Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, Milan 20162, Italy.
Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, Milan 20162, Italy.
Phys Med. 2021 Feb;82:28-39. doi: 10.1016/j.ejmp.2021.01.004. Epub 2021 Jan 28.
Quantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes a patient-independent model for the estimation of well-aerated volume of lungs in CT images (WAVE).
A Gaussian fit, with mean (Mu.f) and width (Sigma.f) values, was applied to the lower CT histogram data points of the lung to provide the estimation of the well-aerated lung volume (WAVE.f). Independence from CT reconstruction parameters and respiratory cycle was analysed using healthy lung CT images and 4DCT acquisitions. The Gaussian metrics and first order radiomic features calculated for a third cohort of COVID-19 patients were compared with those relative to healthy lungs. Each lung was further segmented in 24 subregions and a new biomarker derived from Gaussian fit parameter Mu.f was proposed to represent the local density changes.
WAVE.f resulted independent from the respiratory motion in 80% of the cases. Differences of 1%, 2% and up to 14% resulted comparing a moderate iterative strength and FBP algorithm, 1 and 3 mm of slice thickness and different reconstruction kernel. Healthy subjects were significantly different from COVID-19 patients for all the metrics calculated. Graphical representation of the local biomarker provides spatial and quantitative information in a single 2D picture.
Unlike other metrics based on fixed histogram thresholds, this model is able to consider the inter- and intra-subject variability. In addition, it defines a local biomarker to quantify the severity of the disease, independently of the observer.
肺部计算机断层扫描（CT）图像中的定量指标已被广泛应用，但往往与生理学没有明确的联系。本研究提出了一种用于估计 CT 图像中充气良好的肺体积（WAVE）的患者独立模型。
对肺下部 CT 直方图数据点进行高斯拟合，得到均值（Mu.f）和宽度（Sigma.f）值，用于估计充气良好的肺体积（WAVE.f）。使用健康肺 CT 图像和 4DCT 采集来分析对 CT 重建参数和呼吸周期的独立性。比较了高斯指标和第一阶放射组学特征与健康肺的特征。对 COVID-19 患者的第三组肺进行进一步分割，提出了一个新的基于高斯拟合参数 Mu.f 的生物标志物，以表示局部密度变化。
在 80%的情况下，WAVE.f 与呼吸运动无关。在中等迭代强度和 FBP 算法、1mm 和 3mm 层厚以及不同重建核之间进行比较时，差异为 1%、2%和高达 14%。所有计算的指标都表明健康受试者与 COVID-19 患者存在显著差异。局部生物标志物的图形表示提供了单一二维图像中的空间和定量信息。
与基于固定直方图阈值的其他指标不同，该模型能够考虑个体间和个体内的变异性。此外，它定义了一个局部生物标志物，用于量化疾病的严重程度，而不依赖于观察者。
