Optimum beamforming for sidelobe reduction in ultrasound imaging

A constrained adaptive beamforming in a deterministic sense is considered for side lobe reduction, leading to an adaptive weighting of the uniform delay-and-sum beamformer; based upon this, the coherence factor and other similar methods are interpreted as beamforming methods. A generalized form of the weighting factor for the side lobe reduction is also established. It is shown through simulations that restricting the apodization vector to a parametric representation through a discrete Fourier transform or discrete cosine transform can result in higher quality images with fewer artifacts and enhanced contrast properties compared with images obtained through the coherence factor-like methods.

[1]  E.S. Ebbini,et al.  Blocked element compensation in phased array imaging , 1993, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  A Mahloojifar,et al.  Optimization of point spread function in ultrasound arrays. , 2006, Ultrasonics.

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

[4]  Sverre Holm,et al.  Wiener beamforming and the coherence factor in ultrasound imaging , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[5]  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.

[6]  M. O'Donnell,et al.  Phase-aberration correction using signals from point reflectors and diffuse scatterers: measurements , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[7]  Pai-Chi Li,et al.  Adaptive imaging using the generalized coherence factor. , 2003, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[8]  M. O'Donnell,et al.  Coherence factor of speckle from a multi-row probe , 1999, 1999 IEEE Ultrasonics Symposium. Proceedings. International Symposium (Cat. No.99CH37027).

[9]  G. Cincotti,et al.  Optimization of wide-band linear arrays , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[10]  L. Griffiths,et al.  An adaptive generalized sidelobe canceller with derivative constraints , 1986 .

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

[12]  Stuart R. DeGraaf,et al.  Sidelobe reduction via adaptive FIR filtering in SAR imagery , 1994, IEEE Trans. Image Process..

[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]  Adaptive sidelobe reduction applied to ultrasound imaging , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[15]  M. O'Donnell,et al.  Improved estimation of phase aberration profiles , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[16]  Mok-Kun Jeong A Fourier transform-based sidelobe reduction method in ultrasound imaging , 2000 .

[17]  Stuart R. DeGraaf,et al.  SAR imaging via modern 2-D spectral estimation methods , 1998, IEEE Trans. Image Process..

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