# Minimizing Uncertainty in Breast Ultrasound Imaging with Real-Time Coherence-Based Beamforming > **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $352,016 ## Abstract Project Summary Approximately 280,000 women are expected to be diagnosed with breast cancer in the United States in 2021 and more than 40,000 will die from the disease. It is well documented that early detection results in improved morbidity and mortality. Ultrasound imaging is an important screening and diagnostic breast cancer detection tool, particularly for women with dense breasts when mammography tends to be suboptimal. While suspicious findings may be clarified with ultrasound imaging, a subset of ultrasound images yield inconclusive results, ne- cessitating biopsies or follow-up imaging, which increase patient anxiety and places additional burdens on the time available for clinical care and the resource allocations of our healthcare system. One reason for this out- standing challenge is that dense breasts tend to create images with significant acoustic clutter, which confounds the differentiation of an otherwise anechoic mass (which is indicative of a benign cyst) from a truly hypoechoic mass (which could be indicative of malignancy). In addition, it can be difficult to distinguish a complicated cyst (which has internal echoes due to proteinaceous material and is benign) from either a solid mass or a complex cystic and solid mass (which could be malignant) using standard ultrasound imaging methods alone. The objective of this proposal is to develop new, real-time ultrasound imaging technology that will simplify clin- ical workflows by distinguishing fluid-filled masses from solid masses and from complex cystic and solid masses, which all appear hypoechoic in traditional ultrasound B-mode images. Our novel approach, Robust Short-Lag Spatial Coherence (R-SLSC) imaging, has demonstrated feasibility to make this distinction by incorporating coherence-based beamforming to augment existing beamforming methods available in clinical ultrasound scan- ners. Aim 1 will focus on development of a real-time system for implementing matched B-mode and R-SLSC imaging. Aim 2 will evaluate and compare real-time system performance. Aim 3 will assess the ability of our novel methods to distinguish fluid from solid or complex cystic and solid masses utilizing a combination of quanti- tative analyses and task-oriented reader studies. Aim 4 will investigate advanced methods to retrieve coherence information and diagnostic information regarding mass contents from ultrasound channel data, including recently discovered options that rely on coherence lengths and lag-one coherence values without requiring reader input. Successful completion of these aims will lead to a real-time, ultrasound-based tool to confidently distinguish solid from fluid hypoechoic breast masses and provide a more simplified clinical workflow for the most challenging of these cases. In addition, results from the proposed studies will be applicable to clarifying the content of masses that may appear in multiple organs throughout the human body (e.g., testicular, liver, or pancreatic masses). ## Key facts - **NIH application ID:** 10417922 - **Project number:** 1R01EB032960-01 - **Recipient organization:** JOHNS HOPKINS UNIVERSITY - **Principal Investigator:** Muyinatu A. Lediju Bell - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $352,016 - **Award type:** 1 - **Project period:** 2022-08-15 → 2026-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10417922 ## Citation > US National Institutes of Health, RePORTER application 10417922, Minimizing Uncertainty in Breast Ultrasound Imaging with Real-Time Coherence-Based Beamforming (1R01EB032960-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10417922. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*