Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography.
Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography.
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DOI：
10.1088/0031-9155/61/18/6707
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发表时间：
2016-09-21
影响因子：
3.5
通讯作者：
McCollough CH
中科院分区：
文献类型：
--
作者：
Yu Z;Leng S;Li Z;McCollough CH
光子计数计算机层析成像(PCCT)是一种新兴的成像技术，它只需一次扫描采集即可实现多能量成像。为了实现多能量成像，对应于全x射线光谱的检测到的光子被分成对应于更窄的能量窗口的几个子组的面元数据。因此，与全频谱数据相比，每个能量仓中的噪声都会增加。本文提出了一种迭代重建算法，用于PCCT成像中较窄能区的噪声抑制。该算法基于先验图像约束压缩感知(PICCS)的框架，使用传统的滤波反投影重建的全光谱图像作为先验图像。谱PICCS算法使用具有自适应迭代步长的约束优化方案来实现，使得在大多数情况下仅需要两个调谐参数。该算法首先使用计算机模拟进行评估，然后通过物理模型和使用研究PCCT系统的活体猪研究进行验证。计算机模拟和实验研究的结果表明，在不牺牲CT数精度或空间分辨率的情况下，在较窄的能量仓(43~73%)内可显著降低图像噪声。
Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in-vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43~73%) without sacrificing CT number accuracy or spatial resolution.
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影响因子：
3.8
作者：
Lauzier, Pascal Theriault;Tang, Jie;Chen, Guang-Hong
通讯作者：
Chen, Guang-Hong
影响因子：
10.6
作者：
Chen GH;Theriault-Lauzier P;Tang J;Nett B;Leng S;Zambelli J;Qi Z;Bevins N;Raval A;Reeder S;Rowley H
通讯作者：
Rowley H
影响因子：
3.2
作者：
Heismann, BJ;Leppert, J;Stierstorfer, K
通讯作者：
Stierstorfer, K
影响因子：
10.6
作者：
Bouman, CA;Sauer, K
通讯作者：
Sauer, K
影响因子：
3.5
作者：
Atak, Haluk;Shikhaliev, Polad M.
通讯作者：
Shikhaliev, Polad M.