Low-dose photon-counting CT with penalized-likelihood basis-image reconstruction: image quality evaluation

Abstract

Photon-counting CT scanners promise improvements in terms of noise performance, spatial resolution and material-discrimination capabilities, and their ability to reject electronic noise gives them a particularly large advantage for low-dose imaging compared to energy-integrating CT. Since filtered backprojection is suboptimal for highly noisy image data, model-based iterative reconstruction can be expected to give improved image quality for low-dose CT imaging. Several ”one-step” algorithms have been proposed that combine material decomposition and image reconstruction in a single optimization problem. The purpose of this simulation study is to evaluate the image quality that can be achieved with a one-step model-based iterative reconstruction for photon-counting low-dose CT. To this end, a penalized Poisson-likelihood model is used to reconstruct material basis images and virtual monoenergetic images from simulated measurements with a silicon-based photon-counting CT scanner and study the resulting image quality in terms of the edge-spread function, contrast-to-noise ratio and noise power spectrum, so that the tradeoff between noise and spatial resolution can be studied. The results are compared with a two-step method where projection-space material decomposition is followed by filtered backprojection. Our results show that the unconstrained one-step method can give good image quality even for low-dose images where the unconstrained two-step method fails. These results demonstrate the potential of photon-counting CT for low-dose imaging applications.