# Lung-specific ultrasound beamforming for diagnostic imaging

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $217,430

## Abstract

PROJECT SUMMARY
Accurate diagnosis and monitoring of lung disease, including the urgent need arising from Covid-19, could be
widely addressed by ultrasound imaging. The standard modalities that diagnose and monitor lung disease are
X-ray imaging and computed tomography (CT) due to their extensive diagnostic capabilities. Ultrasound may not
be normally thought of as a primary lung imaging modality, however in the hands of an expert user it has a
sensitivity and specificity ranging from 90% to 100% relative to CT.
For non-expert users the interpretation of lung ultrasound images can be complex because ultrasound cannot
penetrate the soft-tissue/air interface. Thus, lung ultrasound relies on the interpretation of imaging "artefacts"
that appear to come from deep inside the air space of the lung, but are actually complex reverberations from the
pleural interface. These reflections carry information about the underlying lung pathology. This indirect imaging
and clinical interpretation approach is fundamentally different from imaging in soft tissue, where echos come
directly from the structures being imaged. Nevertheless, delay-and-sum beamforming methods currently used
in ultrasound systems are identical for lung imaging and soft tissue imaging. The lack of understanding of the
fundamental acoustics at the complex soft-tissue/air interface remains an impediment to the rational design of
ultrasound imaging sequences that can relate directly to lung acoustics and would be more sensitive to disease.
To overcome this challenge, we propose to develop and validate new ultrasound imaging and beamforming
methods using a physics-based approach that establishes a quantitative link between ultrasound imaging and
the disease state of the lungs. We hypothesize that ultrasound beamforming techniques that are designed
specifically for the lung and its complex reverberation physics will generate higher quality images, improved
clinical interpretability, and diagnostic capabilities. We will develop acoustical simulation tools and simulations of
the human body and lung disease that are experimentally calibrated to accurately represent the relevant
reverberation physics, such as A-line and B-line artefacts. Spatial coherence beamformers, which rely on
reverberation as a source of contrast and machine learning beamformers will be designed and optimized to
detect lung disease. These beamformers will be implemented on a programmable scanner and compared to
conventional B-mode imaging. If successful, this proposal will yield ultrasound imaging methods that are more
sensitive to lung disease, with clearer clinical interpretability, that can be deployed in current ultrasound imaging
systems.

## Key facts

- **NIH application ID:** 10440831
- **Project number:** 1R21EB033150-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Gianmarco Pinton
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $217,430
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440831

## Citation

> US National Institutes of Health, RePORTER application 10440831, Lung-specific ultrasound beamforming for diagnostic imaging (1R21EB033150-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10440831. Licensed CC0.

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