Experimental performance assessment of the sub‐band minimum variance beamformer for ultrasound imaging

HIGHLIGHTSExperimental performance of the sub‐band Minimum Variance beamformer is studied.Lateral FWHM was measured to 16.7 &mgr;m (&lgr;/12), from isolated wire‐targets.This result demonstrates 24‐fold improvement compared to conventional beamforming.No resolution benefits were found for imaging continuous targets.No significant differences were found between temporal and sub‐band MV beamforming. ABSTRACT Recent progress in adaptive beamforming techniques for medical ultrasound has shown that current resolution limits can be surpassed. One method of obtaining improved lateral resolution is the Minimum Variance (MV) beamformer. The frequency domain implementation of this method effectively divides the broadband ultrasound signals into sub‐bands (MVS) to conform with the narrow‐band assumption of the original MV theory. This approach is investigated here using experimental Synthetic Aperture (SA) data from wire and cyst phantoms. A 7 MHz linear array transducer is used with the SARUS experimental ultrasound scanner for the data acquisition. The lateral resolution and the contrast obtained, are evaluated and compared with those from the conventional Delay‐and‐Sum (DAS) beamformer and the MV temporal implementation (MVT). From the wire phantom the Full‐Width‐at‐Half‐Maximum (FWHM) measured at a depth of 52 mm, is 16.7 Symbolm (0.08Symbol) for both MV methods, while the corresponding values for the DAS case are at least 24 times higher. The measured Peak‐Side‐lobe‐Level (PSL) may reach −41 dB using the MVS approach, while the values from the DAS and MVT beamforming are above −24 dB and −33 dB, respectively. From the cyst phantom, the power ratio (PR), the contrast‐to‐noise ratio (CNR), and the speckle signal‐to‐noise ratio (sSNR) measured at a depth of 30 mm are at best similar for MVS and DAS, with values ranging between −29 dB and −30 dB, 1.94 and 2.05, and 2.16 and 2.27 respectively. In conclusion the MVS beamformer is not suitable for imaging continuous targets, and significant resolution gains were obtained only for isolated targets. Symbol. No caption available. Symbol. No caption available.

[1]  Renbiao Wu,et al.  Time-delay- and time-reversal-based robust capon beamformers for ultrasound imaging , 2005, IEEE Transactions on Medical Imaging.

[2]  Bernard Mulgrew,et al.  Performance of spatial smoothing algorithms for correlated sources , 1996, IEEE Trans. Signal Process..

[3]  W.D. O'Brien,et al.  Synthetic aperture techniques with a virtual source element , 1998, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[4]  W. Marczak,et al.  Water as a standard in the measurements of speed of sound in liquids , 1997 .

[5]  Douglas L. Jones,et al.  Performance of time- and frequency-domain binaural beamformers based on recorded signals from real rooms. , 2004, The Journal of the Acoustical Society of America.

[6]  Ryan M. Jones,et al.  Three-Dimensional Transcranial Ultrasound Imaging of Microbubble Clouds Using a Sparse Hemispherical Array , 2014, IEEE Transactions on Biomedical Engineering.

[7]  Andreas Austeng,et al.  The iterative adaptive approach in medical ultrasound imaging , 2014, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

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

[9]  Jin Ho Chang,et al.  Frequency compounded imaging with a high-frequency dual element transducer. , 2010, Ultrasonics.

[10]  Iben Kraglund Minimum Variance Beamforming for High Frame-Rate Ultrasound Imaging , 2009 .

[11]  K. Thomenius,et al.  Evolution of ultrasound beamformers , 1996, 1996 IEEE Ultrasonics Symposium. Proceedings.

[12]  J.A. Jensen,et al.  Effects Influencing Focusing in Synthetic Aperture Vector Flow Imaging , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  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..

[14]  M. Tanter,et al.  Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging , 2015, Nature.

[15]  P. Stoica,et al.  Robust Adaptive Beamforming , 2013 .

[16]  J.A. Jensen,et al.  Fast parametric beamformer for synthetic aperture imaging , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[18]  S. I. Nikolov,et al.  SARUS: A synthetic aperture real-time ultrasound system , 2013, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[19]  Ole Marius Hoel Rindal,et al.  Understanding contrast improvements from capon beamforming , 2014, 2014 IEEE International Ultrasonics Symposium.

