Abstract

The methods available for solving the inverse problem of photoacoustic tomography promote only one feature–either being smooth or sharp–in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental set-ups as well as imaging objects.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (2)

S. Gutta, M. Bhatt, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Modeling errors compensation with total least squares for limited data photoacoustic tomography,” IEEE J. Sel. Top. Quantum Electron. 25, 1–14 (2019).
[Crossref]

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[Crossref] [PubMed]

2018 (3)

P. P. Pai, A. De, and S. Banerjee, “Accuracy enhancement for noninvasive glucose estimation using dual-wavelength photoacoustic measurements and kernel-based calibration,” IEEE Transactions on Instrumentation Meas. 67, 126–136 (2018).
[Crossref]

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[Crossref]

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[Crossref]

2017 (3)

P. K. Upputuri and M. Pramanik, “Recent advances toward preclinical and clinical translation of photoacoustic tomography: a review,” J. Biomed. Opt. 22, 041006 (2017).
[Crossref]

N. Gandhi, M. Allard, S. Kim, P. Kazanzides, and M. A. L. Bell, “Photoacoustic-based approach to surgical guidance performed with and without a da vinci robot,” J. Biomed. Opt. 22, 121606 (2017).
[Crossref]

L. Li, L. Zhu, Y. Shen, and L. V. Wang, “Multiview hilbert transformation in full-ring transducer array-based photoacoustic computed tomography,” J. Biomed. Opt. 22, 076017 (2017).
[Crossref]

2016 (7)

S. K. Kalva and M. Pramanik, “Experimental validation of tangential resolution improvement in photoacoustic tomography using modified delay-and-sum reconstruction algorithm,” J. Biomed. Opt. 21, 086011 (2016).
[Crossref]

D. Van de Sompel, L. S. Sasportas, J. V. Jokerst, and S. S. Gambhir, “Comparison of deconvolution filters for photoacoustic tomography,” PloS One 11, e0152597 (2016).
[Crossref] [PubMed]

Y. Zhou, J. Yao, and L. V. Wang, “Tutorial on photoacoustic tomography,” J. Biomed. Opt. 21, 061007 (2016).
[Crossref]

S. J. Ford, P. L. Bigliardi, T. C. Sardella, A. Urich, N. C. Burton, M. Kacprowicz, M. Bigliardi, M. Olivo, and D. Razansky, “Structural and functional analysis of intact hair follicles and pilosebaceous units by volumetric multispectral optoacoustic tomography,” J. Investig. Dermatol. 136, 753–761 (2016).
[Crossref]

S. R. Arridge, M. M. Betcke, B. T. Cox, F. Lucka, and B. E. Treeby, “On the adjoint operator in photoacoustic tomography,” Inverse Probl. 32, 115012 (2016).
[Crossref]

S. Arridge, P. Beard, M. Betcke, B. Cox, N. Huynh, F. Lucka, O. Ogunlade, and E. Zhang, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Medicine Biol. 61, 8908–8940 (2016).
[Crossref]

M. Bhatt, A. Acharya, and P. K. Yalavarthy, “Computationally efficient error estimate for evaluation of regularization in photoacoustic tomography,” J. Biomed. Opt. 21, 106002 (2016).
[Crossref] [PubMed]

2015 (2)

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

J. Yao, L. Wang, J.-M. Yang, K. I. Maslov, T. T. Wong, L. Li, C.-H. Huang, J. Zou, and L. V. Wang, “High-speed label-free functional photoacoustic microscopy of mouse brain in action,” Nat. Methods 12, 407–410 (2015).
[Crossref] [PubMed]

2014 (2)

J. Prakash, H. Dehghani, B. W. Pogue, and P. K. Yalavarthy, “Model-resolution-based basis pursuit deconvolution improves diffuse optical tomographic imaging,” IEEE Transactions on Med. Imaging 33, 891–901 (2014).
[Crossref]

J. Prakash, A. S. Raju, C. B. Shaw, M. Pramanik, and P. K. Yalavarthy, “Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography,” Biomed. Opt. Express 5, 1363–1377 (2014).
[Crossref] [PubMed]

2013 (6)

