Abstract

Delay-and-sum (DAS) is the most common algorithm used in photoacoustic (PA) image formation. However, this algorithm results in a reconstructed image with a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an adaptive beamformer, overcomes these limitations and improves the image resolution and contrast. In this paper, a novel algorithm, named Modified-Sparse-MV (MS-MV), is proposed in which a 1-norm constraint is added to the MV minimization problem after some modifications, in order to suppress the sidelobes more efficiently, compared to MV. The added constraint can be interpreted as the sparsity of the output of the MV beamformed signals. Since the final minimization problem is convex, it can be solved efficiently using a simple iterative algorithm. The numerical results show that the proposed method, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, in average, compared to MV. Also, the experimental results, using a wire-target phantom, show that MS-MV leads to SNR improvement of about 2.64 dB in comparison with the MV.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Adaptive beamforming for photoacoustic imaging

Suhyun Park, Andrei B. Karpiouk, Salavat R. Aglyamov, and Stanislav Y. Emelianov
Opt. Lett. 33(12) 1291-1293 (2008)

Enhancement of photoacoustic image quality by sound speed correction: ex vivo evaluation

Changhan Yoon, Jeeun Kang, Seunghee Han, Yangmo Yoo, Tai-Kyong Song, and Jin Ho Chang
Opt. Express 20(3) 3082-3090 (2012)

Joint sparse and low rank recovery algorithm for compressive hyperspectral imaging

Tatiana Gelvez, Hoover Rueda, and Henry Arguello
Appl. Opt. 56(24) 6785-6795 (2017)

References

  • View by:
  • |
  • |
  • |

  1. B. Wang, L. Xiang, M. S. Jiang, J. Yang, Q. Zhang, P. R. Carney, and H. Jiang, “Photoacoustic tomography system for noninvasive real-time three-dimensional imaging of epilepsy,” Biomed. Opt. Express 3, 1427–1432 (2012).
    [Crossref] [PubMed]
  2. M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
    [Crossref] [PubMed]
  3. J. Yao and L. V. Wang, “Sensitivity of photoacoustic microscopy,” Photoacoustics 2, 87–101 (2014).
    [Crossref] [PubMed]
  4. S. Y. Nam and S. Y. Emelianov, “Array-based real-time ultrasound and photoacoustic ocular imaging,” J. Opt. Soc. Korea 18, 151–155 (2014).
    [Crossref]
  5. M. Heijblom, W. Steenbergen, and S. Manohar, “Clinical photoacoustic breast imaging: the twente experience,” IEEE Pulse 6, 42–46 (2015).
    [Crossref] [PubMed]
  6. J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61, 1380–1389 (2014).
    [Crossref]
  7. L. V. Wang, “Multiscale photoacoustic microscopy and computed tomography,” Nat. Photon. 3, 503–509 (2009).
    [Crossref]
  8. M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
    [Crossref]
  9. M. Xu, Y. Xu, and L. V. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Trans. Biomed. Eng. 50, 1086–1099 (2003).
    [Crossref] [PubMed]
  10. C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
    [Crossref]
  11. G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
    [Crossref]
  12. M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
    [Crossref]
  13. M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Image enhancement and noise reduction using modified delay-multiply-and-sum beamformer: Application to medical photoacoustic imaging,” in “Iranian Conference on Electrical Engineering (ICEE), 2017,” (IEEE, 2017), pp. 65–69.
  14. J. Capon, “High-resolution frequency-wavenumber spectrum analysis,” Proc. IEEE 57, 1408–1418 (1969).
    [Crossref]
  15. J.-F. Synnevag, A. Austeng, and S. Holm, “Benefits of minimum-variance beamforming in medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 1263 (2009).
    [Crossref]
  16. S.-L. Wang and P.-C. Li, “MVDR-based coherence weighting for high-frame-rate adaptive imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 10943182 (2009).
  17. S. Park, A. B. Karpiouk, S. R. Aglyamov, and S. Y. Emelianov, “Adaptive beamforming for photoacoustic imaging using linear array transducer,” in “Ultrasonics Symposium, 2008. IUS 2008. IEEE,” (IEEE, 2008), pp. 1088–1091.
  18. M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
    [Crossref]
  19. M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Medical photoacoustic beamforming using minimum variance-based delay multiply and sum,” in “Digital Optical Technologies 2017,”, vol. 10335 (International Society for Optics and Photonics, 2017), vol. 10335, p. 1033522.
  20. M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
    [Crossref]
  21. J.-F. Synnevåg, C.-I. Nilsen, and S. Holm, “P2b-13 speckle statistics in adaptive beamforming,” in “Ultrasonics Symposium, 2007. IEEE,” (IEEE, 2007), pp. 1545–1548.
  22. B. M. Asl and A. Mahloojifar, “Contrast enhancement of adaptive ultrasound imaging using eigenspace-based minimum variance beamfoming,” in “Ultrasonics Symposium (IUS), 2009 IEEE International,” (IEEE, 2009), pp. 349–352.
  23. S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
    [Crossref]
  24. M. Mozaffarzadeh, A. Mahloojifar, M. Nasiriavanaki, and M. Orooji, “Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study,” in “Photonics in Dermatology and Plastic Surgery 2018,”, vol. 10467 (International Society for Optics and Photonics, 2018), vol. 10467, p. 1046717.
  25. R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
    [Crossref]
  26. Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics. 52, 1046–1055 (2012).
    [Crossref] [PubMed]
  27. R. Lavarello, F. Kamalabadi, and W. D. O’Brien, “A regularized inverse approach to ultrasonic pulse-echo imaging,” IEEE Trans. Med. Imag. 25, 712–722 (2006).
    [Crossref]
  28. E. Ozkan, V. Vishnevsky, and O. Goksel, “Inverse problem of ultrasound beamforming with sparsity constraints and regularization,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 65, 356–365 (2018).
    [Crossref]
  29. Y. Zhang, Y. Wang, and C. Zhang, “Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction,” J. Biomed. Opt. 18, 066008 (2013).
    [Crossref]
  30. A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
    [Crossref]
  31. X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
    [Crossref]
  32. P. Tsakalides and C. L. Nikias, “Robust adaptive beamforming in alpha-stable noise environments,” 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (IEEE, 1996), vol. 5, pp. 2884–2887.
  33. X. Jiang, A. Yasotharan, and T. Kirubarajan, “Robust beamforming with sidelobe suppression for impulsive signals,” IEEE Signal Process. Lett. 22, 346–350 (2015).
    [Crossref]
  34. Y. Liu and Q. Wan, “Sidelobe suppression for robust capon beamforming with mainlobe-to-sidelobe power ratio maximization,” IEEE Antennas Wirel. Propag. Lett. 11, 1218–1221 (2012).
    [Crossref]
  35. Y. Liu, “Robust capon beamforming via shaping beam pattern,” arXiv preprint arXiv:1302.6173 (2013).
  36. Y. Zhang, B. Ng, and Q. Wan, “Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern,” Electron. Lett. 44, 615–616 (2008).
    [Crossref]
  37. R. A. Monzingo and T. W. Miller, Introduction to adaptive arrays (Scitech publishing, 1980).
  38. J. F. Synnevag, A. Austeng, and S. Holm, “Adaptive beamforming applied to medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 54, 431 (2007).
    [Crossref]
  39. J. F. Sturm, “Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones,” Optim. Methods Softw. 11, 625–653 (1999).
    [Crossref]
  40. B. E. Treeby and B. T. Cox, “k-wave: Matlab toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15, 021314 (2010).
    [Crossref] [PubMed]

