用多视角变换器从心脏CT推断肺气道比例，助力新冠后遗症研究
Multi-View Transformers for Airway-To-Lung Ratio Inference on Cardiac CT Scans: The C4R Study
- 设计多视角Swin Transformer，融合心脏CT多角度信息推断全肺气道比
- 在MESA数据集上达到与全肺CT扫描重测一致性相当的精度
- 为缺乏高分辨率肺CT的流行病学研究提供高效替代方案
在吸气期高分辨率全肺计算机断层扫描（CT）中评估的气道管腔体积与肺部大小之比（ALR），是慢性阻塞性肺疾病（COPD）的重要风险因素。目前有越来越多的研究关注从广泛存在的心脏CT图像中推断ALR，以探索其与严重新冠肺炎及新冠后急性综合征（PASC）的关系。以往的心脏扫描仅包含约2/3的肺体积，且层厚比高分辨率（HR）全肺（FL）CT大5-6倍。本研究提出一种基于注意力机制的多视角Swin Transformer，用于从分割后的心脏CT推断全肺ALR值。监督训练使用来自多族裔动脉粥样硬化研究（MESA）的配对全肺与心脏CT数据。所提网络显著优于直接在心脏CT上进行的代理ALR推断方法，并达到与全肺CT扫描重测一致性相当的准确性和可重复性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。