DOI: 10.1051/jp3:1997242
J. Phys. III France 7 (1997) 2079-2102

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

A. Herment¹, J.-F. Giovannelli², G. Demoment², B. Diebold¹ and A. Delouche<sup>1

1</sup>  INSERM, Unité 66, Imagerie Biomédicale, Morphologique et Fonctionnelle, CHU Pitié Salpétrière, 91 boulevard de l'Hôpital, 75634 Paris Cedex 13, France
²  Laboratoire des Signaux et Systèmes, CNRS - ESE - UPS ESE, Plateau de Moulon, 91192 Gif-Sur-Yvette Cedex, France

(Received 20 December 1996, revised 23 May 1997, accepted 4 July 1997)

Abstract
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.

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