Multi-View Transformers for Airway-to-Lung Ratio Inference on Cardiac CT Scans: The C4R Study

Sneha N. Naik; Elsa D. Angelini; Eric A. Hoffman; Elizabeth C. Oelsner; R. Graham Barr; Benjamin M. Smith; Andrew F. Laine

doi:10.1109/ISBI60581.2025.10981155

Conference proceeding

Multi-View Transformers for Airway-to-Lung Ratio Inference on Cardiac CT Scans: The C4R Study

Sneha N. Naik, Elsa D. Angelini, Eric A. Hoffman, Elizabeth C. Oelsner, R. Graham Barr, Benjamin M. Smith and Andrew F. Laine

Proceedings (International Symposium on Biomedical Imaging), pp.1-5

04/14/2025

DOI: 10.1109/ISBI60581.2025.10981155

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Abstract

The ratio of airway tree lumen to lung size (ALR), assessed at full inspiration on high resolution full-lung computed tomography (CT), is a major risk factor for chronic obstructive pulmonary disease (COPD). There is growing interest to infer ALR from cardiac CT images, which are widely available in epidemiological cohorts, to investigate the relationship of ALR to severe COVID-19 and post-acute sequelae of SARS-CoV-2 infection (PASC). Previously, cardiac scans included approximately 2/3 of the total lung volume with 5-6x greater slice thickness than high-resolution (HR) full-lung (FL) CT. In this study, we present a novel attention-based Multi-view Swin Transformer to infer FL ALR values from segmented cardiac CT scans. For the supervised training we exploit paired full-lung and cardiac CTs acquired in the Multi-Ethnic Study of Atherosclerosis (MESA). Our network significantly outperforms a proxy direct ALR inference on segmented cardiac CT scans and achieves accuracy and reproducibility comparable with a scan-rescan reproducibility of the FL ALR ground-truth.

Computed Tomography

airways

cardiac CT

Chronic obstructive pulmonary disease

COVID-19

deep-learning

Image resolution

Image segmentation

Lumen

lung CT

Lungs

Reproducibility of results

Training

transformer

Transformers

Details

Title: Subtitle: Multi-View Transformers for Airway-to-Lung Ratio Inference on Cardiac CT Scans: The C4R Study
Creators: Sneha N. Naik - Columbia University
Elsa D. Angelini - Columbia University
Eric A. Hoffman - University of Iowa
Elizabeth C. Oelsner - Columbia University
R. Graham Barr - Columbia University
Benjamin M. Smith - Columbia University
Andrew F. Laine - Columbia University
Resource Type: Conference proceeding
Publication Details: Proceedings (International Symposium on Biomedical Imaging), pp.1-5
Publisher: IEEE
DOI: 10.1109/ISBI60581.2025.10981155
ISSN: 1945-7928
eISSN: 1945-8452
Number of pages: 5
Language: English
Date published: 04/14/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984825531802771

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