C. B. Shaw, J. Prakash, M. Pramanik, and P. K. Yalavarthy, “Least squares qr-based decomposition provides an efficient way of computing optimal regularization parameter in photoacoustic tomography,” J. Biomed. Opt. 18, 080501 (2013).
[Crossref]

S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” IEEE Transactions on Image Process. 22, 2864–2875 (2013).
[Crossref]

J. Chen, R. Lin, H. Wang, J. Meng, H. Zheng, and L. Song, “Blind-deconvolution optical-resolution photoacoustic microscopy in vivo,” Opt. Express 21, 7316–7327 (2013).
[Crossref] [PubMed]

J. Prakash and P. K. Yalavarthy, “A lsqr-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography,” Med. Phys. 40, 033101 (2013).
[Crossref] [PubMed]

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9, 318–336 (2013).
[Crossref]

R. A. Kruger, C. M. Kuzmiak, R. B. Lam, D. R. Reinecke, S. P. Del Rio, and D. Steed, “Dedicated 3d photoacoustic breast imaging,” Med. Phys. 4013301 (2013).
[Crossref] [PubMed]

2012 (5)

M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Transactions on Biomed. Eng. 59, 2538–2548 (2012).
[Crossref]

X. L. Dean-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Transactions on Med. Imaging 31, 1154–1162 (2012).
[Crossref]

X. L. Dean-Ben, A. Buehler, V. Ntziachristos, and D. Razansky, “Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography,” IEEE Transactions on Med. Imaging 31, 1922–1928 (2012).
[Crossref]

L. V. Wang and S. Hu, “Photoacoustic tomography: in vivo imaging from organelles to organs,” Science 335, 1458–1462 (2012).
[Crossref] [PubMed]

K. Wang, R. Su, A. A. Oraevsky, and M. A. Anastasio, “Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography,” Phys. Medicine Biol. 57, 5399–5423 (2012).
[Crossref]

2011 (2)

A. Buehler, A. Rosenthal, T. Jetzfellner, A. Dima, D. Razansky, and V. Ntziachristos, “Model-based optoacoustic inversions with incomplete projection data,” Med. Phys. 38, 1694–1704 (2011).
[Crossref] [PubMed]

K. Jansen, A. F. Van Der Steen, H. M. van Beusekom, J. W. Oosterhuis, and G. van Soest, “Intravascular photoacoustic imaging of human coronary atherosclerosis,” Opt. Lett. 36, 597–599 (2011).
[Crossref] [PubMed]

2010 (2)

S. Jiao, M. Jiang, J. Hu, A. Fawzi, Q. Zhou, K. K. Shung, C. A. Puliafito, and H. F. Zhang, “Photoacoustic ophthalmoscopy for in vivo retinal imaging,” Opt. Express 18, 3967–3972 (2010).
[Crossref] [PubMed]

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 (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]

2008 (2)

M. Pramanik, G. Ku, C. Li, and L. V. Wang, “Design and evaluation of a novel breast cancer detection system combining both thermoacoustic (ta) and photoacoustic (pa) tomography,” Med. Phys. 35, 2218–2223 (2008).
[Crossref] [PubMed]

Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM J. on Imaging Sci. 1, 248–272 (2008).
[Crossref]

2004 (4)

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vis. 20, 89–97 (2004).
[Crossref]

Y. Xu, L. V. Wang, G. Ambartsoumian, and P. Kuchment, “Reconstructions in limited-view thermoacoustic tomography,” Med. Phys. 31, 724–733 (2004).
[Crossref] [PubMed]

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Med. Imaging 23, 501–509 (2004).
[Crossref]

X. Song, B. W. Pogue, S. Jiang, M. M. Doyley, H. Dehghani, T. D. Tosteson, and K. D. Paulsen, “Automated region detection based on the contrast-to-noise ratio in near-infrared tomography,” Appl. Opt. 43, 1053–1062 (2004).
[Crossref] [PubMed]

2002 (1)

G. Paltauf, J. Viator, S. Prahl, and S. Jacques, “Iterative reconstruction algorithm for optoacoustic imaging,” The J. Acoust. Soc. Am. 112, 1536–1544 (2002).
[Crossref] [PubMed]

2001 (2)

