Multispectral in-line hologram reconstruction with aberration compensation applied to Gram-stained bacteria microscopy.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 09 2023
02 09 2023
Historique:
received:
13
06
2023
accepted:
21
08
2023
medline:
5
9
2023
pubmed:
3
9
2023
entrez:
2
9
2023
Statut:
epublish
Résumé
In multispectral digital in-line holographic microscopy (DIHM), aberrations of the optical system affect the repeatability of the reconstruction of transmittance, phase and morphology of the objects of interest. Here we address this issue first by model fitting calibration using transparent beads inserted in the sample. This step estimates the aberrations of the optical system as a function of the lateral position in the field of view and at each wavelength. Second, we use a regularized inverse problem approach (IPA) to reconstruct the transmittance and phase of objects of interest. Our method accounts for shift-variant chromatic and geometrical aberrations in the forward model. The multi-wavelength holograms are jointly reconstructed by favouring the colocalization of the object edges. The method is applied to the case of bacteria imaging in Gram-stained blood smears. It shows our methodology evaluates aberrations with good repeatability. This improves the repeatability of the reconstructions and delivers more contrasted spectral signatures in transmittance and phase, which could benefit applications of microscopy, such as the analysis and classification of stained bacteria.
Identifiants
pubmed: 37660181
doi: 10.1038/s41598-023-41079-4
pii: 10.1038/s41598-023-41079-4
pmc: PMC10475072
doi:
Substances chimiques
Excipients
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14437Informations de copyright
© 2023. Springer Nature Limited.
Références
Popescu, G. Quantitative phase imaging of cells and tissues. McGraw-Hill Education, 1st edition edn. (2011).
Aknoun, S. et al. Living cell dry mass measurement using quantitative phase imaging with quadriwave lateral shearing interferometry: An accuracy and sensitivity discussion. J. Biomed. Opt. 20, 126009. https://doi.org/10.1117/1.JBO.20.12.126009 (2015).
doi: 10.1117/1.JBO.20.12.126009
pubmed: 26720876
Boltyanskiy, R., Odete, M. A., Cheong, F. C. & Philips, L. A. Label-free viability assay using in-line holographic video microscopy. Sci. Rep. 12, 12746. https://doi.org/10.1038/s41598-022-17098-y (2022).
doi: 10.1038/s41598-022-17098-y
pubmed: 35882977
pmcid: 9325748
Girshovitz, P. & Shaked, N. T. Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization. Biomed. Opt. Express 3, 1757–1773. https://doi.org/10.1364/BOE.3.001757 (2012).
doi: 10.1364/BOE.3.001757
pubmed: 22876342
pmcid: 3409697
Popescu, G. et al. Optical measurement of cell membrane tension. Phys. Rev. Lett. 97, 218101. https://doi.org/10.1103/PhysRevLett.97.218101 (2006).
doi: 10.1103/PhysRevLett.97.218101
pubmed: 17155774
Wang, A., Rogers, W. B. & Manoharan, V. N. Effects of contact-line pinning on the adsorption of nonspherical colloids at liquid interfaces. Phys. Rev. Lett. 119, 108004. https://doi.org/10.1103/PhysRevLett.119.108004 (2017).
doi: 10.1103/PhysRevLett.119.108004
pubmed: 28949187
Nguyen, T. L. et al. Quantitative phase imaging: Recent advances and expanding potential in biomedicine. ACS Nano 16, 11516–11544. https://doi.org/10.1021/acsnano.1c11507 (2022).
doi: 10.1021/acsnano.1c11507
pubmed: 35916417
pmcid: 10112851
Brault, D. et al. Automatic numerical focus plane estimation in digital holographic microscopy using calibration beads. Appl. Opt. 61, B345–B355. https://doi.org/10.1364/AO.444996 (2022).
doi: 10.1364/AO.444996
pubmed: 35201158
Sirico, D. G. et al. Compensation of aberrations in holographic microscopes: main strategies and applications. Appl. Phys. B 128, 78. https://doi.org/10.1007/s00340-022-07798-8 (2022).
doi: 10.1007/s00340-022-07798-8
Colomb, T. et al. Numerical parametric lens for shifting, magnification, and complete aberration compensation in digital holographic microscopy. JOSA A 23, 3177–3190. https://doi.org/10.1364/JOSAA.23.003177 (2006).
doi: 10.1364/JOSAA.23.003177
pubmed: 17106474
Martin, C., Leahy, B., Manoharan, V. N. & Manoharan, V. N. Improving holographic particle characterization by modeling spherical aberration. Opt. Express 29, 18212–18223. https://doi.org/10.1364/OE.424043 (2021).
doi: 10.1364/OE.424043
pubmed: 34154082
Brault, D. et al. Accurate unsupervised estimation of aberrations in digital holographic microscopy for improved quantitative reconstruction. Opt. Express 30, 38383–38404. https://doi.org/10.1364/OE.471638 (2022).
doi: 10.1364/OE.471638
pubmed: 36258405
Gerchberg, R. W. & Saxton, W. O. Practical algorithm for determination of phase from image and diffraction plane pictures. Optik 35, 237–000 (1972).
