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.
Blurred Ultrasonic Images as ISI-Affected Signals: Joint Tissue Response Estimation and Channel Tracking in the Proposed Paradigm / L. De Marchi; A. Palladini; N. Testoni; N. Speciale. - ELETTRONICO. - (2007), pp. 1270-1273. (Intervento presentato al convegno 2007 IEEE Ultrasonics Symposium tenutosi a New York City, NY, USA nel 28 - 31 October 2007) [10.1109/ULTSYM.2007.319].
Blurred Ultrasonic Images as ISI-Affected Signals: Joint Tissue Response Estimation and Channel Tracking in the Proposed Paradigm
DE MARCHI, LUCA;PALLADINI, ALESSANDRO;TESTONI, NICOLA;SPECIALE, NICOLO'ATTILIO
2007
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.