2018 (4)

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

E. Ozkan, V. Vishnevsky, and O. Goksel, “Inverse problem of ultrasound beamforming with sparsity constraints and regularization,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 65, 356–365 (2018).
[Crossref]

2015 (4)

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
[Crossref]

M. Heijblom, W. Steenbergen, and S. Manohar, “Clinical photoacoustic breast imaging: the twente experience,” IEEE Pulse 6, 42–46 (2015).
[Crossref] [PubMed]

X. Jiang, A. Yasotharan, and T. Kirubarajan, “Robust beamforming with sidelobe suppression for impulsive signals,” IEEE Signal Process. Lett. 22, 346–350 (2015).
[Crossref]

2014 (5)

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

J. Yao and L. V. Wang, “Sensitivity of photoacoustic microscopy,” Photoacoustics 2, 87–101 (2014).
[Crossref] [PubMed]

S. Y. Nam and S. Y. Emelianov, “Array-based real-time ultrasound and photoacoustic ocular imaging,” J. Opt. Soc. Korea 18, 151–155 (2014).
[Crossref]

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61, 1380–1389 (2014).
[Crossref]

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

2013 (2)

Y. Zhang, Y. Wang, and C. Zhang, “Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction,” J. Biomed. Opt. 18, 066008 (2013).
[Crossref]

M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
[Crossref] [PubMed]

2012 (6)

B. Wang, L. Xiang, M. S. Jiang, J. Yang, Q. Zhang, P. R. Carney, and H. Jiang, “Photoacoustic tomography system for noninvasive real-time three-dimensional imaging of epilepsy,” Biomed. Opt. Express 3, 1427–1432 (2012).
[Crossref] [PubMed]

Y. Liu and Q. Wan, “Sidelobe suppression for robust capon beamforming with mainlobe-to-sidelobe power ratio maximization,” IEEE Antennas Wirel. Propag. Lett. 11, 1218–1221 (2012).
[Crossref]

A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
[Crossref]

S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
[Crossref]

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics. 52, 1046–1055 (2012).
[Crossref] [PubMed]

2010 (1)

B. E. Treeby and B. T. Cox, “k-wave: Matlab toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15, 021314 (2010).
[Crossref] [PubMed]

2009 (3)

J.-F. Synnevag, A. Austeng, and S. Holm, “Benefits of minimum-variance beamforming in medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 1263 (2009).
[Crossref]

S.-L. Wang and P.-C. Li, “MVDR-based coherence weighting for high-frame-rate adaptive imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 10943182 (2009).

