Aljahdali Majed H, Woodman Alexander, Al-Jamea Lamiaa, Albatati Saeed M, Williams Chris
Department of Biomedical Engineering, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia.
Vice Deanship of Postgraduate Studies and Research, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia.
Ultrason Imaging. 2021 May;43(3):113-123. doi: 10.1177/0161734621992332. Epub 2021 Feb 15.
The quality assurance (QA) of ultrasound transducers is often identified as an area requiring continuous development in terms of the tools available to users. Periodic evaluation of the transducers as part of the QA protocol is important, since the quality of the diagnostics. Some of the key criteria determining the process of developing a QA protocol include the complexity of setup, the time required, accuracy, and potential automation to achieve scale. For the current study, a total of eight different ultrasound machines (12 transducers) with linear transducers were obtained separately. The results from these 12 transducers were used to validate the protocol. WAD-QC was used as part of this study to assess in-air reverberation patterns obtained from ultrasound transducers. Initially, three in-air reverberation images obtained from normal transducers and three obtained from defective transducers were used to calculate the uniformity parameters. The results were applied to 12 other images obtained from independent sources. Image processing results with WAD-QC were verified with imageJ. A comparison of raw data for uniformity showed consistency, and using controls based on mean absolute deviation yielded identical results. WAD-QC can be considered as a powerful mechanism for quick, efficient, and accurate analysis of in-air reverberation patterns obtained from ultrasound transducers.
超声换能器的质量保证（QA）通常被认为是一个在用户可用工具方面需要持续发展的领域。作为质量保证协议的一部分，对换能器进行定期评估很重要，因为这关系到诊断的质量。确定质量保证协议制定过程的一些关键标准包括设置的复杂性、所需时间、准确性以及实现规模化的潜在自动化程度。在本研究中，分别获取了总共八台配备线性换能器的不同超声机器（12个换能器）。这12个换能器的结果用于验证该协议。本研究使用WAD-QC来评估从超声换能器获得的空气中混响模式。最初，从正常换能器获得的三张空气中混响图像和从有缺陷的换能器获得的三张图像用于计算均匀性参数。结果应用于从独立来源获得的其他12张图像。使用ImageJ验证了WAD-QC的图像处理结果。均匀性原始数据的比较显示出一致性，并且使用基于平均绝对偏差的对照产生了相同的结果。WAD-QC可被视为一种强大的机制，用于快速、高效且准确地分析从超声换能器获得的空气中混响模式。
