Using Edge-Preserving Algorithm with Non-local Mean for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT

TL;DR

This paper introduces HYPR-NLM, an edge-preserving, non-local mean filter integrated into dual-energy CT material decomposition, significantly reducing noise while maintaining image resolution and quantitative accuracy.

Contribution

The paper presents a novel HYPR-NLM algorithm that enhances dual-energy CT material decomposition by effectively reducing noise and preserving spatial resolution using non-local means within the HYPR-LR framework.

Findings

HYPR-NLM reduces noise more effectively than existing methods.

It preserves high-frequency edge information and quantitative accuracy.

The method is robust to parameter variations.

Abstract

Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local HighlY constrained backPRojection Reconstruction (HYPR-LR) framework. With effective use of the non-local mean, the proposed algorithm, which is referred to as HYPR-NLM, reduces the noise in dual energy decomposition while preserving the accuracy of quantitative measurement and spatial resolution of the material-specific dual energy images. We demonstrate the noise reduction and resolution preservation of the algorithm with iodine concentrate numerical phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also show the superior performance of the HYPR-NLM over the existing methods by using two sets of cardiac perfusing imaging data. The reference drawn from the comparison study includes: (1) HYPR-NLM significantly reduces the DECT material decomposition noise while preserving quantitative measurements and high-frequency edge information, and (2) HYPR-NLM is robust with respect to parameter selection.