Abstract

We present a filtering procedure based on singular value decomposition to remove artifacts arising from sample motion during dynamic full field OCT acquisitions. The presented method succeeded in removing artifacts created by environmental noise from data acquired in a clinical setting, including in vivo data. Moreover, we report on a new method based on using the cumulative sum to compute dynamic images from raw signals, leading to a higher signal to noise ratio, and thus enabling dynamic imaging deeper in tissues.

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

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Dynamic full field optical coherence tomography: subcellular metabolic contrast revealed in tissues by interferometric signals temporal analysis

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References

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  1. D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
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  2. W. Drexler and J. G. Fujimoto, eds., Optical coherence tomography: technology and applications (Springer, 2015), 2nd ed.
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  4. A. Dubois, K. Grieve, G. Moneron, R. Lecaque, L. Vabre, and C. Boccara, “Ultrahigh-resolution full-field optical coherence tomography,” Appl. Opt. 43, 2874–2883 (2004).
    [Crossref] [PubMed]
  5. J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
    [Crossref] [PubMed]
  6. M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
    [Crossref]
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    [Crossref] [PubMed]
  8. C.-E. Leroux, F. Bertillot, O. Thouvenin, and A.-C. Boccara, “Intracellular dynamics measurements with full field optical coherence tomography suggest hindering effect of actomyosin contractility on organelle transport,” Biomed. Opt. Express 7, 4501–4513 (2016).
    [Crossref] [PubMed]
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    [Crossref]
  10. A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
    [Crossref] [PubMed]
  11. J. Scholler, V. Mazlin, O. Thouvenin, K. Groux, P. Xiao, J.-A. Sahel, M. Fink, C. Boccara, and K. Grieve, “Probing dynamic processes in the eye at multiple spatial and temporal scales with multimodal full field oct,” Biomed. Opt. Express 10, 731–746 (2019).
    [Crossref] [PubMed]
  12. Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20, 4710–4725 (2012).
    [Crossref] [PubMed]
  13. T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
    [Crossref]
  14. A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
    [Crossref] [PubMed]
  15. H. Ammari, F. Romero, and C. Shi, “A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography,” Multiscale Model. & Simul. 15, 1155–1175 (2017).
    [Crossref]
  16. C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
    [Crossref]
  17. J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
    [Crossref]
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    [Crossref]
  20. P. Lévy, “Sur certains processus stochastiques homogènes,” Compos. Math. 7, 283–339 (1940).
  21. I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
    [Crossref] [PubMed]
  22. V. Mazlin, P. Xiao, E. Dalimier, K. Grieve, K. Irsch, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high resolution human corneal imaging using full-field optical coherence tomography,” Biomed. Opt. Express 9, 557–568 (2018).
    [Crossref] [PubMed]
  23. P. Xiao, V. Mazlin, K. Grieve, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high-resolution human retinal imaging with wavefront-correctionless full-field oct,” Optica 5, 409–412 (2018).
    [Crossref]
  24. P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

2019 (2)

J. Scholler, V. Mazlin, O. Thouvenin, K. Groux, P. Xiao, J.-A. Sahel, M. Fink, C. Boccara, and K. Grieve, “Probing dynamic processes in the eye at multiple spatial and temporal scales with multimodal full field oct,” Biomed. Opt. Express 10, 731–746 (2019).
[Crossref] [PubMed]

P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

2018 (4)

V. Mazlin, P. Xiao, E. Dalimier, K. Grieve, K. Irsch, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high resolution human corneal imaging using full-field optical coherence tomography,” Biomed. Opt. Express 9, 557–568 (2018).
[Crossref] [PubMed]

P. Xiao, V. Mazlin, K. Grieve, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high-resolution human retinal imaging with wavefront-correctionless full-field oct,” Optica 5, 409–412 (2018).
[Crossref]

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

2017 (4)

H. Ammari, F. Romero, and C. Shi, “A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography,” Multiscale Model. & Simul. 15, 1155–1175 (2017).
[Crossref]

O. Thouvenin, C. Boccara, M. Fink, J. Sahel, M. Pâques, and K. Grieve, “Cell motility as contrast agent in retinal explant imaging with full-field optical coherence tomography,” Investig. Opthalmology & Vis. Sci. 58, 4605 (2017).
[Crossref]

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

2016 (3)

2015 (2)

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
[Crossref]

2012 (1)

2011 (2)

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

2004 (1)

1998 (1)

1991 (1)

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

1940 (1)

P. Lévy, “Sur certains processus stochastiques homogènes,” Compos. Math. 7, 283–339 (1940).

a la Guillaume, E. B.

