# Robust Detection of Early Small Airway Disease

> **NIH NIH R21** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $134,250

## Abstract

Project Summary
 Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality. Despite
declines in smoking, mortality from COPD continues to increase and is now the 3rd leading cause of death in
the US. The chronic airflow limitation of COPD is caused by a mixture of small airway disease and
parenchymal destruction (emphysema). Recent studies have suggested a central role of small airway
destruction in the pathogenesis of COPD. This evidence has sparked the interest in in-vivo assessment of
small airway disease overall at the early onset of the disease. Early identification of small airway disease could
lead to better patient diagnosis, early therapeutic intervention and provide more sensitive markers to elucidate
the pathogenesis of the disease and its biomolecular basis that could inform much-needed drug discovery.
 Computed Tomography (CT) is an imaging modality that has proven to be effective in the quantification of
parenchymal destruction. However, the imaging resolution required to obtain direct measures from small
airways is beyond the limits of CT scans. A recent technique called parametric response mapping (PRM)
proposes to distinguish gas trapping due to small airway disease from emphysema by matching inspiratory and
expiratory CT scans and applying density thresholds to distinguish functional small airway disease (FSAD) and
emphysema.
 Despite its success, the PRM shows some limitations that are precluding the accuracy, robustness and
interpretation of its results in early disease: The CT density values highly depend on acquisition parameters
(dosage, reconstruction kernel, changes in body size) that introduce subject- and scanner-dependent
confounders. Although clinical trials use well defined acquisition protocols and phantom-calibrated acquisitions,
the biases and noise patterns still are subject-dependent. In particular, many studies using PRM employ
inspiratory and expiratory images that are obtained at different dose levels.
 This project will take full advantage of our most recent developments in image-driven statistical
characterization of tissues to reduce the harmful effects of the main factors affecting PRM. The harmonization
of CT scans in a statistical framework will enable robust PRM metrics in cross-sectional and longitudinal
studies. The statistical characterization will also lead to define adaptive thresholds to detect the emphysema
and FSAD minimizing type I and II error trade-off. We will validate the robustness of harmonized PRM metrics
in multiparametric acquisitions and study its clinical relevance by studying associations with lung function. Our
preliminary data shows that we can obtain harmonized images that minimize the scanner and subject-
dependent confounders. Our tissue characterization in CT images also has proved its suitability to provide a
statistical framework to define robust adaptive thresholds. Together, the research proposed in the aims of this
award will take full adva...

## Key facts

- **NIH application ID:** 10360612
- **Project number:** 5R21HL156229-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Gonzalo Vegas Sanchez-Ferrero
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $134,250
- **Award type:** 5
- **Project period:** 2021-03-01 → 2025-01-12

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10360612

## Citation

> US National Institutes of Health, RePORTER application 10360612, Robust Detection of Early Small Airway Disease (5R21HL156229-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10360612. Licensed CC0.

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