Estimation and compensation of ultrasonic wavefront distortion using a blind system identification method

A common random input filter model is described for estimation and correction of wavefront aberration in ultrasonic b-scan imaging. In the model, aberration between the focus and the transducer elements is represented by the response of a linear filter bank to a common random signal. The response of...

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Veröffentlicht in:IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1999. - 49(2002), 6 vom: 25. Juni, Seite 739-55
1. Verfasser: Lin, Feng (VerfasserIn)
Weitere Verfasser: Waag, Robert C
Format: Aufsatz
Sprache:English
Veröffentlicht: 2002
Zugriff auf das übergeordnete Werk:IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.
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245 1 0 |a Estimation and compensation of ultrasonic wavefront distortion using a blind system identification method 
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520 |a A common random input filter model is described for estimation and correction of wavefront aberration in ultrasonic b-scan imaging. In the model, aberration between the focus and the transducer elements is represented by the response of a linear filter bank to a common random signal. The response of each filter in the bank is found using a two-level extension of an existing subspace method for blind system identification. The receive waveforms are compensated using an inverse filter, and the transmit waveforms are predistorted using time reversal. To test the model, experiments were conducted using a two-dimensional array system to obtain echoes from a point reflector and from a random medium in each case through an aberrator. The aberrator is a phantom that mimics wavefront distortion produced by human abdominal wall, and the random medium is made to mimic ultrasonic characteristics of human liver. The results indicate the method can improve both the transmit and the receive focus and can outperform time-shift estimation and compensation as well as the method of backpropagation followed by timeshift estimation and compensation 
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