Abstract

Photoacoustic (PA) techniques have shown promise in the imaging of tissue chromophores and exogenous contrast agents in various clinical applications. However, the key drawback of current PA technology is its dependence on a complex and hazardous laser system for the excitation of a tissue sample. Although light-emitting diodes (LED) have the potential to replace the laser, the image quality of an LED-based system is severely corrupted due to the low output power of LED elements. The current standard way to improve the quality is to increase the scanning time, which leads to a reduction in the imaging speed and makes the images prone to motion artifacts. To address the challenges of longer scanning time and poor image quality, in this work we present a deep neural networks based approach that exploits the temporal information in PA images using a recurrent neural network. We train our network using 32 phantom experiments; on the test set of 30 phantom experiments, we achieve a gain in the frame rate of 8 times with a mean peak-signal-to-noise-ratio of 35.4 dB compared to the standard technique.

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

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References

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    [Crossref]
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    [Crossref]
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  20. J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in neural information processing systems, (2012), pp. 341–349.
  21. C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
    [Crossref]
  22. J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision, (Springer, 2016), pp. 694–711.
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    [Crossref]
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    [Crossref] [PubMed]
  30. S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.
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    [Crossref]

2018 (1)

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

2016 (4)

J. Weber, P. C. Beard, and S. E. Bohndiek, “Contrast agents for molecular photoacoustic imaging,” Nat. Methods 13, 639 (2016).
[Crossref] [PubMed]

T. J. Allen and P. C. Beard, “High power visible light emitting diodes as pulsed excitation sources for biomedical photoacoustics,” Biomed. Opt. Express 7, 1260–1270 (2016).
[Crossref] [PubMed]

H. K. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Coded excitation using periodic and unipolar M-sequences for photoacoustic imaging and flow measurement,” Opt. Express 24, 17–29 (2016).
[Crossref] [PubMed]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

2012 (3)

C. Zhang, K. I. Maslov, J. Yao, and L. V. Wang, “In vivo photoacoustic microscopy with 7.6-μm axial resolution using a commercial 125-MHz ultrasonic transducer,” J. Biomed. Opt. 17, 116016 (2012).
[Crossref]

H. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Simultaneous multispectral coded excitation using gold codes for photoacoustic imaging,” Jpn. J. Appl. Phys. 51, 07GF03 (2012).
[Crossref]

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

2011 (1)

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus. 1, rsfs20110028 (2011).
[Crossref]

2010 (2)

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

M. P. Mienkina, C.-S. Friedrich, N. C. Gerhardt, M. F. Beckmann, M. F. Schiffner, M. R. Hofmann, and G. Schmitz, “Multispectral photoacoustic coded excitation imaging using unipolar orthogonal golay codes,” Opt. Express 18, 9076–9087 (2010).
[Crossref] [PubMed]

2009 (1)

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

2007 (1)

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

2006 (1)

M. Xu and L. V. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Meth. 77, 041101 (2006).
[Crossref]

2004 (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

1999 (1)

N. A. George, B. Aneeshkumar, P. Radhakrishnan, and C. Vallabhan, “Photoacoustic study on photobleaching of rhodamine 6g doped in poly (methyl methacrylate),” J. Phys. D: Appl. Phys. 32, 1745 (1999).
[Crossref]

1997 (1)

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

1990 (1)

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis Mach. Intell. 12, 629–639 (1990).
[Crossref]

1988 (1)

D. E. Rumelhart, G. E. Hinton, R. J. Williams, and et al., “Learning representations by back-propagating errors,” Cogn. Model. 5, 1 (1988).

Acosta, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Agarwal, A.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Aitken, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Alahi, A.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision, (Springer, 2016), pp. 694–711.

Allen, T. J.

Aneeshkumar, B.

N. A. George, B. Aneeshkumar, P. Radhakrishnan, and C. Vallabhan, “Photoacoustic study on photobleaching of rhodamine 6g doped in poly (methyl methacrylate),” J. Phys. D: Appl. Phys. 32, 1745 (1999).
[Crossref]

Antholzer, S.

