OSCR

Interferometric ultra-high resolution 3D imaging through brain sections.

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Systematic configuration and 4Pi-BRAINSPOT algorithm › 4Pi in-situ PSF retrieval ↔ Support/PSF Toolbox_4pi/@PSF_4pi/PSF_4pi.m, lines 2–91 · score 0.85 · coherent pupil functions, Pi PSF model, objective misalignment, phase retrieval, cavity phase, depth
  2. [2] § Results › Design of 4Pi-BRAINSPOT ↔ Support/PSF Toolbox_4pi/@PSF_4pi/PSF_4pi.m, lines 2–91 · score 0.81 · coherent pupil functions, Pi PSF model, PSFs generated, objective misalignments, phase retrieval, cavity phase
  3. [3] § Methods › Systematic configuration and 4Pi-BRAINSPOT algorithm › 4Pi in-situ PSF retrieval ↔ 4Pi-BRAINSPOT toolbox/4Pi_insitu_PSF_retrieval/INSPR4Pi_model_generation.m, lines 12–104 · score 0.76 · interferometric patterns, Pi PSF, PSF retrieval, pupil functions, XYZ, refined
  4. [4] § Results › Design of 4Pi-BRAINSPOT ↔ 4Pi-BRAINSPOT toolbox/4Pi_insitu_PSF_retrieval/INSPR4Pi_model_generation.m, lines 12–104 · score 0.69 · Pi interference, Interferometric pattern, Pi PSF, pupil functions, refined, single molecule
  5. [5] § Results › Characterization of 4Pi-BRAINSPOT performance ↔ 4Pi-BRAINSPOT toolbox/brainspot_4pi_GUI.m, lines 140–224 · score 0.63 · vertical astigmatism, Zernike modes, axial directions, PSF model, axial positions, lateral
  6. [6] § Methods › Quantification and statistical analysis › Analysis pipeline of 3D structural morphology and circumference ↔ 4Pi-BRAINSPOT Supplementary analysis/Function/Analysis_spine_area_3D.m, lines 126–173 · score 0.58 · alphaShape algorithm, boundaries, sliced, fitting, positions
  7. [7] § Results › Nanoscale visualization and quantitative analysis of subcellular organisms in mammalian cells ↔ 4Pi-BRAINSPOT toolbox/4Pi_insitu_PSF_retrieval/gen_init_4PiPupil_v2.m, the whole file · a weak match · score 0.58 · immersion oil, cavity phase, refractive, depths, emission, objective
  8. [8] § Results › Characterization of 4Pi-BRAINSPOT performance ↔ 4Pi-BRAINSPOT toolbox/Dynamic_model_update/caliphi0_and_misObj_NCC_v3.m, the whole file · a weak match · score 0.50 · objective misalignment, model update, pupil functions, NCC, guess, median

