单色
计算机科学
计算机视觉
人工智能
插值(计算机图形学)
能量(信号处理)
图像质量
工件(错误)
双重能量
迭代重建
材料科学
图像(数学)
光学
物理
医学
骨矿物
骨质疏松症
量子力学
内分泌学
作者
Tianling Lyu,Zhao Wang,Wei Gao,Jian Zhu,Yan Xiao,Yang Chen,Wentao Zhu
标识
DOI:10.1109/embc40787.2023.10340221
摘要
Metal implants are one of the culprits for image quality degradation in CT imaging, introducing so-called metal artifacts. With the help of the virtual-monochromatic imaging technique, dual-energy CT has been proven to be effective in metal artifact reduction. However, the virtual monochromatic images with suppressed metal artifacts show reduced CNR compared to polychromatic images. To remove metal artifacts on polychromatic images, we propose a dual-energy NMAR (deNMAR) algorithm in this paper that adds material decomposition to the widely used NMAR framework. The dual energy sinograms are first decomposed into water and bone sinograms, and metal regions are replaced with water on the reconstructed material maps. Prior sinograms are constructed by polyenergetically forward projecting the material maps with corresponding spectra, and they are used to guide metal trace interpolation in the same way as in the NMAR algorithm. We performed experiments on authentic human body phantoms, and the results show that the proposed deNMAR algorithm achieves better performance in tissue restoration compared to other compelling methods. Tissue boundaries become clear around metal implants, and CNR rises to 2.58 from ~1.70 on 80 kV images compared to other dual-energy-based algorithms.
科研通智能强力驱动
Strongly Powered by AbleSci AI