The University of Oklahoma Health Sciences Center, 940 NE 13th St. Garrison Tower, Suite 3G3210, Oklahoma City, OK 73104, United States of America.
Biomed Phys Eng Express. 2024 Jun 25;10(4). doi: 10.1088/2057-1976/ad57cd.
Medical physicists routinely perform quality assurance on digital detection systems, part of which involves the testing of flat panel detectors. Flat panels may degrade over time as an increasing number of individual detector elements begin to malfunction. The pixels that correspond to these elements are corrected for using information elsewhere in the detector system, however these corrected elements still constitute a loss in image quality for the system as a whole. These correction methods, as well as the location and number of dead detector elements, are often only available to the vendor of the digital detection system, but not to the medical physicist responsible for the quality assurance of the system.We greatly expand upon a previous work by providing a novel technique for classifying dead detector elements at single pixel resolution. We also demonstrate that this technique can be trained on one detector, and then tested and validated on another with moderate success, which demonstrates some ability to generalize to different detectors. The technique requires 3 flat field, or 'noise', images to be taken to predict the dead detector element maps for the system.Models using only for-processing pixel data were unable to successfully generalize from one detector to the other. Models preprocessed using the standard deviation across three for-processing images were able to classify dead detector element maps with an Fscore ranging from 0.4527 to 0.8107 and recall ranging from 0.5420 to 0.9303 with better performance, on average, observed using the low exposure data set.. Many physicists do not have access to the dead detector maps for their diagnostic digital radiography systems. CNNs are capable of predicting the dead detector maps of flat panel detectors with single pixel resolution. Physicists can implement this tool by acquiring three flat field images and then inputting them into the model. Model performance saw a marginal increase when trained on the low exposure set data, as opposed to the high exposure set data, indicating high exposure, low relative noise images may not be necessary for optimal performance. Model performance across detectors manufactured by different vendors requires further investigation.
医学物理学家通常会对数字检测系统进行质量保证，其中一部分涉及对平板探测器进行测试。随着越来越多的单个探测器元件开始出现故障，平板可能会随着时间的推移而降级。与这些元件相对应的像素会使用探测器系统中其他位置的信息进行校正，但是这些校正后的元件仍然会导致整个系统的图像质量下降。这些校正方法以及死像素的位置和数量通常仅为数字检测系统的供应商所掌握，而负责系统质量保证的医学物理学家则无法获得这些信息。我们在之前的工作基础上进行了重大扩展，提供了一种用于以单个像素分辨率对死像素进行分类的新技术。我们还证明，该技术可以在一个探测器上进行训练，然后在另一个探测器上进行测试和验证，取得了中等程度的成功，这表明该技术具有一定的泛化到不同探测器的能力。该技术需要拍摄 3 张平场或“噪声”图像，以预测系统的死像素图。仅使用预处理像素数据的模型无法成功地从一个探测器推广到另一个探测器。使用三个预处理图像的标准差进行预处理的模型能够以 0.4527 到 0.8107 的 F 分数和 0.5420 到 0.9303 的召回率来分类死像素图，平均而言，使用低曝光数据集观察到的性能更好。许多物理学家无法访问其诊断数字射线照相系统的死像素图。CNN 能够以单个像素分辨率预测平板探测器的死像素图。物理学家可以通过获取三张平场图像并将其输入模型来实现此工具。与使用高曝光数据集相比，在使用低曝光数据集进行训练时，模型性能略有提高，这表明高曝光、低相对噪声的图像可能不是获得最佳性能所必需的。不同供应商制造的探测器的模型性能还需要进一步研究。
