Photon counting CT (PCCT) is an x-ray imaging technique that has undergone great development in the past decade. PCCT has the potential to improve dose efficiency and low-dose performance. In this paper, we propose a statistics-based iterative algorithm to perform a direct reconstruction of material-decomposed images. Compared with the conventional sinogram-based decomposition method which has degraded performance in low- dose scenarios, the multi-energy alternating minimization algorithm for photon counting CT (MEAM-PCCT) can generate accurate material-decomposed image with much smaller biases.
X-ray imaging for security screening is a challenging application that requires simultaneous satisfaction of seemingly incompatible constraints, including low cost, high throughput, and reliable detection of threats. We take a principled computational imaging approach to system design. Mathematical models of the underlying physics and a Huber-class penalty function yield a penalized maximum-likelihood problem. We extend our iterative algorithm for computing linear attenuation coefficients to use multiple energy bins in the SureScan x1000, which has an unconventional, fixed-source geometry. The goal is to maintain the spatial resolution of the single-energy reconstruction while providing information for material characterization used for detection of threats.
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