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.

翻译：气道树管腔与肺容积的比率（ALR）在高分辨率全肺计算机断层扫描（CT）的充分吸气状态下进行评估，是慢性阻塞性肺疾病（COPD）的主要风险因素。目前，越来越多的研究关注从心脏CT图像推断ALR，这些图像在流行病学队列中广泛可得，旨在探究ALR与重症COVID-19以及SARS-CoV-2感染急性后遗症（PASC）之间的关系。以往，心脏扫描仅包含约2/3的总体肺容积，且其切片厚度比高分辨率（HR）全肺（FL）CT厚5-6倍。在本研究中，我们提出了一种新颖的基于注意力的多视角Swin Transformer，用于从分割后的心脏CT扫描推断FL ALR值。对于监督训练，我们利用了在动脉粥样硬化多种族研究（MESA）中采集的配对全肺和心脏CT图像。我们的网络显著优于在分割心脏CT扫描上进行的代理直接ALR推断，并达到了与FL ALR真实值的扫描-再扫描可重复性相当的准确性和可重复性。