DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

TL;DR

This paper introduces DLIMD, a novel dictionary learning-based method for spectral CT material decomposition that enhances accuracy and image quality through a multi-step process involving initial decomposition, dictionary training, and constraint integration.

Contribution

The paper presents a new image-domain material decomposition technique using dictionary learning and constraints, improving spectral CT material imaging accuracy.

Findings

DLIMD improves material decomposition accuracy.

DLIMD preserves edges and features in material images.

DLIMD outperforms traditional methods in experiments.

Abstract

The potential huge advantage of spectral computed tomography (CT) is its capability to provide accuracy material identification and quantitative tissue information. This can benefit clinical applications, such as brain angiography, early tumor recognition, etc. To achieve more accurate material components with higher material image quality, we develop a dictionary learning based image-domain material decomposition (DLIMD) for spectral CT in this paper. First, we reconstruct spectral CT image from projections and calculate material coefficients matrix by selecting uniform regions of basis materials from image reconstruction results. Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique. Third, the trained dictionary is employed to explore the similarities from decomposed material images by constructing the DLIMD model. Fourth, more constraints (i.e., volume conservation and the bounds of each pixel within material maps) are further integrated into the model to improve the accuracy of material decomposition. Finally, both physical phantom and preclinical experiments are employed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.