Low-dose CT image reconstruction method with probabilistic atlas prior

This work investigates the problem of image reconstruction from low-dose x-ray computed tomography (CT). Statistical iterative reconstruction is known to provide higher image quality due to the ability to incorporate prior knowledge to the reconstruction method and accurately model the photon statistics. In this paper, we develop a statistical reconstruction method using prior knowledge extracted from probabilistic atlas. First, we use a set of CT images previously scanned of various patients to generate a probabilistic atlas using Gaussian mixture model (GMM). Then, expectation maximization (EM) clustering algorithm is used to estimate the mixture parameters. Probabilistic atlas and mixture model parameters are then used to formulate the image reconstruction cost function. By merging the atlas information and smoothing penalty into the reconstruction procedure, image quality has been remarkably improved.