Deep-Learning Estimation of Perfusion Kinetic Parameters in Contrast-Enhanced Ultrasound Imaging

Contrast-enhanced ultrasound (CEUS) is a sensitive imaging technique to evaluate blood perfusion and tissue vascularity, whose quantification can assist in characterizing different perfusion patterns, e.g. in cancer or in arthritis. The perfusion parameters are estimated by fitting non-linear parametric models to experimental data, usually through the optimization of non-linear least squares, maximum likelihood, free energy or other methods that evaluate the adherence of a model adherence to the data. However, low signal-to-noise ratio and the nonlinearity of the model make the parameter estimation difficult. We investigate the possibility of providing estimates for the model parameters by directly analyzing the available data, without any fitting procedure, by using a deep convolutional neural network (CNN) that is trained on simulated ultrasound datasets of the model to be used. We demonstrated the feasibility of the proposed method both on simulated data and experimental CEUS data. In the simulations, the trained deep CNN performs better than constrained non-linear least squares in terms of accuracy of the parameter estimates, and is equivalent in term of sum of squared residuals (goodness of fit to the data). In the experimental CEUS data, the deep CNN trained on simulated data performs better than non-linear least squares in term of sum of squared residuals.