A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms

TL;DR

This paper presents a novel metal-artifact reduction scheme integrated into iterative dual-energy CT algorithms, significantly improving image quality in the presence of metal implants for accurate treatment planning.

Contribution

It introduces a new metal-artifact reduction method for iterative DECT algorithms using data substitution and image-domain decomposition to enhance artifact suppression.

Findings

DEAM with the proposed scheme outperforms original DEAM and vendor methods.

The method effectively reduces streaking artifacts from metallic objects.

Quantitative analysis confirms improved image quality and accuracy.

Abstract

CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping-power maps used in treatment planning. However, the presence of metal implants introduces severe streaking artifacts in the reconstructed images, affecting the diagnostic accuracy and treatment performance. In order to reduce the metal artifacts in DECT, we introduce a metal-artifact reduction scheme for iterative DECT algorithms. An estimate is substituted for the corrupt data in each iteration. We utilize normalized metal-artifact reduction (NMAR) composed with image-domain decomposition to initialize the algorithm and speed up the convergence. A fully 3D joint statistical DECT algorithm, dual-energy alternating minimization (DEAM), with the proposed scheme is tested on experimental and clinical helical data acquired on a Philips Brilliance Big Bore scanner. We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without metal-artifact reduction for orthopedic implants (O-MAR). The visualization and quantitative analysis show that DEAM with the proposed method has the best performance in reducing streaking artifacts caused by metallic objects.