Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data.

Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data.
Phys Med Biol. 2017 Aug 01;62(16):6762-6783
Authors: Humphries T, Winn J, Faridani A
Abstract
Recent work in CT image reconstruction has seen increasing interest in the use of total variation (TV) and related penalties to regularize problems involving reconstruction from undersampled or incomplete data. Superiorization is a recently proposed heuristic which provides an automatic procedure to 'superiorize' an iterative image reconstruction algorithm with respect to a chosen objective function, such as TV. Under certain conditions, the superiorized algorithm is guaranteed to find a solution that is as satisfactory as any found by the original algorithm with respect to satisfying the constraints of the problem; this solution is also expected to be superior with respect to the chosen objective. Most work on superiorization has used reconstruction algorithms which assume a linear measurement model, which in the case of CT corresponds to data generated from a monoenergetic x-ray beam. Many CT systems generate x-rays from a polyenergetic spectrum, however, in which the measured data represent an integral of object attenuation over all energies in the spectrum. This inconsistency with the linear model produces the well-known beam hardening artifacts, which impair analysis of CT images. In this work we superiorize an iterative algorit...

Source: Physics in Medicine and Biology - August 1, 2017 Category: Physics Authors: Humphries T, Winn J, Faridani A Tags: Phys Med Biol Source Type: research