Incorporating robustness in diagonally-relaxed orthogonal projections method for proton computed tomography

Abstract

Iterative algorithms such as ART, DROP, and CARP are commonly used in reconstructing computed tomography images, but only account for errors in the measurements. Errors in the predicted path and intersection lengths, or even blocks of missing measurements can result in degraded image quality. Robust techniques allow for errors in other areas of the model and produce good images that show less sensitivity. In this paper we introduce a robust version of DROP and compare its performance advantages to the standard DROP algorithm on on real data.