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” arXiv preprint arXiv:1704.04587 (2017).

J. Schwab, S. Antholzer, R. Nuster, and M. Haltmeier, “DALnet: High-resolution photoacoustic projection imaging using deep learning,” arXiv preprint arXiv:1801.06693 (2018).

S. Antholzer, M. Haltmeier, R. Nuster, and J. Schwab, “Photoacoustic image reconstruction via deep learning,” in Photons Plus Ultrasound: Imaging and Sensing 2018, vol. 10494 (International Society for Optics and Photonics, 2018), p. 104944U.

Ashkenazi, S.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Ba, J.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Bayer, C. L.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Beard, P.

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus. 1, rsfs20110028 (2011).
[Crossref]

Beard, P. C.

Beckmann, M. F.

Bell, M. A. L.

A. Reiter and M. A. L. Bell, “A machine learning approach to identifying point source locations in photoacoustic data,” in Photons Plus Ultrasound: Imaging and Sensing 2017, vol. 10064 (International Society for Optics and Photonics, 2017), p. 100643J.

Boctor, E.

J. Kang, H. Zhang, E. Kulikowicz, E. Graham, R. Koehler, and E. Boctor, “In vivo photoacoustic quantification of brain tissue oxygenation for neonatal piglet graded ischemia model using microsphere administration,” in Ultrasonics Symposium (IUS), 2017 IEEE International, (IEEE, 2017), p. 1.

Bohndiek, S. E.

J. Weber, P. C. Beard, and S. E. Bohndiek, “Contrast agents for molecular photoacoustic imaging,” Nat. Methods 13, 639 (2016).
[Crossref] [PubMed]

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

Caballero, J.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Chao, D. L.

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

Chen, E.

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in neural information processing systems, (2012), pp. 341–349.

Chen, Y.-S.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Chen, Z.

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

Conjusteau, A.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Cunningham, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2015), pp. 3431–3440.

Day, K.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Day, M.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Dong, C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Emelianov, S. Y.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Ermilov, S. A.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Fei-Fei, L.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision, (Springer, 2016), pp. 694–711.

Feng, N.

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

Friedrich, C.-S.

Gao, Q.

T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in 2017 IEEE International Conference on Computer Vision (ICCV), (IEEE, 2017), pp. 4809–4817.
[Crossref]

George, N. A.

N. A. George, B. Aneeshkumar, P. Radhakrishnan, and C. Vallabhan, “Photoacoustic study on photobleaching of rhodamine 6g doped in poly (methyl methacrylate),” J. Phys. D: Appl. Phys. 32, 1745 (1999).
[Crossref]

Gerhardt, N. C.

Graham, E.

J. Kang, H. Zhang, E. Kulikowicz, E. Graham, R. Koehler, and E. Boctor, “In vivo photoacoustic quantification of brain tissue oxygenation for neonatal piglet graded ischemia model using microsphere administration,” in Ultrasonics Symposium (IUS), 2017 IEEE International, (IEEE, 2017), p. 1.

Haltmeier, M.

J. Schwab, S. Antholzer, R. Nuster, and M. Haltmeier, “DALnet: High-resolution photoacoustic projection imaging using deep learning,” arXiv preprint arXiv:1801.06693 (2018).

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” arXiv preprint arXiv:1704.04587 (2017).

S. Antholzer, M. Haltmeier, R. Nuster, and J. Schwab, “Photoacoustic image reconstruction via deep learning,” in Photons Plus Ultrasound: Imaging and Sensing 2018, vol. 10494 (International Society for Optics and Photonics, 2018), p. 104944U.

Hariri, A.

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

A. Hariri, M. Hosseinzadeh, S. Noei, and M. Nasiriavanaki, “Photoacoustic signal enhancement: towards utilization of very low-cost laser diodes in photoacoustic imaging,” in Photons Plus Ultrasound: Imaging and Sensing 2017, vol. 10064 (International Society for Optics and Photonics, 2017), p. 100645L.

