Spectral computed tomography for material classification

Photon counting spectral detectors are finding applications in industrial and medical x-ray imaging. We will show novel imaging techniques and material decomposition approaches for classifying and volumetrically separating materials, chemicals and biological contents in a spectral computed tompgraphic imaging system. Our methods use cutting-edge photon counting detectors (PCD) developed in CERN (Medipix3) originally for particle tracking. We show the correction and calibration methods required to use these detectors along with novel algorithms involving Guassian mixture models and multi-step material decomposition developed by our group to separate multiple (upto 6 materials) of various concentrations. The high resolution and low noise from the PCDs combined with novel decomposition algorithms has the potential for applications of these mtheods in an array of industrial and biomedical imaging applications.