Path-based Iterative Reconstruction (PBIR) for X-ray Computed Tomography

TL;DR

This paper introduces path seeking algorithms for efficient generation of multiple MBIR images with varying penalty strengths in X-ray CT, enabling comprehensive data analysis and improved reconstruction quality.

Contribution

It presents two novel path seeking algorithms that efficiently produce a series of MBIR images with different penalty parameters, enhancing reconstruction flexibility.

Findings

Root-mean-squared-differences below 4 HU across the reconstruction path

Efficient generation of multiple MBIR images with different penalty strengths

Improved reconstruction process for X-ray CT imaging

Abstract

Model-based iterative reconstruction (MBIR) techniques have demonstrated many advantages in X-ray CT image reconstruction. The MBIR approach is often modeled as a convex optimization problem including a data fitting function and a penalty function. The tuning parameter value that regulates the strength of the penalty function is critical for achieving good reconstruction results but difficult to choose. In this work, we describe two path seeking algorithms that are capable of efficiently generating a series of MBIR images with different strengths of the penalty function. The root-mean-squared-differences of the proposed path seeking algorithms are below 4 HU throughout the entire reconstruction path. With the efficient path seeking algorithm, we suggest a path-based iterative reconstruction (PBIR) to obtain complete information from the scanned data and reconstruction model.