Implementing capon beamforming on a GPU for real-time cardiac ultrasound imaging

Capon beamforming is associated with a high computational complexity, which limits its use as a real-time method in many applications. In this paper, we present an implementation of the Capon beamformer that exhibits realtime performance when applied in a typical cardiac ultrasound imaging setting. To achieve this performance, we make use of the parallel processing power found in modern graphics processing units (GPUs), combined with beamspace processing to reduce the computational complexity as the number of array elements increases. For a three-dimensional beamspace, we show that processing rates supporting real-time cardiac ultrasound imaging are possible, meaning that images can be processed faster than the image acquisition rate for a wide range of parameters. Image quality is investigated in an in vivo cardiac data set. These results show that Capon beamforming is feasible for cardiac ultrasound imaging, providing images with improved lateral resolution both in element-space and beamspace.

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

[2]  Andreas Austeng,et al.  Adapting the minimum variance beamformer to a graphics processing unit for active sonar imaging systems , 2013 .

[3]  Sverre Holm,et al.  Speckle Statistics in Adaptive Beamforming , 2007 .

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

[5]  K. Kristoffersen,et al.  Parallel beamforming using synthetic transmit beams , 2007, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[6]  Junying Chen,et al.  Medical Ultrasound Imaging: To GPU or Not to GPU? , 2011, IEEE Micro.

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

[8]  A. Mahloojifar,et al.  A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[9]  Yangmo Yoo,et al.  An effective beamforming algorithm for a GPU-based ultrasound imaging system , 2012, 2012 IEEE International Ultrasonics Symposium.

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

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

[12]  Thomas Kailath,et al.  Performance analysis of the optimum beamformer in the presence of correlated sources and its behavior under spatial smoothing , 1987, IEEE Trans. Acoust. Speech Signal Process..

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

[14]  Sverre Holm,et al.  Implementing Capon beamforming on the GPU for real time cardiac ultrasound imaging , 2012, 2012 IEEE International Ultrasonics Symposium.

[15]  E. Jacobsen,et al.  The sliding DFT , 2003, IEEE Signal Process. Mag..

[16]  Junying Chen,et al.  Design considerations of real-time adaptive beamformer for medical ultrasound research using FPGA and GPU , 2012, 2012 International Conference on Field-Programmable Technology.

[17]  Junying Chen,et al.  Real-time GPU-based adaptive beamformer for high quality ultrasound imaging , 2011, 2011 IEEE International Ultrasonics Symposium.

[18]  T. J. Shepherd,et al.  A systolic array processor for MVDR beamforming , 1989 .

[19]  K. Boone,et al.  Effect of skin impedance on image quality and variability in electrical impedance tomography: a model study , 1996, Medical and Biological Engineering and Computing.

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

[21]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[22]  Andreas Austeng,et al.  An Optimized GPU Implementation of the MVDR Beamformer for Active Sonar Imaging , 2015, IEEE Journal of Oceanic Engineering.

[23]  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.

[24]  Junying Chen,et al.  Design space exploration of adaptive beamforming acceleration for bedside and portable medical ultrasound imaging , 2011, CARN.

[25]  J-F Synnevåg,et al.  A low-complexity data-dependent beamformer , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[26]  Pai-Chi Li,et al.  MVDR-based coherence weighting for high-frame-rate adaptive imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[27]  J. Arendt.  Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging: Field: A Program for Simulating Ultrasound Systems , 1996 .

[28]  Kyuhong Kim,et al.  Flexible Minimum Variance weights estimation using principal component analysis , 2012, 2012 IEEE International Ultrasonics Symposium.

[29]  Francois Vignon,et al.  Capon beamforming in medical ultrasound imaging with focused beams , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[30]  Marc Moonen.  Systolic MVDR beamforming with inverse updating , 1993 .

[31]  A. Austeng,et al.  An approach to multibeam covariance matrices for adaptive beamforming in ultrasonography , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[32]  Alan D. George,et al.  PARALLEL ALGORITHMS FOR ROBUST BROADBAND MVDR BEAMFORMING , 2002 .

[33]  H. Mewes,et al.  A novel method to improve the performance of Capon's minimum variance estimator , 1997 .

[34]  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.