Block Matching Frame based Material Reconstruction for Spectral CT
This work addresses material identification and artifact suppression in spectral CT, offering a domain-specific improvement that appears incremental over existing regularization techniques.
The authors tackled the problem of improving material reconstruction in spectral CT by proposing a block matching frame (BMF) regularization method, which outperformed total variation and non-local mean regularizations in suppressing artifacts and enhancing image quality, as validated by numerical simulations and physical phantom experiments.
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.