Abstract

To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme, where a concept of virtual parallel-projection matching the measurement condition is introduced to aid the “compressive sensing” in the reconstruction procedure, and meanwhile, the non-local spatially adaptive filtering exploring the a priori information of the mutual similarities in natural images is adopted to recover the unknowns in the transformed sparse domain. Consequently, the reconstructed images with the proposed sparse-view scheme can be evidently improved in comparison to those with the universal back-projection method, for the cases of same sparse views. The proposed approach has been validated by the simulations and ex vivo experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.

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

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References

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  1. X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
    [Crossref] [PubMed]
  2. L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
    [Crossref] [PubMed]
  3. J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61(5), 1380–1389 (2014).
    [Crossref] [PubMed]
  4. J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
    [Crossref]
  5. M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
    [Crossref] [PubMed]
  6. M. Heijblom, D. Piras, W. Xia, J. C. G. van Hespen, J. M. Klaase, F. M. van den Engh, T. G. van Leeuwen, W. Steenbergen, and S. Manohar, “Visualizing breast cancer using the Twente photoacoustic mammoscope: what do we learn from twelve new patient measurements?” Opt. Express 20(11), 11582–11597 (2012).
    [Crossref] [PubMed]
  7. L. Nie, Z. Guo, and L. V. Wang, “Photoacoustic tomography of monkey brain using virtual point ultrasonic transducers,” J. Biomed. Opt. 16(7), 076005 (2011).
    [Crossref] [PubMed]
  8. Z. Deng, W. Li, and C. Li, “Slip-ring-based multi-transducer photoacoustic tomography system,” Opt. Lett. 41(12), 2859–2862 (2016).
    [Crossref] [PubMed]
  9. M. Xu and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E 71(1), 016706 (2005).
    [Crossref] [PubMed]
  10. X. L. Deán-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Trans. Med. Imaging 31(5), 1154–1162 (2012).
    [Crossref] [PubMed]
  11. C. G. Graff and E. Y. Sidky, “Compressive sensing in medical imaging,” Appl. Opt. 54(8), C23–C44 (2015).
    [Crossref] [PubMed]
  12. J. Provost and F. Lesage, “The application of compressed sensing for photo-acoustic tomography,” IEEE Trans. Med. Imaging 28(4), 585–594 (2009).
    [Crossref] [PubMed]
  13. Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics 52(8), 1046–1055 (2012).
    [Crossref] [PubMed]
  14. Y. Dong, T. Gorner, and S. Kunis, “An algorithm for total variation regularized photoacoustic imaging,” Adv. Comput. Math. 41(2), 423–438 (2015).
    [Crossref]
  15. Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
    [Crossref] [PubMed]
  16. C. Zhang and Y. Wang, “High total variation-based method for sparse-view photoacoustic reconstruction,” Chin. Opt. Lett. 12(11), 111703 (2014).
    [Crossref]
  17. J. Wang, C. Zhang, and Y. Wang, “A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity,” Biomed. Eng. Online 16(1), 64 (2017).
    [Crossref] [PubMed]
  18. M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
    [Crossref]
  19. Y. Han, L. Ding, X. L. D. Ben, D. Razansky, J. Prakash, and V. Ntziachristos, “Three-dimensional optoacoustic reconstruction using fast sparse representation,” Opt. Lett. 42(5), 979–982 (2017).
    [Crossref] [PubMed]
  20. J. Meng, L. V. Wang, L. Ying, D. Liang, and L. Song, “Compressed-sensing photoacoustic computed tomography in vivo with partially known support,” Opt. Express 20(15), 16510–16523 (2012).
    [Crossref]
  21. J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
    [Crossref] [PubMed]
  22. M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
    [Crossref]
  23. M. Sun, N. Feng, Y. Shen, X. Shen, L. Ma, J. Li, and Z. Wu, “Photoacoustic imaging method based on arc-direction compressed sensing and multi-angle observation,” Opt. Express 19(16), 14801–14806 (2011).
    [Crossref] [PubMed]
  24. 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. Med. Biol. 61(24), 8908–8940 (2016).
    [Crossref] [PubMed]
  25. C. Lutzweiler and D. Razansky, “Optoacoustic imaging and tomography: reconstruction approaches and outstanding challenges in image performance and quantification,” Sensors (Basel) 13(6), 7345–7384 (2013).
    [Crossref] [PubMed]
  26. X. Fei, Z. Wei, and L. Xiao, “Iterative directional total variation refinement for compressive sensing image reconstruction,” IEEE Signal Process. Lett. 20(11), 1070–1073 (2013).
    [Crossref]
  27. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
    [Crossref] [PubMed]
  28. G. L. Zeng, Medical image reconstruction: A conceptual tutorial (Higher Education Press, 2010).
  29. A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9(4), 318–336 (2014).
    [Crossref] [PubMed]
  30. P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
    [Crossref]
  31. G. Paltauf, R. Nuster, M. Haltmeier, and P. Burgholzer, “Photoacoustic tomography using a Mach-Zehnder interferometer as an acoustic line detector,” Appl. Opt. 46(16), 3352–3358 (2007).
    [Crossref] [PubMed]
  32. M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
    [Crossref]
  33. L. V. Wang and L. Gao, “Photoacoustic microscopy and computed tomography: from bench to bedside,” Annu. Rev. Biomed. Eng. 16(1), 155–185 (2014).
    [Crossref] [PubMed]
  34. A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Spatially adaptive filtering as regularization in inverse imaging: Compressive sensing, super-resolution, and upsampling,” in Super-Resolution Imaging, M. Peyman, ed. (CRC, 2010).
  35. S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389–399 (1997).
    [Crossref]
  36. B. E. Treeby and B. T. Cox, “k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15(2), 021314 (2010).
    [Crossref] [PubMed]

