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首页> 外文期刊>Physics in medicine and biology. >A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging
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A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging

机译：在CT成像中模拟现实的肺，肝和肾病理的通用框架

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摘要

Realistic three-dimensional (3D) mathematical models of subtle lesions are essential for many computed tomography (CT) studies focused on performance evaluation and optimization. In this paper, we develop a generic mathematical framework that describes the 3D size, shape, contrast, and contrast-profile characteristics of a lesion, as well as a method to create lesion models based on CT data of real lesions. Further, we implemented a technique to insert the lesion models into CT images in order to create hybrid CT datasets. This framework was used to create a library of realistic lesion models and corresponding hybrid CT images. The goodness of fit of the models was assessed using the coefficient of determination (R2) and the visual appearance of the hybrid images was assessed with an observer study using images of both real and simulated lesions and receiver operator characteristic (ROC) analysis. The average R2 of the lesion models was 0.80, implying that the models provide a good fit to real lesion data. The area under the ROC curve was 0.55, implying that the observers could not readily distinguish between real and simulated lesions. Therefore, we conclude that the lesion-modeling framework presented in this paper can be used to create realistic lesion models and hybrid CT images. These models could be instrumental in performance evaluation and optimization of novel CT systems.
机译：细微病变的逼真的三维（3D）数学模型对于许多专注于性能评估和优化的计算机断层扫描（CT）研究至关重要。在本文中，我们开发了一个通用的数学框架，该框架描述了病变的3D大小，形状，对比度和对比度轮廓特征，以及基于实际病变的CT数据创建病变模型的方法。此外，我们实现了一种将病变模型插入CT图像的技术，以创建混合CT数据集。该框架用于创建实际病变模型和相应的混合CT图像库。使用确定系数（R2）评估模型的拟合优度，并使用观察者研究评估混合图像的视觉外观，使用真实和模拟病变图像以及接收者操作员特征（ROC）分析进行观察。病变模型的平均R2为0.80，这表明这些模型非常适合实际病变数据。 ROC曲线下的面积为0.55，这意味着观察者无法轻易地区分真实病变和模拟病变。因此，我们得出的结论是，本文提出的病变建模框架可用于创建逼真的病变模型和混合CT图像。这些模型可能有助于新型CT系统的性能评估和优化。

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