Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT

Physics in Medicine and Biology
Wei ZhaoJames K Min

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 an 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 DECT material decomposition comparison study shows that all four algorithms yield acceptable quantitative measurements of iodine concentrate. Direct matrix inversion yields the highest noise level, followed by HYPR-LR and Iter-DECT. HYPR-NLM in an iterative formulation signif...Continue Reading

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Citations

Aug 24, 2018·Physics in Medicine and Biology·Shengzhen TaoShuai Leng
Nov 9, 2018·Journal of Computer Assisted Tomography·Yuan YuanHengyong Yu
May 14, 2019·Journal of X-ray Science and Technology·Yanbo ZhangHengyong Yu
Jul 22, 2020·Physics in Medicine and Biology·Weiwen WuHengyong Yu
Jun 15, 2021·Physics in Medicine and Biology·Wei FangQuanzheng Li

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