2017 (5)

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

J. Wang, C. Zhang, and Y. Wang, “A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity,” Biomed. Eng. Online 16(1), 64 (2017).
[Crossref] [PubMed]

Y. Han, L. Ding, X. L. D. Ben, D. Razansky, J. Prakash, and V. Ntziachristos, “Three-dimensional optoacoustic reconstruction using fast sparse representation,” Opt. Lett. 42(5), 979–982 (2017).
[Crossref] [PubMed]

2016 (5)

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
[Crossref]

Z. Deng, W. Li, and C. Li, “Slip-ring-based multi-transducer photoacoustic tomography system,” Opt. Lett. 41(12), 2859–2862 (2016).
[Crossref] [PubMed]

M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
[Crossref] [PubMed]

2015 (4)

Y. Dong, T. Gorner, and S. Kunis, “An algorithm for total variation regularized photoacoustic imaging,” Adv. Comput. Math. 41(2), 423–438 (2015).
[Crossref]

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

C. G. Graff and E. Y. Sidky, “Compressive sensing in medical imaging,” Appl. Opt. 54(8), C23–C44 (2015).
[Crossref] [PubMed]

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

2014 (4)

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9(4), 318–336 (2014).
[Crossref] [PubMed]

L. V. Wang and L. Gao, “Photoacoustic microscopy and computed tomography: from bench to bedside,” Annu. Rev. Biomed. Eng. 16(1), 155–185 (2014).
[Crossref] [PubMed]

C. Zhang and Y. Wang, “High total variation-based method for sparse-view photoacoustic reconstruction,” Chin. Opt. Lett. 12(11), 111703 (2014).
[Crossref]

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61(5), 1380–1389 (2014).
[Crossref] [PubMed]

2013 (2)

C. Lutzweiler and D. Razansky, “Optoacoustic imaging and tomography: reconstruction approaches and outstanding challenges in image performance and quantification,” Sensors (Basel) 13(6), 7345–7384 (2013).
[Crossref] [PubMed]

X. Fei, Z. Wei, and L. Xiao, “Iterative directional total variation refinement for compressive sensing image reconstruction,” IEEE Signal Process. Lett. 20(11), 1070–1073 (2013).
[Crossref]

2012 (4)

J. Meng, L. V. Wang, L. Ying, D. Liang, and L. Song, “Compressed-sensing photoacoustic computed tomography in vivo with partially known support,” Opt. Express 20(15), 16510–16523 (2012).
[Crossref]

M. Heijblom, D. Piras, W. Xia, J. C. G. van Hespen, J. M. Klaase, F. M. van den Engh, T. G. van Leeuwen, W. Steenbergen, and S. Manohar, “Visualizing breast cancer using the Twente photoacoustic mammoscope: what do we learn from twelve new patient measurements?” Opt. Express 20(11), 11582–11597 (2012).
[Crossref] [PubMed]

