Abstract

Singular value decomposition (SVD) was used to identify and remove laser-induced noise in photoacoustic images acquired with a clinical ultrasound scanner. This noise, which was prominent in the radiofrequency data acquired in parallel from multiple transducer elements, was induced by the excitation light source. It was modelled by truncating the SVD matrices so that only the first few largest singular value components were retained, and subtracted prior to image reconstruction. The dependency of the signal amplitude and the number of the largest singular value components used for noise modeling was investigated for different photoacoustic source geometries. Validation was performed with simulated data and measured noise, and with photoacoustic images acquired from the human forearm and finger in vivo using L14-5/38 and L40-8/12 linear array clinical imaging probes. The use of only one singular value component was found to be sufficient to achieve near-complete removal of laser-induced noise from reconstructed images. This method has strong potential to increase image quality for a wide range of photoacoustic imaging systems with parallel data acquisition.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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References

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2016 (3)

2015 (2)

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

J. M. Mari, W. Xia, S. J. West, and A. E. Desjardins, “Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study,” J. Biomed. Opt. 20(11), 110503 (2015).
[Crossref] [PubMed]

2014 (3)

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

2013 (4)

A. Rajwade, A. Rangarajan, and A. Banerjee, “Image denoising using the higher order singular value decomposition,” IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013).
[Crossref] [PubMed]

D. Piras, C. Grijsen, P. Schütte, W. Steenbergen, and S. Manohar, “Photoacoustic needle: minimally invasive guidance to biopsy,” J. Biomed. Opt. 18(7), 070502 (2013).
[Crossref] [PubMed]

M. A. Lediju Bell, N. Kuo, D. Y. Song, and E. M. Boctor, “Short-lag spatial coherence beamforming of photoacoustic images for enhanced visualization of prostate brachytherapy seeds,” Biomed. Opt. Express 4(10), 1964–1977 (2013).
[Crossref] [PubMed]

M. Jaeger, J. C. Bamber, and M. Frenz, “Clutter elimination for deep clinical optoacoustic imaging using localised vibration tagging (LOVIT),” Photoacoustics 1(2), 19–29 (2013).
[Crossref] [PubMed]

2011 (2)

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus 1(4), 602–631 (2011).
[Crossref] [PubMed]

Y. He, T. Gan, W. Chen, and H. Wang, “Adaptive denoising by singular value decomposition,” IEEE Signal Process. Lett. 18(4), 215–218 (2011).
[Crossref]

2010 (2)

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]

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

2008 (1)

S. H. Holan and J. A. Viator, “Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction,” Phys. Med. Biol. 53(12), N227–N236 (2008).
[Crossref] [PubMed]

Abolmaesumi, P.

Achilefu, S.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Ai, M.

Akers, W. J.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Alles, E. J.

E. J. Alles, M. Jaeger, and J. C. Bamber, “Photoacoustic clutter reduction using short-lag spatial coherence weighted imaging,” IEEE International Ultrasonics Symposium, 41–44 (2014).
[Crossref]

Bamber, J. C.

M. Jaeger, J. C. Bamber, and M. Frenz, “Clutter elimination for deep clinical optoacoustic imaging using localised vibration tagging (LOVIT),” Photoacoustics 1(2), 19–29 (2013).
[Crossref] [PubMed]

E. J. Alles, M. Jaeger, and J. C. Bamber, “Photoacoustic clutter reduction using short-lag spatial coherence weighted imaging,” IEEE International Ultrasonics Symposium, 41–44 (2014).
[Crossref]

Banerjee, A.

A. Rajwade, A. Rangarajan, and A. Banerjee, “Image denoising using the higher order singular value decomposition,” IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013).
[Crossref] [PubMed]

Beard, P.

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus 1(4), 602–631 (2011).
[Crossref] [PubMed]

Beard, P. C.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Boctor, E. M.

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

M. A. Lediju Bell, N. Kuo, D. Y. Song, and E. M. Boctor, “Short-lag spatial coherence beamforming of photoacoustic images for enhanced visualization of prostate brachytherapy seeds,” Biomed. Opt. Express 4(10), 1964–1977 (2013).
[Crossref] [PubMed]

Carson, P. L.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Chen, W.

Y. He, T. Gan, W. Chen, and H. Wang, “Adaptive denoising by singular value decomposition,” IEEE Signal Process. Lett. 18(4), 215–218 (2011).
[Crossref]

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]

David, A. L.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

de Jong, N.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

Deng, C. X.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Deprest, J.