Fienup, J. R. Reconstruction of an object from the modulus of its Fourier transform. Opt. Lett. 3, 27–29. https://doi.org/10.1364/OL.3.000027 (1978).
doi: 10.1364/OL.3.000027
pubmed: 19684685
Latychevskaia, T. & Fink, H.-W. Solution to the twin image problem in holography. Phys. Rev. Lett. 98, 233901. https://doi.org/10.1103/PhysRevLett.98.233901 (2007).
doi: 10.1103/PhysRevLett.98.233901
pubmed: 17677906
Denis, L., Lorenz, D., Thiébaut, E., Fournier, C. & Trede, D. Inline hologram reconstruction with sparsity constraints. Opt. Lett. 34, 3475–3477. https://doi.org/10.1364/OL.34.003475 (2009).
doi: 10.1364/OL.34.003475
pubmed: 19927182
Momey, F., Denis, L., Olivier, T. & Fournier, C. From Fienup’s phase retrieval techniques to regularized inversion for in-line holography: Tutorial. JOSA A 36, D62–D80. https://doi.org/10.1364/JOSAA.36.000D62 (2019).
doi: 10.1364/JOSAA.36.000D62
pubmed: 31873388
Lee, S.-H. et al. Characterizing and tracking single colloidal particles with video holographic microscopy. Opt. Express 15, 18275–18282. https://doi.org/10.1364/OE.15.018275 (2007).
doi: 10.1364/OE.15.018275
pubmed: 19551125
Soulez, F., Denis, L., Fournier, C., Thiébaut, É. & Goepfert, C. Inverse-problem approach for particle digital holography: Accurate location based on local optimization. JOSA A 24, 1164–1171. https://doi.org/10.1364/JOSAA.24.001164 (2007).
doi: 10.1364/JOSAA.24.001164
pubmed: 17361304
Martin, C. et al. In-line holographic microscopy with model-based analysis. Nat. Rev. Methods Primers 2, 1–17. https://doi.org/10.1038/s43586-022-00165-z (2022).
doi: 10.1038/s43586-022-00165-z
Olivier, T. et al. Effects of some model approximations in the reconstructions of digital in-line holograms: simulations, experiments on calibrated objects and model refinement assessment. In: Georges, M. P., Popescu, G. & Verrier, N. (eds.) Unconventional Optical Imaging III, 10, https://doi.org/10.1117/12.2621668 SPIE, Strasbourg, France, (2022).
Lakshminarayanan, V. & Fleck, A. Zernike polynomials: A guide. J. Mod. Opt. 58, 545–561. https://doi.org/10.1080/09500340.2011.554896 (2011).
doi: 10.1080/09500340.2011.554896
Noll, R. J. Zernike polynomials and atmospheric turbulence. JOSA 66, 207–211 (1976).
doi: 10.1364/JOSA.66.000207
Herve, L. et al. Multispectral total-variation reconstruction applied to lens-free microscopy. Biomed. Opt. Express 9, 5828–5836. https://doi.org/10.1364/BOE.9.005828 (2018).
doi: 10.1364/BOE.9.005828
pubmed: 30460165
pmcid: 6238925
Rivenson, Y. et al. Sparsity-based multi-height phase recovery in holographic microscopy. Sci. Rep. 6, 37862. https://doi.org/10.1038/srep37862 (2016).
doi: 10.1038/srep37862
pubmed: 27901048
pmcid: 5129015
Luo, W., Zhang, Y., Göröcs, Z., Feizi, A. & Ozcan, A. Propagation phasor approach for holographic image reconstruction. Sci. Rep. 6, 22738. https://doi.org/10.1038/srep22738 (2016).
doi: 10.1038/srep22738
pubmed: 26964671
pmcid: 4786813
Berdeu, A. et al. Lens-free microscopy for 3D + time acquisitions of 3D cell culture. Sci. Rep. 8, 16135. https://doi.org/10.1038/s41598-018-34253-6 (2018).
doi: 10.1038/s41598-018-34253-6
pubmed: 30382136
pmcid: 6208343
Noom, D. W. E., Flaes, D. E. B., Labordus, E., Eikema, K. S. E. & Witte, S. High-speed multi-wavelength Fresnel diffraction imaging. Opt. Exp. 22, 30504–30511. https://doi.org/10.1364/OE.22.030504 (2014).
doi: 10.1364/OE.22.030504
Zhang, H., Stangner, T., Wiklund, K. & Andersson, M. Object plane detection and phase retrieval from single-shot holograms using multi-wavelength in-line holography. Appl. Opt. 57, 9855–9862. https://doi.org/10.1364/AO.57.009855 (2018).
doi: 10.1364/AO.57.009855
pubmed: 30462021
Gao, Y. & Cao, L. Projected refractive index framework for multi-wavelength phase retrieval. Opt. Lett. 47, 5965–5968. https://doi.org/10.1364/OL.476707 (2022).
