Coherent 3-D echo detection for ultrasonic imaging

The purpose of the present paper is to present an ultrasonic processing set-up by which three-dimensional (3-D) echo location can be computed more efficiently than by other one-dimensional (1-D) methods. This set-up contains three successive tasks. The first one deals with a model for representing echoes. This model is based on a generic wavelet, which is a cosine function with variable amplitude and phase. To estimate the wavelet, we propose to use a spline representation of its complex envelope in order to reduce amplitude and phase dimension. The second task deals with 1-D detection and is conducted within a Bayesian framework. Using an Ascan decomposition on a family of wavelets resulting from the first task, we propose a specific procedure to carry on constrained least-squares in order to alleviate the bias inherent to this criterion. The third task deals with the spatial regularization of the detected echo location field resulting from the second task. We propose a Bayes-Markov model for removing isolated wrong detections and simultaneously improving, under regularization constraint, the spatial location of the detected echoes. In fact, this model deals with the general problem of nonorganized point approximation. All the proposed techniques are illustrated on real ultrasonic data.