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本文引用的文献

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Sparse-view x-ray CT reconstruction via total generalized variation regularization.基于全广义变分正则化的稀疏视图X射线CT重建
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Total variation-stokes strategy for sparse-view X-ray CT image reconstruction.总变差-斯多克斯策略用于稀疏视角 X 射线 CT 图像重建。
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SR-NLM: a sinogram restoration induced non-local means image filtering for low-dose computed tomography.SR-NLM:一种基于射线重建的非局部均值图像滤波方法,用于低剂量计算机断层扫描。
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Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction.自适应加权全变差最小化在稀疏数据低剂量 X 射线计算机断层成像重建中的应用。
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Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior.基于增强前扫描的边缘保持先验的脑灌注 CT 迭代重建。
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Low-dose computed tomography image restoration using previous normal-dose scan.低剂量计算机断层扫描图像使用先前的正常剂量扫描进行恢复。
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采用低毫安秒与稀疏视图组合方案的低剂量X射线计算机断层扫描图像重建

Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol.

作者信息

Gao Yang, Bian Zhaoying, Huang Jing, Zhang Yunwan, Niu Shanzhou, Feng Qianjin, Chen Wufan, Liang Zhengrong, Ma Jianhua

出版信息

Opt Express. 2014 Jun 16;22(12):15190-210. doi: 10.1364/OE.22.015190.

DOI:10.1364/OE.22.015190
PMID:24977611
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4083059/
Abstract

To realize low-dose imaging in X-ray computed tomography (CT) examination, lowering milliampere-seconds (low-mAs) or reducing the required number of projection views (sparse-view) per rotation around the body has been widely studied as an easy and effective approach. In this study, we are focusing on low-dose CT image reconstruction from the sinograms acquired with a combined low-mAs and sparse-view protocol and propose a two-step image reconstruction strategy. Specifically, to suppress significant statistical noise in the noisy and insufficient sinograms, an adaptive sinogram restoration (ASR) method is first proposed with consideration of the statistical property of sinogram data, and then to further acquire a high-quality image, a total variation based projection onto convex sets (TV-POCS) method is adopted with a slight modification. For simplicity, the present reconstruction strategy was termed as "ASR-TV-POCS." To evaluate the present ASR-TV-POCS method, both qualitative and quantitative studies were performed on a physical phantom. Experimental results have demonstrated that the present ASR-TV-POCS method can achieve promising gains over other existing methods in terms of the noise reduction, contrast-to-noise ratio, and edge detail preservation.

摘要

为了在X射线计算机断层扫描(CT)检查中实现低剂量成像,降低毫安秒(低mAs)或减少围绕身体每旋转一圈所需的投影视图数量(稀疏视图)作为一种简单有效的方法已得到广泛研究。在本研究中,我们专注于从采用低mAs和稀疏视图组合协议采集的正弦图进行低剂量CT图像重建,并提出一种两步图像重建策略。具体而言,为了抑制噪声大且不完整的正弦图中的显著统计噪声,首先考虑正弦图数据的统计特性提出了一种自适应正弦图恢复(ASR)方法,然后为了进一步获得高质量图像,对基于总变分的凸集投影(TV-POCS)方法进行了轻微修改后采用。为简单起见,当前的重建策略被称为“ASR-TV-POCS”。为了评估当前的ASR-TV-POCS方法,在物理模型上进行了定性和定量研究。实验结果表明,当前的ASR-TV-POCS方法在降噪、对比度噪声比和边缘细节保留方面比其他现有方法有显著提升。