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A Multi-Model Framework to Estimate Perfusion Parameters Using Contrast-Enhanced Ultrasound Imaging.

Medical Physics 2018 December 17
PURPOSE: Contrast-enhanced ultrasound imaging (CEUS) 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.

METHODS: 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 (TIC). 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 (R2 ), and the normalized root mean square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.

RESULTS: Our multi-model framework outperforms conventional single perfusion model approaches with least squares optimization, providing more robust perfusion parameter estimation. R2 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 R2 and NRMSE are 0.91 and 0.31, respectively.

CONCLUSIONS: 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 labelled 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, ROI size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs. This article is protected by copyright. All rights reserved.

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