title: Strong reflector-based beamforming in ultrasound medical imaging.
abstract: This paper investigates the use of sparse priors in creating original two-dimensional beamforming methods for ultrasound imaging. The proposed approaches detect the strong reflectors from the scanned medium based on the well known Bayesian Information Criteria used in statistical modeling. Moreover, they allow a parametric selection of the level of speckle in the final beamformed image. These methods are applied on simulated data and on recorded experimental data. Their performance is evaluated considering the standard image quality metrics: contrast ratio (CR), contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). A comparison is made with the classical delay-and-sum and minimum variance beamforming methods to confirm the ability of the proposed methods to precisely detect the number and the position of the strong reflectors in a sparse medium and to accurately reduce the speckle and highly enhance the contrast in a non-sparse medium. We confirm that our methods improve the contrast of the final image for both simulated and experimental data. In all experiments, the proposed approaches tend to preserve the speckle, which can be of major interest in clinical examinations, as it can contain useful information. In sparse mediums we achieve a highly improvement in contrast compared with the classical methods.
authors.affiliation: Université de Toulouse, IRIT, UMR CNRS 5505, France.
authors.forename: Teodora
authors.initials: T
authors.lastname: Szasz
authors.affiliation: Université de Toulouse, IRIT, UMR CNRS 5505, France.
authors.forename: Adrian
authors.initials: A
authors.lastname: Basarab
authors.affiliation: Université de Toulouse, IRIT, UMR CNRS 5505, France.
authors.forename: Denis
authors.initials: D
authors.lastname: Kouamé
mesh_terms: D001499:Bayes Theorem; D004452:Echocardiography; D006801:Humans; D008962:Models, Theoretical; D014463:Ultrasonography / Q000379:methods*
keywords: Adaptive beamforming; Bayesian Information Criteria; Sparse signal representation; Ultrasound imaging