title: Hearing the unheard
title: single CT volume surrogates of regional lung function
titleType: Subtitle
Creators: name: Chaudhary, Muhammad Faizyab Ali
Publisher: name: University of Iowa
Year: 2024
Subjects: COPD
Generative Adversarial Networks
Hospitalizations
Image Registration
Medical Image Analysis
Computed Tomography

description: Chronic obstructive pulmonary disease (COPD) is a complex disorder that leads to progressive decline in lung function and poor quality of life. A major source of burden in COPD comes from exacerbations, which are hard to characterize and often lead to emergency room visits or hospitalization. Over the past decade, a number of interesting developments in quantitative computed tomography (QCT) imaging have enabled the early characterization of small airways disease, a well-known precursor to COPD. Concurrently, image registration of multiple CT volumes has enabled local characterization of biomechanical defects across the lungs. These spatial measures of functional abnormalities have been associated with several outcomes in COPD, including overall lung function, symptom burden, and the risk of exacerbations. The strength of these measures stems from their ability to localize disease phenotypes, thereby opening possible avenues into mechanisms. These developments have thus introduced the notion of locality for understanding lung disease. Although several large multicenter studies have carefully acquired computed tomography scans at different volumes for a better functional assessment of COPD, this practice remains far from being protocolled in the clinic. Typically, single CT volumes at inspiration are acquired in most clinical settings, which precludes a large bulk of clinical data from being analyzed for functional characterization. In addition, several retrospective multicenter studies were not designed to acquire multiple CT images.
description: Our work attempts to solve this problem by introducing deep generative modeling as a new paradigm for deriving functional surrogates of local lung function from single-volume CT scans. We hypothesized that a CT scan acquired at a single volume can be used to derive different measures of local lung function by learning a deep generative adversarial image synthesis model. The first part of our work investigated and reaffirmed the effectiveness of local measures of lung function, such as local tissue expansion and QCT texture, in predicting various clinical outcomes in COPD. We identified registration-invariant biomarkers of local tissue mechanics for the prediction and staging of COPD. Our initial exploration into robust local tissue mechanics for characterizing COPD led to a broader investigation into predicting COPD exacerbations using QCT textures from the adaptive multiple feature method. We discovered a novel radiological texture pattern that was highly predictive of severe COPD exacerbations. This signature outperformed the current clinical standard-of-practice, the history of exacerbations.
description: The second body of our work concerns the development of a generative adversarial learning model that can predict different measures of local lung function from a single CT volume, either at inspiration or expiration. To achieve these goals, we proposed a novel high-resolution medical image-to-image translation framework. We demonstrated that our models could be used to estimate different measures of local lung function, including the parametric response mapping-based functional small airways disease, image registration-based local tissue expansion, and the hyperpolarized gas magnetic resonance images.