Robust Photoacoustic Beamforming Using Dense Convolutional Neural Networks

  • Emran Mohammad Abu AnasEmail author
  • Haichong K. Zhang
  • Chloé Audigier
  • Emad M. Boctor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11042)


Photoacoustic (PA) is a promising technology for imaging of endogenous tissue chromophores and exogenous contrast agents in a wide range of clinical applications. The imaging technique is based on excitation of a tissue sample using short light pulse, followed by acquisition of the resultant acoustic signal using an ultrasound (US) transducer. To reconstruct an image of the tissue from the received US signals, the most common approach is to use the delay-and-sum (DAS) beamforming technique that assumes a wave propagation with a constant speed of sound. Unfortunately, such assumption often leads to artifacts such as sidelobes and tissue aberration; in addition, the image resolution is degraded. With an aim to improve the PA image reconstruction, in this work, we propose a deep convolutional neural networks-based beamforming approach that uses a set of densely connected convolutional layers with dilated convolution at higher layers. To train the network, we use simulated images with various sizes and contrasts of target objects, and subsequently simulating the PA effect to obtain the raw US signals at an US transducer. We test the network on an independent set of 1,500 simulated images and we achieve a mean peak-to-signal-ratio of 38.7 dB between the estimated and reference images. In addition, a comparison of our approach with the DAS beamforming technique indicates a statistical significant improvement of the proposed technique.


Photoacoustic Beamforming Delay-and-sum Convolutional neural networks Dense convolution Dilated convolution 



We would like to thank the National Institute of Health (NIH) Brain Initiative (R24MH106083-03) and NIH National Institute of Biomedical Imaging and Bioengineering (R01EB01963) for funding this project.


  1. 1.
    Agarwal, A., et al.: Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging. J. Appl. Phys. 102(6), 064701 (2007)CrossRefGoogle Scholar
  2. 2.
    Antholzer, S., Haltmeier, M., Schwab, J.: Deep learning for photoacoustic tomography from sparse data. arXiv preprint arXiv:1704.04587 (2017)
  3. 3.
    Beard, P.: Biomedical photoacoustic imaging. Interface Focus (2011). Scholar
  4. 4.
    Bell, M.A.L., Kuo, N., Song, D.Y., Boctor, E.M.: Short-lag spatial coherence beamforming of photoacoustic images for enhanced visualization of prostate brachytherapy seeds. Biomed. Optics Express 4(10), 1964–1977 (2013)CrossRefGoogle Scholar
  5. 5.
    Hoelen, C.G., de Mul, F.F.: Image reconstruction for photoacoustic scanning of tissue structures. Appl. Opt. 39(31), 5872–5883 (2000)CrossRefGoogle Scholar
  6. 6.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)Google Scholar
  7. 7.
    Kang, J., et al.: Validation of noninvasive photoacoustic measurements of sagittal sinus oxyhemoglobin saturation in hypoxic neonatal piglets. J. Appl. Physiol. (2018)Google Scholar
  8. 8.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  9. 9.
    Luchies, A., Byram, B.: Deep neural networks for ultrasound beamforming. In: 2017 IEEE International Ultrasonics Symposium (IUS), pp. 1–4. IEEE (2017)Google Scholar
  10. 10.
    Luchies, A., Byram, B.: Suppressing off-axis scattering using deep neural networks. In: Medical Imaging 2018: Ultrasonic Imaging and Tomography, vol. 10580, p. 105800G. International Society for Optics and Photonics (2018)Google Scholar
  11. 11.
    Mozaffarzadeh, M., Mahloojifar, A., Orooji, M.: Medical photoacoustic beamforming using minimum variance-based delay multiply and sum. In: Digital Optical Technologies 2017, vol. 10335, p. 1033522. International Society for Optics and Photonics (2017)Google Scholar
  12. 12.
    Mozaffarzadeh, M., Mahloojifar, A., Orooji, M., Adabi, S., Nasiriavanaki, M.: Double-stage delay multiply and sum beamforming algorithm: application to linear-array photoacoustic imaging. IEEE Trans. Biomed. Eng. 65(1), 31–42 (2018)CrossRefGoogle Scholar
  13. 13.
    Mozaffarzadeh, M., Yan, Y., Mehrmohammadi, M., Makkiabadi, B.: Enhanced linear-array photoacoustic beamforming using modified coherence factor. J. Biomed. Opt. 23(2), 026005 (2018)Google Scholar
  14. 14.
    Nair, A.A., Tran, T.D., Reiter, A., Bell, M.A.L.: A deep learning based alternative to beamforming ultrasound images (2018)Google Scholar
  15. 15.
    Park, S., Karpiouk, A.B., Aglyamov, S.R., Emelianov, S.Y.: Adaptive beamforming for photoacoustic imaging. Opt. Lett. 33(12), 1291–1293 (2008)CrossRefGoogle Scholar
  16. 16.
    Treeby, B.E., Cox, B.T.: k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. J. Biomed. Opt. 15(2), 021314 (2010)CrossRefGoogle Scholar
  17. 17.
    Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
  18. 18.
    Zhang, H.K., et al.: Prostate specific membrane antigen (PSMA)-targeted photoacoustic imaging of prostate cancer in vivo. J. Biophotonics 13, e201800021 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Emran Mohammad Abu Anas
    • 1
    Email author
  • Haichong K. Zhang
    • 1
  • Chloé Audigier
    • 2
  • Emad M. Boctor
    • 1
    • 2
  1. 1.Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Radiology and Radiological ScienceJohns Hopkins UniversityBaltimoreUSA

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