Image texture aided spectral CT material decomposition

Spectral capabilities of photon counting detectors (PCDs) can allow material decomposition. We recently showed multi-material decomposition using high resolution photon counting detectors (Medipix3). High-resolution photon counting spectral detectors have unique advantages and challenges. Some of the challenges arise from noise properties as well as spectral distortions. We show benefits of an empirical correction method to obtain accurate attenuation values in a spectral CT even with high resolution detectors and a combination of spectral distortions. Aided with accurate spectral correction, we show that a Gaussian mixture model assisted iterative decomposition can separate multiple materials at once. Our group has been investigating the role of image texture features in signal detection performance in tomographic images. Here we will explore utilizing variations in image texture features in spectral CT material decomposition. Along with attenuation variations for each energy bin, second order statistical texture feature variations associated with spectral data will be used to reduce the number of energy bins and imaging dose to perform multi-material decomposition. With promising preliminary results, we will show a more thorough investigation of image texture variations in spectral data to assist efficient and low dose material decomposition in spectral CT.