CRII: SCH: Multi-modal Soft Tissue Characterization for Non-invasive Breast Imaging

University Of Massachusetts, Dartmouth, North Dartmouth MA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Breast cancer is the most common type of cancer among women worldwide, and its early detection is one of the most important enabling factors for better post-treatment prognosis which significantly affects the quality of life for survivors. This project will develop a fundamental understanding of soft tissue behaviors using an optimization framework with ultrasound and electrical impedance tomography to provide an accessible and reliable tool for potential usages in breast cancer screening. The specification of a distinct feature of collagen in the human breast will provide a clue for a new breast cancer biomarker. The successful completion of the project will lead to improved technologies associated with an advanced breast cancer screening technique employing multi-modal characterization such as wearable sensors, artificial intelligence in medical imaging, and point-of-care diagnostic systems. The research team will implement an optimization framework that can learn from the physical responses of soft tissue in order to visualize the cross-sectional human breast non-invasively using an integrated imaging technique combining ultrasound and electrical impedance tomography. The first specific aim is to establish the optimization algorithms and multi-modal parameters that present a line of demarcation for actively responding soft tissues against the electro-mechanical stimuli with minimal artifacts. The resultant images will illustrate how the soft tissues respond against ultrasound stimulation under different patterns of electric fields with temperature distribution information across the soft tissues. The second specific aim is to conduct a repeated measure study involving healthy subjects so as to identify critical factors that affect the multi-modal characterization results over time. This observational study will lead to not only an improved understanding of the algorithm parameters required to account for temporal variability for patient-specific adaptation of the algorithm while bridging the gap between ex vivo and in vivo electromechanical characteristics of human breast tissues. 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.