X. L. Deán-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Trans. Med. Imaging 31(5), 1154–1162 (2012).
[Crossref] [PubMed]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics 52(8), 1046–1055 (2012).
[Crossref] [PubMed]

2011 (2)

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(2), 021314 (2010).
[Crossref] [PubMed]

2009 (1)

J. Provost and F. Lesage, “The application of compressed sensing for photo-acoustic tomography,” IEEE Trans. Med. Imaging 28(4), 585–594 (2009).
[Crossref] [PubMed]

2007 (3)

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

G. Paltauf, R. Nuster, M. Haltmeier, and P. Burgholzer, “Photoacoustic tomography using a Mach-Zehnder interferometer as an acoustic line detector,” Appl. Opt. 46(16), 3352–3358 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

2005 (1)

M. Xu and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E 71(1), 016706 (2005).
[Crossref] [PubMed]

2004 (1)

M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
[Crossref]

1997 (1)

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389–399 (1997).
[Crossref]

Aguirre, J.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
[Crossref] [PubMed]

Arridge, S.

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Baraniuk, R. G.

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389–399 (1997).
[Crossref]

Bauer-Marschallinger, J.

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

Beard, P.

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Ben, X. L. D.

Berer, T.

M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
[Crossref]

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

Betcke, M.

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Buehler, A.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
[Crossref] [PubMed]

Burgholzer, P.

M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
[Crossref]

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

G. Paltauf, R. Nuster, M. Haltmeier, and P. Burgholzer, “Photoacoustic tomography using a Mach-Zehnder interferometer as an acoustic line detector,” Appl. Opt. 46(16), 3352–3358 (2007).
[Crossref] [PubMed]

M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
[Crossref]

Chen, W.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

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(2), 021314 (2010).
[Crossref] [PubMed]

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

Deán-Ben, X. L.

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

X. L. Deán-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Trans. Med. Imaging 31(5), 1154–1162 (2012).
[Crossref] [PubMed]

Deng, Z.

Ding, L.

Dong, Y.

Y. Dong, T. Gorner, and S. Kunis, “An algorithm for total variation regularized photoacoustic imaging,” Adv. Comput. Math. 41(2), 423–438 (2015).
[Crossref]

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

Eyerich, K.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

Fei, X.

X. Fei, Z. Wei, and L. Xiao, “Iterative directional total variation refinement for compressive sensing image reconstruction,” IEEE Signal Process. Lett. 20(11), 1070–1073 (2013).
[Crossref]

Feng, N.

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

Gao, L.

L. V. Wang and L. Gao, “Photoacoustic microscopy and computed tomography: from bench to bedside,” Annu. Rev. Biomed. Eng. 16(1), 155–185 (2014).
[Crossref] [PubMed]

Garzorz, N.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

Ghael, S. P.

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389–399 (1997).
[Crossref]

Gorner, T.

Y. Dong, T. Gorner, and S. Kunis, “An algorithm for total variation regularized photoacoustic imaging,” Adv. Comput. Math. 41(2), 423–438 (2015).
[Crossref]

Gottschalk, S.

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

Graff, C. G.

Gruen, H.

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

Guo, Z.

L. Nie, Z. Guo, and L. V. Wang, “Photoacoustic tomography of monkey brain using virtual point ultrasonic transducers,” J. Biomed. Opt. 16(7), 076005 (2011).
[Crossref] [PubMed]

Haltmeier, M.

M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
[Crossref]

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

G. Paltauf, R. Nuster, M. Haltmeier, and P. Burgholzer, “Photoacoustic tomography using a Mach-Zehnder interferometer as an acoustic line detector,” Appl. Opt. 46(16), 3352–3358 (2007).
[Crossref] [PubMed]

M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
[Crossref]

Han, Y.

Y. Han, L. Ding, X. L. D. Ben, D. Razansky, J. Prakash, and V. Ntziachristos, “Three-dimensional optoacoustic reconstruction using fast sparse representation,” Opt. Lett. 42(5), 979–982 (2017).
[Crossref] [PubMed]

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

Heijblom, M.

Huynh, N.

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Jiang, Z.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

Kim, C.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

Klaase, J. M.

Krahmer, F.

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

Kunis, S.

