Conformable Sparse-Array Ultrasound Patch for Wearable 3D Breast Imaging

About This Grant

Breast cancer is the most commonly diagnosed cancer among women in the United States, yet many individuals face barriers to routine screening due to limited access, high costs, and discomfort. While mammography is the clinical standard, it is radiative and less effective for younger women and those with dense breast tissue. In response, the U.S. Food and Drug Administration now requires that patients be notified about their breast density and advised that additional imaging methods may improve cancer detection. Some aggressive tumors—known as interval cancers—can develop between mammograms and progress rapidly, making early detection especially important. Ultrasound is a safe, noninvasive, and accessible tool for breast cancer screening. However, current handheld ultrasound (HHUS) imaging is highly operator dependent, requires significant training, and typically offers only two-dimensional (2D) imaging, making it easy to miss anomalies unless the imaging plane intersects them precisely. Automated Breast Ultrasound (ABUS) systems provide standardized three-dimensional (3D) imaging with reduced operator variability but are expensive, clinic-bound, and reliant on uncomfortable compression and mechanical scanning. To overcome these limitations, this project introduces a wearable, real-time 3D ultrasound system that is low-power, and optimized for wide-angle, high-resolution volumetric imaging. The system is intended as an adjunct to routine mammography, designed to improve access to early detection, making breast imaging more accessible and frequent outside of clinical settings—particularly for individuals with dense breast tissue or limited imaging access. The system will be evaluated in clinical studies to assess diagnostic performance and generate datasets for developing AI tools to assist in early anomaly detection, which aligns with NSF’s mission to improve national health outcomes. The project includes educational and outreach efforts to spark interest in STEM at the K–12, undergraduate, and graduate levels. Novel fabrication techniques and wearable imaging concepts will be shared through new course modules and public demonstrations, aiming to engage students and educators beyond traditional physics and materials science disciplines. This project proposes a fully integrated, wearable ultrasound system to enable autonomous, longitudinal breast health monitoring. It combines innovations in patch design, transducer fabrication, miniaturized electronics, efficient three-dimensional (3D) imaging on curved anatomy, and AI-driven analysis to deliver safe, accessible, and gel-free imaging in a portable format. We introduce new laser-based micromachining strategies for piezoceramic dicing and electrode patterning, which will overcome limitations of conventional planar fabrication techniques such as dice-and-fill. These methods reduce mechanical stress, improve yield, and streamline the manufacturing process for 2D transducer arrays. Newly engineered matching and backing layers will improve bandwidth, enhance acoustic coupling, and suppress transducer ringing. Combined with biocompatible dry coupling materials, this approach eliminates the need for ultrasound gel and enables conformal, reusable skin contact. A soft substrate designed to have minimal-stress on the component will facilitate conformable and repeatable probe positioning, while a ring-array architecture will enable acoustic triangulation, allowing accurate 3D image reconstruction over complex curvilinear targets such as the breast. A novel signal acquisition architecture, “Chirped Data Acquisition System (cDAQ),” will be implemented to achieve high signal-to-noise ratio at sub-Nyquist sampling rates. In parallel, a “Convolutional Optimally Degenerate Array (CODA)” will be developed to replicate full-matrix resolution with significantly fewer transducer elements. In vivo trials with Massachusetts General Hospital will assess diagnostic performance, and the data will also be used to train AI algorithms for real-time detection and classification of breast anomalies. This work advances ultrasound transducer manufacturing, portable low-power imaging systems, and scalable clinical validation, providing a foundation for broader deployment of breast health screening technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.