doi: 10.1364/OL.476707
pubmed: 37219148
Samuel, L. P., Balada-Llasat, J.-M., Harrington, A. & Cavagnolo, R. Multicenter assessment of gram stain error rates. J. Clin. Microbiol. 54, 1442–1447 (2016).
doi: 10.1128/JCM.03066-15
pubmed: 26888900
pmcid: 4879281
Samuel, L. & Plebani, M. Targeting errors in microbiology: The case of the gram stain. Clin. Chem. Lab. Med. (CCLM) 55, 309–310 (2017).
doi: 10.1515/cclm-2016-0828
pubmed: 27718489
Thomson, R. B. Jr. Commentary: One small step for the gram stain, one giant leap for clinical microbiology. J. Clin. Microbiol. 54, 1416–1417 (2016).
doi: 10.1128/JCM.00303-16
pubmed: 27008876
pmcid: 4879305
Fischer, A. et al. Performances of automated digital imaging of gram-stained slides with on-screen reading against manual microscopy. Eur. J. Clin. Microbiol. Infect. Dis. 40, 2171–2176 (2021).
doi: 10.1007/s10096-021-04233-2
pubmed: 33963927
pmcid: 8449764
Goodman, J. Introduction to Fourier Optics. Roberts and Company Publishers, Englewood, Colo, 3rd edition edn. (2004)
Denis, L., Thiébaut, E. & Soulez, F. Fast model of space-variant blurring and its application to deconvolution in astronomy. In 2011 18th IEEE International Conference on Image Processing, 2817–2820. IEEE, (2011).
Sai, T., Saba, M., Dufresne, E. R., Steiner, U. & Wilts, B. D. Designing refractive index fluids using the Kramers-Kronig relations. Faraday Disc. 223, 136–144. https://doi.org/10.1039/D0FD00027B (2020).
doi: 10.1039/D0FD00027B
Slimani, F., Gréhan, G., Gouesbet, G. & Allano, D. Near-field lorenz-mie theory and its application to microholography. Appl. Opt. 23, 4140–4148 (1984).
doi: 10.1364/AO.23.004140
pubmed: 18213288
Lee, S.-H. et al. Characterizing and tracking single colloidal particles with video holographic microscopy. Opt. Express 15, 18275–18282 (2007).
doi: 10.1364/OE.15.018275
pubmed: 19551125
Cheong, F. C., Krishnatreya, B. J. & Grier, D. G. Strategies for three-dimensional particle tracking with holographic video microscopy. Opt. Express 18, 13563–13573 (2010).
doi: 10.1364/OE.18.013563
pubmed: 20588488
Méès, L. et al. Evaporating droplet hologram simulation for digital in-line holography setup with divergent beam. JOSA A 30, 2021–2028 (2013).
doi: 10.1364/JOSAA.30.002021
pubmed: 24322858
Holland, P. W. & Welsch, R. E. Robust regression using iteratively reweighted least-squares. Commun. Stat. Theory Methods 6, 813–827 (1977).
doi: 10.1080/03610927708827533
Flasseur, O., Denis, L., Fournier, C. & Thiébaut, E. Robust object characterization from lensless microscopy videos. In 2017 25th European Signal Processing Conference (EUSIPCO), 1445–1449. IEEE, (2017).
Brault, D. et al. Fast and robust pattern detection: Application to spherical bead localization in holographic microscopy. accepted in IEEE European Signal Processing Conference (EUSIPCO), 09/2022, (2022).
Goodman, J. W. Introduction to Fourier optics 3rd edn. (Roberts & Co., Publishers, Englewood, Colorado, 2004).
Karl, W. C. Regularization in image restoration and reconstruction. In Handbook of Image and Video Processing, 183–V Elsevier, (2005).
Lawson, C. L. & Hanson, R. J. Solving least squares problems (SIAM, 1995).
Stein, C. M. Estimation of the mean of a multivariate normal distribution. Ann. Stat. 1135–1151 (1981).
Ramani, S., Blu, T. & Unser, M. Monte-carlo sure: A black-box optimization of regularization parameters for general denoising algorithms. IEEE Trans. Image Process. 17, 1540–1554 (2008).
doi: 10.1109/TIP.2008.2001404
pubmed: 18701393
Denneulin, L. et al. Gsure criterion for unsupervised regularized reconstruction in tomographic diffractive microscopy. JOSA A 39, A52–A61. https://doi.org/10.1364/JOSAA.444890 (2022).
doi: 10.1364/JOSAA.444890
pubmed: 35200955
Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).
doi: 10.1080/01621459.1979.10481038
Thiébaut, E. Optimization issues in blind deconvolution algorithms. In Astronomical Data Analysis II, vol. 4847, 174–183 SPIE, (2002).
Unser, M., Soubies, E., Soulez, F., McCann, M. & Donati, L. Globalbioim: A unifying computational framework for solving inverse problems. In Computational Optical Sensing and Imaging, CTu1B–1 Optical Society of America, (2017).
Soubies, E. et al. Pocket guide to solve inverse problems with globalbioim. Inverse Prob. 35, 104006 (2019).
doi: 10.1088/1361-6420/ab2ae9