图像质量
光子计数
迭代重建
张量(固有定义)
计算机科学
能量(信号处理)
光子
算法
人工智能
数学
计算机视觉
光学
物理
图像(数学)
统计
纯数学
作者
Weiwen Wu,Dianlin Hu,Kang An,Shaoyu Wang,Fulin Luo
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:70: 1-14
被引量:40
标识
DOI:10.1109/tim.2020.3026804
摘要
Photon-counting X-ray computed tomography (CT) has been attracting great attention in tissue characterization, material discrimination, and so on. The emitting X-ray energy spectrum cutting into several energy bins that can result in only a part of X-ray photons can be collected within each narrow bin. This can compromise the image quality. In this case, how to obtain high-quality tomography is a big challenge. In this study, to overcome these issues, we mainly focus on developing an advanced imaging software based on the latest photon-counting CT system (MARS scanner). Specifically, we first design a weight adaptive total variation (TV) using compressed sensing theory. Then, combining the weight adaptive TV and nonlocal low-rank tensor factorization to formulate a new weight adaptive total-variation and image-spectral tensor factorization (WATITF) model for high-quality imaging. Finally, the optimization model is performed to obtain its solution. The studies including the numerical and preclinical mice are performed to validate and evaluate its outperformance.
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