# Accuracy and Precision in CT Quantification of COPD Through Virtual Imaging Trials

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $447,580

## Abstract

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 ou...

## Key facts

- **NIH application ID:** 10435577
- **Project number:** 5R01HL155293-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Ehsan Abadi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $447,580
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10435577

## Citation

> US National Institutes of Health, RePORTER application 10435577, Accuracy and Precision in CT Quantification of COPD Through Virtual Imaging Trials (5R01HL155293-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10435577. Licensed CC0.

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