Pathour Teja, Rastegar Ghazal, Sirsi Shashank R, Fei Baowei
University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.
University of Texas at Dallas, Center for Imaging and Surgical Innovation, Richardson, Texas, United States.
J Med Imaging (Bellingham). 2025 Jul;12(4):047001. doi: 10.1117/1.JMI.12.4.047001. Epub 2025 Jul 12.
We aim to investigate and isolate the distinctive acoustic properties generated by chemically crosslinked microbubble clusters (CCMCs) using machine learning (ML) techniques, specifically using an anomaly detection model based on autoencoders.
CCMCs were synthesized via copper-free click chemistry and subjected to acoustic analysis using a clinical transducer. Radiofrequency data were acquired, processed, and organized into training and testing datasets for the ML models. We trained an anomaly detection model with the nonclustered microbubbles (MBs) and tested the model on the CCMCs to isolate the unique acoustics. We also had a separate set of control experiments that was performed to validate the anomaly detection model.
The anomaly detection model successfully identified frames exhibiting unique acoustic signatures associated with CCMCs. Frequency domain analysis further confirmed that these frames displayed higher amplitude and energy, suggesting the occurrence of potential coalescence events. The specificity of the model was validated through control experiments, in which both groups contained only individual MBs without clustering. As anticipated, no anomalies were detected in this control dataset, reinforcing the model's ability to distinguish clustered MBs from nonclustered ones.
We highlight the feasibility of detecting and distinguishing the unique acoustic characteristics of CCMCs, thereby improving the detectability and localization of contrast agents in ultrasound imaging. The elevated acoustic amplitudes produced by CCMCs offer potential advantages for more effective contrast agent detection, which is particularly valuable in super-resolution ultrasound imaging. Both the contrast agent and the ML-based analysis approach hold promise for a wide range of applications.
我们旨在利用机器学习（ML）技术，特别是基于自动编码器的异常检测模型，研究并分离化学交联微泡簇（CCMCs）产生的独特声学特性。
通过无铜点击化学合成CCMCs，并使用临床换能器进行声学分析。采集射频数据，进行处理，并整理成用于ML模型的训练和测试数据集。我们用非簇状微泡（MBs）训练了一个异常检测模型，并在CCMCs上测试该模型以分离独特的声学特性。我们还进行了一组单独的对照实验来验证异常检测模型。
异常检测模型成功识别出表现出与CCMCs相关的独特声学特征的帧。频域分析进一步证实，这些帧显示出更高的幅度和能量，表明可能发生了聚并事件。通过对照实验验证了模型的特异性，在对照实验中两组均仅包含未聚集的单个MBs。正如预期的那样，在该对照数据集中未检测到异常，这增强了模型区分聚集MBs和非聚集MBs的能力。
我们强调了检测和区分CCMCs独特声学特征的可行性，从而提高了超声成像中造影剂的可检测性和定位。CCMCs产生的升高的声学幅度为更有效的造影剂检测提供了潜在优势，这在超分辨率超声成像中尤为有价值。造影剂和基于ML的分析方法在广泛的应用中都具有前景。