He, K.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE CVPR, (2016), pp. 770–778.

Hinton, G. E.

D. E. Rumelhart, G. E. Hinton, R. J. Williams, and et al., “Learning representations by back-propagating errors,” Cogn. Model. 5, 1 (1988).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

Hochreiter, S.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

Hofmann, M. R.

Homan, K. A.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Hopfield, J. J.

J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” in Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications, (World Scientific, 1987), pp. 411–415.

Hosseinzadeh, M.

A. Hariri, M. Hosseinzadeh, S. Noei, and M. Nasiriavanaki, “Photoacoustic signal enhancement: towards utilization of very low-cost laser diodes in photoacoustic imaging,” in Photons Plus Ultrasound: Imaging and Sensing 2017, vol. 10064 (International Society for Optics and Photonics, 2017), p. 100645L.

Huang, G.

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017), p. 3.

Huang, S.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Huszár, F.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Jeevarathinam, A. S.

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

Johnson, J.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision, (Springer, 2016), pp. 694–711.

Jokerst, J. V.

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

Kang, J.

J. Kang, H. Zhang, E. Kulikowicz, E. Graham, R. Koehler, and E. Boctor, “In vivo photoacoustic quantification of brain tissue oxygenation for neonatal piglet graded ischemia model using microsphere administration,” in Ultrasonics Symposium (IUS), 2017 IEEE International, (IEEE, 2017), p. 1.

Khamapirad, T.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Kim, S.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Kingma, D.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Koehler, R.

J. Kang, H. Zhang, E. Kulikowicz, E. Graham, R. Koehler, and E. Boctor, “In vivo photoacoustic quantification of brain tissue oxygenation for neonatal piglet graded ischemia model using microsphere administration,” in Ultrasonics Symposium (IUS), 2017 IEEE International, (IEEE, 2017), p. 1.

Kondo, K.

H. K. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Coded excitation using periodic and unipolar M-sequences for photoacoustic imaging and flow measurement,” Opt. Express 24, 17–29 (2016).
[Crossref] [PubMed]

H. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Simultaneous multispectral coded excitation using gold codes for photoacoustic imaging,” Jpn. J. Appl. Phys. 51, 07GF03 (2012).
[Crossref]

Kotov, N.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

Kulikowicz, E.

J. Kang, H. Zhang, E. Kulikowicz, E. Graham, R. Koehler, and E. Boctor, “In vivo photoacoustic quantification of brain tissue oxygenation for neonatal piglet graded ischemia model using microsphere administration,” in Ultrasonics Symposium (IUS), 2017 IEEE International, (IEEE, 2017), p. 1.

Lacewell, R.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Ledig, C.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Lemaster, J.

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

Leonard, M. H.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Li, G.

T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in 2017 IEEE International Conference on Computer Vision (ICCV), (IEEE, 2017), pp. 4809–4817.
[Crossref]

Li, J.

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

Liu, X.

T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in 2017 IEEE International Conference on Computer Vision (ICCV), (IEEE, 2017), pp. 4809–4817.
[Crossref]

Liu, Z.

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017), p. 3.

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2015), pp. 3431–3440.

Loy, C. C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Malik, J.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis Mach. Intell. 12, 629–639 (1990).
[Crossref]

Maslov, K. I.

C. Zhang, K. I. Maslov, J. Yao, and L. V. Wang, “In vivo photoacoustic microscopy with 7.6-μm axial resolution using a commercial 125-MHz ultrasonic transducer,” J. Biomed. Opt. 17, 116016 (2012).
[Crossref]

Mehta, K.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Mienkina, M. P.

Miller, T.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Nasiriavanaki, M.

