In patients with the acute respiratory distress syndrome (ARDS), lung imaging is a fundamental tool in the study of the morphological and mechanistic features of the lungs. Chest computed tomography studies led to major advances in the understanding of ARDS physiology. They allowed the in vivo study of the syndrome’s lung features in relation with its impact on respiratory physiology and physiology, but also explored the lungs’ response to mechanical ventilation, be it alveolar recruitment or ventilator-induced lung injuries. Coupled with positron emission tomography, morphological findings were put in relation with ventilation, perfusion or acute lung inflammation. Lung imaging has always been central in the care of patients with ARDS, with modern point-of-care tools such as electrical impedance tomography or lung ultrasounds guiding clinical reasoning beyond macro-respiratory mechanics. Finally, artificial intelligence and machine learning now assist imaging post-processing software, which allows real-time analysis of quantitative parameters that describe the syndrome’s complexity. This narrative review aims to draw a didactic and comprehensive picture of how modern imaging techniques improved our understanding of the syndrome, and have the potential to help the clinician guide ventilatory treatment and refine patient prognostication.

在患有急性呼吸窘迫综合征 (ARDS) 的患者中，肺部成像是研究肺部形态学和机械特征的基本工具。胸部计算机断层扫描研究导致对 ARDS 生理学的理解取得重大进展。他们允许在体内研究该综合征的肺部特征及其对呼吸生理学和生理学的影响，还探讨了肺部对机械通气的反应，无论是肺泡复张还是呼吸机引起的肺损伤。结合正电子发射断层扫描，形态学发现与通气、灌注或急性肺部炎症有关。肺部成像一直是 ARDS 患者护理的核心，使用现代现场护理工具，如电阻抗断层扫描或肺部超声，指导宏观呼吸力学之外的临床推理。最后，人工智能和机器学习现在辅助成像后处理软件，可以实时分析描述综合征复杂性的定量参数。这篇叙述性综述旨在对现代成像技术如何提高我们对该综合征的理解进行全面的教学描述，并有可能帮助临床医生指导通气治疗和改进患者预后。