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Desjardins, A. E.

J. M. Mari, W. Xia, S. J. West, and A. E. Desjardins, “Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study,” J. Biomed. Opt. 20(11), 110503 (2015).
[Crossref] [PubMed]

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

dos Santos, G. S.

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Erpelding, T. N.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Fowlkes, J. B.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Frenz, M.

Gan, T.

Y. He, T. Gan, W. Chen, and H. Wang, “Adaptive denoising by singular value decomposition,” IEEE Signal Process. Lett. 18(4), 215–218 (2011).
[Crossref]

Grijsen, C.

D. Piras, C. Grijsen, P. Schütte, W. Steenbergen, and S. Manohar, “Photoacoustic needle: minimally invasive guidance to biopsy,” J. Biomed. Opt. 18(7), 070502 (2013).
[Crossref] [PubMed]

He, Y.

Y. He, T. Gan, W. Chen, and H. Wang, “Adaptive denoising by singular value decomposition,” IEEE Signal Process. Lett. 18(4), 215–218 (2011).
[Crossref]

Holan, S. H.

S. H. Holan and J. A. Viator, “Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction,” Phys. Med. Biol. 53(12), N227–N236 (2008).
[Crossref] [PubMed]

Jaeger, M.

M. K. A. Singh, M. Jaeger, M. Frenz, and W. Steenbergen, “In vivo demonstration of reflection artifact reduction in photoacoustic imaging using synthetic aperture photoacoustic-guided focused ultrasound (PAFUSion),” Biomed. Opt. Express 7(8), 2955–2972 (2016).
[Crossref] [PubMed]

M. Jaeger, J. C. Bamber, and M. Frenz, “Clutter elimination for deep clinical optoacoustic imaging using localised vibration tagging (LOVIT),” Photoacoustics 1(2), 19–29 (2013).
[Crossref] [PubMed]

E. J. Alles, M. Jaeger, and J. C. Bamber, “Photoacoustic clutter reduction using short-lag spatial coherence weighted imaging,” IEEE International Ultrasonics Symposium, 41–44 (2014).
[Crossref]

Jankovic, L.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Kang, J. U.

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

Kim, C.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Kruizinga, P.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

Kuo, N.

Kuo, N. P.

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

Lediju Bell, M. A.

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

M. A. Lediju Bell, N. Kuo, D. Y. Song, and E. M. Boctor, “Short-lag spatial coherence beamforming of photoacoustic images for enhanced visualization of prostate brachytherapy seeds,” Biomed. Opt. Express 4(10), 1964–1977 (2013).
[Crossref] [PubMed]

Lin, J. D.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Liu, X.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Lutzweiler, C.

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

Maneas, E.

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Manohar, S.

D. Piras, C. Grijsen, P. Schütte, W. Steenbergen, and S. Manohar, “Photoacoustic needle: minimally invasive guidance to biopsy,” J. Biomed. Opt. 18(7), 070502 (2013).
[Crossref] [PubMed]

Margenthaler, J. A.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Mari, J. M.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

J. M. Mari, W. Xia, S. J. West, and A. E. Desjardins, “Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study,” J. Biomed. Opt. 20(11), 110503 (2015).
[Crossref] [PubMed]

Maslov, K.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Meng, Z. X.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Mosse, C. A.

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Nikitichev, D. I.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Ntziachristos, V.

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

Ourselin, S.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Pashley, M. D.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Piras, D.

D. Piras, C. Grijsen, P. Schütte, W. Steenbergen, and S. Manohar, “Photoacoustic needle: minimally invasive guidance to biopsy,” J. Biomed. Opt. 18(7), 070502 (2013).
[Crossref] [PubMed]

Pratt, R.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

Rajwade, A.

A. Rajwade, A. Rangarajan, and A. Banerjee, “Image denoising using the higher order singular value decomposition,” IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013).
[Crossref] [PubMed]

Rangarajan, A.

A. Rajwade, A. Rangarajan, and A. Banerjee, “Image denoising using the higher order singular value decomposition,” IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013).
[Crossref] [PubMed]

Razansky, D.

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

Robertus, J. L.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

Rohling, R.

Rosenthal, A.

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

Salcudean, T.

Schütte, P.

