Block Matching Frame based Material Reconstruction for Spectral CT

TL;DR

This paper introduces a novel spectral CT material reconstruction method that leverages block matching frames to improve image quality and reduce artifacts, validated through simulations and experiments.

Contribution

It proposes a new BMF-based regularization technique for spectral CT material reconstruction, outperforming existing regularization methods.

Findings

BMFMR outperforms total variation regularization.

The method effectively suppresses beam hardening artifacts.

Numerical and physical experiments validate the approach.

Abstract

Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations.