Gubbi Mardava R, Gonzalez Eduardo A, Bell Muyinatu A Lediju
IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Jun;69(6):2098-2114. doi: 10.1109/TUFFC.2022.3169082. Epub 2022 May 26.
The successful integration of computer vision, robotic actuation, and photoacoustic imaging to find and follow targets of interest during surgical and interventional procedures requires accurate photoacoustic target detectability. This detectability has traditionally been assessed with image quality metrics, such as contrast, contrast-to-noise ratio, and signal-to-noise ratio (SNR). However, predicting target tracking performance expectations when using these traditional metrics is difficult due to unbounded values and sensitivity to image manipulation techniques like thresholding. The generalized contrast-to-noise ratio (gCNR) is a recently introduced alternative target detectability metric, with previous work dedicated to empirical demonstrations of applicability to photoacoustic images. In this article, we present theoretical approaches to model and predict the gCNR of photoacoustic images with an associated theoretical framework to analyze relationships between imaging system parameters and computer vision task performance. Our theoretical gCNR predictions are validated with histogram-based gCNR measurements from simulated, experimental phantom, ex vivo, and in vivo datasets. The mean absolute errors between predicted and measured gCNR values ranged from 3.2 ×10 to 2.3 ×10 for each dataset, with channel SNRs ranging -40 to 40 dB and laser energies ranging 0.07 [Formula: see text] to 68 mJ. Relationships among gCNR, laser energy, target and background image parameters, target segmentation, and threshold levels were also investigated. Results provide a promising foundation to enable predictions of photoacoustic gCNR and visual servoing segmentation accuracy. The efficiency of precursory surgical and interventional tasks (e.g., energy selection for photoacoustic-guided surgeries) may also be improved with the proposed framework.
在外科手术和介入操作过程中，要成功整合计算机视觉、机器人驱动和光声成像以找到并追踪感兴趣的目标，就需要准确的光声目标可检测性。传统上，这种可检测性是通过图像质量指标来评估的，如对比度、对比噪声比和信噪比（SNR）。然而，由于这些指标的值无界且对诸如阈值处理等图像处理技术敏感，因此使用这些传统指标来预测目标跟踪性能预期很困难。广义对比噪声比（gCNR）是最近引入的一种替代目标可检测性指标，此前的工作致力于对其在光声图像中的适用性进行实证演示。在本文中，我们提出了理论方法来建模和预测光声图像的gCNR，并给出了一个相关的理论框架，以分析成像系统参数与计算机视觉任务性能之间的关系。我们基于理论的gCNR预测通过对模拟、实验体模、离体和体内数据集进行基于直方图的gCNR测量得到验证。每个数据集的预测gCNR值与测量值之间的平均绝对误差范围为3.2×10至2.3×10，通道SNR范围为 - 40至40 dB，激光能量范围为0.07 [公式：见正文] 至68 mJ。我们还研究了gCNR、激光能量、目标和背景图像参数、目标分割以及阈值水平之间的关系。研究结果为预测光声gCNR和视觉伺服分割精度提供了一个有前景的基础。所提出的框架还可能提高前期外科手术和介入任务（例如光声引导手术的能量选择）的效率。