Y. Dong, T. Gorner, and S. Kunis, “An algorithm for total variation regularized photoacoustic imaging,” Adv. Comput. Math. 41(2), 423–438 (2015).
[Crossref]

Lesage, F.

J. Provost and F. Lesage, “The application of compressed sensing for photo-acoustic tomography,” IEEE Trans. Med. Imaging 28(4), 585–594 (2009).
[Crossref] [PubMed]

Li, C.

Li, J.

Li, L.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Li, W.

Liang, D.

Lin, L.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Lutzweiler, C.

C. Lutzweiler and D. Razansky, “Optoacoustic imaging and tomography: reconstruction approaches and outstanding challenges in image performance and quantification,” Sensors (Basel) 13(6), 7345–7384 (2013).
[Crossref] [PubMed]

Ma, C.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Ma, L.

Manohar, S.

Maslov, K.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Mc Larney, B.

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

Meng, J.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

J. Meng, L. V. Wang, L. Ying, D. Liang, and L. Song, “Compressed-sensing photoacoustic computed tomography in vivo with partially known support,” Opt. Express 20(15), 16510–16523 (2012).
[Crossref]

Moon, S.

M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
[Crossref]

Nie, L.

L. Nie, Z. Guo, and L. V. Wang, “Photoacoustic tomography of monkey brain using virtual point ultrasonic transducers,” J. Biomed. Opt. 16(7), 076005 (2011).
[Crossref] [PubMed]

Ntziachristos, V.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

Y. Han, L. Ding, X. L. D. Ben, D. Razansky, J. Prakash, and V. Ntziachristos, “Three-dimensional optoacoustic reconstruction using fast sparse representation,” Opt. Lett. 42(5), 979–982 (2017).
[Crossref] [PubMed]

M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
[Crossref] [PubMed]

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9(4), 318–336 (2014).
[Crossref] [PubMed]

X. L. Deán-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Trans. Med. Imaging 31(5), 1154–1162 (2012).
[Crossref] [PubMed]

Nunes, A.

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

Nuster, R.

Ogunlade, O.

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Omar, M.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

Paltauf, G.

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

G. Paltauf, R. Nuster, M. Haltmeier, and P. Burgholzer, “Photoacoustic tomography using a Mach-Zehnder interferometer as an acoustic line detector,” Appl. Opt. 46(16), 3352–3358 (2007).
[Crossref] [PubMed]

M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
[Crossref]

Park, J.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

Piras, D.

Prakash, J.

Provost, J.

J. Provost and F. Lesage, “The application of compressed sensing for photo-acoustic tomography,” IEEE Trans. Med. Imaging 28(4), 585–594 (2009).
[Crossref] [PubMed]

Razansky, D.

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

Y. Han, L. Ding, X. L. D. Ben, D. Razansky, J. Prakash, and V. Ntziachristos, “Three-dimensional optoacoustic reconstruction using fast sparse representation,” Opt. Lett. 42(5), 979–982 (2017).
[Crossref] [PubMed]

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9(4), 318–336 (2014).
[Crossref] [PubMed]

C. Lutzweiler and D. Razansky, “Optoacoustic imaging and tomography: reconstruction approaches and outstanding challenges in image performance and quantification,” Sensors (Basel) 13(6), 7345–7384 (2013).
[Crossref] [PubMed]

X. L. Deán-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Trans. Med. Imaging 31(5), 1154–1162 (2012).
[Crossref] [PubMed]

Rosenthal, A.

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9(4), 318–336 (2014).
[Crossref] [PubMed]

Sandbichler, M.

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

Sayeed, A. M.

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389–399 (1997).
[Crossref]

Scherzer, O.

M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
[Crossref]

Schwarz, M.

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
[Crossref] [PubMed]

Shen, X.

Shen, Y.

Shi, J.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Shoham, S.

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

Sidky, E. Y.

Song, L.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

J. Meng, L. V. Wang, L. Ying, D. Liang, and L. Song, “Compressed-sensing photoacoustic computed tomography in vivo with partially known support,” Opt. Express 20(15), 16510–16523 (2012).
[Crossref]

Steenbergen, W.

Sun, M.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

M. Sun, N. Feng, Y. Shen, X. Shen, L. Ma, J. Li, and Z. Wu, “Photoacoustic imaging method based on arc-direction compressed sensing and multi-angle observation,” Opt. Express 19(16), 14801–14806 (2011).
[Crossref] [PubMed]

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(2), 021314 (2010).
[Crossref] [PubMed]

Tzoumas, S.

