Séminaire CREATIS (présentiel - La Doua) : Quantitative ultrasound (QUS) markers in lung tissue

Marie MULLER
Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
&
 Biomedical Engineering, North Carolina State University, University of North Carolina at Chapel Hill, NC, USA

Résumé + bio au format pdf

Lung ultrasound has often played a role in management of Covid patients during the first waves of the pandemic. But conventional ultrasound imaging relies on hypotheses that do not apply to lung tissue, and lung ultrasound only allows the visualization of artifacts, whose appearance depends on the ultrasound scanner and probe. It is critical that specific ultrasound methods are developed for lung applications. In addition, the advent of ultra-portable ultrasound and wearable devices has the potential to boost lung ultrasound further as a true point-of-care modality, but only if quantitative lung ultrasound markers of lung disease are developed. For ultrasound to be used at home to monitor chronic lung diseases by non-expert users, it is necessary to develop easy-to-read parameters. QUS markers of lung diseases will allow objectively and unequivocally detecting and quantifying changes in lung tissue. This will avoid subjectivity of interpreting conventional ultrasound images, and the need for highly skilled and highly trained technicians, especially in remote areas and for underserved populations.
To achieve this, we exploit the purported weakness of lung ultrasound: scattering of ultrasound waves by the air-filled alveoli. Healthy lungs are expected to scatter ultrasound waves more than diseased lungs, due a higher alveolar density. Multiple scattering-based ultrasound markers should be developed, because changes in lung microstructure such as changes in alveolar size, alveolar density or alveolar spacing will differently affect various QUS parameters. We investigate the use of QUS in lungs
by exploring four families of QUS parameters, and their potential to assess, follow up and discriminate diseases such as fibrosis, edema, and other lung diseases.