A. Hariri, M. Hosseinzadeh, S. Noei, and M. Nasiriavanaki, “Photoacoustic signal enhancement: towards utilization of very low-cost laser diodes in photoacoustic imaging,” in Photons Plus Ultrasound: Imaging and Sensing 2017, vol. 10064 (International Society for Optics and Photonics, 2017), p. 100645L.

Noei, S.

A. Hariri, M. Hosseinzadeh, S. Noei, and M. Nasiriavanaki, “Photoacoustic signal enhancement: towards utilization of very low-cost laser diodes in photoacoustic imaging,” in Photons Plus Ultrasound: Imaging and Sensing 2017, vol. 10064 (International Society for Optics and Photonics, 2017), p. 100645L.

Nuster, R.

S. Antholzer, M. Haltmeier, R. Nuster, and J. Schwab, “Photoacoustic image reconstruction via deep learning,” in Photons Plus Ultrasound: Imaging and Sensing 2018, vol. 10494 (International Society for Optics and Photonics, 2018), p. 104944U.

J. Schwab, S. Antholzer, R. Nuster, and M. Haltmeier, “DALnet: High-resolution photoacoustic projection imaging using deep learning,” arXiv preprint arXiv:1801.06693 (2018).

O’donnell, M.

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

Oraevsky, A. A.

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

Perona, P.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis Mach. Intell. 12, 629–639 (1990).
[Crossref]

Radhakrishnan, P.

N. A. George, B. Aneeshkumar, P. Radhakrishnan, and C. Vallabhan, “Photoacoustic study on photobleaching of rhodamine 6g doped in poly (methyl methacrylate),” J. Phys. D: Appl. Phys. 32, 1745 (1999).
[Crossref]

Reiter, A.

A. Reiter and M. A. L. Bell, “A machine learning approach to identifying point source locations in photoacoustic data,” in Photons Plus Ultrasound: Imaging and Sensing 2017, vol. 10064 (International Society for Optics and Photonics, 2017), p. 100643J.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE CVPR, (2016), pp. 770–778.

Rumelhart, D. E.

D. E. Rumelhart, G. E. Hinton, R. J. Williams, and et al., “Learning representations by back-propagating errors,” Cogn. Model. 5, 1 (1988).

Schiffner, M. F.

Schmidhuber, J.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

Schmitz, G.

Schwab, J.

S. Antholzer, M. Haltmeier, R. Nuster, and J. Schwab, “Photoacoustic image reconstruction via deep learning,” in Photons Plus Ultrasound: Imaging and Sensing 2018, vol. 10494 (International Society for Optics and Photonics, 2018), p. 104944U.

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” arXiv preprint arXiv:1704.04587 (2017).

J. Schwab, S. Antholzer, R. Nuster, and M. Haltmeier, “DALnet: High-resolution photoacoustic projection imaging using deep learning,” arXiv preprint arXiv:1801.06693 (2018).

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2015), pp. 3431–3440.

Shen, X.

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

Shen, Y.

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

Shiina, T.

H. K. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Coded excitation using periodic and unipolar M-sequences for photoacoustic imaging and flow measurement,” Opt. Express 24, 17–29 (2016).
[Crossref] [PubMed]

H. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Simultaneous multispectral coded excitation using gold codes for photoacoustic imaging,” Jpn. J. Appl. Phys. 51, 07GF03 (2012).
[Crossref]

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

Su, J. L.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE CVPR, (2016), pp. 770–778.

Sun, M.

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

Tang, X.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Tejani, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Theis, L.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Tong, T.

T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in 2017 IEEE International Conference on Computer Vision (ICCV), (IEEE, 2017), pp. 4809–4817.
[Crossref]

Totz, J.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Vallabhan, C.

N. A. George, B. Aneeshkumar, P. Radhakrishnan, and C. Vallabhan, “Photoacoustic study on photobleaching of rhodamine 6g doped in poly (methyl methacrylate),” J. Phys. D: Appl. Phys. 32, 1745 (1999).
[Crossref]

van der Maaten, L.

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017), p. 3.

