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Solving the boundary artifact for the enhanced deconvolution algorithm SUPPOSe applied to fluorescence microscopy
M. Toscani 1, S. Martínez 2

1 Laboratorio de Fotónica, Instituto de Ingeniería Biomédica, FI-UBA, CONICET, Buenos Aires, Argentina,
2 Departamento de Matemática, FCEyN-UBA, IMAS, CONICET, Buenos Aires, Argentina

 PDF, 1287 kB

DOI: 10.18287/2412-6179-CO-825

Страницы: 418-426.

Язык статьи: English

Аннотация:
The SUPPOSe enhanced deconvolution algorithm relies in assuming that the image source can be described by an incoherent superposition of virtual point sources of equal intensity and finding the number and position of such virtual sources. In this work we describe the recent advances in the implementation of the method to gain resolution and remove artifacts due to the presence of fluorescent molecules close enough to the image frame boundary. The method was modified removing the invariant used before given by the product of the flux of the virtual sources times the number of virtual sources, and replacing it by a new invariant given by the total flux within the frame, thus allowing the location of virtual sources outside the frame but contributing to the signal inside the frame.

Ключевые слова:
deconvolution, fluorescence, microscopy, boundary, artifact.

Благодарности
Authors want to thanks Oscar E. Martínez for fruitful discussions and constructive comments in relation to this work. The work was partially funded by Agencia Nacional de Promoción Científica y Tecnológica, PICT 2015-1523; Universidad de Buenos Aires, ubacyt2018 20020170100137BA; and AFOSR, FA9550-18-1-0470 P00001.

Citation:
Toscani M, Martínez S. Solving the boundary artifact for the enhanced deconvolution algorithm SUPPOSe applied to fluorescence microscopy. Computer Optics 2021; 45(3): 418-426. DOI: 10.18287/2412-6179-CO-825.

