A New Iterative Method for CT Reconstruction with Uncertain View Angles

Abstract

In this paper, we propose a new iterative algorithm for Computed Tomography (CT) reconstruction when the problem has uncertainty in the view angles. The algorithm models this uncertainty by an additive model-discrepancy term leading to an estimate of the uncertainty in the likelihood function. This means we can combine state-of-theart regularization priors such as total variation with this likelihood. To achieve a good reconstruction the algorithm alternates between updating the CT image and the uncertainty estimate in the likelihood. In simulated 2D numerical experiments, we show that our method is able to improve the relative reconstruction error and visual quality of the CT image for the uncertain-angle CT problem.

Hybrid Projection Methods with Recycling for Inverse Problems

“…are slightly modified. The need to solve multiple problems with modified projection angles arises in various scenarios including optimal experimental design frameworks [42] and optimization to correct for uncertain angles [41]. We investigate the use of hybrid projection methods with recycling to reuse the solution and solution basis vectors acquired from one reconstruction to efficiently solve another reconstruction problem with modified angles.…”