Quantitative computed tomography (CT) imaging in chronic obstructive pulmonary disease (COPD) patients provides measurements of the underlying disease features, small airway disease and emphysema, but tools that can interrogate the inter-relationships are limited. We hypothesize that there is a spatial relationship between disease features that is related to disease severity. We will use image processing methods to generate three-dimensional maps for visualizing and quantifying the spatial inter-relationships.
Computed tomography (CT) provides high resolution images of the underlying structural abnormalities in patients with lung disease. We hypothesize that the spatial distribution of structural abnormalities, not just the extent of disease, is related to clinically meaningful measures in chronic obstructive pulmonary disease (COPD) patients. We will use image processing and optimize methods to quantify various aspects of spatial heterogeneity, such as shape of disease regions, complexity in the distribution, and clustering of disease regions in the lung.
In Physics, our understanding of phenomena is expressed in terms of math expressions, e.g. F=ma or E=mc^2, which also allows us to make verifiable predictions beyond experimental observations. In Virology, there is little or no math, and this makes robust validation of knowledge and prediction impossible: let's work together to fix this! In your project, you will use scientific programming (probably coding in python) and math (mostly ODEs) to describe and study a specific aspect of a virus infection, like flu or HIV. Specifics of the project will depend on your skills and interests.