SNR AND NOISE MEASUREMENTS FOR MEDICAL IMAGING - IA PRACTICAL APPROACH BASED ON STATISTICAL DECISION-THEORY
SNR AND NOISE MEASUREMENTS FOR MEDICAL IMAGING - IA PRACTICAL APPROACH BASED ON STATISTICAL DECISION-THEORY
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DOI：
10.1088/0031-9155/38/1/006
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发表时间：
1993-01-01
影响因子：
3.5
通讯作者：
WAGNER, RF
中科院分区：
文献类型：
--
作者：
TAPIOVAARA, MJ;WAGNER, RF
在统计决策理论的框架下，研究了一种医学成像设备图像质量的测量方法。在该方法中，将图像视为随机向量，并且在可用于执行指定检测或识别任务的图像信息的上下文中定义图像质量。该方法提供了一种测量与检测感兴趣的图像细节相关的图像质量的手段，而无需参考图像形成中涉及的实际物理机制，并且无需单独测量信号传输特性或图像噪声。然而，测量不考虑图像中的确定性误差；他们需要对他们关心的成像方式进行单独的评估。图像细节的可检测性可以用理想观测者在决策层的信噪比来表示。通常，可以通过使用次优观测器来获得较好的信噪比近似值，其性能也与人类观测器的性能很好地相关。在本文中，信噪比的测量是基于实现特定观测器的算法实现，并在实际执行特定的感兴趣的检测任务时分析它们的响应。考虑了三种观测器：理想预白化匹配滤波器、非预白化匹配滤波器和直流抑制非预白化匹配滤波器。除了最简单的成像情况外，理想观测器的构造需要大量的数据和计算，因此，建议使用次优观测器，并讨论了它们在检测特定信号时的性能。噪声和信噪比的测量已经扩展到包括时间变化的图像和动态成像系统。
A method of measuring the image quality of medical imaging equipment is considered within the framework of statistical decision theory. In this approach, images are regarded as random vectors and image quality is defined in the context of the image information available for performing a specified detection or discrimination task. The approach provides a means of measuring image quality, as related to the detection of an image detail of interest, without reference to the actual physical mechanisms involved in image formation and without separate measurements of signal transfer characteristics or image noise. The measurement does not, however, consider deterministic errors in the image; they need a separate evaluation for imaging modalities where they am of concern. The detectability of an image detail can be expressed in terms of the ideal observer's signal-to-noise ratio (SNR) at the decision level. Often a good approximation to this SNR can be obtained by employing sub-optimal observers, whose performance correlates well with the performance of human observers as well. In this paper the measurement Of SNR is based on implementing algorithmic realizations of specified observers and analysing their responses while actually performing a specified detection task of interest. Three observers are considered: the ideal prewhitening matched filter, the non-prewhitening matched filter, and the DC-suppressing non-prewhitening matched filter. The construction of the ideal observer requires an impractical amount of data and computing, except for the most simple imaging situations, Therefore, the utilization of sub-optimal observers is advised and their performance in detecting a specified signal is discussed. Measurement of noise and SNR has been extended to include temporally varying images and dynamic imaging systems.