Dictionary learning based statistical interior reconstruction without a prior knowledge

Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint.