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:10.48550/arxiv.2501.08902

Back

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

Preprint

Open access

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

arXiv.org

Cornell University

01/15/2025

DOI: 10.48550/arxiv.2501.08902

Files and links (1)

url

https://doi.org/10.48550/arxiv.2501.08902View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Computer Science - Computer Vision and Pattern Recognition

Computer Science - Learning

Details

Title: Subtitle: Multi-View Transformers for Airway-To-Lung Ratio Inference on Cardiac CT Scans: The C4R Study
Creators: Sneha N Naik
Elsa D Angelini
Eric A Hoffman
Elizabeth C Oelsner
R. Graham Barr
Benjamin M Smith
Andrew F Laine
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2501.08902
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 01/15/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984773416802771

Metrics

8 Record Views