Ultrasound Laboratory Trento (ULTRa), Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Trento, Italy.
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA.
J Acoust Soc Am. 2021 Dec;150(6):4151. doi: 10.1121/10.0007274.
The potential of lung ultrasound (LUS) has become manifest in the light of the recent COVID-19 pandemic. The need for a point-of care, quantitative, and widely available assessment of lung condition is critical. However, conventional ultrasound imaging was never designed for lung assessment. This limits LUS to the subjective and qualitative interpretation of artifacts and imaging patterns visible on ultrasound images. A number of research groups have begun to tackle this limitation, and this special issue reports on their most recent findings. Through in silico, in vitro, and in vivo studies (preclinical animal studies and pilot clinical studies on human subjects), the research presented aims at understanding and modelling the physical phenomena involved in ultrasound propagation, and at leveraging these phenomena to extract semi-quantitative and quantitative information relevant to estimate changes in lung structure. These studies are the first steps in unlocking the full potential of lung ultrasound as a relevant tool for lung assessment.
在最近的 COVID-19 大流行中，肺部超声（LUS）的潜力已经显现。迫切需要一种能够在现场进行、定量且广泛可用的肺部状况评估方法。然而，传统的超声成像并不是为肺部评估而设计的。这限制了 LUS 只能对超声图像上可见的伪影和成像模式进行主观和定性的解释。许多研究小组已经开始解决这一限制，本特刊报告了他们的最新研究结果。通过计算机模拟、体外和体内研究（临床前动物研究和针对人体的初步临床研究），所呈现的研究旨在理解和模拟超声传播中涉及的物理现象，并利用这些现象提取与估计肺部结构变化相关的半定量和定量信息。这些研究是充分发挥肺部超声作为肺部评估相关工具潜力的第一步。