D. Piras, C. Grijsen, P. Schütte, W. Steenbergen, and S. Manohar, “Photoacoustic needle: minimally invasive guidance to biopsy,” J. Biomed. Opt. 18(7), 070502 (2013).
[Crossref] [PubMed]

Shu, W.

Singh, M. K. A.

Song, D. Y.

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

M. A. Lediju Bell, N. Kuo, D. Y. Song, and E. M. Boctor, “Short-lag spatial coherence beamforming of photoacoustic images for enhanced visualization of prostate brachytherapy seeds,” Biomed. Opt. Express 4(10), 1964–1977 (2013).
[Crossref] [PubMed]

Song, L.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Springeling, G.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

Steenbergen, W.

Tang, S.

Tao, C.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[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.

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

van der Lugt, A.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

van der Steen, A. F.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

van Soest, G.

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

Vercauteren, T.

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Viator, J. A.

S. H. Holan and J. A. Viator, “Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction,” Phys. Med. Biol. 53(12), N227–N236 (2008).
[Crossref] [PubMed]

Wang, H.

Y. He, T. Gan, W. Chen, and H. Wang, “Adaptive denoising by singular value decomposition,” IEEE Signal Process. Lett. 18(4), 215–218 (2011).
[Crossref]

Wang, L. V.

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

Wang, X.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

West, S. J.

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

J. M. Mari, W. Xia, S. J. West, and A. E. Desjardins, “Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study,” J. Biomed. Opt. 20(11), 110503 (2015).
[Crossref] [PubMed]

Xia, W.

J. M. Mari, W. Xia, S. J. West, and A. E. Desjardins, “Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study,” J. Biomed. Opt. 20(11), 110503 (2015).
[Crossref] [PubMed]

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

Xu, G.

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Biomed. Opt. Express (2)

IEEE Signal Process. Lett. (1)

Y. He, T. Gan, W. Chen, and H. Wang, “Adaptive denoising by singular value decomposition,” IEEE Signal Process. Lett. 18(4), 215–218 (2011).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

A. Rajwade, A. Rangarajan, and A. Banerjee, “Image denoising using the higher order singular value decomposition,” IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 849–862 (2013).
[Crossref] [PubMed]

Interface Focus (1)

P. Beard, “Biomedical photoacoustic imaging,” Interface Focus 1(4), 602–631 (2011).
[Crossref] [PubMed]

J. Biomed. Opt. (7)

C. Kim, T. N. Erpelding, K. Maslov, L. Jankovic, W. J. Akers, L. Song, S. Achilefu, J. A. Margenthaler, M. D. Pashley, and L. V. Wang, “Handheld array-based photoacoustic probe for guiding needle biopsy of sentinel lymph nodes,” J. Biomed. Opt. 15(4), 046010 (2010).
[Crossref] [PubMed]

D. Piras, C. Grijsen, P. Schütte, W. Steenbergen, and S. Manohar, “Photoacoustic needle: minimally invasive guidance to biopsy,” J. Biomed. Opt. 18(7), 070502 (2013).
[Crossref] [PubMed]

M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014).
[Crossref] [PubMed]

W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015).
[Crossref] [PubMed]

J. M. Mari, W. Xia, S. J. West, and A. E. Desjardins, “Interventional multispectral photoacoustic imaging with a clinical ultrasound probe for discriminating nerves and tendons: an ex vivo pilot study,” J. Biomed. Opt. 20(11), 110503 (2015).
[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(2), 021314 (2010).
[Crossref] [PubMed]

P. Kruizinga, A. F. van der Steen, N. de Jong, G. Springeling, J. L. Robertus, A. van der Lugt, and G. van Soest, “Photoacoustic imaging of carotid artery atherosclerosis,” J. Biomed. Opt. 19(11), 110504 (2014).
[Crossref] [PubMed]

Med. Phys. (1)

S. Tzoumas, A. Rosenthal, C. Lutzweiler, D. Razansky, and V. Ntziachristos, “Spatiospectral denoising framework for multispectral optoacoustic imaging based on sparse signal representation,” Med. Phys. 41(11), 113301 (2014).
[Crossref] [PubMed]

Opt. Express (1)

Photoacoustics (1)

M. Jaeger, J. C. Bamber, and M. Frenz, “Clutter elimination for deep clinical optoacoustic imaging using localised vibration tagging (LOVIT),” Photoacoustics 1(2), 19–29 (2013).
[Crossref] [PubMed]