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

Ammari, H.

H. Ammari, F. Romero, and C. Shi, “A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography,” Multiscale Model. & Simul. 15, 1155–1175 (2017).
[Crossref]

Apelian, C.

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

C. Apelian, F. Harms, O. Thouvenin, and A. C. Boccara, “Dynamic full field optical coherence tomography: subcellular metabolic contrast revealed in tissues by interferometric signals temporal analysis,” Biomed. Opt. Express 7, 1511–1524 (2016).
[Crossref] [PubMed]

Arganda-Carreras, I.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Arnal, B.

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

Aubry, A.

A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
[Crossref] [PubMed]

Badon, A.

A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
[Crossref] [PubMed]

Baranger, J.

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

Baud, O.

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Beaurepaire, E.

Ben Arous, J.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Bergel, A.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Bertillot, F.

Binding, J.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Biran, V.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Blanchot, L.

Boccara, A. C.

Boccara, A.-C.

Boccara, C.

J. Scholler, V. Mazlin, O. Thouvenin, K. Groux, P. Xiao, J.-A. Sahel, M. Fink, C. Boccara, and K. Grieve, “Probing dynamic processes in the eye at multiple spatial and temporal scales with multimodal full field oct,” Biomed. Opt. Express 10, 731–746 (2019).
[Crossref] [PubMed]

P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

O. Thouvenin, C. Boccara, M. Fink, J. Sahel, M. Pâques, and K. Grieve, “Cell motility as contrast agent in retinal explant imaging with full-field optical coherence tomography,” Investig. Opthalmology & Vis. Sci. 58, 4605 (2017).
[Crossref]

A. Dubois, K. Grieve, G. Moneron, R. Lecaque, L. Vabre, and C. Boccara, “Ultrahigh-resolution full-field optical coherence tomography,” Appl. Opt. 43, 2874–2883 (2004).
[Crossref] [PubMed]

Bourdieu, L.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Cardona, A.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Casado, M.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Chang, W.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Chassagnon, G.

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

Chen, C.-L.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Cohen, I.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Correas, J.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Dalimier, E.

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

V. Mazlin, P. Xiao, E. Dalimier, K. Grieve, K. Irsch, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high resolution human corneal imaging using full-field optical coherence tomography,” Biomed. Opt. Express 9, 557–568 (2018).
[Crossref] [PubMed]

de Carlo, T. E.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
[Crossref]

Deffieux, T.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Demené, C.

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Dubois, A.

Duker, J. S.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
[Crossref]

Eliceiri, K. W.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Fink, M.

J. Scholler, V. Mazlin, O. Thouvenin, K. Groux, P. Xiao, J.-A. Sahel, M. Fink, C. Boccara, and K. Grieve, “Probing dynamic processes in the eye at multiple spatial and temporal scales with multimodal full field oct,” Biomed. Opt. Express 10, 731–746 (2019).
[Crossref] [PubMed]

P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

P. Xiao, V. Mazlin, K. Grieve, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high-resolution human retinal imaging with wavefront-correctionless full-field oct,” Optica 5, 409–412 (2018).
[Crossref]

V. Mazlin, P. Xiao, E. Dalimier, K. Grieve, K. Irsch, J.-A. Sahel, M. Fink, and A. C. Boccara, “In vivo high resolution human corneal imaging using full-field optical coherence tomography,” Biomed. Opt. Express 9, 557–568 (2018).
[Crossref] [PubMed]

O. Thouvenin, C. Boccara, M. Fink, J. Sahel, M. Pâques, and K. Grieve, “Cell motility as contrast agent in retinal explant imaging with full-field optical coherence tomography,” Investig. Opthalmology & Vis. Sci. 58, 4605 (2017).
[Crossref]

A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
[Crossref] [PubMed]

Flotte, T.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Franqui, S.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Fujimoto, J. G.