Wang, B.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Wang, H.

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

Wang, J.

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

Wang, L. V.

C. Zhang, K. I. Maslov, J. Yao, and L. V. Wang, “In vivo photoacoustic microscopy with 7.6-μm axial resolution using a commercial 125-MHz ultrasonic transducer,” J. Biomed. Opt. 17, 116016 (2012).
[Crossref]

M. Xu and L. V. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Meth. 77, 041101 (2006).
[Crossref]

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

Weber, J.

J. Weber, P. C. Beard, and S. E. Bohndiek, “Contrast agents for molecular photoacoustic imaging,” Nat. Methods 13, 639 (2016).
[Crossref] [PubMed]

Weinberger, K. Q.

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017), p. 3.

Williams, R. J.

D. E. Rumelhart, G. E. Hinton, R. J. Williams, and et al., “Learning representations by back-propagating errors,” Cogn. Model. 5, 1 (1988).

Wilson, K. E.

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

Wong, W.-K.

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

Woo, W.-c.

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

Xie, J.

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in neural information processing systems, (2012), pp. 341–349.

Xingjian, S.

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

Xu, L.

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in Advances in neural information processing systems, (2012), pp. 341–349.

Xu, M.

M. Xu and L. V. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Meth. 77, 041101 (2006).
[Crossref]

Yamakawa, M.

H. K. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Coded excitation using periodic and unipolar M-sequences for photoacoustic imaging and flow measurement,” Opt. Express 24, 17–29 (2016).
[Crossref] [PubMed]

H. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Simultaneous multispectral coded excitation using gold codes for photoacoustic imaging,” Jpn. J. Appl. Phys. 51, 07GF03 (2012).
[Crossref]

Yao, J.

C. Zhang, K. I. Maslov, J. Yao, and L. V. Wang, “In vivo photoacoustic microscopy with 7.6-μm axial resolution using a commercial 125-MHz ultrasonic transducer,” J. Biomed. Opt. 17, 116016 (2012).
[Crossref]

Yeung, D.-Y.

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

Zhang, C.

C. Zhang, K. I. Maslov, J. Yao, and L. V. Wang, “In vivo photoacoustic microscopy with 7.6-μm axial resolution using a commercial 125-MHz ultrasonic transducer,” J. Biomed. Opt. 17, 116016 (2012).
[Crossref]

Zhang, H.

H. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Simultaneous multispectral coded excitation using gold codes for photoacoustic imaging,” Jpn. J. Appl. Phys. 51, 07GF03 (2012).
[Crossref]

J. Kang, H. Zhang, E. Kulikowicz, E. Graham, R. Koehler, and E. Boctor, “In vivo photoacoustic quantification of brain tissue oxygenation for neonatal piglet graded ischemia model using microsphere administration,” in Ultrasonics Symposium (IUS), 2017 IEEE International, (IEEE, 2017), p. 1.

Zhang, H. K.

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE CVPR, (2016), pp. 770–778.

Adv. Adapt. Data Analysis (1)

M. Sun, N. Feng, Y. Shen, X. Shen, and J. Li, “Photoacoustic signals denoising based on empirical mode decomposition and energy-window method,” Adv. Adapt. Data Analysis 4, 1250004 (2012).
[Crossref]

Biomed. Opt. Express (1)

Cogn. Model. (1)

D. E. Rumelhart, G. E. Hinton, R. J. Williams, and et al., “Learning representations by back-propagating errors,” Cogn. Model. 5, 1 (1988).

