P1A-8 Blurred Ultrasonic Images as ISI-Affected Signals: Joint Tissue Response Estimation and Channel Tracking in the Proposed Paradigm

Biomedical ultrasound (US) image quality is limited due to the blurring of tissue reflectivity introduced by the transducer Point Spread Function (PSF). We present a method based on a Maximum Likelihood (ML) estimation of tissue response. We adopt efficient equalization techniques usually applied in digital communications: the ultrasonic RF signal is considered as a sequence of discrete values (symbols) affected by channel intersymbol interference (ISI), and processed with a reduced-complexity Viterbi algorithm. Spatial variations of the channel are then tracked by coupling the Viterbi algorithm with a least mean square (LMS) real-time updating procedure. Finally, an adaptive symbol-quantization is defined to overcome the qualitative limitation due to a finite-length alphabet. The results show that the fast LMS adaptation of the channel allows for a real-time spatial analysis and compensation of tissue attenuation effects and inhomogeneities, thus enhancing the diagnostic capability of US images.