfully when differentiated by the likelihood approach.