Paper

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The authors' code

MATLAB · 359 lines · 16 KB · other · 2 matches

  2. classdef PSF_4pi < handle
  3. % (C) Copyright Huang Lab, Weldon School of Biomedical Engineering,
  4. % All rights reserved Purdue University, West Lafayette, IN, USA
  5. %
  6. %
  7. %
  8. % PSF_4pi class for generating 4PI PSF models
  9. % create object: obj = PSF_4pi(PRstruct)
  10. %
  11. % PSF_4pi Methods:
  12. % precomputeParam - generate images for k space operation
  13. % gen2Pupil - generate pupil functions for top and bottom emission paths from user defined or phase retrieved Zernike coefficients
  14. % genPupil - generate pupil functions from user defined or phase retrieved Zernike coefficients
  15. % genPupil_4pi - generate coherent pupil functions for the four detection channels
  16. % genPSF - generate PSF from a given pupil function
  17. % genPSF_4pi - generate interferometric PSFs for the four detection channels assuming complete interference where the modulation depth is at the maximum
  18. % genPSF_4pi_md - generate interferometric PSFs for the four detection channels assuming partial interference
  19. % scalePSF - generate OTF rescaled PSFs
  20. % addMisalign - add additional objective misalignment into pupil function
  22. properties
  23. % PRstruct - define necessary parameters for a PSF model
  24. % NA
  25. % Lambda
  26. % RefractiveIndex
  27. % Pupil: phase retrieved pupil function
  28. % phase: phase image
  29. % mag: magnitude image
  30. % Zernike_phase: coefficient of zernike polynomials representing the pupil phase
  31. % Zernike_mag: coefficient of zernike polynomials representing the pupil phase
  32. % SigmaX: sigmax of Gaussian filter for OTF rescale, unit is 1/micron in k space, the conversion to real space is 1/(2*pi*SigmaX), unit is micron
  33. % SigmaY: sigmay of Gaussian filter for OTF rescale, unit is 1/micron in k space, the conversion to real space is 1/(2*pi*SigmaY), unit is micron
  34. PRstruct;
  35. PRstruct1;
  36. PRstruct2;
  37. Xpos;% x positions of simulated emitters, a vector of N elements, unit is pixel
  38. Ypos;% y positions of simulated emitters, a vector of N elements, unit is pixel
  39. Zpos;% z positions of simulated emitters, a vector of N elements, unit is micron
  40. nMed;% refractive index of the sample medium
  41. PSFsize; % image size of the pupil function
  42. Boxsize; % image size of out put PSF
  43. Pixelsize;% pixel size at sample plane, unit is micron
  44. Z;% object from Zernike_Polynomials class
  45. StagePosUp;% used for PSF model with index mismatch aberration, the distance the top objective moved to focus on the bead away from the coverslip, unit: micron
  46. StagePosDown;% used for PSF model with index mismatch aberration, the distance the sample stage (relative to bottom objective) moved to focus on the bead away from the coverslip, unit: micron
  47. Phasediff;% phase difference between s- and p-polarizations
  48. Iratio = 1;% transmission ratio between top and bottom emission path, from 0 to 1
  49. Phi0;% cavity phase
  50. Zoffset = 0; % z offset caused by index mismatch aberration, set to zero when using 'mvbead'
  51. ChamberH; % the height of the sample chamber
  52. PlaneDis = 0; % parameter used for dual focal plane method in 4pi
  53. ModulationDepth = 1;% modulation strength of interferometric PSFs,from 0 to 1
  54. % PSFs - out put PSFs from Fourier transform of the pupil function,
  55. % it's a 3D matrix of Boxsize x Boxsize x N, N is the number of
  56. % elements in Xpos.
  57. PSFs;
  59. end
  61. properties (SetAccess = private, GetAccess = private)
  62. % precompute images for k space operation
  63. Zo;% r coordinates of out put PSF, it's a image of PSFsize x PSFsize