M. E. Kilmer and D. P. O’Leary, “Choosing regularization parameters in iterative methods for ill-posed problems,” SIAM J. on Matrix Analysis Appl. 22, 1204–1221 (2001).
[Crossref]

A. E. Cerussi, A. J. Berger, F. Bevilacqua, N. Shah, D. Jakubowski, J. Butler, R. F. Holcombe, and B. J. Tromberg, “Sources of absorption and scattering contrast for near-infrared optical mammography,” Acad. Radiol. 8, 211–218 (2001).
[Crossref] [PubMed]

2000 (1)

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Med. Imaging 19, 203–210 (2000).
[Crossref]

1992 (1)

J. Eckstein and D. P. Bertsekas, “On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators,” Math. Program. 55, 293–318 (1992).
[Crossref]

1982 (1)

C. C. Paige and M. A. Saunders, “Lsqr: An algorithm for sparse linear equations and sparse least squares,” ACM Transactions on Math. Softw. 8, 43–71 (1982).
[Crossref]

Abadi, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

Abràmoff, M. D.

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Med. Imaging 23, 501–509 (2004).
[Crossref]

Acharya, A.

M. Bhatt, A. Acharya, and P. K. Yalavarthy, “Computationally efficient error estimate for evaluation of regularization in photoacoustic tomography,” J. Biomed. Opt. 21, 106002 (2016).
[Crossref] [PubMed]

Agarwal, A.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

Allard, M.

N. Gandhi, M. Allard, S. Kim, P. Kazanzides, and M. A. L. Bell, “Photoacoustic-based approach to surgical guidance performed with and without a da vinci robot,” J. Biomed. Opt. 22, 121606 (2017).
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S. R. Arridge, M. M. Betcke, B. T. Cox, F. Lucka, and B. E. Treeby, “On the adjoint operator in photoacoustic tomography,” Inverse Probl. 32, 115012 (2016).
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N. Awasthi, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Image-guided filtering for improving photoacoustic tomographic image reconstruction,” J. Biomed. Opt. 23, 091413 (2018).
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N. Awasthi, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography,” J. Biomed. Opt. 23, 071204 (2018).
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D. Kingma and J. A. Ba, “A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

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M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Transactions on Biomed. Eng. 59, 2538–2548 (2012).
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S. Arridge, P. Beard, M. Betcke, B. Cox, N. Huynh, F. Lucka, O. Ogunlade, and E. Zhang, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Medicine Biol. 61, 8908–8940 (2016).
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N. Gandhi, M. Allard, S. Kim, P. Kazanzides, and M. A. L. Bell, “Photoacoustic-based approach to surgical guidance performed with and without a da vinci robot,” J. Biomed. Opt. 22, 121606 (2017).
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S. Arridge, P. Beard, M. Betcke, B. Cox, N. Huynh, F. Lucka, O. Ogunlade, and E. Zhang, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Medicine Biol. 61, 8908–8940 (2016).
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S. R. Arridge, M. M. Betcke, B. T. Cox, F. Lucka, and B. E. Treeby, “On the adjoint operator in photoacoustic tomography,” Inverse Probl. 32, 115012 (2016).
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A. E. Cerussi, A. J. Berger, F. Bevilacqua, N. Shah, D. Jakubowski, J. Butler, R. F. Holcombe, and B. J. Tromberg, “Sources of absorption and scattering contrast for near-infrared optical mammography,” Acad. Radiol. 8, 211–218 (2001).
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S. J. Ford, P. L. Bigliardi, T. C. Sardella, A. Urich, N. C. Burton, M. Kacprowicz, M. Bigliardi, M. Olivo, and D. Razansky, “Structural and functional analysis of intact hair follicles and pilosebaceous units by volumetric multispectral optoacoustic tomography,” J. Investig. Dermatol. 136, 753–761 (2016).
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

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X. L. Dean-Ben, A. Buehler, V. Ntziachristos, and D. Razansky, “Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography,” IEEE Transactions on Med. Imaging 31, 1922–1928 (2012).
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S. J. Ford, P. L. Bigliardi, T. C. Sardella, A. Urich, N. C. Burton, M. Kacprowicz, M. Bigliardi, M. Olivo, and D. Razansky, “Structural and functional analysis of intact hair follicles and pilosebaceous units by volumetric multispectral optoacoustic tomography,” J. Investig. Dermatol. 136, 753–761 (2016).
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Citro, C.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

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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).
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

Cox, B.

