基于残差去噪的CT图像噪声盲分析

Sohini Roychowdhury, Nathan Hollraft, A. Alessio
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引用次数: 5

摘要

CT方案设计和质量控制将受益于自动化工具来估计生成的CT图像的质量。这些工具可用于识别错误的CT采集或改进方案,以获得特定的信噪比特征。本文研究了一种确定胸部CT图像全局信号强度和噪声水平的盲估计方法。方法:我们提出了新的性能指标,对应于噪声和信号估计的准确性。我们实现并评估了六种基于空间和频率的方法的噪声估计性能,这些方法源自传统的图像滤波算法。算法在患者数据集上进行了测试,这些数据集来自于在同一扫描区域使用高剂量和低剂量技术进行的全身重复CT采集。结果:提出的性能指标可以评估滤波器参数和噪声估计性能的相对权衡。所提出的自动化方法往往低估了低通量水平下CT图像的噪声。该方法的初步应用表明,各向异性扩散和基于小波变换的滤波器提供了最佳的噪声估计。此外,方法并不能准确估计绝对噪音声级,但可以估计噪音声级的相对变化及/或趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind analysis of CT image noise using residual denoised images
CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images. These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics. This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images. Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation. We implement and evaluate the noise estimation performance of six spatial- and frequency-based methods, derived from conventional image filtering algorithms. Algorithms were tested on patient data sets from whole-body repeat CT acquisitions performed with a higher and lower dose technique over the same scan region. Results: The proposed performance metrics can evaluate the relative tradeoff of filter parameters and noise estimation performance. The proposed automated methods tend to underestimate CT image noise at low-flux levels. Initial application of methodology suggests that anisotropic diffusion and Wavelet-transform based filters provide optimal estimates of noise. Furthermore, methodology does not provide accurate estimates of absolute noise levels, but can provide estimates of relative change and/or trends in noise levels.
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