SBIR Phase I: Compact, Low-cost, Automated 3D Ultrasound System for Regular and Accessible Breast Imaging

This SBIR Phase I project introduces a new paradigm for early monitoring and detection of breast cancer: the Quantified Self Exam. In the United States over 300,000 women are diagnosed and 40,000 women die from breast cancer every year. Breast cancer has a 99% survival rate if detected early, but limitations in cost, sensitivity, accessibility, and convenience of existing screening technologies result in one third of breasts cancers getting missed at early stages. Since treatment for early stage cancer is an order of magnitude less costly than treatment for stage 3 and 4 cancers, there is a clear economic and societal benefit for the development of better breast cancer monitoring and screening tools. To address this challenge, the technology proposed in this project leverages the proven benefits of ultrasound imaging and the newfound power of cloud-based artificial intelligence to provide a regular and accessible self-monitoring tool that can quantify and track suspicious changes in breast tissue. The device portability, low cost, 2 minute scan time, and automated analysis of breast image data will greatly increase the accessibility of breast cancer monitoring for women, which in turn stands to decrease the cost burden of this disease for the US healthcare system. This SBIR Phase I project proposes to develop a new tool for early detection and monitoring of breast cancer: the Quantified Self Exam (QSE) that combines a low-cost, compact 3D ultrasound device and positioning accessory with artificial intelligence to empower women and their physicians with appropriate and actionable data. The QSE technology proposed in this project operates independent of user skill and captures 3D volumetric images of the whole breast in 2 minutes. The system architecture allows for simplified and low-cost ultrasound hardware that connects wirelessly to a smart phone/tablet and transfers captured data to secure cloud for advanced image processing and storage. The ultrasound scanner attaches to a positioning accessory for repeatable imaging that enables longitudinal 3D monitoring of abnormal growth using machine learning-based image analysis. The proposed Phase I R&D efforts focus on four objectives: (i) optimize electrical hardware and low-level imaging software for spatial resolution and image quality; (ii) build a QSE scanner that maximizes field of view and volumetric integrity; (iii) build a positioning accessory for positioning of the QSE scanner; (iv) demonstrate the longitudinal repeatability of QSE imaging by validating the alignment of 3D ultrasound volumes on a breast phantom.