S. Arridge, P. Beard, M. Betcke, B. Cox, N. Huynh, F. Lucka, O. Ogunlade, and E. Zhang, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Medicine Biol. 61, 8908–8940 (2016).
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S. R. Arridge, M. M. Betcke, B. T. Cox, F. Lucka, and B. E. Treeby, “On the adjoint operator in photoacoustic tomography,” Inverse Probl. 32, 115012 (2016).
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De, A.

P. P. Pai, A. De, and S. Banerjee, “Accuracy enhancement for noninvasive glucose estimation using dual-wavelength photoacoustic measurements and kernel-based calibration,” IEEE Transactions on Instrumentation Meas. 67, 126–136 (2018).
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

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X. L. Dean-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Transactions on Med. Imaging 31, 1154–1162 (2012).
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J. Prakash, H. Dehghani, B. W. Pogue, and P. K. Yalavarthy, “Model-resolution-based basis pursuit deconvolution improves diffuse optical tomographic imaging,” IEEE Transactions on Med. Imaging 33, 891–901 (2014).
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467 (2016).

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A. Buehler, A. Rosenthal, T. Jetzfellner, A. Dima, D. Razansky, and V. Ntziachristos, “Model-based optoacoustic inversions with incomplete projection data,” Med. Phys. 38, 1694–1704 (2011).
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B. Pourebrahimi, S. Yoon, D. Dopsa, and M. C. Kolios, “Improving the quality of photoacoustic images using the short-lag spatial coherence imaging technique,” in Photons Plus Ultrasound: Imaging and Sensing 2013, vol. 8581 (International Society for Optics and Photonics, 2013), p. 85813Y.

Doyley, M. M.

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J. Eckstein and D. P. Bertsekas, “On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators,” Math. Program. 55, 293–318 (1992).
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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).
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Ford, S. J.

S. J. Ford, P. L. Bigliardi, T. C. Sardella, A. Urich, N. C. Burton, M. Kacprowicz, M. Bigliardi, M. Olivo, and D. Razansky, “Structural and functional analysis of intact hair follicles and pilosebaceous units by volumetric multispectral optoacoustic tomography,” J. Investig. Dermatol. 136, 753–761 (2016).
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M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Transactions on Biomed. Eng. 59, 2538–2548 (2012).
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D. Van de Sompel, L. S. Sasportas, J. V. Jokerst, and S. S. Gambhir, “Comparison of deconvolution filters for photoacoustic tomography,” PloS One 11, e0152597 (2016).
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N. Gandhi, M. Allard, S. Kim, P. Kazanzides, and M. A. L. Bell, “Photoacoustic-based approach to surgical guidance performed with and without a da vinci robot,” J. Biomed. Opt. 22, 121606 (2017).
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S. Gutta, M. Bhatt, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Modeling errors compensation with total least squares for limited data photoacoustic tomography,” IEEE J. Sel. Top. Quantum Electron. 25, 1–14 (2019).
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A. E. Cerussi, A. J. Berger, F. Bevilacqua, N. Shah, D. Jakubowski, J. Butler, R. F. Holcombe, and B. J. Tromberg, “Sources of absorption and scattering contrast for near-infrared optical mammography,” Acad. Radiol. 8, 211–218 (2001).
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A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Med. Imaging 19, 203–210 (2000).
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M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach applied to retinal blood vessel segmentation,” IEEE Transactions on Biomed. Eng. 59, 2538–2548 (2012).
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J. Yao, L. Wang, J.-M. Yang, K. I. Maslov, T. T. Wong, L. Li, C.-H. Huang, J. Zou, and L. V. Wang, “High-speed label-free functional photoacoustic microscopy of mouse brain in action,” Nat. Methods 12, 407–410 (2015).
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S. Arridge, P. Beard, M. Betcke, B. Cox, N. Huynh, F. Lucka, O. Ogunlade, and E. Zhang, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Medicine Biol. 61, 8908–8940 (2016).
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A. E. Cerussi, A. J. Berger, F. Bevilacqua, N. Shah, D. Jakubowski, J. Butler, R. F. Holcombe, and B. J. Tromberg, “Sources of absorption and scattering contrast for near-infrared optical mammography,” Acad. Radiol. 8, 211–218 (2001).
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Jetzfellner, T.