Expert. Opin. on Med. Diagn. (1)

J. L. Su, B. Wang, K. E. Wilson, C. L. Bayer, Y.-S. Chen, S. Kim, K. A. Homan, and S. Y. Emelianov, “Advances in clinical and biomedical applications of photoacoustic imaging,” Expert. Opin. on Med. Diagn. 4, 497–510 (2010).
[Crossref]

IEEE Transactions on Image Process. (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

IEEE Transactions on Pattern Analysis Mach. Intell. (2)

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis Mach. Intell. 12, 629–639 (1990).
[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Interface Focus. (1)

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus. 1, rsfs20110028 (2011).
[Crossref]

J. Appl. Phys. (1)

A. Agarwal, S. Huang, M. O’donnell, K. Day, M. Day, N. Kotov, and S. Ashkenazi, “Targeted gold nanorod contrast agent for prostate cancer detection by photoacoustic imaging,” J. Appl. Phys. 102, 064701 (2007).
[Crossref]

J. Biomed. Opt. (2)

S. A. Ermilov, T. Khamapirad, A. Conjusteau, M. H. Leonard, R. Lacewell, K. Mehta, T. Miller, and A. A. Oraevsky, “Laser optoacoustic imaging system for detection of breast cancer,” J. Biomed. Opt. 14, 024007 (2009).
[Crossref] [PubMed]

C. Zhang, K. I. Maslov, J. Yao, and L. V. Wang, “In vivo photoacoustic microscopy with 7.6-μm axial resolution using a commercial 125-MHz ultrasonic transducer,” J. Biomed. Opt. 17, 116016 (2012).
[Crossref]

J. Phys. D: Appl. Phys. (1)

N. A. George, B. Aneeshkumar, P. Radhakrishnan, and C. Vallabhan, “Photoacoustic study on photobleaching of rhodamine 6g doped in poly (methyl methacrylate),” J. Phys. D: Appl. Phys. 32, 1745 (1999).
[Crossref]

Jpn. J. Appl. Phys. (1)

H. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Simultaneous multispectral coded excitation using gold codes for photoacoustic imaging,” Jpn. J. Appl. Phys. 51, 07GF03 (2012).
[Crossref]

Nat. Methods (1)

J. Weber, P. C. Beard, and S. E. Bohndiek, “Contrast agents for molecular photoacoustic imaging,” Nat. Methods 13, 639 (2016).
[Crossref] [PubMed]

Neural Comput. (1)

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput. 9, 1735–1780 (1997).
[Crossref] [PubMed]

Opt. Express (2)

Photoacoustics. (1)

A. Hariri, J. Lemaster, J. Wang, A. S. Jeevarathinam, D. L. Chao, and J. V. Jokerst, “The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging,” Photoacoustics. 9, 10–20 (2018).
[Crossref]

Rev. Sci. Meth. (1)

M. Xu and L. V. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Meth. 77, 041101 (2006).
[Crossref]

Other (17)

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in neural information processing systems, (2015), pp. 802–810.

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017), p. 3.

J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” in Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications, (World Scientific, 1987), pp. 411–415.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision, (Springer, 2016), pp. 694–711.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-realistic single image super-resolution using a generative adversarial network,” arXiv preprint (2016).

T. Tong, G. Li, X. Liu, and Q. Gao, “Image super-resolution using dense skip connections,” in 2017 IEEE International Conference on Computer Vision (ICCV), (IEEE, 2017), pp. 4809–4817.
[Crossref]

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” arXiv preprint arXiv:1704.04587 (2017).

J. Schwab, S. Antholzer, R. Nuster, and M. Haltmeier, “DALnet: High-resolution photoacoustic projection imaging using deep learning,” arXiv preprint arXiv:1801.06693 (2018).

S. Antholzer, M. Haltmeier, R. Nuster, and J. Schwab, “Photoacoustic image reconstruction via deep learning,” in Photons Plus Ultrasound: Imaging and Sensing 2018, vol. 10494 (International Society for Optics and Photonics, 2018), p. 104944U.

