Improved Characterization of Non-Stationary Flows Using a Regularized Spectral Analysis of Ultrasound Doppler Signals

This paper addresses the problem of ultrasound Doppler spectral estimation when only a short observation set is available. Following the work of Kitagawa and Gersch, the spectra are described by a long autoregressive model whose coefficients are estimated in a Bayesian regularized least squares framework accounting for spectral smoothness in order to avoid too spiky spectra. The critical computation of the tradeoff parameters is addressed using both maximum likelihood and generalized cross validation criteria in order to automatically tune the smoothness constraint. The practical potential of the method is demonstrated using both simulated and in vitro signals. In a Monte-Carlo simulation study, investigation of quantitative indices such as quadratic distances shows interesting improvements with respect to the usual least squares method whatever the window data length and the signal to noise ratio. When applied to actual Doppler signals, the proposed method offers better description of the Doppler spectrum morphology than the usual least squares one