A. Buehler, A. Rosenthal, T. Jetzfellner, A. Dima, D. Razansky, and V. Ntziachristos, “Model-based optoacoustic inversions with incomplete projection data,” Med. Phys. 38, 1694–1704 (2011).
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Jiang, S.

Jiao, S.

Jokerst, J. V.

D. Van de Sompel, L. S. Sasportas, J. V. Jokerst, and S. S. Gambhir, “Comparison of deconvolution filters for photoacoustic tomography,” PloS One 11, e0152597 (2016).
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S. J. Ford, P. L. Bigliardi, T. C. Sardella, A. Urich, N. C. Burton, M. Kacprowicz, M. Bigliardi, M. Olivo, and D. Razansky, “Structural and functional analysis of intact hair follicles and pilosebaceous units by volumetric multispectral optoacoustic tomography,” J. Investig. Dermatol. 136, 753–761 (2016).
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Kalva, S. K.

S. Gutta, M. Bhatt, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Modeling errors compensation with total least squares for limited data photoacoustic tomography,” IEEE J. Sel. Top. Quantum Electron. 25, 1–14 (2019).
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N. Awasthi, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography,” J. Biomed. Opt. 23, 071204 (2018).
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N. Awasthi, S. K. Kalva, M. Pramanik, and P. K. Yalavarthy, “Image-guided filtering for improving photoacoustic tomographic image reconstruction,” J. Biomed. Opt. 23, 091413 (2018).
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S. K. Kalva and M. Pramanik, “Experimental validation of tangential resolution improvement in photoacoustic tomography using modified delay-and-sum reconstruction algorithm,” J. Biomed. Opt. 21, 086011 (2016).
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S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” IEEE Transactions on Image Process. 22, 2864–2875 (2013).
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N. Gandhi, M. Allard, S. Kim, P. Kazanzides, and M. A. L. Bell, “Photoacoustic-based approach to surgical guidance performed with and without a da vinci robot,” J. Biomed. Opt. 22, 121606 (2017).
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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).
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N. Gandhi, M. Allard, S. Kim, P. Kazanzides, and M. A. L. Bell, “Photoacoustic-based approach to surgical guidance performed with and without a da vinci robot,” J. Biomed. Opt. 22, 121606 (2017).
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D. Kingma and J. A. Ba, “A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Kolios, M. C.

B. Pourebrahimi, S. Yoon, D. Dopsa, and M. C. Kolios, “Improving the quality of photoacoustic images using the short-lag spatial coherence imaging technique,” in Photons Plus Ultrasound: Imaging and Sensing 2013, vol. 8581 (International Society for Optics and Photonics, 2013), p. 85813Y.

Kouznetsova, V.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Med. Imaging 19, 203–210 (2000).
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R. A. Kruger, C. M. Kuzmiak, R. B. Lam, D. R. Reinecke, S. P. Del Rio, and D. Steed, “Dedicated 3d photoacoustic breast imaging,” Med. Phys. 4013301 (2013).
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Y. Xu, L. V. Wang, G. Ambartsoumian, and P. Kuchment, “Reconstructions in limited-view thermoacoustic tomography,” Med. Phys. 31, 724–733 (2004).
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R. A. Kruger, C. M. Kuzmiak, R. B. Lam, D. R. Reinecke, S. P. Del Rio, and D. Steed, “Dedicated 3d photoacoustic breast imaging,” Med. Phys. 4013301 (2013).
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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).
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R. A. Kruger, C. M. Kuzmiak, R. B. Lam, D. R. Reinecke, S. P. Del Rio, and D. Steed, “Dedicated 3d photoacoustic breast imaging,” Med. Phys. 4013301 (2013).
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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).
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M. Pramanik, G. Ku, C. Li, and L. V. Wang, “Design and evaluation of a novel breast cancer detection system combining both thermoacoustic (ta) and photoacoustic (pa) tomography,” Med. Phys. 35, 2218–2223 (2008).
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S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” IEEE Transactions on Image Process. 22, 2864–2875 (2013).
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Lin, R.

