Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death. Increasing in prevalence, COPD is a major burden to patients and providers. Computed tomography (CT) can provide valuable information about the structural and functional abnormalities of the disease as demonstrated in numerous studies where quantitative CT is deployed to characterize and evaluate the treatment. For instance, the COPDGene study has recently shown the substantial role of quantitative CT in the redefinition of COPD diagnosis, and in evaluating the progression of emphysema over time. However, these biomarkers vary across different scanners, settings, and patient attributes. There is a crucial need to manage this variability by optimizing and harmonizing CT images for reliable biomarker quantifications across both current and emerging scanners. This goal is not possible through conventional methods of using physical phantoms or patient images. Physical phantoms are often oversimplified and not representative of the complex anatomy and physiology of COPD patients. Patient images are ground-truth-limited, i.e., the exact anatomy and physiology of the patient is not fully known. Further, patient-based comparisons require multiple acquisitions of the same subjects across different scanners and settings. This is not ethically possible since repeated imaging increases the absorbed radiation dose. These challenges can be overcome through the use of virtual imaging trials (VITs) where studies are performed in silico using computational models of patients and scanners. VITs can provide reliable and practical solution to the challenge of COPD imaging provided realistic models of patients and scanners. Such models are currently lacking in the context of COPD. We develop and then utilize realistic virtual imaging toolsets to systematically evaluate and optimize CT biomarkers in COPD patients across scanners, imaging parameters, and patient attributes. We develop the first library of realistic COPD patient models with diverse attributes and severities. Coupled with accurate models of different scanners, the phantoms will be used to generate sets of ground-truth-known virtual CT cases, to be disseminated to the research community and to be used to systematically evaluate the effects of current and emerging scanners, various patient attributes, and the effects of image processing algorithms (through a national challenge), on the accuracy and precision of COPD biomarkers. Further, we develop and optimize a truth-based artificial intelligence-based algorithm for COPD quantifications. We optimize the algorithm for accuracy and reproducibility, taking advantage of the ground-truth known simulated images . We then harmonize CT settings across different scanners to accurately and precisely assess COPD imaging biomarkers for both single time-point and longitudinal studies. The studies will be done for the top two image processing algorithms, identified in the challenge, as well as our developed algorithm. Through these efforts, the project will position CT as a more reliable method for improved characterization and monitoring of COPD.

Narrative The project aims to systematically evaluate and optimize quantitative CT imaging biomarkers in Chronic Obstructive Pulmonary Disease (COPD) patients, across scanner makes and models, imaging parameters, and patient attributes. This will be performed by developing and using in silico models of COPD patients and CT scanners. The project will position CT alongside other diagnostic methods to accurately and precisely characterize COPD.