IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248301.html
   My bibliography  Save this article

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach

Author

Listed:
  • Daniel U Campos-Delgado
  • Omar Gutierrez-Navarro
  • Ricardo Salinas-Martinez
  • Elvis Duran
  • Aldo R Mejia-Rodriguez
  • Miguel J Velazquez-Duran
  • Javier A Jo

Abstract

The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses (FluoIRs) of the sample are decoupled from the instrument response (InstR). In blind deconvolution estimation (BDE), the FluoIRs and InstR are jointly extracted from a dataset with minimal a priori information. In this work, two BDE algorithms are introduced based on linear combinations of multi-exponential functions to model each FluoIR in the sample. For both schemes, the InstR is assumed with a free-form and a sparse structure. The local perspective of the BDE methodology assumes that the characteristic parameters of the exponential functions (time constants and scaling coefficients) are estimated based on a single spatial point of the dataset. On the other hand, the same exponential functions are used in the whole dataset in the global perspective, and just the scaling coefficients are updated for each spatial point. A least squares formulation is considered for both BDE algorithms. To overcome the nonlinear interaction in the decision variables, an alternating least squares (ALS) methodology iteratively solves both estimation problems based on non-negative and constrained optimizations. The validation stage considered first synthetic datasets at different noise types and levels, and a comparison with the standard deconvolution techniques with a multi-exponential model for FLIM measurements, as well as, with two BDE methodologies in the state of the art: Laguerre basis, and exponentials library. For the experimental evaluation, fluorescent dyes and oral tissue samples were considered. Our results show that local and global perspectives are consistent with the standard deconvolution techniques, and they reached the fastest convergence responses among the BDE algorithms with the best compromise in FluoIRs and InstR estimation errors.

Suggested Citation

  • Daniel U Campos-Delgado & Omar Gutierrez-Navarro & Ricardo Salinas-Martinez & Elvis Duran & Aldo R Mejia-Rodriguez & Miguel J Velazquez-Duran & Javier A Jo, 2021. "Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-29, March.
  • Handle: RePEc:plo:pone00:0248301
    DOI: 10.1371/journal.pone.0248301
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248301
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248301&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0248301?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. Sean C Warren & Anca Margineanu & Dominic Alibhai & Douglas J Kelly & Clifford Talbot & Yuriy Alexandrov & Ian Munro & Matilda Katan & Chris Dunsby & Paul M W French, 2013. "Rapid Global Fitting of Large Fluorescence Lifetime Imaging Microscopy Datasets," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-17, August.
    2. Bryan Kaye & Peter J Foster & Tae Yeon Yoo & Daniel J Needleman, 2017. "Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-13, January.
    3. Johannes Friedrich & Pengcheng Zhou & Liam Paninski, 2017. "Fast online deconvolution of calcium imaging data," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-26, March.
    4. Forrest Young & Jan Leeuw & Yoshio Takane, 1976. "Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 505-529, December.
    5. Thomas Pengo & Arrate Muñoz-Barrutia & Isabel Zudaire & Carlos Ortiz-de-Solorzano, 2013. "Efficient Blind Spectral Unmixing of Fluorescently Labeled Samples Using Multi-Layer Non-Negative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-11, November.
    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. Kadziński, MiŁosz & Greco, Salvatore & SŁowiński, Roman, 2012. "Extreme ranking analysis in robust ordinal regression," Omega, Elsevier, vol. 40(4), pages 488-501.
    2. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    3. Grigoroudis, Evangelos & Noel, Laurent & Galariotis, Emilios & Zopounidis, Constantin, 2021. "An ordinal regression approach for analyzing consumer preferences in the art market," European Journal of Operational Research, Elsevier, vol. 290(2), pages 718-733.
    4. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    5. Celia M. Gagliardi & Marc E. Normandin & Alexandra T. Keinath & Joshua B. Julian & Matthew R. Lopez & Manuel-Miguel Ramos-Alvarez & Russell A. Epstein & Isabel A. Muzzio, 2024. "Distinct neural mechanisms for heading retrieval and context recognition in the hippocampus during spatial reorientation," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    6. Dagum, Camilo & Slottje, Daniel J., 2000. "A new method to estimate the level and distribution of household human capital with application," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 67-94, July.
    7. van Rosmalen, J.M. & Koning, A.J. & Groenen, P.J.F., 2007. "Optimal Scaling of Interaction Effects in Generalized Linear Models," Econometric Institute Research Papers EI 2007-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Gerhard Tutz & Jan Gertheiss, 2014. "Rating Scales as Predictors—The Old Question of Scale Level and Some Answers," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 357-376, July.
    9. Hye Won Suk & Heungsun Hwang, 2016. "Functional Generalized Structured Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 940-968, December.
    10. Michio Yamamoto, 2012. "Clustering of functional data in a low-dimensional subspace," 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. 6(3), pages 219-247, October.
    11. Vittadini, Giorgio & Lovaglio, Pietro Giorgio, 2007. "Evaluation of the Dagum-Slottje method to estimate household human capital," Structural Change and Economic Dynamics, Elsevier, vol. 18(2), pages 270-278, June.
    12. Matthias Klemm & Dietrich Schweitzer & Sven Peters & Lydia Sauer & Martin Hammer & Jens Haueisen, 2015. "FLIMX: A Software Package to Determine and Analyze the Fluorescence Lifetime in Time-Resolved Fluorescence Data from the Human Eye," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    13. Jan Leeuw & Forrest Young & Yoshio Takane, 1976. "Additive structure in qualitative data: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 471-503, December.
    14. Takane, Yoshio, 2016. "My Early Interactions with Jan and Some of His Lost Papers," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i07).
    15. Zhou, Lixing & Takane, Yoshio & Hwang, Heungsun, 2016. "Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 93-109.
    16. Philipp Berens & Jeremy Freeman & Thomas Deneux & Nikolay Chenkov & Thomas McColgan & Artur Speiser & Jakob H Macke & Srinivas C Turaga & Patrick Mineault & Peter Rupprecht & Stephan Gerhard & Rainer , 2018. "Community-based benchmarking improves spike rate inference from two-photon calcium imaging data," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-13, May.
    17. Jacquet-Lagreze, Eric & Siskos, Yannis, 2001. "Preference disaggregation: 20 years of MCDA experience," European Journal of Operational Research, Elsevier, vol. 130(2), pages 233-245, April.
    18. Daniel M. Virga & Stevie Hamilton & Bertha Osei & Abigail Morgan & Parker Kneis & Emiliano Zamponi & Natalie J. Park & Victoria L. Hewitt & David Zhang & Kevin C. Gonzalez & Fiona M. Russell & D. Grah, 2024. "Activity-dependent compartmentalization of dendritic mitochondria morphology through local regulation of fusion-fission balance in neurons in vivo," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    19. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    20. Benedetto Manganelli & Pierluigi Morano & Francesco Tajani & Francesca Salvo, 2019. "Affordability Assessment of Energy-Efficient Building Construction in Italy," Sustainability, MDPI, vol. 11(1), pages 1-17, January.

    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:plo:pone00:0248301. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.