L. V. Wang, “Multiscale photoacoustic microscopy and computed tomography,” Nat. Photon. 3, 503–509 (2009).
[Crossref]

2008 (1)

Y. Zhang, B. Ng, and Q. Wan, “Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern,” Electron. Lett. 44, 615–616 (2008).
[Crossref]

2007 (1)

J. F. Synnevag, A. Austeng, and S. Holm, “Adaptive beamforming applied to medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 54, 431 (2007).
[Crossref]

2006 (1)

R. Lavarello, F. Kamalabadi, and W. D. O’Brien, “A regularized inverse approach to ultrasonic pulse-echo imaging,” IEEE Trans. Med. Imag. 25, 712–722 (2006).
[Crossref]

2003 (1)

M. Xu, Y. Xu, and L. V. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Trans. Biomed. Eng. 50, 1086–1099 (2003).
[Crossref] [PubMed]

1999 (1)

J. F. Sturm, “Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones,” Optim. Methods Softw. 11, 625–653 (1999).
[Crossref]

1969 (1)

J. Capon, “High-resolution frequency-wavenumber spectrum analysis,” Proc. IEEE 57, 1408–1418 (1969).
[Crossref]

Adabi, S.

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

Aglyamov, S. R.

S. Park, A. B. Karpiouk, S. R. Aglyamov, and S. Y. Emelianov, “Adaptive beamforming for photoacoustic imaging using linear array transducer,” in “Ultrasonics Symposium, 2008. IUS 2008. IEEE,” (IEEE, 2008), pp. 1088–1091.

Arnal, B.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

Asl, B. M.

B. M. Asl and A. Mahloojifar, “Contrast enhancement of adaptive ultrasound imaging using eigenspace-based minimum variance beamfoming,” in “Ultrasonics Symposium (IUS), 2009 IEEE International,” (IEEE, 2009), pp. 349–352.

Austeng, A.

S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
[Crossref]

J.-F. Synnevag, A. Austeng, and S. Holm, “Benefits of minimum-variance beamforming in medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 1263 (2009).
[Crossref]

J. F. Synnevag, A. Austeng, and S. Holm, “Adaptive beamforming applied to medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 54, 431 (2007).
[Crossref]

Bauer, A. Q.

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

Caliano, G.

G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
[Crossref]

Capon, J.

J. Capon, “High-resolution frequency-wavenumber spectrum analysis,” Proc. IEEE 57, 1408–1418 (1969).
[Crossref]

Carney, P. R.

Cox, B. T.

B. E. Treeby and B. T. Cox, “k-wave: Matlab toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15, 021314 (2010).
[Crossref] [PubMed]

Culver, J. P.

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

Dapp, R.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Emelianov, S. Y

M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
[Crossref] [PubMed]

Emelianov, S. Y.

S. Y. Nam and S. Y. Emelianov, “Array-based real-time ultrasound and photoacoustic ocular imaging,” J. Opt. Soc. Korea 18, 151–155 (2014).
[Crossref]

S. Park, A. B. Karpiouk, S. R. Aglyamov, and S. Y. Emelianov, “Adaptive beamforming for photoacoustic imaging using linear array transducer,” in “Ultrasonics Symposium, 2008. IUS 2008. IEEE,” (IEEE, 2008), pp. 1088–1091.

Fousek, J.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Goksel, O.

E. Ozkan, V. Vishnevsky, and O. Goksel, “Inverse problem of ultrasound beamforming with sparsity constraints and regularization,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 65, 356–365 (2018).
[Crossref]

Heijblom, M.

M. Heijblom, W. Steenbergen, and S. Manohar, “Clinical photoacoustic breast imaging: the twente experience,” IEEE Pulse 6, 42–46 (2015).
[Crossref] [PubMed]

Holm, S.

S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
[Crossref]

J.-F. Synnevag, A. Austeng, and S. Holm, “Benefits of minimum-variance beamforming in medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 1263 (2009).
[Crossref]

J. F. Synnevag, A. Austeng, and S. Holm, “Adaptive beamforming applied to medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 54, 431 (2007).
[Crossref]

J.-F. Synnevåg, C.-I. Nilsen, and S. Holm, “P2b-13 speckle statistics in adaptive beamforming,” in “Ultrasonics Symposium, 2007. IEEE,” (IEEE, 2007), pp. 1545–1548.

Jan, J.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Jetzfellner, T.

A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
[Crossref]

Jiang, H.

Jiang, M. S.

Jiang, X.

X. Jiang, A. Yasotharan, and T. Kirubarajan, “Robust beamforming with sidelobe suppression for impulsive signals,” IEEE Signal Process. Lett. 22, 346–350 (2015).
[Crossref]

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

Jirik, R.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Johansen, T. F.

S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
[Crossref]

Joon Yoon, S.

M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
[Crossref] [PubMed]

Kamalabadi, F.

R. Lavarello, F. Kamalabadi, and W. D. O’Brien, “A regularized inverse approach to ultrasonic pulse-echo imaging,” IEEE Trans. Med. Imag. 25, 712–722 (2006).
[Crossref]

Karpiouk, A. B.

S. Park, A. B. Karpiouk, S. R. Aglyamov, and S. Y. Emelianov, “Adaptive beamforming for photoacoustic imaging using linear array transducer,” in “Ultrasonics Symposium, 2008. IUS 2008. IEEE,” (IEEE, 2008), pp. 1088–1091.

Kirubarajan, T.

X. Jiang, A. Yasotharan, and T. Kirubarajan, “Robust beamforming with sidelobe suppression for impulsive signals,” IEEE Signal Process. Lett. 22, 346–350 (2015).
[Crossref]

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

Kratkiewicz, K.

