Pathour Teja, Akter Nasrin, Dormer James D, Chaudhary Sugandha, Fei Baowei, Sirsi Shashank
Department of Bioengineering, The University of Texas at Dallas, TX.
Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12038. doi: 10.1117/12.2611572. Epub 2022 Apr 4.
Ultrasound contrast agents (UCA) are gas encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing a backscattered signal which can be used for improved ultrasound imaging and drug delivery. UCA's are being used widely for contrast-enhanced ultrasound imaging, but there is a need for improved UCAs to develop faster and more accurate contrast agent detection algorithms. Recently, we introduced a new class of lipid based UCAs called Chemically Cross-linked Microbubble Clusters (CCMCs). CCMCs are formed by the physical tethering of individual lipid microbubbles into a larger aggregate cluster. The advantages of these novel CCMCs are their ability to fuse together when exposed to low intensity pulsed ultrasound (US), potentially generating unique acoustic signatures that can enable better contrast agent detection. In this study, our main objective is to demonstrate that the acoustic response of CCMCs is unique and distinct when compared to individual UCAs using deep learning algorithms. Acoustic characterization of CCMCs and individual bubbles was performed using a broadband hydrophone or a clinical transducer attached to a Verasonics Vantage 256. A simple artificial neural network (ANN) was trained and used to classify raw 1D RF ultrasound data as either from CCMC or non-tethered individual bubble populations of UCAs. The ANN was able to classify CCMCs at an accuracy of 93.8% for data collected from broadband hydrophone and 90% for data collected using Verasonics with a clinical transducer. The results obtained suggest the acoustic response of CCMCs is unique and has the potential to be used in developing a novel contrast agent detection technique.
超声造影剂（UCA）是气体包裹的微球，当暴露于超声场时会发生体积振荡，产生反向散射信号，可用于改善超声成像和药物递送。UCA被广泛用于超声造影成像，但需要改进的UCA来开发更快、更准确的造影剂检测算法。最近，我们引入了一类新型的基于脂质的UCA，称为化学交联微泡簇（CCMC）。CCMC是通过将单个脂质微泡物理连接成更大的聚集簇而形成的。这些新型CCMC的优点是，当暴露于低强度脉冲超声（US）时，它们能够融合在一起，潜在地产生独特的声学特征，从而实现更好的造影剂检测。在本研究中，我们的主要目标是使用深度学习算法证明，与单个UCA相比，CCMC的声学响应是独特且不同的。使用连接到Verasonics Vantage 256的宽带水听器或临床换能器对CCMC和单个气泡进行声学表征。训练了一个简单的人工神经网络（ANN），并用于将原始一维射频超声数据分类为来自CCMC或UCA的非连接单个气泡群体。对于从宽带水听器收集的数据，ANN能够以93.8%的准确率对CCMC进行分类；对于使用Verasonics临床换能器收集的数据，准确率为90%。获得的结果表明，CCMC的声学响应是独特的，有潜力用于开发一种新型的造影剂检测技术。