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Figures (9)

Fig. 1
Fig. 1 A schematic of our proposed approach. (a) The densenet-based CNN architecture to improve the quality of a single PA image. The architecture consists of three dense blocks, each dense block includes two 3 × 3 dense convolutional layers followed by rectified linear units. (b) A schematic of ConvLSTM cell. In addition to current input Xt, it exploits previous hidden and cell states to generate current states. (c) The proposed architecture that integrates CNN and ConvLSTM together to extract the spatial features and the temporal dependencies, respectively. We also incorporate dense skip connections throughout the network for an improved prediction. The increasing number of features maps are mentioned in the figure.
Fig. 2
Fig. 2 A schematic of the cross-sectional view of the gold magnetic nanoparticle phantom. The phantom consists of five cylindrical tubes. Tubes 1–3 are placed at successively decreasing depth, where a same concentration of nanoparticles is used in each of them. In addition to tube 3, we include two more tubes (tubes 4 and 5) at the upper row of tubes, with a successively decreasing concentration from tube 3 to 5.
Fig. 3
Fig. 3 An example of generating an input sequence of length of 4 for N0 = 800 and Ns = 200. The sequence starts with 200 with a step of Ns and ends at 800.
Fig. 4
Fig. 4 A comparison of PSNR and SSIM of our (RNN+CNN) method with those from the simple averaging and CNN-only methods. (a) PSNR vs. averaging frame numbers. An improvement at all of the averaging frame numbers is noticed for our method compared to two other methods. We can also observe a higher improvement rate of our method compared to the CNN-only method. (b) SSIM vs. averaging frame numbers. Unlike CNN-only method, we observe an increasing trend of improvement with the averaging frame numbers for our method. (c) Gain in frame rate vs. mean PSNR.
Fig. 5
Fig. 5 Qualitative comparison of our method with the simple averaging and CNN-only techniques for a wire phantom example for three different values of the averaging frame numbers, where the imaging plane consists of a line object. Though the network has been trained using point spread function, we can observe its robustness on line target function.
Fig. 6
Fig. 6 A comparison of our method with the averaging and CNN-only techniques for an in vivo example. The in vivo data consists of proper digital arteries of three fingers of a volunteer. We can notice improvements in our results compared to those of other two methods in recovering the blood vessels (marked by arrows).
Fig. 7
Fig. 7 An example effect of depth on the PA image quality. We choose tubes 1–3 of the nanoparticle phantom for this purpose. A comparison among the simple averaging, CNN-only and RNN+CNN techniques is shown for three different values of the averaging frame numbers. As shown by the arrows, a decrease in the image quality can be observed with an increase in the imaging depth. It is also interesting to notice the increased image quality with an increase in the averaging frame number.
Fig. 8
Fig. 8 Sensitivity analysis of our method. (a) Effect of depth on SNR vs. averaging frame numbers. We can notice a successively reduced accuracy from lower to higher depth. (b) Effect of optical contrast and scattering of phantom medium on image quality. The image quality is degraded with a decrease in the concentration of optical absorber. In addition, a higher optical scattering medium leads to a reduction in image quality.
Fig. 9
Fig. 9 Comparative performance of the simple averaging, CNN-only and RNN+CNN methods in detection of point target objects with different concentrations. We can notice a successive decrease in signal quality with a decrease in concentration. In addition, the improved performance of our RNN+CNN method can be observed (marked by arrow) with respect to two other methods.

Equations (5)

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i t = σ ( W x i * X t + W h i * H t 1 + b i ) , f t = σ ( W x f * X t + W h f * H t 1 + b f ) , o t = σ ( W x o * X t + W h o * H t 1 + b o ) , C t = f t C t 1 + i t tanh ( W x c * X t + W h c * H t 1 + b c ) , H t = o t tanh ( C t ) .
SNR = 20 log 10 ( μ I σ b ) .
PSNR = 20 log 10 ( I max MSE ) ,
MSE = 1 MN m = 0 M 1 n = 0 N 1 ( I ref ( m , n ) I est ( m , n ) ) 2 .
SSIM = ( 2 μ ref μ est + c 1 ) ( 2 σ cov + c 2 ) ( μ ref 2 + μ est 2 + c 1 ) ( σ ref 2 + σ est 2 + c 2 ) .

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