IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-29166-y.html
   My bibliography  Save this article

Large field-of-view non-invasive imaging through scattering layers using fluctuating random illumination

Author

Listed:
  • Lei Zhu

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France
    Xidian University)

  • Fernando Soldevila

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France)

  • Claudio Moretti

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France)

  • Alexandra d’Arco

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France)

  • Antoine Boniface

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France)

  • Xiaopeng Shao

    (Xidian University)

  • Hilton B. Aguiar

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France)

  • Sylvain Gigan

    (ENS–Université PSL, CNRS, Sorbonne Université, College de France)

Abstract

Non-invasive optical imaging techniques are essential diagnostic tools in many fields. Although various recent methods have been proposed to utilize and control light in multiple scattering media, non-invasive optical imaging through and inside scattering layers across a large field of view remains elusive due to the physical limits set by the optical memory effect, especially without wavefront shaping techniques. Here, we demonstrate an approach that enables non-invasive fluorescence imaging behind scattering layers with field-of-views extending well beyond the optical memory effect. The method consists in demixing the speckle patterns emitted by a fluorescent object under variable unknown random illumination, using matrix factorization and a novel fingerprint-based reconstruction. Experimental validation shows the efficiency and robustness of the method with various fluorescent samples, covering a field of view up to three times the optical memory effect range. Our non-invasive imaging technique is simple, neither requires a spatial light modulator nor a guide star, and can be generalized to a wide range of incoherent contrast mechanisms and illumination schemes.

Suggested Citation

  • Lei Zhu & Fernando Soldevila & Claudio Moretti & Alexandra d’Arco & Antoine Boniface & Xiaopeng Shao & Hilton B. Aguiar & Sylvain Gigan, 2022. "Large field-of-view non-invasive imaging through scattering layers using fluctuating random illumination," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29166-y
    DOI: 10.1038/s41467-022-29166-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-29166-y
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-29166-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ori Katz & François Ramaz & Sylvain Gigan & Mathias Fink, 2019. "Controlling light in complex media beyond the acoustic diffraction-limit using the acousto-optic transmission matrix," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
    3. Jacopo Bertolotti & Elbert G. van Putten & Christian Blum & Ad Lagendijk & Willem L. Vos & Allard P. Mosk, 2012. "Non-invasive imaging through opaque scattering layers," Nature, Nature, vol. 491(7423), pages 232-234, November.
    4. Antoine Boniface & Jonathan Dong & Sylvain Gigan, 2020. "Non-invasive focusing and imaging in scattering media with a fluorescence-based transmission matrix," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Y. Jauregui-Sánchez & H. Penketh & J. Bertolotti, 2022. "Tracking moving objects through scattering media via speckle correlations," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    2. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    3. Takehiro Sano & Tsuyoshi Migita & Norikazu Takahashi, 2022. "A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence," Journal of Global Optimization, Springer, vol. 84(3), pages 755-781, November.
    4. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    5. Shanika L Wickramasuriya & Berwin A Turlach & Rob J Hyndman, 2019. "Optimal Non-negative Forecast Reconciliation," Monash Econometrics and Business Statistics Working Papers 15/19, Monash University, Department of Econometrics and Business Statistics.
    6. Yunmin Yang & Binbin Chu & Jiayi Cheng & Jiali Tang & Bin Song & Houyu Wang & Yao He, 2022. "Bacteria eat nanoprobes for aggregation-enhanced imaging and killing diverse microorganisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Yoshi Fujiwara & Rubaiyat Islam, 2021. "Bitcoin's Crypto Flow Network," Papers 2106.11446, arXiv.org, revised Jul 2021.
    8. Yin Liu & Sam Davanloo Tajbakhsh, 2023. "Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient," Journal of Optimization Theory and Applications, Springer, vol. 198(1), pages 239-289, July.
    9. Immanuel Bomze & Werner Schachinger & Gabriele Uchida, 2012. "Think co(mpletely)positive ! Matrix properties, examples and a clustered bibliography on copositive optimization," Journal of Global Optimization, Springer, vol. 52(3), pages 423-445, March.
    10. Hiroyasu Abe & Hiroshi Yadohisa, 2019. "Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 825-853, December.
    11. GILLIS, Nicolas & GLINEUR, François, 2010. "On the geometric interpretation of the nonnegative rank," LIDAM Discussion Papers CORE 2010051, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Yaoyao Shi & Wei Sheng & Yangyang Fu & Youwen Liu, 2023. "Overlapping speckle correlation algorithm for high-resolution imaging and tracking of objects in unknown scattering media," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    13. Guowei Yang & Lin Zhang & Minghua Wan, 2022. "Exponential Graph Regularized Non-Negative Low-Rank Factorization for Robust Latent Representation," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
    14. Jingu Kim & Yunlong He & Haesun Park, 2014. "Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework," Journal of Global Optimization, Springer, vol. 58(2), pages 285-319, February.
    15. GILLIS, Nicolas & GLINEUR, François, 2010. "A multilevel approach for nonnegative matrix factorization," LIDAM Discussion Papers CORE 2010047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. N. Venkata Sailaja & L. Padma Sree & N. Mangathayaru, 2018. "New Rough Set-Aided Mechanism for Text Categorisation," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-19, June.
    17. Soodabeh Asadi & Janez Povh, 2021. "A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization," Mathematics, MDPI, vol. 9(5), pages 1-22, March.
    18. Thiel, Michel & Sauwen, Nicolas & Khamiakova, Tastian & Maes, Tor & Govaerts, Bernadette, 2021. "Comparison of chemometrics strategies for the spectroscopic monitoring of active pharmaceutical ingredients in chemical reactions," LIDAM Discussion Papers ISBA 2021009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    19. GILLIS, Nicolas & GLINEUR, François, 2008. "Nonnegative factorization and the maximum edge biclique problem," LIDAM Discussion Papers CORE 2008064, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    20. Tao Liu & Jiayuan Yu & Yuanjin Zheng & Chao Liu & Yanxiong Yang & Yunfei Qi, 2022. "A Nonlinear Multigrid Method for the Parameter Identification Problem of Partial Differential Equations with Constraints," Mathematics, MDPI, vol. 10(16), pages 1-12, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29166-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.