Automated Respiratory Pattern Analysis for Dynamic MRI of the Lung in Post COVID-19 patients at 0.55 T

Motivation: There is a lack of non-invasive approaches for quantitatively analyzing the patterns of respiration motion in proton MRI in patients with lung diseases such as post-COVID symptoms. Goal(s): The goal of this work is to determine whether post-COVID-19 patients can be classified as having either long COVID or no symptoms by analyzing dynamic MRI motion fields within various regions of lungs. Approach: A deep learning-assisted framework was developed for automatically analyzing localized respiratory motion in lung MRI. Results: The framework was able to successfully categorize patients into different categories based on their degrees of no symptoms using the proposed image analysis framework. Impact: This work develops an automated framework that can aid radiologists in quickly determining not only the presence but also the severity of long COVID. It can also be extended for applications in other lung diseases.