Robust Photoacoustic Beamforming Using Dense Convolutional Neural Networks
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.
KeywordsPhotoacoustic 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.
- 2.Antholzer, S., Haltmeier, M., Schwab, J.: Deep learning for photoacoustic tomography from sparse data. arXiv preprint arXiv:1704.04587 (2017)
- 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.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.Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
- 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.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.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
- 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.Nair, A.A., Tran, T.D., Reiter, A., Bell, M.A.L.: A deep learning based alternative to beamforming ultrasound images (2018)Google Scholar
- 17.Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
- 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