Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
Med Phys. 2019 Feb;46(2):590-600. doi: 10.1002/mp.13340. Epub 2019 Jan 21.
Contrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application.
Our multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R ), and the normalized root-mean-square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.
Our multi-model framework outperforms conventional single perfusion model approaches with least-squares optimization, providing more robust perfusion parameter estimation. R and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R and NRMSE are 0.91 and 0.31, respectively.
The proposed multi-model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs.
对比增强超声成象通过实现组织灌注的实时成象和定量，扩展了超声的诊断潜力。已经开发了几种灌注模型和曲线拟合方法来定量示踪剂信号的时间行为并使灌注定量标准化。虽然最小二乘法传统上被应用于曲线拟合，但对于噪声和复杂数据，它可能不够充分。此外，先前的研究表明，某些灌注模型可能根据所成像的器官或组织而更相关。我们提出了一种多模型框架，以选择最适合每个诊断应用的灌注模型和曲线拟合方法。
我们的多模型方法使用系统识别方法，该方法从与给定时间强度曲线拟合最好的模型中估计灌注参数。我们比较了当前使用单一灌注模型和曲线拟合方法的灌注定量方法以及我们在体内植入结肠癌细胞的小鼠的 3D 动态对比增强超声（DCE-US）的 bolus 图像以及模拟数据上的多模型框架。使用 Spearman 相关系数、决定系数（R）和归一化均方根误差（NRMSE）评估了估计灌注参数的拟合质量，以确保多模型框架找到最佳的灌注模型和曲线拟合算法。
我们的多模型框架优于使用最小二乘优化的传统单一灌注模型方法，提供了更稳健的灌注参数估计。对于我们的方法，R 和 NRMSE 分别为 0.98 和 0.18。相比之下，传统方法的性能更多地取决于适当模型的选择。R 和 NRMSE 分别为 0.91 和 0.31。
与使用最小二乘优化的当前单一灌注模型方法相比，用于灌注建模的建议的多模型框架在使用专家标记的经验数据作为金标准时表现更好，并且更稳健地估计灌注参数。我们的技术对影响灌注参数估计准确性的问题（包括上升时间、噪声、感兴趣区域大小和帧率）的敏感性最小。该框架可在不同组织和器官中对不同的灌注系统建模中发挥关键作用。