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

Lavarello, R.

R. Lavarello, F. Kamalabadi, and W. D. O’Brien, “A regularized inverse approach to ultrasonic pulse-echo imaging,” IEEE Trans. Med. Imag. 25, 712–722 (2006).
[Crossref]

Li, P.-C.

S.-L. Wang and P.-C. Li, “MVDR-based coherence weighting for high-frame-rate adaptive imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 10943182 (2009).

Liu, Y.

Y. Liu and Q. Wan, “Sidelobe suppression for robust capon beamforming with mainlobe-to-sidelobe power ratio maximization,” IEEE Antennas Wirel. Propag. Lett. 11, 1218–1221 (2012).
[Crossref]

Y. Liu, “Robust capon beamforming via shaping beam pattern,” arXiv preprint arXiv:1302.6173 (2013).

Magenes, G.

G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
[Crossref]

Mahloojifar, A.

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Medical photoacoustic beamforming using minimum variance-based delay multiply and sum,” in “Digital Optical Technologies 2017,”, vol. 10335 (International Society for Optics and Photonics, 2017), vol. 10335, p. 1033522.

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Image enhancement and noise reduction using modified delay-multiply-and-sum beamformer: Application to medical photoacoustic imaging,” in “Iranian Conference on Electrical Engineering (ICEE), 2017,” (IEEE, 2017), pp. 65–69.

B. M. Asl and A. Mahloojifar, “Contrast enhancement of adaptive ultrasound imaging using eigenspace-based minimum variance beamfoming,” in “Ultrasonics Symposium (IUS), 2009 IEEE International,” (IEEE, 2009), pp. 349–352.

M. Mozaffarzadeh, A. Mahloojifar, M. Nasiriavanaki, and M. Orooji, “Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study,” in “Photonics in Dermatology and Plastic Surgery 2018,”, vol. 10467 (International Society for Optics and Photonics, 2018), vol. 10467, p. 1046717.

Makkiabadi, B.

M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
[Crossref]

Manohar, S.

M. Heijblom, W. Steenbergen, and S. Manohar, “Clinical photoacoustic breast imaging: the twente experience,” IEEE Pulse 6, 42–46 (2015).
[Crossref] [PubMed]

Matrone, G.

G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
[Crossref]

Mehdizadeh, S.

S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
[Crossref]

Mehrmohammadi, M.

M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
[Crossref]

M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
[Crossref] [PubMed]

Miller, T. W.

R. A. Monzingo and T. W. Miller, Introduction to adaptive arrays (Scitech publishing, 1980).

Monzingo, R. A.

R. A. Monzingo and T. W. Miller, Introduction to adaptive arrays (Scitech publishing, 1980).

Mozaffarzadeh, M.

M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Image enhancement and noise reduction using modified delay-multiply-and-sum beamformer: Application to medical photoacoustic imaging,” in “Iranian Conference on Electrical Engineering (ICEE), 2017,” (IEEE, 2017), pp. 65–69.

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Medical photoacoustic beamforming using minimum variance-based delay multiply and sum,” in “Digital Optical Technologies 2017,”, vol. 10335 (International Society for Optics and Photonics, 2017), vol. 10335, p. 1033522.

M. Mozaffarzadeh, A. Mahloojifar, M. Nasiriavanaki, and M. Orooji, “Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study,” in “Photonics in Dermatology and Plastic Surgery 2018,”, vol. 10467 (International Society for Optics and Photonics, 2018), vol. 10467, p. 1046717.

Nam, S. Y.

Nasiriavanaki, M.

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Nasiriavanaki, and M. Orooji, “Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study,” in “Photonics in Dermatology and Plastic Surgery 2018,”, vol. 10467 (International Society for Optics and Photonics, 2018), vol. 10467, p. 1046717.

Ng, B.

Y. Zhang, B. Ng, and Q. Wan, “Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern,” Electron. Lett. 44, 615–616 (2008).
[Crossref]

Nguyen, T.-M.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

Nikias, C. L.

P. Tsakalides and C. L. Nikias, “Robust adaptive beamforming in alpha-stable noise environments,” 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (IEEE, 1996), vol. 5, pp. 2884–2887.

Nilsen, C.-I.

J.-F. Synnevåg, C.-I. Nilsen, and S. Holm, “P2b-13 speckle statistics in adaptive beamforming,” in “Ultrasonics Symposium, 2007. IEEE,” (IEEE, 2007), pp. 1545–1548.

Ntziachristos, V.

A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
[Crossref]

O’Brien, W. D.

R. Lavarello, F. Kamalabadi, and W. D. O’Brien, “A regularized inverse approach to ultrasonic pulse-echo imaging,” IEEE Trans. Med. Imag. 25, 712–722 (2006).
[Crossref]

O’Donnell, M.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

Orooji, M.

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Medical photoacoustic beamforming using minimum variance-based delay multiply and sum,” in “Digital Optical Technologies 2017,”, vol. 10335 (International Society for Optics and Photonics, 2017), vol. 10335, p. 1033522.

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Image enhancement and noise reduction using modified delay-multiply-and-sum beamformer: Application to medical photoacoustic imaging,” in “Iranian Conference on Electrical Engineering (ICEE), 2017,” (IEEE, 2017), pp. 65–69.

