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Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging.

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  1. [1] § Methods › Dual deconvolution algorithm › Algorithm: ↔ main.m, lines 77–96 · score 0.78 · iteration stops, Wiener filters, emission OTFs, DC, ratios, error

Paper

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The authors' code

MATLAB · 144 lines · 5.3 KB · no license · 1 match

  2. % Description:
  3. % Example code for displaying two-photon fluorescence microscopy (TPFM), confocal TPFM,
  4. % SIM, and dual-deconvoluved SIM images.
  5. % Three raw Experimental datasets for differnt type of samples can be loaded and processed.
  7. %% ---- clear --------------------------------------
  8. clearvars; close all; clc;
  9. addpath(genpath('./'));
  11. %% --- load raw data from file --------------------------------------
  12. % Select the filename of the data to load.
  13. % Three raw data sets are aviable:
  15. filename = '../data/USAF.mat'; % (1) USAF target in Fig. 4(f-i)
  16. % filename = '../data/Beads_A488.mat'; % (2) Alexa-488 gold beads in Fig. 4(a-c)
  17. % filename = '../data/COS7_Cell.mat'; % (3) COS-7 cell in Fig. 5(a-b)
  19. psim_data = load_raw_data(filename);
  21. %% ---- set parameters -----------------------------------------------
  22. precision = 'single'; % specify precision: 'sinlge' or 'double'
  23. % Sinlge precision is recommended due to the memory-intensive nature of the code.
  24. green = flip(circshift(hot(256),1,2),2); % green colormap
  25. x_coord = psim_data.scan_info.x_coords; % X and Y coordinates for displaying images
  26. y_coord = psim_data.scan_info.y_coords;
  28. %% ---- image preprocessing ------------------------------------------
  29. psim_data = remove_bg_offset(psim_data, precision);
  31. %% --- generate two-photon fluorescence microscopy image -------------
  32. TPFM_image = gen_two_photon_image(psim_data, precision);
  34. h_fig = figure(1);
  35. h_fig.Name = 'Conventional two-photon fluorescence image';
  36. imagesc(x_coord, y_coord, TPFM_image)
  37. axis image
  38. colormap(green)
  39. colorbar
  40. title('2PFM image')
  41. xlabel ('x (\mum)')
  42. ylabel ('y (\mum)')
  43. drawnow
  45. %% --- generate confocal two-photon fluorescence image ----------------
  46. pinhole_radius_in_um = 0.2; % radius of confocal pinhole in micrometer
  47. CTPFM_image = gen_confocal_image(psim_data, pinhole_radius_in_um, precision);
  49. h_fig = figure(2);
  50. h_fig.Name = 'Two-photon excitation and confocal detection';
  51. imagesc(x_coord, y_coord, CTPFM_image)
  52. axis image
  53. colormap(green)
  54. colorbar
  55. title('Confocal 2PFM image')
  56. xlabel ('x (\mum)')
  57. ylabel ('y (\mum)')
  58. drawnow
  60. %% ---- construct incoherent response matrix, Fkk = F(ko, ki) -------
  61. [Fkk, otf_params] = gen_Fkk(psim_data, precision);
  63. %% ---- reconstruct 2PSIM image without aberration correction -------
  64. TPSIM_image = SIM_image_from_Fkk(Fkk, otf_params);
  66. h_fig = figure(3);
  67. h_fig.Name = 'Two-photon SIM image without aberration correction';
  68. imagesc(x_coord, y_coord, TPSIM_image)
  69. axis image
  70. colormap(green)
  71. colorbar
  72. title('2PSIM image')
  73. xlabel ('x (\mum)')
  74. ylabel ('y (\mum)')
  75. drawnow
  77. %% ---------- generate AO-2PSIM image -------------------------------
  79. % Setting parameters for dual deconvolution
  80. deconv_params = psim_data.deconv_params; % By default, optimal parameters for dual deconvolution are preloaded.
  82. % You can customize the parameters by uncommenting and modifying the lines below:
  83. % deconv_params.max_iter = 5; % Maximum number of iterations
  84. % deconv_params.iter_tol = 1E-4; % Iteration Stopping criterion for OTF error (tolerance)
  85. % deconv_params.N_cutoff_k = 8; % Cutoff frequency for DC filtering, specified in pixels
  86. % deconv_params.alpha = 9E-5; % Noise-to-signal ratio (NSR) for the Wiener filter applied to W(ko, ki)
  87. % deconv_params.beta = 0.15; % NSR for the Wiener filter applied to the object spectrum G(dk)
  88. % deconv_params.gamma = 3E-3; % NSR for the Wiener filters applied to excitation and emission OTFs, H_ex(ki) and H_em(ko)
  89. % deconv_params.iterN_lucy = 2; % Number of iterations for Lucy-Richardson deconvolution (used when dec_type = 0)
  90. % deconv_params.dec_type = 0; % Deconvolution method: 0 = Lucy-Richardson (faster, avoids ringing), 1 = Wiener
  92. deconv_params.x_coord = x_coord; % for figure
  93. deconv_params.y_coord = y_coord;
  95. % dual deconvolution: reconstruct AO_2PSIM image by dual deconvolution
  96. [AO2PSIM_image, H_ex_map, H_em_map] = dual_deconv(Fkk, otf_params, deconv_params);
  98. %% comparison with blind deconvolution
  100. % ---- blind deconvolution of TPFM image -------
  101. initial_PSF = fspecial('gaussian', size(TPFM_image,1), 3); % initial guess
  102. [TPFM_image_deconv, ~] = deconvblind(TPFM_image, initial_PSF, 5);
  104. h_fig = figure(11);
  105. h_fig.Name = 'blind deconvolution of two-photon image';
  106. imagesc(x_coord, y_coord, TPFM_image_deconv)
  107. axis image
  108. colormap(green)
  109. colorbar
  110. title('TPFM\_image\_deconv')
  111. xlabel ('x (\mum)')
  112. ylabel ('y (\mum)')
  114. % ---- blind deconvolution of confocal TPFM image -------
  115. initial_PSF = fspecial('gaussian', size(TPFM_image,1), 3); % initial guess
  116. [CTPFM_image_deconv, ~] = deconvblind(CTPFM_image, initial_PSF, 10);
  118. h_fig = figure(12);
  119. h_fig.Name = 'blind deconvolution of confocal two-photon image';
  120. imagesc(x_coord, y_coord, CTPFM_image_deconv)
  121. axis image
  122. colormap(green)
  123. colorbar
  124. title('CTPFM\_image\_deconv')
  125. xlabel ('x (\mum)')
  126. ylabel ('y (\mum)')
  128. % ---- blind deconvolution of 2PSIM image -------
  129. initial_PSF = fspecial('gaussian', size(TPFM_image,1), 6); % initial guess
  130. [TPSIM_image_deconv, ~] = deconvblind(TPSIM_image, initial_PSF, 5);
  132. h_fig = figure(32);
  133. h_fig.Name = 'blind deconvolution of confocal 2PSIM image';
  134. imagesc(x_coord, y_coord, TPSIM_image_deconv)
  135. axis image
  136. colormap(green)
  137. colorbar
  138. title('TPSIM\_image\_deconv')
  139. xlabel ('x (\mum)')
  140. ylabel ('y (\mum)')
  142. drawnow