Lucka, F.

S. Arridge, P. Beard, M. Betcke, B. Cox, N. Huynh, F. Lucka, O. Ogunlade, and E. Zhang, “Accelerated high-resolution photoacoustic tomography via compressed sensing,” Phys. Medicine Biol. 61, 8908–8940 (2016).
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S. R. Arridge, M. M. Betcke, B. T. Cox, F. Lucka, and B. E. Treeby, “On the adjoint operator in photoacoustic tomography,” Inverse Probl. 32, 115012 (2016).
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Manohar, S.

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S. K. Kalva, P. K. Upputuri, and M. Pramanik, “High-speed, low-cost, pulsed-laser-diode-based second-generation desktop photoacoustic tomography system,” Opt. Lett. 44, 81–84 (2019).
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Figures (10)

Fig. 1
Fig. 1 Flowchart of the fusion process. The typical computational times for each process is given below each block.
Fig. 2
Fig. 2 Network architecture of proposed PA-Fuse model. It consists of three stages: feature encoder to extract salient features, feature aggregator to combine them, and feature decoder to reconstruct fused image. In the feature encoder section, feature extraction of input images I1 and I2 using two convolutional layers with tied weights were achieved. The extracted features are fused using sum operation and fused image was reconstructed using four convolutional layers.
Fig. 3
Fig. 3 Schematic diagram of photoacoustic data acquisition geometry along with depiction of position of 100 acoustic detectors (shown by dots) around the imaging domain. The data generation grid had a dimension of 401×401 and reconstruction grid was 201×201.
Fig. 4
Fig. 4 Target blood vessel (BV) phantom is shown in (a). The reconstructed initial pressure distribution with SNR of measurement data being 40 dB using (b) LBP, (c) LTO, and (d) TV regularization. The results obtained using modified guided filter with (b) as guiding image and to be guided image being (c) and (d) are correspondingly given in (e) and (g). The results using the PA-Fuse with one input image being (b) and another being (c) and (d) are correspondingly given in (f) and (h).The RMSE and CNR of these images are correspondingly presented in Table-1.
Fig. 5
Fig. 5 The logarithm of amplitude of the Fourier spectrum of results pertaining to (a) Target BV Phantom (spatial distribution is given in Fig. 4(a)), (b) Linear Back-Projection (LBP) (spatial distribution is given in Fig. 4(b)), (c) Lanczos-Tikhonov-Optimal (LTO) (spatial distribution is given in Fig. 4(c)), (d) Total Variation (TV) (spatial distribution is given in Fig. 4(d)), (e) Modified guided filter based fused output with LBP and LTO as inputs (spatial distribution is given in Fig. 4(e)), (f) Proposed PA-Fuse output with LBP and LTO as inputs (spatial distribution is given in Fig. 4(f)), (g) Modified guided filter based fused output with LBP and TV as inputs (spatial distribution is given in Fig. 4(g)), and (h) Proposed PA-Fuse output with LBP and TV as inputs (spatial distribution is given in Fig. 4(h)).
Fig. 6
Fig. 6 Target modified Derenzo phantom is shown in (a). The reconstructed initial pressure distribution with SNR of measurement data being 40 dB using (b) LBP, (c) LTO, and (d) TV regularization. The results obtained using modified guided filter with (b) as guiding image and to be guided image being (c) and (d) are correspondingly given in (e) and (g). The results using the PA-Fuse with one input image being (b) and another being (c) and (d) are correspondingly given in (f) and (h). The RMSE and CNR of these images are correspondingly presented in Table-1. The red arrows in the TV (reconstructed or fused) images show non-uniform background induced artifacts.
Fig. 7
Fig. 7 Target PAT phantom is shown in (a). The reconstructed initial pressure distribution with SNR of measurement data being 40 dB using (b) LBP, (c) LTO, and (d) TV regularization. The results obtained using modified guided filter with (b) as guiding image and to be guided image being (c) and (d) are correspondingly given in (e) and (g). The results using the PA-Fuse with one input image being (b) and another being (c) and (d) are correspondingly given in (f) and (h).The RMSE and CNR of these images are correspondingly presented in Table-1.
Fig. 8
Fig. 8 The initial pressure distribution reconstruction for the experimental horse hair phantom data obtained using Nd:YAG laser with LBP, LTO, and TV regularization methods are given in (a), (b), and (c) respectively. The results obtained using modified guided filter with (a) as guiding image and to be guided image being (b) and (c) are correspondingly given in (d) and (f). The results using the PA-Fuse with one input image being (a) and another being (b) and (c) are correspondingly given in (e) and (g). SNRr (in dB), of each PA image is given below.
Fig. 9
Fig. 9 The initial pressure distribution reconstruction for experimental horse hair phantom data obtained using PLD laser with LBP, LTO, and TV regularization methods are given in (a), (b), and (c) respectively. The results obtained using modified guided filter with (a) as guiding image and to be guided image being (b) and (c) are correspondingly given in (d) and (f). The results using the PA-Fuse with one input image being (a) and another being (b) and (c) are correspondingly given in (e) and (g). SNRr (in dB), of each PA image is given below.
Fig. 10
Fig. 10 The initial pressure distribution reconstruction for the rat brain in-vivo data with LBP, Delay-and-Sum (DAS), LTO, and TV regularization methods are given in (a), (b), (c), and (d) respectively. The results obtained using the modified guided filter with (a) as guiding image and to be guided image as (c) and (d) are shown in (e) and (g) respectively. The results using the PA-Fuse with one input image being (a) and another being (b) and (c) are correspondingly given in (f) and (h). The results obtained using the modified guided filter with (b) as guiding image and to be guided image as (c) and (d) are shown in (i) and (k) respectively. The results using the PA-Fuse with one input image being (b) and another being (c) and (d) are correspondingly given in (j) and (l). SNRr (in dB), of each PA image is given below.