M. Mozaffarzadeh, A. Mahloojifar, M. Nasiriavanaki, and M. Orooji, “Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study,” in “Photonics in Dermatology and Plastic Surgery 2018,”, vol. 10467 (International Society for Optics and Photonics, 2018), vol. 10467, p. 1046717.

Ozkan, E.

E. Ozkan, V. Vishnevsky, and O. Goksel, “Inverse problem of ultrasound beamforming with sparsity constraints and regularization,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 65, 356–365 (2018).
[Crossref]

Park, S.

S. Park, A. B. Karpiouk, S. R. Aglyamov, and S. Y. Emelianov, “Adaptive beamforming for photoacoustic imaging using linear array transducer,” in “Ultrasonics Symposium, 2008. IUS 2008. IEEE,” (IEEE, 2008), pp. 1088–1091.

Pelivanov, I. M.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

Peterlik, I.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Razansky, D.

A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
[Crossref]

Rosenthal, A.

A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
[Crossref]

Ruiter, N.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Savoia, A. S.

G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
[Crossref]

So, H.-C.

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

Steenbergen, W.

M. Heijblom, W. Steenbergen, and S. Manohar, “Clinical photoacoustic breast imaging: the twente experience,” IEEE Pulse 6, 42–46 (2015).
[Crossref] [PubMed]

Sturm, J. F.

J. F. Sturm, “Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones,” Optim. Methods Softw. 11, 625–653 (1999).
[Crossref]

Synnevag, J. F.

J. F. Synnevag, A. Austeng, and S. Holm, “Adaptive beamforming applied to medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 54, 431 (2007).
[Crossref]

Synnevag, J.-F.

J.-F. Synnevag, A. Austeng, and S. Holm, “Benefits of minimum-variance beamforming in medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 1263 (2009).
[Crossref]

Synnevåg, J.-F.

J.-F. Synnevåg, C.-I. Nilsen, and S. Holm, “P2b-13 speckle statistics in adaptive beamforming,” in “Ultrasonics Symposium, 2007. IEEE,” (IEEE, 2007), pp. 1545–1548.

Treeby, B. E.

B. E. Treeby and B. T. Cox, “k-wave: Matlab toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15, 021314 (2010).
[Crossref] [PubMed]

Tsakalides, P.

P. Tsakalides and C. L. Nikias, “Robust adaptive beamforming in alpha-stable noise environments,” 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (IEEE, 1996), vol. 5, pp. 2884–2887.

Vishnevsky, V.

E. Ozkan, V. Vishnevsky, and O. Goksel, “Inverse problem of ultrasound beamforming with sparsity constraints and regularization,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 65, 356–365 (2018).
[Crossref]

Wan, H.

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

Wan, Q.

Y. Liu and Q. Wan, “Sidelobe suppression for robust capon beamforming with mainlobe-to-sidelobe power ratio maximization,” IEEE Antennas Wirel. Propag. Lett. 11, 1218–1221 (2012).
[Crossref]

Y. Zhang, B. Ng, and Q. Wan, “Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern,” Electron. Lett. 44, 615–616 (2008).
[Crossref]

Wang, B.

Wang, L. V.

J. Yao and L. V. Wang, “Sensitivity of photoacoustic microscopy,” Photoacoustics 2, 87–101 (2014).
[Crossref] [PubMed]

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61, 1380–1389 (2014).
[Crossref]

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

L. V. Wang, “Multiscale photoacoustic microscopy and computed tomography,” Nat. Photon. 3, 503–509 (2009).
[Crossref]

M. Xu, Y. Xu, and L. V. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Trans. Biomed. Eng. 50, 1086–1099 (2003).
[Crossref] [PubMed]

Wang, S.-L.

S.-L. Wang and P.-C. Li, “MVDR-based coherence weighting for high-frame-rate adaptive imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 10943182 (2009).

Wang, Y.

Y. Zhang, Y. Wang, and C. Zhang, “Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction,” J. Biomed. Opt. 18, 066008 (2013).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics. 52, 1046–1055 (2012).
[Crossref] [PubMed]

Wei, C.-W.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

Wong, E. Y.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

Xia, J.

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61, 1380–1389 (2014).
[Crossref]

Xiang, L.

Xu, M.

M. Xu, Y. Xu, and L. V. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Trans. Biomed. Eng. 50, 1086–1099 (2003).
[Crossref] [PubMed]

Xu, Y.

M. Xu, Y. Xu, and L. V. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Trans. Biomed. Eng. 50, 1086–1099 (2003).
[Crossref] [PubMed]

Yan, Y.

M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
[Crossref]

Yang, J.

Yao, J.

J. Yao and L. V. Wang, “Sensitivity of photoacoustic microscopy,” Photoacoustics 2, 87–101 (2014).
[Crossref] [PubMed]

Yasotharan, A.

X. Jiang, A. Yasotharan, and T. Kirubarajan, “Robust beamforming with sidelobe suppression for impulsive signals,” IEEE Signal Process. Lett. 22, 346–350 (2015).
[Crossref]

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

Yeager, D.

M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
[Crossref] [PubMed]

Zapf, M.

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

Zeng, W.-J.

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

Zhang, C.