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

van den Engh, F. M.

van Hespen, J. C. G.

van Leeuwen, T. G.

Wang, J.

J. Wang, C. Zhang, and Y. Wang, “A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity,” Biomed. Eng. Online 16(1), 64 (2017).
[Crossref] [PubMed]

Wang, L.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Wang, L. V.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

L. V. Wang and L. Gao, “Photoacoustic microscopy and computed tomography: from bench to bedside,” Annu. Rev. Biomed. Eng. 16(1), 155–185 (2014).
[Crossref] [PubMed]

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61(5), 1380–1389 (2014).
[Crossref] [PubMed]

J. Meng, L. V. Wang, L. Ying, D. Liang, and L. Song, “Compressed-sensing photoacoustic computed tomography in vivo with partially known support,” Opt. Express 20(15), 16510–16523 (2012).
[Crossref]

L. Nie, Z. Guo, and L. V. Wang, “Photoacoustic tomography of monkey brain using virtual point ultrasonic transducers,” J. Biomed. Opt. 16(7), 076005 (2011).
[Crossref] [PubMed]

M. Xu and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E 71(1), 016706 (2005).
[Crossref] [PubMed]

Wang, Y.

J. Wang, C. Zhang, and Y. Wang, “A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity,” Biomed. Eng. Online 16(1), 64 (2017).
[Crossref] [PubMed]

C. Zhang and Y. Wang, “High total variation-based method for sparse-view photoacoustic reconstruction,” Chin. Opt. Lett. 12(11), 111703 (2014).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics 52(8), 1046–1055 (2012).
[Crossref] [PubMed]

Wei, Z.

X. Fei, Z. Wei, and L. Xiao, “Iterative directional total variation refinement for compressive sensing image reconstruction,” IEEE Signal Process. Lett. 20(11), 1070–1073 (2013).
[Crossref]

Wu, Z.

Xia, J.

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61(5), 1380–1389 (2014).
[Crossref] [PubMed]

Xia, W.

Xiao, L.

X. Fei, Z. Wei, and L. Xiao, “Iterative directional total variation refinement for compressive sensing image reconstruction,” IEEE Signal Process. Lett. 20(11), 1070–1073 (2013).
[Crossref]

Xu, M.

M. Xu and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E 71(1), 016706 (2005).
[Crossref] [PubMed]

Yao, J.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Ying, L.

Zhang, C.

J. Wang, C. Zhang, and Y. Wang, “A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity,” Biomed. Eng. Online 16(1), 64 (2017).
[Crossref] [PubMed]

C. Zhang and Y. Wang, “High total variation-based method for sparse-view photoacoustic reconstruction,” Chin. Opt. Lett. 12(11), 111703 (2014).
[Crossref]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics 52(8), 1046–1055 (2012).
[Crossref] [PubMed]

Zhang, E.

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Zhang, R.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Zhang, Y.

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics 52(8), 1046–1055 (2012).
[Crossref] [PubMed]

Zhu, L.

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Adv. Comput. Math. (1)

Y. Dong, T. Gorner, and S. Kunis, “An algorithm for total variation regularized photoacoustic imaging,” Adv. Comput. Math. 41(2), 423–438 (2015).
[Crossref]

Annu. Rev. Biomed. Eng. (1)

L. V. Wang and L. Gao, “Photoacoustic microscopy and computed tomography: from bench to bedside,” Annu. Rev. Biomed. Eng. 16(1), 155–185 (2014).
[Crossref] [PubMed]

Appl. Opt. (2)

Biomed. Eng. Online (1)

J. Wang, C. Zhang, and Y. Wang, “A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity,” Biomed. Eng. Online 16(1), 64 (2017).
[Crossref] [PubMed]

Chem. Soc. Rev. (1)

X. L. Deán-Ben, S. Gottschalk, B. Mc Larney, S. Shoham, and D. Razansky, “Advanced optoacoustic methods for multiscale imaging of in vivo dynamics,” Chem. Soc. Rev. 46(8), 2158–2198 (2017).
[Crossref] [PubMed]

Chin. Opt. Lett. (1)

Curr. Med. Imaging Rev. (1)