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20, 4710–4725 (2012).
[Crossref] [PubMed]

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Gahm, J. K.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Gennisson, J.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Gigan, S.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Gregory, K.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Grieve, K.

Groux, K.

Harms, F.

Hee, M.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Hornegger, J.

Huang, D.

Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto, J. Hornegger, and D. Huang, “Split-spectrum amplitude-decorrelation angiography with optical coherence tomography,” Opt. Express 20, 4710–4725 (2012).
[Crossref] [PubMed]

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Irsch, K.

Jain, M.

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

Jia, Y.

Kashani, A. H.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Kaynig, V.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Kraus, M. F.

Lebec, M.

Lecaque, R.

Léger, J.-F.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Lerosey, G.

A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
[Crossref] [PubMed]

Leroux, C.-E.

Lévy, P.

P. Lévy, “Sur certains processus stochastiques homogènes,” Compos. Math. 7, 283–339 (1940).

Li, D.

A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
[Crossref] [PubMed]

Lin, C.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Liu, J. J.

Mansuet-Lupo, A.

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

Manzoor, M.

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

Mazlin, V.

Mecê, P.

P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

Moneron, G.

Mukherjee, S.

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

Nadolny, S.

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

Osmanski, B.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Pâques, M.

O. Thouvenin, C. Boccara, M. Fink, J. Sahel, M. Pâques, and K. Grieve, “Cell motility as contrast agent in retinal explant imaging with full-field optical coherence tomography,” Investig. Opthalmology & Vis. Sci. 58, 4605 (2017).
[Crossref]

Pernot, M.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Perren, F.

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

Potsaid, B.

Puliafito, C.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Revel, M.-P.

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

Richter, G. M.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Romano, A.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
[Crossref]

Romero, F.

H. Ammari, F. Romero, and C. Shi, “A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography,” Multiscale Model. & Simul. 15, 1155–1175 (2017).
[Crossref]

Rosenfeld, P. J.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Rueden, C.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Sahel, J.

O. Thouvenin, C. Boccara, M. Fink, J. Sahel, M. Pâques, and K. Grieve, “Cell motility as contrast agent in retinal explant imaging with full-field optical coherence tomography,” Investig. Opthalmology & Vis. Sci. 58, 4605 (2017).
[Crossref]

Sahel, J.-A.

Saint-Jalmes, H.

Schindelin, J.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Scholler, J.

P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

J. Scholler, V. Mazlin, O. Thouvenin, K. Groux, P. Xiao, J.-A. Sahel, M. Fink, C. Boccara, and K. Grieve, “Probing dynamic processes in the eye at multiple spatial and temporal scales with multimodal full field oct,” Biomed. Opt. Express 10, 731–746 (2019).
[Crossref] [PubMed]

Schuman, J.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Sebastian Seung, H.

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Shi, C.

H. Ammari, F. Romero, and C. Shi, “A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography,” Multiscale Model. & Simul. 15, 1155–1175 (2017).
[Crossref]

Shi, Y.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Shukla, N.

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

Sieu, L.

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Stinson, W.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Subhash, H.

Swanson, E.

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Tan, O.

Tanter, M.

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

Thouvenin, O.

Tokayer, J.

Topilko, P.

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Vabre, L.

Waheed, N. K.

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
[Crossref]

Wang, R. K.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Wang, Y.

Xiao, P.

Zheng, F.

