Quantitative statistical methods for image quality assessment.
Quantitative statistical methods for image quality assessment.
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DOI：
10.7150/thno.6815
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发表时间：
2013-10-04
期刊：
影响因子：
12.4
通讯作者：
Li Q
中科院分区：
文献类型：
--
作者：
Dutta J;Ahn S;Li Q
图像质量和可靠性的定量测量对于医学图像的定性解释和定量分析都是至关重要的。虽然在理论上，可以通过使用大量噪声实现的蒙特卡罗模拟来分析重建图像，但相关的计算负担使该方法不切实际。此外，这种方法在临床场景中意义不大，因为在临床场景中，多个噪声实现通常不可用。实用的替代方法是计算用于图像质量测量的闭合形式的解析表达式。本文的目的是回顾统计分析技术，使我们能够计算两个关键指标：分辨率(由局部脉冲响应确定)和协方差。基本方法包括定点方法和基于迭代的方法，定点方法在与所使用的迭代算法无关的固定点(唯一且稳定的解)上计算这些度量，而迭代方法产生依赖于算法、初始化和迭代次数的结果。我们还探索了这些方法中的一些方法在一系列特殊情况下的扩展，包括动态和运动补偿图像重建。虽然所讨论的大多数技术都是为发射断层成像开发的，但通用方法也可以扩展到其他成像方式。除了实现图像表征外，这些分析技术还允许我们控制和增强成像系统性能。我们回顾了通过将这些思想应用于硬件(优化扫描仪设计)和图像重建(设计产生统一分辨率或最大化任务特定品质因数的正则化函数)来实现性能改进的实际应用。
Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit).
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影响因子：
3.5
作者：
BARRETT, HH;WILSON, DW;TSUI, BMW
通讯作者：
TSUI, BMW
DOI：
10.1073/pnas.90.21.9758
发表时间：
1993-11-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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作者：
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HERO, AO
影响因子：
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作者：
Fessler, JA;Booth, SD
通讯作者：
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