A. Rosenthal, V. Ntziachristos, and D. Razansky, “Acoustic inversion in optoacoustic tomography: A review,” Curr. Med. Imaging Rev. 9(4), 318–336 (2014).
[Crossref] [PubMed]

IEEE Signal Process. Lett. (1)

X. Fei, Z. Wei, and L. Xiao, “Iterative directional total variation refinement for compressive sensing image reconstruction,” IEEE Signal Process. Lett. 20(11), 1070–1073 (2013).
[Crossref]

IEEE Trans. Biomed. Eng. (1)

J. Xia and L. V. Wang, “Small-animal whole-body photoacoustic tomography: a review,” IEEE Trans. Biomed. Eng. 61(5), 1380–1389 (2014).
[Crossref] [PubMed]

IEEE Trans. Image Process. (1)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

X. L. Deán-Ben, V. Ntziachristos, and D. Razansky, “Acceleration of optoacoustic model-based reconstruction using angular image discretization,” IEEE Trans. Med. Imaging 31(5), 1154–1162 (2012).
[Crossref] [PubMed]

J. Provost and F. Lesage, “The application of compressed sensing for photo-acoustic tomography,” IEEE Trans. Med. Imaging 28(4), 585–594 (2009).
[Crossref] [PubMed]

Inverse Probl. (2)

P. Burgholzer, J. Bauer-Marschallinger, H. Gruen, M. Haltmeier, and G. Paltauf, “Temporal back-projection algorithms for photoacoustic tomography with integrating line detectors,” Inverse Probl. 23(6), S65–S80 (2007).
[Crossref]

M. Haltmeier, O. Scherzer, P. Burgholzer, and G. Paltauf, “Thermoacoustic computed tomography with large planar receivers,” Inverse Probl. 20(5), 1663–1673 (2004).
[Crossref]

J. Biomed. Opt. (3)

B. E. Treeby and B. T. Cox, “k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields,” J. Biomed. Opt. 15(2), 021314 (2010).
[Crossref] [PubMed]

J. Meng, Z. Jiang, L. V. Wang, J. Park, C. Kim, M. Sun, Y. Zhang, and L. Song, “High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis,” J. Biomed. Opt. 21(7), 076007 (2016).
[Crossref] [PubMed]

L. Nie, Z. Guo, and L. V. Wang, “Photoacoustic tomography of monkey brain using virtual point ultrasonic transducers,” J. Biomed. Opt. 16(7), 076005 (2011).
[Crossref] [PubMed]

J. Biophotonics (1)

M. Schwarz, A. Buehler, J. Aguirre, and V. Ntziachristos, “Three-dimensional multispectral optoacoustic mesoscopy reveals melanin and blood oxygenation in human skin in vivo,” J. Biophotonics 9(1-2), 55–60 (2016).
[Crossref] [PubMed]

J. Optics-UK (1)

M. Haltmeier, T. Berer, S. Moon, and P. Burgholzer, “Compressed sensing and sparsity in photoacoustic tomography,” J. Optics-UK 18(11), 114004 (2016).
[Crossref]

Med. Phys. (1)

Y. Han, S. Tzoumas, A. Nunes, V. Ntziachristos, and A. Rosenthal, “Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging,” Med. Phys. 42(9), 5444–5452 (2015).
[Crossref] [PubMed]

Nat Biomed Eng (1)

L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, and L. V. Wang, “Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution,” Nat Biomed Eng 1(5), 0071 (2017).
[Crossref] [PubMed]

Nat. Biomed. Eng. (1)

J. Aguirre, M. Schwarz, N. Garzorz, M. Omar, A. Buehler, K. Eyerich, and V. Ntziachristos, “Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy,” Nat. Biomed. Eng. 1(5), 0068 (2017).
[Crossref]

Opt. Express (3)

Opt. Lett. (2)

Phys. Med. Biol. (1)

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. Med. Biol. 61(24), 8908–8940 (2016).
[Crossref] [PubMed]

Phys. Rev. E (1)

M. Xu and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E 71(1), 016706 (2005).
[Crossref] [PubMed]

Proc. SPIE (1)

S. P. Ghael, A. M. Sayeed, and R. G. Baraniuk, “Improved wavelet denoising via empirical Wiener filtering,” Proc. SPIE 3169, 389–399 (1997).
[Crossref]

