Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
Med Phys. 2012 Feb;39(2):906-11. doi: 10.1118/1.3676183.
Since the introduction of clinical x-ray phase-contrast mammography (PCM), a technique that exploits refractive-index variations to create edge enhancement at tissue boundaries, a number of optimization studies employing physical image-quality metrics have been performed. Ideally, task-based assessment of PCM would have been conducted with human readers. These studies have been limited, however, in part due to the large parameter-space of PCM system configurations and the difficulty of employing expert readers for large-scale studies. It has been proposed that numerical observers can be used to approximate the statistical performance of human readers, thus enabling the study of task-based performance over a large parameter-space.
Methods are presented for task-based image quality assessment of PCM images with a numerical observer, the most significant of which is an adapted lumpy background from the conventional mammography literature that accounts for the unique wavefield propagation physics of PCM image formation and will be used with a numerical observer to assess image quality. These methods are demonstrated by performing a PCM task-based image quality study using a numerical observer. This study employs a signal-known-exactly, background-known-statistically Bayesian ideal observer method to assess the detectability of a calcification object in PCM images when the anode spot size and calcification diameter are varied.
The first realistic model for the structured background in PCM images has been introduced. A numerical study demonstrating the use of this background model has compared PCM and conventional mammography detection of calcification objects. The study data confirm the strong PCM calcification detectability dependence on anode spot size. These data can be used to balance the trade-off between enhanced image quality and the potential for motion artifacts that comes with use of a reduced spot size and increased exposure time.
A method has been presented for the incorporation of structured breast background data into task-based numerical observer assessment of PCM images. The method adapts conventional background simulation techniques to the wavefield propagation physics necessary for PCM imaging. This method is demonstrated with a simple detection task.
自从引入利用折射率变化在组织边界产生边缘增强的临床 X 射线相衬乳腺摄影（PCM）技术以来，已经进行了许多使用物理图像质量指标的优化研究。理想情况下，PCM 的基于任务的评估将由人类读者进行。然而，这些研究受到限制，部分原因是 PCM 系统配置的参数空间大，以及难以在大规模研究中使用专家读者。有人提出，可以使用数字观察者来近似人类读者的统计性能，从而能够在大参数空间中研究基于任务的性能。
本文提出了一种使用数字观察者对 PCM 图像进行基于任务的图像质量评估的方法，其中最重要的方法是改编自传统乳腺摄影文献中的块状背景，该背景考虑了 PCM 图像形成的独特波场传播物理，将与数字观察者一起用于评估图像质量。这些方法通过使用数字观察者进行 PCM 基于任务的图像质量研究来证明。该研究采用信号已知完全，背景已知统计贝叶斯理想观察者方法来评估 PCM 图像中钙化物体的可检测性，当阳极光斑尺寸和钙化直径变化时。
介绍了 PCM 图像中结构化背景的第一个现实模型。使用该背景模型的数值研究比较了 PCM 和传统乳腺摄影对钙化物体的检测。该研究数据证实了 PCM 钙化检测强烈依赖于阳极光斑尺寸。这些数据可用于平衡增强图像质量和使用较小光斑和增加曝光时间带来的潜在运动伪影之间的权衡。
提出了一种将结构化乳房背景数据纳入 PCM 图像基于任务的数字观察者评估的方法。该方法将传统的背景模拟技术改编为 PCM 成像所需的波场传播物理。该方法通过简单的检测任务进行了演示。