Phys. Med. Biol. (1)

S. H. Holan and J. A. Viator, “Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction,” Phys. Med. Biol. 53(12), N227–N236 (2008).
[Crossref] [PubMed]

Sci. Rep. (1)

G. Xu, Z. X. Meng, J. D. Lin, C. X. Deng, P. L. Carson, J. B. Fowlkes, C. Tao, X. Liu, and X. Wang, “High resolution Physio-chemical Tissue Analysis: Towards Non-invasive In Vivo Biopsy,” Sci. Rep. 6, 16937 (2016).
[Crossref] [PubMed]

Other (5)

A. Oraevsky, S. Ermilov, K. Mehta, T. Miller, B. Bell, E. Orihuela, and M. Motamedi, “In vivo testing of laser optoacoustic system for image-guided biopsy of prostate,” In Biomedical Optics 60860B (2006).

W. Xia, E. Maneas, D. I. Nikitichev, C. A. Mosse, G. S. dos Santos, T. Vercauteren, A. L. David, J. Deprest, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Interventional photoacoustic imaging of the human placenta with ultrasonic tracking for minimally invasive fetal surgeries,” In International Conference on Medical Image Computing and Computer-Assisted Intervention, 371–378 (2015).
[Crossref]

ANSI_Z136.1. American National Standard for the Safe Use of Lasers (American National Standards Institute, Washington DC, 2007).

E. J. Alles, M. Jaeger, and J. C. Bamber, “Photoacoustic clutter reduction using short-lag spatial coherence weighted imaging,” IEEE International Ultrasonics Symposium, 41–44 (2014).
[Crossref]

B. Pourebrahimi, S. Yoon, D. Dopsa, and M. C. Kolios, “Improving the quality of photoacoustic images using the short-lag spatial coherence imaging technique,” SPIE Proc. 85813Y (2013).
[Crossref]

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Figures (6)