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Appl. Opt. (1)

Bioinformatics (1)

I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, and H. Sebastian Seung, “Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification,” Bioinformatics 33, 2424–2426 (2017).
[Crossref] [PubMed]

Biomed. Opt. Express (4)

Compos. Math. (1)

P. Lévy, “Sur certains processus stochastiques homogènes,” Compos. Math. 7, 283–339 (1940).

IEEE Transactions on Med. Imaging (2)

C. Demené, T. Deffieux, M. Pernot, B. Osmanski, V. Biran, J. Gennisson, L. Sieu, A. Bergel, S. Franqui, J. Correas, I. Cohen, O. Baud, and M. Tanter, “Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity,” IEEE Transactions on Med. Imaging 34, 2271–2285 (2015).
[Crossref]

J. Baranger, B. Arnal, F. Perren, O. Baud, M. Tanter, and C. Demené, “Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors,” IEEE Transactions on Med. Imaging 37, 1574–1586 (2018).
[Crossref]

Int. J. Retin. Vitreous (1)

T. E. de Carlo, A. Romano, N. K. Waheed, and J. S. Duker, “A review of optical coherence tomography angiography (OCTA),” Int. J. Retin. Vitreous 1, 5 (2015).
[Crossref]

Investig. Opthalmology & Vis. Sci. (1)

O. Thouvenin, C. Boccara, M. Fink, J. Sahel, M. Pâques, and K. Grieve, “Cell motility as contrast agent in retinal explant imaging with full-field optical coherence tomography,” Investig. Opthalmology & Vis. Sci. 58, 4605 (2017).
[Crossref]

J. Pathol. Informatics (1)

M. Jain, N. Shukla, M. Manzoor, S. Nadolny, and S. Mukherjee, “Modified full-field optical coherence tomography: A novel tool for rapid histology of tissues,” J. Pathol. Informatics 2, 28 (2011).
[Crossref]

Journal of Biomedical Optics (1)

J. Ben Arous, J. Binding, J.-F. Léger, M. Casado, P. Topilko, S. Gigan, A. C. Boccara, and L. Bourdieu, “Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy,” Journal of Biomedical Optics 16, 116012 (2011).
[Crossref] [PubMed]

Multiscale Model. & Simul. (1)

H. Ammari, F. Romero, and C. Shi, “A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography,” Multiscale Model. & Simul. 15, 1155–1175 (2017).
[Crossref]

Opt. Express (1)

Opt. Lett. (1)

Optica (1)

Proc. SPIE (1)

P. Mecê, P. Xiao, V. Mazlin, J. Scholler, K. Grieve, J.-A. Sahel, M. Fink, and C. Boccara, “Towards lens-based wavefront sensorless adaptive optics full-field oct for in-vivo retinal imaging,” Proc. SPIE 108671086722 (2019).

Proc. SPIE 10470, Endosc. Microsc. XIII (1)

E. B. a la Guillaume, C. Apelian, E. Dalimier, C. Boccara, A. Mansuet-Lupo, G. Chassagnon, and M.-P. Revel, “Lung biopsy assessment with dynamic cell optical imaging,” Proc. SPIE 10470, Endosc. Microsc. XIII 10470104700C (2018).
[Crossref]

Prog. Retin. Eye Res. (1)

A. H. Kashani, C.-L. Chen, J. K. Gahm, F. Zheng, G. M. Richter, P. J. Rosenfeld, Y. Shi, and R. K. Wang, “Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications,” Prog. Retin. Eye Res. 60, 66 – 100 (2017).
[Crossref] [PubMed]

Sci. Adv. (1)

A. Badon, D. Li, G. Lerosey, A. C. Boccara, M. Fink, and A. Aubry, “Smart optical coherence tomography for ultra-deep imaging through highly scattering media,” Sci. Adv. 2e1600370(2016).
[Crossref] [PubMed]

Science (1)

D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Other (2)

W. Drexler and J. G. Fujimoto, eds., Optical coherence tomography: technology and applications (Springer, 2015), 2nd ed.
[Crossref]

J. Scholler, “FFOCT control and acquisition software,” (2019). https://doi.org/10.5281/zenodo.3137245 .

Supplementary Material (2)

NameDescription
» Visualization 1       DFFOCT image complete field of view (1260 by 1260 micrometers) of a lung biopsy before and after applying the proposed motion artifact filtering.
» Visualization 2       DFFOCT image complete field of view (1260 by 1260 micrometers) of a lung biopsy before and after applying the proposed motion artifact filtering.

