Real-Time Freehand Ultrasound Molecular Imaging with Deep Learning

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Project Summary Earlier detection has reduced breast cancer mortality in recent years; however, current imaging tools have poor positive predictive value and likely contribute to overdiagnosis. Ultrasound molecular imaging (UMI) is a promising tool that can provide noninvasive, non-ionizing, real-time, freehand breast cancer tumor assessment at the point of care. UMI uses targeted ultrasound contrast agents (UCAs) to differentiate between benign and malignant lesions and has the potential to reduce false positive rates and overdiagnosis. However, poor UMI image quality has led researchers to trade the benefits of real-time and freehand imaging for better image quality, resulting in longer exam times, higher UCA dosage, and potentially missed targets. This project will develop a new real-time freehand UMI approach based on deep learning to achieve excellent detection of breast cancer. A spatiotemporal convolutional neural network (CNN) approach is proposed to specifically detect adherent UCAs, which indicate disease, in real-time while suppressing background noise from tissue and free UCAs. A physics- driven simulator of breast UMI will be constructed and calibrated with phantom and water tank measurements. A database of simulated UMI time series will be assembled and used to train the spatiotemporal CNN to identify adherent UCAs. Finally, the CNN will be deployed on a prototype real-time freehand UMI system, operating in excess of 30 frames per second. This system will be tested in a UMI study of breast cancer detection in a transgenic mouse model. This study will use UCAs targeted to a novel biomarker (B7-H3) that is highly specific for cancer and is believed to correlate strongly with likelihood of progression to invasive breast cancer. The real-time freehand approach is significant because it enables the operator to freely interrogate (and revisit) multiple targets with a single UCA injection, reducing exam times and UCA dosage. The proposed approach is innovative because it uses CNNs to better utilize data that is already acquired during UMI. The proposed system will be able to detect breast cancer with high image quality using real-time freehand UMI of B7-H3, potentially reducing false positives and overdiagnosis and thus unnecessary tests, biopsies, costs, and patient distress. The K99 phase will provide dedicated training and career growth opportunities in molecular imaging, UCA synthe- sis and targeting, animal study design, small animal UMI, clinical UMI, and other dedicated preparation needed to transition to a faculty position and an independent research career studying imaging methods for UMI. These training activities will provide the skills necessary to complete the proposed animal study in the R00 phase.