Y. Zhang, Y. Wang, and C. Zhang, “Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction,” J. Biomed. Opt. 18, 066008 (2013).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics. 52, 1046–1055 (2012).
[Crossref] [PubMed]

Zhang, Q.

Zhang, Y.

Y. Zhang, Y. Wang, and C. Zhang, “Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction,” J. Biomed. Opt. 18, 066008 (2013).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics. 52, 1046–1055 (2012).
[Crossref] [PubMed]

Y. Zhang, B. Ng, and Q. Wan, “Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern,” Electron. Lett. 44, 615–616 (2008).
[Crossref]

Biomed. Opt. Express (1)

Curr. Mol. Imaging (1)

M. Mehrmohammadi, S. Joon Yoon, D. Yeager, and S. Y Emelianov, “Photoacoustic imaging for cancer detection and staging,” Curr. Mol. Imaging 2, 89–105 (2013).
[Crossref] [PubMed]

Electron. Lett. (1)

Y. Zhang, B. Ng, and Q. Wan, “Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern,” Electron. Lett. 44, 615–616 (2008).
[Crossref]

IEEE Antennas Wirel. Propag. Lett. (1)

Y. Liu and Q. Wan, “Sidelobe suppression for robust capon beamforming with mainlobe-to-sidelobe power ratio maximization,” IEEE Antennas Wirel. Propag. Lett. 11, 1218–1221 (2012).
[Crossref]

IEEE Pulse (1)

M. Heijblom, W. Steenbergen, and S. Manohar, “Clinical photoacoustic breast imaging: the twente experience,” IEEE Pulse 6, 42–46 (2015).
[Crossref] [PubMed]

IEEE Signal Process. Lett. (1)

X. Jiang, A. Yasotharan, and T. Kirubarajan, “Robust beamforming with sidelobe suppression for impulsive signals,” IEEE Signal Process. Lett. 22, 346–350 (2015).
[Crossref]

IEEE Trans. Biomed. Eng. (3)

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61, 1380–1389 (2014).
[Crossref]

M. Xu, Y. Xu, and L. V. Wang, “Time-domain reconstruction algorithms and numerical simulations for thermoacoustic tomography in various geometries,” IEEE Trans. Biomed. Eng. 50, 1086–1099 (2003).
[Crossref] [PubMed]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, S. Adabi, and M. Nasiriavanaki, “Double-stage delay multiply and sum beamforming algorithm: Application to linear-array photoacoustic imaging,” IEEE Trans. Biomed. Eng. 65, 31–42 (2018).
[Crossref]

IEEE Trans. Med. Imag. (4)

G. Matrone, A. S. Savoia, G. Caliano, and G. Magenes, “The delay multiply and sum beamforming algorithm in ultrasound b-mode medical imaging,” IEEE Trans. Med. Imag. 34, 940–949 (2015).
[Crossref]

A. Rosenthal, T. Jetzfellner, D. Razansky, and V. Ntziachristos, “Efficient framework for model-based tomographic image reconstruction using wavelet packets,” IEEE Trans. Med. Imag. 31, 1346–1357 (2012).
[Crossref]

S. Mehdizadeh, A. Austeng, T. F. Johansen, and S. Holm, “Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues,” IEEE Trans. Med. Imag. 31, 1912–1921 (2012).
[Crossref]

R. Lavarello, F. Kamalabadi, and W. D. O’Brien, “A regularized inverse approach to ultrasonic pulse-echo imaging,” IEEE Trans. Med. Imag. 25, 712–722 (2006).
[Crossref]

IEEE Trans. Signal Process. (1)

X. Jiang, W.-J. Zeng, A. Yasotharan, H.-C. So, and T. Kirubarajan, “Minimum dispersion beamforming for non-gaussian signals,” IEEE Trans. Signal Process. 62, 1879–1893 (2014).
[Crossref]

IEEE Trans. Ultrason., Ferroelectr., Freq. Control. (6)

E. Ozkan, V. Vishnevsky, and O. Goksel, “Inverse problem of ultrasound beamforming with sparsity constraints and regularization,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 65, 356–365 (2018).
[Crossref]

R. Jirik, I. Peterlik, N. Ruiter, J. Fousek, R. Dapp, M. Zapf, and J. Jan, “Sound-speed image reconstruction in sparse-aperture 3-d ultrasound transmission tomography,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 59, 2185 (2012).
[Crossref]

J.-F. Synnevag, A. Austeng, and S. Holm, “Benefits of minimum-variance beamforming in medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 1263 (2009).
[Crossref]

S.-L. Wang and P.-C. Li, “MVDR-based coherence weighting for high-frame-rate adaptive imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 56, 10943182 (2009).

