An expectation maximization framework for an improved ultrasound-based tissue characterization

Ultrasonic tissue characterization has been gaining increasing attention. This procedure is generally based on the analysis of the echo signal. As the ultrasound echo is degraded by the system Point Spread Function, deconvolution could be employed to provide a tissue response estimate, exploitable for a better characterization. In this context, we present a deconvolution framework expressively designed to improve tissue characterization. Thanks to a new model for tissue reflectivity the proposed framework overcomes limitations associated with standard ones. The performance was evaluated from several tissue-mimicking phantoms. Obtained results show relevant improvements in classification accuracy. From a comparison with standard schemes the superiority of the proposed algorithm was attested.