Elastic-net based beamforming in medical ultrasound imaging

This paper presents a new way of addressing beamforming in ultrasound imaging, by formulating it, for each image depth, as an inverse problem solved using elastic-net regularization. This approach was evaluated on both simulated and in vivo data showing a gain in contrast, while maintaining an increased value of the signal-to-noise ratio compared to two standard ultrasound beamforming methods.

[1]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

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

[3]  Xin Yang,et al.  Spatio-temporally smoothed coherence factor for ultrasound imaging [Correspondence] , 2014, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[4]  Lorenzo Rosasco,et al.  Elastic-net regularization in learning theory , 2008, J. Complex..

[5]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[6]  Lorenzo Rosasco,et al.  Solving Structured Sparsity Regularization with Proximal Methods , 2010, ECML/PKDD.

[7]  Michael D. Zoltowski,et al.  Beamspace DOA estimation featuring multirate eigenvector processing , 1996, IEEE Trans. Signal Process..

[8]  Jean-Jacques Fuchs.  Linear programming in spectral estimation. Application to array processing , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

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

[10]  H.L. Van Trees,et al.  Beamspace MODE , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

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

[12]  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.

[13]  A. Mahloojifar,et al.  Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[14]  Adrian Basarab,et al.  Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging , 2015, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

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