Linear and nonlinear characterization of microbubbles and tissue using the Nakagami statistical model.

The goal of this work is to exploit the statistical signatures for discrimination between biological tissues and contrast microbubbles in order to develop new strategies for contrast imaging and tissue characterization. For this purpose, the efficiency of the Nakagami statistical model, for describing the ultrasonic echoes of both contrast microbubbles and tissues, was investigated. Experimental measurements have been performed using a linear array probe connected to an open research platform. A commercially available in vitro phantom was used to mimic biological tissue in which SonoVue contrast microbubbles were flowing. Experimental ultrasound echoes have been filtered around the transmitted frequency (fundamental at 2.5MHz) and around twice the transmitted frequency (at 5MHz) for 2nd harmonic analysis, and a logarithmic compression was applied. The signals have been analyzed in order to evaluate the Nakagami parameter m, the scaling parameter Ω and the probability density function at both frequencies. Parametric images based on the Nakagami parameters map (Nakagami-mode images) were reconstructed and compared to B-mode images. Contrary to the B-mode image which is influenced by the system settings and user operations, the Nakagami parametric image is only based on the backscattered statistics of the ultrasonic signals in a local phantom. Such an imaging principle allows the Nakagami image to quantify the local scatterer concentrations in the phantom and to extract the backscattering information from the regions of the weakest echoes that may be lost in the conventional B-mode image. Results show that the tissue and microbubbles characterization is more sensitive in the 2nd harmonic mode when a logarithmic transform is used. These results would be useful for improving the ultrasound image quality and contrast detection in nonlinear mode.