Fig. 1
Fig. 1 Steps performed to obtain a display of a denoised photoacoustic image. First, radiofrequency (RF) photoacoustic data from transducer elements of a linear-array US imaging probe were acquired in parallel (step 1). Laser-induced noise was identified using SVD (step 2) and then removed (step 3) prior to image reconstruction (step 4). Steps 1-4 were repeated when averaging over multiple PA acquisitions was required. Subsequently, envelope detection was performed with the Hilbert transform (step 5) and the resulting photoacoustic image was displayed on a logarithmic scale (step 6). For B-mode US imaging (steps not shown), acquisitions were performed using electronic focusing; the resulting images were inherently co-registered with the photoacoustic images since they were acquired with the same imaging probe.
Fig. 2
Fig. 2 Radiofrequency (RF) data from transducer elements of the L14-5/38 US imaging probe. When the laser source was on but light was not delivered to tissue, laser-induced noise in the raw RF data manifested as horizontal and vertical bands (prominent instances pointed to with purple arrows) that varied across acquisitions (a). These bands were absent when the laser source was off (b). The power spectrum of the laser-induced noise, averaged across data from all transducer elements, overlapped with the nominal bandwidth of the transducer elements (c). Band-pass frequency filtering across the nominal bandwidth of the transducer elements (5 – 14 MHz) was insufficient to remove the laser-induced noise bands (d). In (a), (b), and (d), the absolute values of the RF data are displayed on a linear scale.
Fig. 3
Fig. 3 Laser-induced noise identification with singular value decomposition (SVD) using simulated signals from circular sources. Radiofrequency data for each transducer element originating from four circular sources at varying depths was simulated [(a), bottom]. Reconstructions were performed in the absence of laser-induced noise [(a), top] and in the presence of this noise [(b), top]. Laser-induced noise was experimentally acquired from the imaging probe with the laser on and with light not delivered to tissue, and added to the raw simulated data [(b), bottom]. Laser-induced noise identification with one singular value and subsequent denoising yielded a substantial improvement in photoacoustic image quality [(c), left]. When ten singular value components (SVCs) were used, the magnitudes of the signals originating from the circular sources were smaller relative to the background noise [(c), right]. The signal-to-noise ratio of the reconstructed images, with the signal for each circular source defined as the mean pixel magnitude across a 2 × 2 mm square region enclosing the circular source and the noise as the standard deviation across a spatial region indicated by the dashed box [(b), top], remained approximately constant when 1 to 7 SVCs were used and it decreased monotonically for larger numbers of SVCs [(d), left]. The peak signal magnitudes from the four sources decreased monotonically with the number of SVCs [(d), right]. The data in (a), (b), and (c) are plotted on linear scales.
Fig. 4
Fig. 4 Laser-induced noise identification with singular value decomposition (SVD) using simulated signals from angled line sources. The five angled line sources, from which radiofrequency data for each transducer element was simulated, intersected at their centres (a). Experimentally-acquired laser-induced noise, which was the same as that used for simulations from circular sources (Fig. 3), manifested prominently in the reconstructed photoacoustic image (b). Laser-induced noise identification with one singular value component (SVC) and subsequent denoising yielded a substantial improvement in photoacoustic image quality overall (c). The signal from the horizontal line was apparent but its magnitude was smaller. When ten SVCs were used, the signal magnitudes from all lines relative to the background noise were smaller (d); the signal from the horizontal line was absent. All reconstructed photoacoustic images were normalised to their maximum values and displayed in the same linear scale (0-1).
Fig. 5
Fig. 5 Laser-induced noise identification with singular value decomposition (SVD) in photoacoustic images acquired from a human forearm in vivo. These data, which were acquired with an L14-5/38 ultrasound (US) imaging probe, were co-registered with a B-mode pulse-echo US image (a). With B-mode US imaging, four superficial blood vessels were apparent (top and bottom of each vessel indicated with white arrows). Beneath the overlying agar block, there was a thin layer of US gel before the skin surface (dashed green arrow). Laser-induced noise manifested as prominent bands [(b), thick purple arrow] with a magnitude comparable to that of signals from the skin surface and larger than that of signals from blood vessels (not apparent). Laser-induced noise identification with 1 singular value component (SVC) and subsequent denoising yielded a substantial improvement in photoacoustic image quality, with the noise band absent (c). When 10 SVCs were used, the magnitude of the signal from the skin surface was reduced (d). In the raw radiofrequency data, vertical and horizontal noise bands were apparent [(e), prominent examples indicated with thick purple arrows]. When averaging across 31 PA images was performed, signals from the blood vessels were apparent but laser-induced noise across the image (prominent examples indicated with thick purple arrows) had comparable magnitudes (f). When averaging across PA images and SVD-denoising with 1 SVC were performed, the laser-induced noise was absent and signals from the blood vessels were clearly visible (g). The signals from the skin surface and the blood vessels were smaller when 10 SVCs were used (h). All reconstructed photoacoustic images were normalised to their maximum values and displayed on logarithmic scales with the same dynamic range (30 dB). The raw data in (e) is displayed on a linear scale.
Fig. 6
Fig. 6 Laser-induced noise identification with singular value decomposition (SVD) in photoacoustic images acquired from a human finger in vivo. These data, which were acquired with an L40-8/12 high frequency (US) imaging probe, were co-registered with a B-mode pulse-echo US image (a). With B-mode US imaging, three superficial blood vessels (upward white arrows) were identified based on slight intensity variations over time (data not shown). Laser-induced noise manifested as prominent bands [(b), thick purple arrow] with a magnitude larger than that of signals from the blood vessels (upward white arrows). Laser-induced noise identification with 1 singular value component (SVC) and subsequent denoising yielded a substantial improvement in photoacoustic image quality, with the noise band absent and with signals from the skin surface present (c). When 10 SVCs were used, the magnitude of the signals from the skin surface and from the blood vessels was reduced relative to the background noise (d). In the raw radiofrequency data, vertical and horizontal noise bands were apparent [(e), prominent example indicated with a thick purple arrow]. When averaging across 31 PA images was performed, signals from the blood vessels were apparent but laser-induced noise across the image (prominent example indicated with a thick purple arrow) was present (f). When averaging across PA images and SVD-denoising with 1 SVC were performed, the laser-induced noise was absent and signals from the blood vessels and skin surface were clearly visible (g). The signals from the skin surface and the blood vessels were smaller relative to the background noise when 10 SVCs were used (h). All reconstructed photoacoustic images were normalised to their maximum values and displayed on logarithmic scales with the same dynamic range (20 dB). The raw data in (e) is displayed on a linear scale.

Equations (4)

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X=US V T
X= X pa + X L +ε.
s L ( i )={ s( i ), 1ik 0,i>k
X ˜ PA =X X ˜ L. .

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