x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung City, Taiwan.
Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, Taiwan.
J Ultrasound Med. 2022 Jul;41(7):1699-1711. doi: 10.1002/jum.15851. Epub 2021 Oct 26.
Lung ultrasound (LUS) is a radiation-free, affordable, and bedside monitoring method that can detect changes in pulmonary aeration before hypoxic damage. However, visual scoring methods of LUS only enable subjective diagnosis. Therefore, quantitative analysis of LUS is necessary for obtaining objective information on pulmonary aeration. Because raw data are not always available in conventional ultrasound systems, Shannon entropy (ShanEn) of information theory without the requirement of raw data is valuable. In this study, we explored the feasibility of ShanEn estimated through grayscale histogram (GSH) analysis of LUS images for the quantification of pulmonary aeration.
Different degrees of pulmonary aeration caused by edema was induced in 32 male New Zealand rabbits intravenously injected with 0.1 mL/kg saline (the control group) and 0.025, 0.05, and 0.1 mL/kg oleic acid (mild, moderate, and severe groups, respectively). In vivo grayscale LUS images were acquired using a commercial point-of-care ultrasound system for estimation of GSH and corresponding ShanEn. Both lungs of each rabbit were dissected, weighed, and dried to determine the wet weight-to-dry weight ratio (W/D) through gravimetry.
The determination coefficients of linear correlations between ShanEn and W/D increased from 0.0487 to 0.7477 with gain and dynamic range (DR). In contrast to visual scoring methods of pulmonary aeration that use median gain and low DR, ShanEn for quantifying pulmonary aeration requires high gain and DR.
The current findings indicate that ShanEn estimated through GSH analysis of LUS images acquired using conventional ultrasonic imaging systems has great potential to provide objective information on pulmonary aeration.
肺部超声（LUS）是一种无辐射、经济实惠且可在床边进行的监测方法，可在缺氧损伤前检测肺通气的变化。然而，LUS 的视觉评分方法仅能进行主观诊断。因此，需要对 LUS 进行定量分析，以获得有关肺通气的客观信息。由于常规超声系统中并非总是提供原始数据，因此无需原始数据的信息论 Shannon 熵（ShanEn）具有重要价值。在本研究中，我们探讨了通过 LUS 图像灰度直方图（GSH）分析估计 ShanEn 来量化肺通气的可行性。
通过向 32 只雄性新西兰兔静脉注射 0.1ml/kg 生理盐水（对照组）和 0.025、0.05 和 0.1ml/kg 油酸（轻度、中度和重度组），在体内诱导不同程度的肺水肿引起的肺通气。使用商业即时护理超声系统获取体内灰度 LUS 图像，用于估计 GSH 和相应的 ShanEn。对每只兔的双肺进行解剖、称重和干燥，通过重量法测定湿重/干重比（W/D）。
ShanEn 与 W/D 的线性相关的决定系数从 0.0487 增加到 0.7477，增益和动态范围（DR）增加。与使用中值增益和低 DR 的肺通气视觉评分方法相比，用于量化肺通气的 ShanEn 需要高增益和 DR。
目前的研究结果表明，通过常规超声成像系统获取的 LUS 图像的 GSH 分析估算的 ShanEn 具有提供肺通气客观信息的巨大潜力。
