Improved Material Decomposition with a Two-step Regularization for spectral CT

TL;DR

This paper introduces a two-step regularization approach for spectral CT that enhances material decomposition accuracy by integrating priors into both image reconstruction and post-processing, validated through simulations and experiments.

Contribution

It proposes a novel two-step regularization method that combines priors in image reconstruction and post-decomposition, improving material accuracy in spectral CT.

Findings

Enhanced material decomposition accuracy demonstrated in simulations.

Improved material maps in preclinical mouse experiments.

The method outperforms existing single-step approaches.

Abstract

One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decomposition is a common method to obtain specific material components, and it can be divided into two steps: image reconstruction and post material decomposition. To obtain accurate material maps, the image reconstruction method mainly focuses on improving image quality by incorporating regularization priors. Very recently, the regularization priors are introduced into the post material decomposition procedure in the iterative image-based methods. Since the regularization priors can be incorporated into image reconstruction and post image-domain material decomposition, the performance of regularization by combining these two cases is still an open problem. To realize this goal, the material accuracy from those steps are first analyzed and compared. Then, to further improve the accuracy of decomposition materials, a two-step regularization based method is developed by incorporating priors into image reconstruction and post material decomposition. Both numerical simulation and preclinical mouse experiments are performed to demonstrate the advantages of the two-step regularization based method in improving material accuracy.