Tables (3)

Tables Icon

Algorithm 1 SALSA Algorithm

Tables Icon

Algorithm 2 Modified Guided Filter(xI, xG, w, , α, β), go(.) represents performing operation ‘o’ on the arguments

Tables Icon

Table 1 The figures of merit, RMSE and CNR, for the resultant images using reconstruction methods examined here with SNR in measurement data being 30 dB, 40 dB, and 50 dB utilizing three numerical phantoms (blood vessel (BV), Derenzo, and PAT). For the case of guided filter, guiding image was always LBP and other input being given being given as argument. The results of the proposed method, PA-Fuse, are given in bottom two rows with one input being LBP image and another image given as argument. The highest RMSE and CNR in each column are marked in bold.

Equations (18)

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2 P ( x , t ) 1 c 2 2 P ( x , t ) t 2 = β C p H ( x , t ) t ,
A x = b ,
x LBP = A T b ,
Ω = A x b 2 2 + δ x 2 2
( A T A + δ I ) x Tikhonov = A T b
E k + 1 ( γ 0 e 1 ) = b
A F k = E k + 1 B k
A T E k + 1 = F K B k T + δ k + 1 n k + 1 e k + 1 T
b A x = E K + 1 ( γ 0 e 1 B K x ( k ) ) ; x = F K x ( k )
Ω ˜ = γ 0 e 1 B K x ( k ) 2 2 + δ x ( k ) 2 2
x estimate ( k ) = ( B K T B K + δ I ) 1 γ 0 B K T e 1
x estimate = F K x estimate ( k )
Γ = A x b 2 2 + η x TV ,
F 1 1 = I 1 f 1 ; F 1 2 = I 2 f 1 F 2 1 = F 1 1 f 2 ; F 2 2 = F 1 2 f 2 F fused = F 2 1 + F 2 2 F 3 = F fused f 3 F 4 = F l 3 f 4 F 5 = F 4 f 5 Output = F 5 f 6
Loss = 𝒩 ( I 1 , I 2 ) O gt 2
RMSE ( x target , x recon ) = Σ ( x target x recon ) N
CNR = m roi m back ( v roi 2 a roi + v back 2 a back ) 1 / 2
SNR r ( in dB ) = 20 × log 10 ( P Pressure n I )

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