PDF(Pubmed)
Abstract：
Several lung diseases lead to alterations in regional lung mechanics, including ventilator- and radiation-induced lung injuries. Such alterations can lead to localized underventilation of the affected areas, resulting in the overdistension of the surrounding healthy regions. Thus, there has been growing interest in quantifying the dynamics of the lung parenchyma using regional biomechanical markers. Image registration through dynamic imaging has emerged as a powerful tool to assess lung parenchyma\'s kinematic and deformation behaviors during respiration. However, the difficulty in validating the image registration estimation of lung deformation, primarily due to the lack of ground-truth deformation data, has limited its use in clinical settings. To address this barrier, we developed a method to convert a finite-element (FE) mesh of the lung into a phantom computed tomography (CT) image, advantageously possessing ground-truth information included in the FE model. The phantom CT images generated from the FE mesh replicated the geometry of the lung and large airways that were included in the FE model. Using spatial frequency response, we investigated the effect of \" imaging parameters\" such as voxel size (resolution) and proximity threshold values on image quality. A series of high-quality phantom images generated from the FE model simulating the respiratory cycle will allow for the validation and evaluation of image registration-based estimations of lung deformation. In addition, the present method could be used to generate synthetic data needed to train machine-learning models to estimate kinematic biomarkers from medical images that could serve as important diagnostic tools to assess heterogeneous lung injuries.
摘要：
几种肺部疾病导致局部肺力学的改变，包括呼吸机和辐射引起的肺损伤。这种改变可能导致受影响地区局部通风不足，导致周围健康区域的过度扩张。因此，人们对使用区域生物力学标志物定量肺实质的动力学越来越感兴趣。通过动态成像的图像配准已成为评估呼吸过程中肺实质的运动学和变形行为的有力工具。然而,难以验证肺变形的图像配准估计，主要是由于缺乏地面实况变形数据，限制了其在临床环境中的使用。为了解决这个障碍，我们开发了一种将肺的有限元（FE）网格转换为体模计算机断层扫描（CT）图像的方法，有利地拥有包括在FE模型中的地面实况信息。从FE网格生成的体模CT图像复制了包括在FE模型中的肺和大气道的几何形状。使用空间频率响应，我们研究了"成像参数"如体素大小(分辨率)和接近阈值对图像质量的影响.从模拟呼吸周期的FE模型生成的一系列高质量体模图像将允许对基于图像配准的肺变形估计的验证和评估。此外,本方法可用于生成训练机器学习模型所需的合成数据,以从医学图像中估计运动学生物标志物,这些生物标志物可作为评估异质性肺损伤的重要诊断工具.
