Methods for Few-View CT Image Reconstruction

TL;DR

This paper introduces advanced constrained and regularized optimization algorithms for reconstructing high-quality CT images from significantly fewer projections, reducing scan time and dose while maintaining image fidelity.

Contribution

It presents novel data fidelity and regularization techniques, combined in multi-stage optimization, that outperform existing methods in few-view CT reconstruction.

Findings

Outperforms state-of-the-art few-view CT methods

Effective with as few as 4 projections

Demonstrated on measured and simulated datasets

Abstract

Computed Tomography (CT) is an essential non-destructive three dimensional imaging modality used in medicine, security screening, and inspection of manufactured components. Typical CT data acquisition entails the collection of a thousand or more projections through the object under investigation through a range of angles covering one hundred eighty degrees or more. It may be desirable or required that the number of projections angles be reduced by one or two orders of magnitude for reasons such as acquisition time or dose. Unless specialized reconstruction algorithms are applied, reconstructing with fewer views will result in streak artifacts and failure to resolve object boundaries at certain orientations. These artifacts may substantially diminish the usefulness of the reconstructed CT volumes. Here we develop constrained and regularized numerical optimization methods to reconstruct CT volumes from 4-28 projections. These methods entail utilization of novel data fidelity and convex and non-convex regularization terms. In addition, the methods outlined here are usually carried out by a sequence of two or three numerical optimization methods in sequence. The efficacy of our methods is demonstrated on four measured and three simulated few-view CT data sets. We show that these methods outperform other state of the art few-view numerical optimization methods.