main.m at commit 283ea44, no license · at the source

Overview

Authors: Sumin Lim1,2, Sungsam Kang1,2, Jin Hee Hong1,2, Young-Ho Jin1,2, Kalpak Gupta1,2, Moonseok Kim3,4, Suhyun Kim5, Wonshik Choi1,2, Seokchan Yoon6
ORCID iDs: Sungsam Kang, Jin Hee Hong, Young-Ho Jin, Suhyun Kim, Wonshik Choi, Seokchan Yoon
  1. Department of Physics, Korea University, Seoul, Korea
  2. Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science, Seoul, Korea
  3. Department of Medical Life Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
  4. Department of Medical Sciences, Graduate School of The Catholic University of Korea, Seoul, Korea
  5. Department of Biomedical Sciences, Korea University, Ansan, Korea
  6. School of Biomedical Convergence Engineering, Pusan National University, Yangsan, Korea
Institutions: Korea University (South Korea); Institute for Basic Science (South Korea); Catholic University of Korea (South Korea); Pusan National University (South Korea)
Journal: Nature communications, volume 17, issue 1, article 2123
Dates: received 12 April 2025; accepted 9 February 2026; published online 4 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-69798-y · PMID 41781392 · PMCID PMC12960828 · OpenAlex W7133526154
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism)
Methods: Spectral & time-frequency, Evoked potentials, fMRI & imaging
Keywords: Adaptive optics, Multiphoton microscopy, Fluorescence imaging, Super-resolution microscopy
MeSH: Brain*, Image Processing, Computer-Assisted*, Microscopy, Fluorescence, Multiphoton*, Algorithms, Animals, Mice (* major topic)
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Research Foundation of Korea (RS-2024-00442818); Institute for Basic Science, IBS-R023-D1.; Institute of Information & Communications Technology Planning & Evaluation (IITP)
Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

CenterForDeepImaging/Dual-Deconvolution

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 283ea4406cc5e8f40e609976806eb62eaed5d52a, 15 March 2025
Languages: MATLAB (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/CenterForDeepImaging/Dual-Deconvolution

figshare 28600847

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-69798-y.

Tracing map

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  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-69798-y.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 4 keywords, 6 MeSH terms, 3 funders, 44 references.

Cite

This paper

Lim, S., Kang, S., Hong, J. H., Jin, Y.-H., Gupta, K., Kim, M., Kim, S., Choi, W., & Yoon, S. (2026). Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging. Nature communications, 17(1), 2123. https://doi.org/10.1038/s41467-026-69798-y

BibTeX

@article{lim2026dual,
author = {Lim, Sumin and Kang, Sungsam and Hong, Jin Hee and Jin, Young-Ho and Gupta, Kalpak and Kim, Moonseok and Kim, Suhyun and Choi, Wonshik and Yoon, Seokchan},
title = {{Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {2123},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-69798-y},
url = {https://doi.org/10.1038/s41467-026-69798-y},
pmid = {41781392},
pmcid = {PMC12960828}
}

RIS

TY - JOUR
AU - Lim, Sumin
AU - Kang, Sungsam
AU - Hong, Jin Hee
AU - Jin, Young-Ho
AU - Gupta, Kalpak
AU - Kim, Moonseok
AU - Kim, Suhyun
AU - Choi, Wonshik
AU - Yoon, Seokchan
TI - Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/04
VL - 17
IS - 1
SP - 2123
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-69798-y
UR - https://doi.org/10.1038/s41467-026-69798-y
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
"author": [
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"PMCID": "PMC12960828",
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