Advancing Ultrasound Superharmonic Imaging Towards Clinical Translation

Abstract

The microvasculature holds important information for improved early detection of disease. Early detection greatly improves survival outcomes, such as in breast cancer where survival rates increase significantly when tumors are detected in the early stages of development. Superharmonic imaging is a contrast-enhanced ultrasound imaging technique specifically designed to image microvasculature. Using dual-frequency band transducers, it exploits the nonlinear echoes of microbubbles at superharmonic frequencies to achieve significantly higher vascular sensitivity than other medical imaging modalities. Additionally, it retains the hallmark advantages of ultrasound in safety, accessibility, and temporal resolution. However, its clinical translation has been hindered by poorly optimized hardware and limited image processing. This dissertation directly addresses these limitations towards advancing superharmonic imaging to clinical use. First, dual-frequency transducer parameters are evaluated in vitro and in vivo to increase imaging depth and improve imaging sensitivity. The deepest application of superharmonic imaging (up to 55 mm) while maintaining high sensitivity (with contrast signal enhancement 30 dB) is demonstrated. Second, the advantage of superharmonic imaging for ultrasound localization microscopy (ULM) to image at resolutions beyond the diffraction limit is investigated in a rodent kidney. Direct comparison with traditional ULM processing based on Singular Value Decomposition shows improved detection of low-velocity vessels using superharmonic ULM. Last, deep learning analysis using convolutional neural networks is applied to streamline tumor detection in superharmonic volumes at high accuracies (up to 92.8%). Gradient-based class-activation visualization also reveal qualitative and quantitative correlation between network attention and vascular morphology metrics, offering potentially interpretable models for clinical decision support in oncology. Together, these innovations in dual-frequency transducer design, ULM processing, and deep-learning-based image analysis elevate superharmonic imaging towards clinical translation as a high-resolution, high-sensitivity, and computationally efficient method for microvascular imaging.Doctor of Philosoph