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.