Литература:
  1. Sage D, Donati L, Soulez F, Fortun D, Schmit G, Seitz A, Guiet R, Vonesch C, Unser M. Deconvolutionlab2: An open-source software for deconvolution microscopy. Methods 2017; 115: 28-41.
  2. Landweber L. An iteration formula for fredholm integral equations of the first kind. Am J Math 1951; 73(3): 615-624.
  3. Richardson WH. Bayesian-based iterative method of image restoration. J Opt Soc Am 1972; 62(1): 55-59.
  4. Lucy LB. An iterative technique for the rectification of observed distributions. Astron J 1974; 79: 745.
  5. Tikhonov AN. On the solution of ill-posed problems and the method of regularization [In Russian]. Doklady Akademii Nauk SSSR 1963; 151(3): 501-504.
  6. Wiener, N. Extrapolation, interpolation, and smoothing of stationary time series: with engineering applications. MIT Press; 1964.
  7. Beck A, Teboulle M. A fast iterative shrinkagethresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2009; 2(1): 183-202.
  8. Dey N, Blanc-Feraud L, Zimmer C, Roux P, Kam Z, Olivo-Marin J-C, Zerubia J. Richardson–lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microsc Res Tech 2006; 69(4): 260-266.
  9. Donoho DL. Superresolution via sparsity constraints. SIAM J Math Anal 1992; 23(5): 1309-1331.
  10. Candès E, Romberg JJ, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006; 52(2): 489-509.
  11. Morgenshtern VI, Candes EJ. Superresolution of positive sources: The discrete setup. SIAM J Imaging Sci 2016; 9(1): 412-444.
  12. Martínez S, Toscani M, Martínez OE. Superresolution method for a single wide-field image deconvolution by superposition of point sources. J Microsc 2019; 275(1): 51-65.
  13. Toscani M, Martínez S, Martínez OE. Single image deconvolution with super-resolution using the suppose algorithm. Proc SPIE 2019; 10884; 1088415.
  14. Vazquez GDB, Martínez S, Martínez OE. Super-resolved edge detection in optical microscopy images by superposition of virtual point sources. Opt Express 2020; 28(17): 25319-25334.
  15. Park SC, Park MK, Kang MG. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 2003; 20(3): 21-36.
  16. Hell SW, Wichmann J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt Lett 1994; 19(11): 780-782.
  17. Klar TA, Jakobs S, Dyba M, Egner A, Hell SW. Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission. Proc Natl Acad Sci U S A 2000; 97(15): 8206-8210.
  18. Hell SW, Kroug M. Ground-state-depletion fluorscence microscopy: A concept for breaking the diffraction resolution limit. Appl Phys B 1995; 60(5): 495-497.
  19. Rittweger E, Wildanger D, Hell SW. Far-field fluorescence nanoscopy of diamond color centers by ground state depletion. EPL (Europhys Lett) 2009; 86(1): 14001.
  20. Gustafsson MG. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J Microsc 2000; 198(2): 82-87.
  21. Heintzmann R, Jovin TM, Cremer C. Saturated patterned excitation microscopy–a concept for optical resolution improvement. J Opt Soc Am A 2002; 19(8): 1599-1609.
  22. Gustafsson MG. Nonlinear structured illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” Proc Natl Acad Sci U S A 2005; 102(37): 13081-13086.
  23. Schwentker MA, Bock H, Hofmann M, Jakobs S, Bewersdorf J, Eggeling C, Hell SW. Widefield subdiffraction resolft microscopy using fluorescent protein photoswitching. Microsc Res Tech 2007; 70(3): 269-280.
  24. Heintzmann R, Gustafsson MG. Subdiffraction resolution in continuous samples. Nat Photon 2009; 3(7): 362-364.
  25. Rosen J, Siegel N, Brooker G. Theoretical and experimental demonstration of resolution beyond the rayleigh limit by finch fluorescence microscopic imaging. Opt Express 2011; 19(27): 26249-26268.
  26. Siegel N, Lupashin V, Storrie B, Brooker G. High-magnification super-resolution finch microscopy using birefringent crystal lens interferometers. Nat Photon 2016; 10(12): 802-808.
  27. Betzig E, Patterson GH, Sougrat R, Lindwasser OW, Olenych S, Bonifacino JS, Davidson MW, Lippincott-Schwartz J, Hess HF. Imaging intracellular fluorescent proteins at nanometer resolution. Science 2006; 313(5793): 1642-1645.
  28. Hess ST, Girirajan TP, Mason MD. Ultrahigh resolution imaging by fluorescence photoactivation localization microscopy. Biophys J 2006; 91(11): 4258-4272.
  29. Rust MJ, Bates M, Zhuang X. Subdiffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 2006; 3(10): 793-796.
  30. Heilemann M, Van De Linde S, Schüttpelz M, Kasper R, Seefeldt B, Mukherjee A, Tinnefeld P, Sauer M. Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes. Angew Chem Int Ed 2008; 47(33): 6172-6176.
  31. Bates M, Jones SA, Zhuang X. Stochastic optical reconstruction microscopy (STORM): a method for superresolution fluorescence imaging. Cold Spring Harb Protoc 2013; 2013(6): 498-520.
  32. Sauer M, Heilemann M. Single-molecule localization microscopy in eukaryotes. Chem Rev 2017; 117(11): 7478-7509.
  33. Stone MB, Shelby SA, Veatch SL. Superresolution microscopy: shedding light on the cellular plasma membrane. Chem Rev 2017; 117(11): 7457-7477.
  34. Tam J, Merino D. Stochastic optical reconstruction microscopy (storm) in comparison with stimulated emission depletion (sted) and other imaging methods. J Neurochem 2015; 135(4): 643-658.
  35. Samanta S, Gong W, Li W, Sharma A, Shim I, Zhang W, Das P, Pan W, Liu L, Yang Z, Qua J, Kima JS. Organic fluorescent probes for stochastic optical reconstruction microscopy (storm): Recent highlights and future possibilities. Coord Chem Rev 2019; 380: 17-34.
  36. Huang B, Wang W, Bates M, Zhuang X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 2008; 319(5864): 810-813.
  37. Backlund MP, Lew MD, Backer AS, Sahl SJ, Grover G, Agrawal A, Piestun R, Moerner W. Simultaneous, accurate measurement of the 3D position and orientation of single molecules. Proc Natl Acad Sci U S A 2012; 109(47): 19087-19092.
  38. Shtengel G, Galbraith JA, Galbraith CG, Lippincott-Schwartz J, Gillette JM, Manley S, Sougrat R, Waterman CM, Kanchanawong P, Davidson MW, Fetter RD, Hess HF. Interferometric fluorescent super-resolution microscopy resolves 3D cellular ultrastructure. Proc Natl Acad Sci U S A 2009; 106(9): 3125-3130.
  39. Aquino D, Schönle A, Geisler C, Middendorff CV, Wurm CA, Okamura Y, Lang T, Hell SW, Egner A. Two-color nanoscopy of threedimensional volumes by 4pi detection of stochastically switched fluorophores. Nat Methods 2011; 8(4): 353-359.
  40. Bourg N, Mayet C, Dupuis G, Barroca T, Bon P, Lécart S, Fort E, Lévêque-Fort S. Direct optical nanoscopy with axially localized detection. Nat Photon 2015; 9(9): 587-593.
  41. Bon P, Linares-Loyez J, Feyeux M, Alessandri K, Lounis B, Nassoy P, Cognet L. Selfinterference 3D super-resolution microscopy for deep tissue investigations. Nat Methods 2018; 15(6): 449-454.
  42. Ghosh A, Sharma A, Chizhik AI, Isbaner S, Ruhlandt D, Tsukanov R, Gregor I, Karedla N, Enderlein J. Graphene-based metal-induced energy transfer for sub-nanometre optical localization. Nat Photon 2019; 13(12): 860-865.
  43. Zhu L, Zhang W, Elnatan D, Huang B. Faster storm using compressed sensing. Nat Methods 2012; 9(7): 721-723.
  44. Min J, Vonesch C, Kirshner H, Carlini L, Olivier N, Holden S, Manley S, Ye JC, Unser M. Falcon: fast and unbiased reconstruction of high-density super-resolution microscopy data. Sci Rep 2014; 4(1): 1-9.
  45. Hugelier S, De Rooi JJ, Bernex R, Duwé S, Devos O, Sliwa M, Dedecker P, Eilers PH, Ruckebusch C. Sparse deconvolution of highdensity super-resolution images. Sci Rep 2016; 6: 21413.
  46. Hugelier S, Eilers P, Devos O, Ruckebusch C. Improved superresolution microscopy imaging by sparse deconvolution with an interframe penalty. J Chemom 2017; 31(4): e2847.
  47. Huang F, Schwartz SL, Byars JM, Lidke KA. Simultaneous multiple-emitter fitting for single molecule super-resolution imaging. Biomed Opt Express 2011; 2(5): 1377-1393.
  48. Nelson A, Hess S. Molecular imaging with neural training of identification algorithm (neural network localization identification). Microsc Res Tech 2018; 81(9): 966-972.
  49. Xu K, Zhong G, Zhuang X. Actin, spectrin, and associated proteins form a periodic cytoskeletal structure in axons. Science 2013; 339(6118): 452-456.
  50. Barabas FM, Masullo LA, Bordenave MD, Giusti SA, Unsain N, Refojo D, Cáceres A, Stefani FD. Automated quantification of protein periodic nanostructures in fluorescence nanoscopy images: abundance and regularity of neuronal spectrin membrane-associated skeleton. Sci Rep 2017; 7(1): 1-10.

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