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Previous Normal Dose Scan Induced Nonlocal Means Gibbs Prior for Low Dose Computed Tomography Maximum A Posteriori Reconstruction

机译:先前的正常剂量扫描诱导的非局部均值Gibbs先于低剂量计算机断层扫描的最大后验重建

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Lower X-ray radiation to patient in computed tomography (CT) examination is clinically desired. Although reducing the level of milliampere-seconds (mAs) can yield lowdose CT scan, the associated image quality will be strongly degraded by excessive quantum noise. In this paper, for obtaining high-quality image in low-mAs or low-dose CT scan, we propose a previous normal dose scan induced nonlocal means Gibbs prior, named as ndiNLMGP. The ndiNLMGP was designed based on the normal-dose scan induced nonlocal means filter (ndiNLM) by exploiting the huge redundancy information from the high-quality reference image. And the associated optimization algorithm was developed based on maximum a posteriori (MAP) criterion for image reconstruction. To validate and evaluate the performance of the present ndiNLMGP, we carried out the low-dose image reconstruction from the computer simulated and real data. The results show that the present ndiNLMGP can impressively outperform the simplex nonlocal means Gibbs prior (NLMGP) model in lowering the noise, and preserving the edge and details in the desired images.
机译:临床上希望在计算机断层扫描(CT)检查中向患者提供更低的X射线辐射。尽管降低毫安秒(mAs)的水平可以产生低剂量的CT扫描,但是过多的量子噪声会严重降低相关的图像质量。在本文中,为了在低mAs或低剂量CT扫描中获得高质量的图像,我们提出了先前的常规剂量扫描诱发的非局部均值Gibbs,称为ndiNLMGP。 ndiNLMGP是在正常剂量扫描诱导的非局部均值滤波器(ndiNLM)的基础上设计的,它利用了高质量参考图像中的大量冗余信息。并基于最大后验(MAP)准则开发了相关的优化算法,用于图像重建。为了验证和评估当前ndiNLMGP的性能,我们从计算机模拟和真实数据中进行了低剂量图像重建。结果表明,当前的ndiNLMGP在降低噪声,保留所需图像的边缘和细节方面,可以明显优于单纯形非局部均值Gibbs先验(NLMGP)模型。

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