Quantitative Lung Ultrasound Spectroscopy for the Characterization of Lung Injury in a Preclinical Porcine Model

Quantitative LUS (Q-LUS) is a safe, real-time imaging tool that exploits the frequency dependency of imaging artifacts to extract biomarkers that characterize the lung disease. However, the direct relationship between Q-LUS biomarkers and the severity of lung injury remains insufficiently characterized. Without this grounding, Q-LUS metrics remain difficult to interpret and cannot be linked to damage severity or disease evolution.

This project investigates the correlation between Q-LUS biomarkers and the severity of the lung injury. Ultrasound data from preclinical porcine models with healthy lung and induced lung injury will be acquired. Data will be processed to extract Q-LUS biomarkers to statistically associate with different levels of injured severity.

University of Toronto will benefit of advance expertise in Q-LUS, including study design, ultrasound platform, and data analysis. A unique lung ultrasound dataset on controlled large animal models will be generated, positioning Canada as an active and important contributor to the development of Q-LUS.

The university of Trento will benefits of preclinical porcine models presenting an induced, controlled lung injury to acquire data. The outcome of this project will fill a knowledge gap on the conducted research line. Furthermore, this work represent an important component of the intern’s doctoral thesis.

Faculty Supervisor:

Laurent Brochard

Student:

Partner:

University of Trento

Discipline:

Life Sciences

Sector:

Education

University:

University of Toronto

Program: