A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development.

High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible. Then high resolution ultrasound image is reconstructed from the few measurements of DCS directly by the proposed MABF algorithm without recovering the raw sensor signals with complex convex optimization algorithm. The simulated results show the effectiveness of the proposed method.

[1]  Fredrik Lingvall A method of improving overall resolution in ultrasonic array imaging using spatio-temporal deconvolution. , 2004, Ultrasonics.

[2]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[3]  O. L. Frost,et al.  An algorithm for linearly constrained adaptive array processing , 1972 .

[4]  Richard G. Baraniuk,et al.  Distributed Compressive Sensing , 2009, ArXiv.

[5]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

[6]  F. Gran,et al.  Broadband minimum variance beamforming for ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[7]  Jian Li,et al.  Time-delay- and time-reversal-based robust capon beamformers for ultrasound imaging , 2005, IEEE Trans. Medical Imaging.

[8]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[9]  J. Capon High-resolution frequency-wavenumber spectrum analysis , 1969 .

[10]  A. Austeng,et al.  Adaptive Beamforming Applied to Medical Ultrasound Imaging , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[11]  William D. O'Brien,et al.  A regularized inverse approach to ultrasonic pulse-echo imaging , 2006, IEEE Transactions on Medical Imaging.

[12]  Magali Sasso,et al.  Medical ultrasound imaging using the fully adaptive beamformer , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[13]  A. Austeng,et al.  Minimum variance adaptive beamforming applied to medical ultrasound imaging , 2005, IEEE Ultrasonics Symposium, 2005..

[14]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[15]  Michael V Greening,et al.  Adaptive beamforming for nonstationary arrays. , 2002, The Journal of the Acoustical Society of America.

[16]  B. Shapo,et al.  Single snapshot spatial processing: optimized and constrained , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[17]  Roland Stoughton,et al.  Source imaging with minimum mean‐squared error , 1993 .

[18]  Thomas Kailath,et al.  Adaptive beamforming for coherent signals and interference , 1985, IEEE Trans. Acoust. Speech Signal Process..

[19]  Michael Elad,et al.  Optimized Projections for Compressed Sensing , 2007, IEEE Transactions on Signal Processing.

[20]  J. Jensen,et al.  Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers , 1992, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.