Receive channel FIR filters for enhanced contrast in medical ultrasound

Aperture weighting functions are critical design parameters in the development of ultrasound systems because beam characteristics determine the contrast and point resolution of the final image. In previous work by our group, we developed a general apodization design method that optimizes a broadband imaging system's contrast resolution performance [1, 2]. In that algorithm we used constrained least squares (CLS) techniques and a linear algebra formulation of the system point spread function (PSF) as a function of the scalar aperture weights. In this work we replace the receive aperture weights with individual channel finite impulse response (FIR) filters to produce PSFs with narrower mainlobe widths and lower sidelobe levels compared to PSFs produced with conventional apodization functions. Our approach minimizes the energy of the PSF outside a defined boundary while imposing a quadratic constraint on the energy of the PSF inside the boundary. We present simulation results showing that FIR filters of modest tap lengths (3-7) can yield marked improvement in image contrast and point resolution. Specifically we show results that 7-tap FIR filters can reduce sidelobe and grating lobe energy by 30dB and improve cystic contrast [3] by as much as 20dB compared to conventional apodization profiles. We also show experimental results where multi-tap FIR filters decrease sidelobe energy in the resulting 2D PSF and maintain a narrow mainlobe. Our algorithm has the potential to significantly improve ultrasound beamforming in any application where the system response is well characterized. Furthermore, this algorithm can be used to increase contrast and resolution in novel receive only beamforming systems [4, 5].

[1]  A. Nuttall Some windows with very good sidelobe behavior , 1981 .

[2]  W.F. Walker,et al.  A spline-based approach for computing spatial impulse responses , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[3]  S. W. Smith,et al.  A contrast-detail analysis of diagnostic ultrasound imaging. , 1982, Medical physics.

[4]  Massoud Motamedi,et al.  Combined ultrasound, optoacoustic, and elasticity imaging , 2004, SPIE BiOS.

[5]  W. Gander Least squares with a quadratic constraint , 1980 .

[6]  David Vilkomerson,et al.  Towards a resolution metric for medical ultrasonic imaging , 1995, 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium.

[7]  R. Schwann,et al.  Optimization of frequency dependent receive apodization , 2005, IEEE Ultrasonics Symposium, 2005..

[8]  W.F. Walker,et al.  Optimal apodization design for medical ultrasound using constrained least squares part I: theory , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[9]  W.F. Walker,et al.  Optimal apodization design for medical ultrasound using constrained least squares part II simulation results , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[10]  Mathias Fink,et al.  Ultra high speed imaging of elasticity , 2002, 2002 IEEE Ultrasonics Symposium, 2002. Proceedings..