PDF(Pubmed)
Abstract：
Optoacoustic (photoacoustic) mesoscopy bridges the gap between optoacoustic microscopy and macroscopy and enables high-resolution visualization deeper than optical microscopy. Nevertheless, as images may be affected by motion and noise, it is critical to develop methodologies that offer standardization and quality control to ensure that high-quality datasets are reproducibly obtained from patient scans. Such development is particularly important for ensuring reliability in applying machine learning methods or for reliably measuring disease biomarkers. We propose herein a quality control scheme to assess the quality of data collected. A reference scan of a suture phantom is performed to characterize the system noise level before each raster-scan optoacoustic mesoscopy (RSOM) measurement. Using the recorded RSOM data, we develop a method that estimates the amount of motion in the raw data. These motion metrics are employed to classify the quality of raw data collected and derive a quality assessment index (QASIN) for each raw measurement. Using simulations, we propose a selection criterion of images with sufficient QASIN, leading to the compilation of RSOM datasets with consistent quality. Using 160 RSOM measurements from healthy volunteers, we show that RSOM images that were selected using QASIN were of higher quality and fidelity compared to non-selected images. We discuss how this quality control scheme can enable the standardization of RSOM images for clinical and biomedical applications.
摘要：
光声（光声）介观桥接光声显微镜和宏观之间的差距，使高分辨率可视化比光学显微镜更深。然而，因为图像可能会受到运动和噪声的影响，开发提供标准化和质量控制的方法至关重要,以确保从患者扫描中可重复获得高质量数据集.这种发展对于确保应用机器学习方法的可靠性或可靠地测量疾病生物标志物尤为重要。我们在此提出了一种质量控制方案来评估收集的数据的质量。在每次光栅扫描光声介观(RSOM)测量之前,进行缝线体模的参考扫描以表征系统噪声水平。使用记录的RSOM数据，我们开发了一种估计原始数据中运动量的方法。采用这些运动度量来对所收集的原始数据的质量进行分类,并得出每个原始测量的质量评估指数(QASIN)。使用模拟，我们提出了具有足够QASIN的图像的选择标准，导致汇编质量一致的RSOM数据集。使用来自健康志愿者的160个RSOM测量值，我们表明，与未选择的图像相比，使用QASIN选择的RSOM图像具有更高的质量和保真度。我们讨论了这种质量控制方案如何使临床和生物医学应用的RSOM图像标准化。