[20]  Jørgen Arendt Jensen,et al.  A comparison between temporal and subband minimum variance adaptive beamforming , 2014, Medical Imaging.

[21]  Robert J. Eckersley,et al.  In Vivo Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles , 2015, IEEE Transactions on Medical Imaging.

[22]  W N McDicken,et al.  The behaviour of individual contrast agent microbubbles. , 2003, Ultrasound in medicine & biology.

[23]  W. Walker,et al.  Adaptive signal processing in medical ultrasound beamforming , 2005, IEEE Ultrasonics Symposium, 2005..

[24]  F. Harris On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[25]  Jian Li,et al.  Robust Capon beamforming , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[26]  W.F. Walker,et al.  A constrained adaptive beamformer for medical ultrasound: initial results , 2002, 2002 IEEE Ultrasonics Symposium, 2002. Proceedings..

[27]  Billy Y S Yiu,et al.  GPU-based minimum variance beamformer for synthetic aperture imaging of the eye. , 2015, Ultrasound in medicine & biology.

[28]  Sverre Holm,et al.  Implementing capon beamforming on a GPU for real-time cardiac ultrasound imaging , 2014, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

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

[30]  C.-I.C. Nilsen,et al.  Beamspace adaptive beamforming for ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[31]  Henry Cox,et al.  Robust adaptive beamforming , 2005, IEEE Trans. Acoust. Speech Signal Process..

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

[33]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[34]  Kaamran Raahemifar,et al.  A frequency domain MVDR beamformer for UWB microwave breast cancer imaging in dispersive mediums , 2013, IEEE International Symposium on Signal Processing and Information Technology.

[35]  Sverre Holm,et al.  Eigenspace Based Minimum Variance Beamforming Applied to Ultrasound Imaging of Acoustically Hard Tissues , 2012, IEEE Transactions on Medical Imaging.

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

[37]  Ali Mahloojifar,et al.  Synthetic Aperture Ultrasound Fourier Beamformation Using Virtual Sources , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[38]  Sergiy A. Vorobyov,et al.  Principles of minimum variance robust adaptive beamforming design , 2013, Signal Process..

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

[40]  A. Austeng,et al.  Sensitivity of minimum variance beamforming to tissue aberrations , 2008, 2008 IEEE Ultrasonics Symposium.

[41]  A. Austeng,et al.  Benefits of minimum-variance beamforming in medical ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[42]  F. Gran,et al.  P2B-12 Minimum Variance Beamforming for High Frame-Rate Ultrasound Imaging , 2007, 2007 IEEE Ultrasonics Symposium Proceedings.

[43]  Avinash C. Kak,et al.  Array signal processing , 1985 .

[44]  C. Dunsby,et al.  3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles , 2015, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[45]  A. Austeng,et al.  Minimum variance beamforming applied to ultrasound imaging with a partially shaded aperture , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[46]  Sverre Holm,et al.  Capon Beamforming for Active Ultrasound Imaging Systems , 2009, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop.

[47]  Bruno Clerckx,et al.  MIMO techniques in WiMAX and LTE: a feature overview , 2010, IEEE Communications Magazine.

[48]  Yong-Ping Zheng,et al.  Subarray coherence based postfilter for eigenspace based minimum variance beamformer in ultrasound plane-wave imaging. , 2016, Ultrasonics.

[49]  Jin S. Lee,et al.  Computationally Efficient Adaptive Beamformer for Ultrasound Imaging Based on QR Decomposition , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[50]  Randy L. Haupt,et al.  Introduction to Adaptive Arrays , 1980 .

[51]  J. Jensen,et al.  In-vivo synthetic aperture flow imaging in medical ultrasound , 2003, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[52]  Alfred C. H. Yu,et al.  Towards establishing a design rule for aperture parameters in minimum-variance beamforming , 2013, 2013 IEEE International Ultrasonics Symposium (IUS).

[53]  A Mahloojifar,et al.  Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.