Motion Compensated X-ray CT Algorithm for Moving Objects

Abstract

In this study a motion compensated X-ray CT algorithm based on a statistical model is proposed. The important feature of our motion compensated X-ray CT algorithm is that the target object is assumed to move or deform along the time. Then the projections of the deforming target object are described by a state-space model. The deformation is described by motion vectors each attached to each pixel. To reduce the ill-posed ness we incorporate into the prior distribution our a priori knowledge that the target object is composed of a restricted number of materials whose X-ray absorption coefficients are roughly known. To perform Bayesian inference based on our statistical model, the posterior distribution is approximated by a computationally tractable distribution such to minimize Kullback-Leibler (KL) divergence between the posterior and the tractable distributions. Computer simulations using phantom images show the effectiveness of our CT algorithm, suggesting the state-space model works even when the target object is deforming.