C.-W. Wei, T.-M. Nguyen, J. Xia, B. Arnal, E. Y. Wong, I. M. Pelivanov, and M. O’Donnell, “Real-time integrated photoacoustic and ultrasound (paus) imaging system to guide interventional procedures: ex vivo study,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 62, 319–328 (2015).
[Crossref]

J. F. Synnevag, A. Austeng, and S. Holm, “Adaptive beamforming applied to medical ultrasound imaging,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control. 54, 431 (2007).
[Crossref]

J. Biomed. Opt. (4)

B. E. Treeby and B. T. Cox, “k-wave: Matlab toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15, 021314 (2010).
[Crossref] [PubMed]

M. Mozaffarzadeh, Y. Yan, M. Mehrmohammadi, and B. Makkiabadi, “Enhanced linear-array photoacoustic beamforming using modified coherence factor,” J. Biomed. Opt. 23, 026005 (2018).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction,” J. Biomed. Opt. 18, 066008 (2013).
[Crossref]

M. Mozaffarzadeh, A. Mahloojifar, M. Orooji, K. Kratkiewicz, S. Adabi, and M. Nasiriavanaki, “Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm,” J. Biomed. Opt. 23, 026002 (2018).
[Crossref]

J. Opt. Soc. Korea (1)

Nat. Photon. (1)

L. V. Wang, “Multiscale photoacoustic microscopy and computed tomography,” Nat. Photon. 3, 503–509 (2009).
[Crossref]

Optim. Methods Softw. (1)

J. F. Sturm, “Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones,” Optim. Methods Softw. 11, 625–653 (1999).
[Crossref]

Photoacoustics (1)

J. Yao and L. V. Wang, “Sensitivity of photoacoustic microscopy,” Photoacoustics 2, 87–101 (2014).
[Crossref] [PubMed]

Proc. IEEE (1)

J. Capon, “High-resolution frequency-wavenumber spectrum analysis,” Proc. IEEE 57, 1408–1418 (1969).
[Crossref]

Proc. Natl. Acad. Sci. (1)

M. Nasiriavanaki, J. Xia, H. Wan, A. Q. Bauer, J. P. Culver, and L. V. Wang, “High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain,” Proc. Natl. Acad. Sci. 111, 21–26 (2014).
[Crossref]

Ultrasonics. (1)

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics. 52, 1046–1055 (2012).
[Crossref] [PubMed]

Other (9)

J.-F. Synnevåg, C.-I. Nilsen, and S. Holm, “P2b-13 speckle statistics in adaptive beamforming,” in “Ultrasonics Symposium, 2007. IEEE,” (IEEE, 2007), pp. 1545–1548.

B. M. Asl and A. Mahloojifar, “Contrast enhancement of adaptive ultrasound imaging using eigenspace-based minimum variance beamfoming,” in “Ultrasonics Symposium (IUS), 2009 IEEE International,” (IEEE, 2009), pp. 349–352.

M. Mozaffarzadeh, A. Mahloojifar, M. Nasiriavanaki, and M. Orooji, “Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study,” in “Photonics in Dermatology and Plastic Surgery 2018,”, vol. 10467 (International Society for Optics and Photonics, 2018), vol. 10467, p. 1046717.

P. Tsakalides and C. L. Nikias, “Robust adaptive beamforming in alpha-stable noise environments,” 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (IEEE, 1996), vol. 5, pp. 2884–2887.

Y. Liu, “Robust capon beamforming via shaping beam pattern,” arXiv preprint arXiv:1302.6173 (2013).

R. A. Monzingo and T. W. Miller, Introduction to adaptive arrays (Scitech publishing, 1980).

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Image enhancement and noise reduction using modified delay-multiply-and-sum beamformer: Application to medical photoacoustic imaging,” in “Iranian Conference on Electrical Engineering (ICEE), 2017,” (IEEE, 2017), pp. 65–69.

M. Mozaffarzadeh, A. Mahloojifar, and M. Orooji, “Medical photoacoustic beamforming using minimum variance-based delay multiply and sum,” in “Digital Optical Technologies 2017,”, vol. 10335 (International Society for Optics and Photonics, 2017), vol. 10335, p. 1033522.

S. Park, A. B. Karpiouk, S. R. Aglyamov, and S. Y. Emelianov, “Adaptive beamforming for photoacoustic imaging using linear array transducer,” in “Ultrasonics Symposium, 2008. IUS 2008. IEEE,” (IEEE, 2008), pp. 1088–1091.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (14)

Fig. 1
Fig. 1 The lateral variations of a single point target using MS-MV with different values of β.
Fig. 2
Fig. 2 The numerical reconstructed PA images of 10 point targets using (a) DAS, (b) MV, (c) DS-MV. Noise is added to the received signals having a SNR of 50 dB.
Fig. 3
Fig. 3 The lateral variations of the images shown in Fig. 2 at the depth of (a) 30 mm, (b) 40 mm, (c) 55 mm and (d) 65 mm. Noise is added to the received signals having a SNR of 50 dB.
Fig. 4
Fig. 4 The reconstructed PA images of 10 point targets using (a) DAS, (b) MV, (c) DS-MV. Noise is added to the received signals having a SNR of 10 dB.
Fig. 5
Fig. 5 The lateral variations of the images shown in Fig. 4 at the depth of (a) 40 mm and (b) 50 mm . Noise is added to the received signals having a SNR of 10 dB.
Fig. 6
Fig. 6 The schematic of the setup used for the experimental PAI.
Fig. 7
Fig. 7 Reconstructed experimental PA images using (a) DAS, (b) MV and (c) MS-MV (β = 1). All the images are shown with a dynamic range of 50 dB. A wire target was used as the imaging target.
Fig. 8
Fig. 8 Reconstructed experimental PA images using (a) DAS, (b) MV and (c) MS-MV (β = 1). All the images are shown with a dynamic range of 50 dB. Two wires were used as the imaging target.
Fig. 9
Fig. 9 The lateral variations for the reconstructed experimental PA images shown in Fig. 8. Arrows and circles demonstrate the improvement caused by MS-MV algorithm.
Fig. 10
Fig. 10 Reconstructed experimental PA images using (a) MV, (b) MV+CF, (c) MS-MV and (d) MS-MV+CF.Two wires were used as the imaging target.
Fig. 11
Fig. 11 The lateral variations for the reconstructed experimental PA images shown in Fig. 10.
Fig. 12
Fig. 12 (a) The phantom used for the experiment. (b) The ex vivo imaging setup.
Fig. 13
Fig. 13 Reconstructed ex vivo images using (a) DAS, (b) MV and (c) MS-MV. A linear-array and the phantom shown in Fig. 12 were used for the experimental design. All the images are shown with a dynamic range of 50 dB.
Fig. 14
Fig. 14 Lateral variations of the reconstructed images shown in Fig. 13 at the depths of 31.3 mm.

