Accelerated Alternating Minimization for X-ray Tomographic Reconstruction

TL;DR

This paper introduces an accelerated alternating minimization method to improve X-ray CT image reconstruction by simultaneously correcting geometric errors, enhancing image quality especially for portable, error-prone CT devices.

Contribution

The paper proposes a novel accelerated alternating minimization algorithm that jointly reconstructs images and corrects geometric errors in CT, improving upon existing methods.

Findings

Enhanced image quality in error-prone CT reconstructions

Faster convergence of the reconstruction algorithm

Effective correction of geometric errors in portable CT devices

Abstract

While Computerized Tomography (CT) images can help detect disease such as Covid-19, regular CT machines are large and expensive. Cheaper and more portable machines suffer from errors in geometry acquisition that downgrades CT image quality. The errors in geometry can be represented with parameters in the mathematical model for image reconstruction. To obtain a good image, we formulate a nonlinear least squares problem that simultaneously reconstructs the image and corrects for errors in the geometry parameters. We develop an accelerated alternating minimization scheme to reconstruct the image and geometry parameters.