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Figures (6)

Fig. 1
Fig. 1 (a) FFOCT setup in inverted configuration top view. Microscope objectives are Olympus UPlanSApo 30× 1.05 NA. OCT Camera: ADIMEC Q-2A750-CXP. Light source: Thorlabs M660L3. - PZT: piezoelectric translation - BS: 50/50 beam splitter. (b) Schematic of the sample axial oscillations around the coherence volume due to mechanical vibrations, simulated on a graph in the top right corner. The setup is illustrated with oil immersed objectives where the probed volume depth is 1 μm.
Fig. 2
Fig. 2 (a) First few temporal eigenvectors. Top: for an acquisition with motion artifacts where V1 and V2 were detected as motion artifact and removed. Bottom: for an acquisition without visible artifacts. (b) Zero crossing rate computed for each temporal eigenvectors. (c) Absolute value of the derivative of the zero crossing rate computed for each temporal eigenvector. Artifacts were detected by thresholding the curve above 3 standard deviations. The baseline was arbitrarily increased for the red curves in (b) and (c) in order to increase readability.
Fig. 3
Fig. 3 Lung biopsy for cancer detection taken on the LLTech clinical setup. Artifacts arise mainly from mechanical vibration and air conditioning. (a)(d) Original D-FFOCT images computed on the raw stack. (b)(e) Denoised images computed with SVD filtering. (c)(f) Sum of the spatial eigenvectors absolute value removed by the SVD filtering. Red arrows are highlighting cells that were partially masked by motion artifacts. See Visualization 1 for complete 1260×1260 μm fields of view.
Fig. 4
Fig. 4 Fibroblast image taken on the setup presented Fig. 1. Artifacts arise mainly from mechanical vibrations leading to fringes pattern. (a) Original D-FFOCT image computed on the raw stack. (b) SVD-denoised image computed with SVD filtering. Subcellular features appear with a much better contrast enabling segmentation and tracking. (c) Sum of the spatial eigenvectors absolute value removed by the SVD filtering. Red arrows are highlighting subcellular features, only the bottom one was visible on the original image.
Fig. 5
Fig. 5 (a) Simulation of Gaussian noise with and without bias and their cumulative sum. The maximum reached by the cumulative sum is 3 times higher with the bias. (b) Dynamic image of a macaque photoreceptor layer using standard deviation. (c) Dynamic image of a macaque photoreceptor layer using the proposed method based on cumulative sum. (d) Histogram of SNR for 192 single cells segmented in (b) and (c). Segmentation results are shown in the top right corner. The gain in SNR for this data set is 1.96 on average for each cell.
Fig. 6
Fig. 6 In vivo mouse liver dynamic image taken on LLTech clinical setup with custom mount. Artifacts mainly arise from the breathing and heartbeat. (a) In vivo D-FFOCT image of a mouse liver computed on the raw stack with the standard deviation. (b)In vivo D-FFOCT image of a mouse liver computed on the SVD-denoised stack with the standard deviation. (c) in vivo D-FFOCT image of a mouse liver computed on the SVD-denoised stack with the cumulative sum. See Visualization 2 for complete 1260×1260 μm fields of view.

Tables (1)

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Table 1 Setup Characteristics.

Equations (9)

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I ( r , t ) = η I 0 4 ( R ( r , t ) + R i n c + R r e f + 2 R ( r , t ) R r e f c o s ( Δ ϕ ( r , t ) ) ) )
I d y n ( r ) = 1 N i S D ( η I 0 2 R s ( r , t [ i , i + τ ] ) R r e f c o s ( Δ ϕ s ( r , t [ i , i + τ ] ) ) )
I m e s ( r ) = I d y n ( r ) + I a r t ( r )
I a r t ( r ) = 1 N i S D ( η I 0 2 R s ( r , t [ i , i + τ ] ) R r e f c o s ( 4 π λ z ( t [ i , i + τ ] ) ) )
M u = U Σ V = i σ i U i V i
M ^ u = i σ i ^ U i V i
W M = s u p { W s : s [ 0 , 1 ] }
[ W M u ] = 1 e 2 u 2
I d y n ( r ) = 1 N i m a x ( | C u m S u m ( I ( r , t [ i , i + τ ] ) I ¯ ( r , t [ i , i + τ ] ) ) | )

Metrics