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

TL;DR

This study introduces a novel Multi-view Swin Transformer model to accurately infer airway-to-lung ratio (ALR) from cardiac CT scans, enabling COPD risk assessment using widely available imaging data.

Contribution

The paper presents a new attention-based transformer architecture that improves ALR inference accuracy from cardiac CT scans compared to previous methods.

Findings

Model significantly outperforms proxy inference methods.

Achieves accuracy comparable to full-lung CT scan reproducibility.

Demonstrates potential for COPD risk assessment from cardiac scans.

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.