Sensors (Basel) (1)

C. Lutzweiler and D. Razansky, “Optoacoustic imaging and tomography: reconstruction approaches and outstanding challenges in image performance and quantification,” Sensors (Basel) 13(6), 7345–7384 (2013).
[Crossref] [PubMed]

SIAM J. Appl. Math. (1)

M. Sandbichler, F. Krahmer, T. Berer, P. Burgholzer, and M. Haltmeier, “A novel compressed sensing scheme for photoacoustic tomography,” SIAM J. Appl. Math. 75(6), 2475–2494 (2015).
[Crossref]

Ultrasonics (1)

Y. Zhang, Y. Wang, and C. Zhang, “Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction,” Ultrasonics 52(8), 1046–1055 (2012).
[Crossref] [PubMed]

Other (2)

G. L. Zeng, Medical image reconstruction: A conceptual tutorial (Higher Education Press, 2010).

A. Danielyan, A. Foi, V. Katkovnik, and K. Egiazarian, “Spatially adaptive filtering as regularization in inverse imaging: Compressive sensing, super-resolution, and upsampling,” in Super-Resolution Imaging, M. Peyman, ed. (CRC, 2010).

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Figures (14)

Fig. 1
Fig. 1 Sketches of the different back-projection modes of the PA signals: (a) Arc projections and (b) Virtual parallel-projections.
Fig. 2
Fig. 2 Schematics of (a) the superposition process of the signals back projected from a series of virtual point-transducers arranged along the surface of the transducer, and (b) calculating the minimum rotation radius of the transducer.
Fig. 3
Fig. 3 Simulation results of the projection paths back projected from (a) Point transducer, and (b) Transducer with the proposed imaging conditions.
Fig. 4
Fig. 4 Flowchart of the implementation of IRT-BM3D.
Fig. 5
Fig. 5 Schematic of the PAT measuring system.
Fig. 6
Fig. 6 Experimental performance of the imaging system: (a) Image reconstruction of the microspheres (with a diameter of 200 μm); (b) Profile along line A-A’, as marked in (a).
Fig. 7
Fig. 7 Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.
Fig. 8
Fig. 8 Values of (a) d, and (b) PSNR of the reconstructed results versus the iterations.
Fig. 9
Fig. 9 Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.
Fig. 10
Fig. 10 Values of (a) d, and (b) PSNR of the reconstructed results versus the iterations.
Fig. 11
Fig. 11 Reconstructed images of the tumor-mimicking tissue sample: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k)-(l) Photographs of the biological tissue sample; (m) Reconstructed details in yellow dotted boxes in (e) and (j).
Fig. 12
Fig. 12 Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.
Fig. 13
Fig. 13 Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Fig. 14
Fig. 14 Reconstructed results of the tumor-mimicking tissue sample: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.

Tables (5)

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Table 1 Algorithm 1. Iterative sparse-view PAT reconstruction cooperating with BM3D filtering.

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Table 1 The PSNR and d values of the final reconstructed UBP and IRT-BM3D results

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Table 2 The PSNR and d values of the final reconstructed UBP and IRT-BM3D results

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Table 3 The PSNR and d values of the final reconstructed TV-GD and IRT-BM3D results of the vessel phantom

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Table 4 The PSNR and d values of the final reconstructed TV-GD and IRT-BM3D results of the tumor-mimicking tissue sample

Equations (9)

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P(ω,α)=F( ω x , ω y )| ω x =ωcosα, ω y =ωsinα ,
RR1dl+ L 2 ,R1= ( LD 2 ) 2 +d l 2 2cos[ arctan( LD 2dl ) ] ,
f(x,y)= 0 2π p(s,α) s 1 2 π 2 (xcosα+ysinαs) dsdα,
d= i=1 M j=1 N ( u(i,j)v(i,j) ) 2 / i=1 M j=1 N v (i,j) 2 ,
PSNR=10× log 10 ( M×N× v max 2 i=1 M j=1 N ( u(i,j)v(i,j) ) 2 ),
R1= L H cosα ,
L H = 1 2 EF= 1 2 ( E G 2 +d l 2 )= 1 2 ( ( LD 2 ) 2 +d l 2 ),
α=arctan( EG dl )=arctan( LD 2dl ).
R1= ( LD 2 ) 2 +d l 2 2cos[ arctan( LD 2dl ) ] .

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