Tables (3)

Tables Icon

Table 1 The calculated SNR (dB) in different depths.

Tables Icon

Table 2 The calculated FWHM (mm) in different depths.

Tables Icon

Table 3 The calculated FWHM (mm) for wire phantom shown in Fig. 10.

Equations (32)

Equations on this page are rendered with MathJax. Learn more.

y ( k ) = m = 1 M w m ( k ) x m ( k Δ m ( k ) ) ,
y ( k ) = W H ( k ) X ( k ) ,
min W ( k ) W ( k ) H R ( k ) W ( k ) , s . t . W ( k ) H a = 1 ,
W ( k ) = R ( k ) 1 a a H R ( k ) 1 a .
R ^ ( k ) = 1 ( 2 K + 1 ) ( M L + 1 ) n = K K l = 1 M L + 1 X ¯ l ( k + n ) X ¯ l ( k + n ) H ,
y ˜ ( k ) = 1 M L + 1 l = 1 M L + 1 W ( k ) H X ¯ l ( k ) .
min W W H R W + α C H W p p , s . t . a H W = 1 ,
min W S W S H RW S + ( α C H W S 1 + β X T H W S 1 ) , s . t . a H W S = 1 .
min W S W S H RW S + BW S 1 , s . t . a H W S = 1 ,
min W S W S H RW S + Φ 1 BW S 2 , s . t . a H W S = 1 .
Φ 1 = diag { | BW S ( 1 ) | p 2 2 , , | BW S ( N ) | p 2 2 } .
f ( W S , γ ) = W S H RW S + Φ 1 BW S 2 γ ( a H W S 1 ) = W S H RW S + W S H B H D 1 ( W S ) BW S γ ( a H W S 1 ) ,
D 1 ( W S ) = Φ 1 H Φ 1 = diag { | BW S ( 1 ) | p 2 , , | BW S ( N ) | p 2 } .
f W S = 0 RW S + B H D 1 ( W S ) BW S γ a = 0 W S = γ ( R + B H D 1 ( W S ) B ) 1 a
f γ = 0 a H W S = 1
a T W S = γ a H ( R + B H D 1 ( W S ) B ) 1 a γ = 1 a H ( R + B H D 1 ( W S ) B ) 1 a ,
W S k + 1 = ( R + B H D 1 ( W S k ) B ) 1 a a H ( R + B H D 1 ( W S k ) B ) 1 a ,
1 L [ W S k + 1 W S k ] T [ W S k + 1 W S k ] = 10 5
min W MS W MS H RW MS + β X T H W MS 1 , s . t . a H W MS = 1 .
W MS k + 1 = ( R + β X T D 2 ( W MS k ) X T H ) 1 a a H ( R + β X T D 2 ( W MS k ) X T H ) 1 a ,
Φ 2 = diag { | X T H W MS ( 1 ) | p 2 2 , , | X T H W MS ( N ) | p 2 2 } .
SNR = 20 log 10 P signal / P noise ,
w = ( R + α C D ( w ) C H ) 1 a a H ( R + α C D ( W ) C H ) 1 a ,
D ( W ) = [ | n = 1 N w ( n ) | 1 0 0 | n = 1 N w ( n ) | 1 ] K × K
α CD ( W ) C H = α × [ 1 1 1 1 ] N × K [ 1 0 0 1 ] K × K [ 1 1 1 1 ] K × N = [ α N α N α N α N ] N × N
w = ( R + α N ) 1 a a H ( R + α N ) 1 a .
R = [ a b c d ]
R 1 = 1 a d b c A [ d b c a ]
w MV = R 1 a a H R 1 a = 1 A [ d b c + a ] 1 A ( d b c + a ) = 1 ( d b c + a ) [ d b a c ]
( R + α N ) = [ a + α N b + α N c + α N d + α N ]
( R + α N ) 1 = 1 a d b c + α N ( a + d b c ) B × [ d + α N b α N c α N a + α N ]
w SC = ( R + α N a ) 1 a a H ( R + α N ) 1 a = 1 B [ d + α N b α N c α N + a + α N ] 1 B ( d b c + a ) = 1 ( d b c + a ) [ d b a c ]

Metrics