  64. k_r;% k_r coordinates of out put OTF, it's a image of PSFsize x PSFsize
  65. k_z;% k_z coordinates of out put OTF, it's a image of PSFsize x PSFsize
  66. Phi;% phi coordinates out put PSF, it's a image of PSFsize x PSFsize
  67. NA_constrain;% a circle function defining the limit of k_r, it's a image of PSFsize x PSFsize
  68. Cos1;% cos(theta1), theta1 is the angle of between the k vector and the optical axis in the sample medium
  69. Cos3;% cos(theta3), theta3 is the angle of between the k vector and the optical axis in the immersion medium
  70. end
  72. properties (SetAccess = private, GetAccess = public)
  73. % IMMPSFs - out put PSFs from Fourier transform of the pupil
  74. % function modified with the index mismatch aberration, it's a 3D
  75. % matrix of Boxsize x Boxsize x N, N is the number of elements in
  76. % Xpos.
  77. IMMPSFs;
  78. % ScaledPSFs - out put PSFs after OTF rescale, it's a 3D matrix of
  79. % Boxsize x Boxsize x N, N is the number of elements in Xpos.
  80. ScaledPSFs;
  81. % Pupil - pupil function generated from a set of zernike polynomials
  82. % phase: phase image of PSFsize x PSFsize
  83. % mag: magnitude image of PSFsize x PSFsize
  84. Pupila; % Pupil from top objective lens
  85. Pupilb; % Pupil from bottom objective lens
  86. Pupil;
  87. Pupil4pi;% coherent pupil function for each quadrant
  88. PSF4pi;% 4PiPSF model, containing PSFs from the four quadrants
  89. end
  91. methods
  92. function obj=PSF_4pi(PRstruct)
  93. obj.PRstruct=PRstruct;
  94. end
  96. function precomputeParam(obj)
  97. % precomputeParam - generate images for k space operation, and saved in
  98. % precomputed parameters.
  99. [X,Y]=meshgrid(-obj.PSFsize/2:obj.PSFsize/2-1,-obj.PSFsize/2:obj.PSFsize/2-1);
  100. obj.Zo=sqrt(X.^2+Y.^2);
  101. scale=obj.PSFsize*obj.Pixelsize;
  102. obj.k_r=obj.Zo./scale;
  103. obj.Phi=atan2(Y,X);
  104. n=obj.PRstruct.RefractiveIndex;
  105. Freq_max=obj.PRstruct.NA/obj.PRstruct.Lambda;
  106. obj.NA_constrain=obj.k_r<Freq_max;
  107. obj.k_z=sqrt((n/obj.PRstruct.Lambda)^2-obj.k_r.^2).*obj.NA_constrain;
  108. sin_theta3=obj.k_r.*obj.PRstruct.Lambda./n;
  109. sin_theta1=n./obj.nMed.*sin_theta3;
  111. obj.Cos1=sqrt(1-sin_theta1.^2);
  112. obj.Cos3=sqrt(1-sin_theta3.^2);
  115. end
  117. function genZernike(obj)
  118. % create Zernike_Polynomials object
  119. zk = Zernike_Polynomials();
  120. zk.Ordering = 'Wyant';
  121. %zk.Ordering = 'Noll';
  122. ZN=sqrt(numel(obj.PRstruct.Zernike_phase))-1;
  123. zk.setN(ZN);
  124. zk.initialize();
  125. [Zrho, Ztheta, Zinit] = ...
  126. zk.params3_Zernike(obj.Phi, obj.k_r, obj.PRstruct.NA, obj.PRstruct.Lambda);
  127. zk.matrix_Z(Zrho, Ztheta, Zinit);
  128. obj.Z = zk;
  129. end
  131. function gen2Pupil(obj,PRstruct1,PRstruct2)
  132. obj.PRstruct = PRstruct1;
  133. obj.precomputeParam();
  134. obj.genZernike();
  135. obj.genPupil();
  136. obj.Pupila.phase = obj.Pupil.phase;
  137. obj.Pupila.mag = obj.Pupil.mag;
  138. obj.PRstruct1.Pupil.phase = exp(1i.*obj.Pupila.phase);
  139. obj.PRstruct1.Pupil.mag = obj.Pupila.mag;
  141. obj.PRstruct = PRstruct2;
  142. obj.precomputeParam();
  143. obj.genZernike();
  144. obj.genPupil();
  145. obj.Pupilb.phase = obj.Pupil.phase;
  146. obj.Pupilb.mag = obj.Pupil.mag;
  147. obj.PRstruct2.Pupil.phase = exp(1i.*obj.Pupilb.phase);
  148. obj.PRstruct2.Pupil.mag = obj.Pupilb.mag;
  149. end
  150. function genPupil(obj)
  151. % genPupil - generate pupil function from Zernike polynomials
  152. % Zernike polynomials are a set of images generated by using
  153. % Zernike_Polynomials class. The coefficients of the Zernike
  154. % polynomials are given from the 'PRstruct'. The resulting
  155. % pupil function includes a phase image and a magnitude image
  156. %
  157. % see also Zernike_Polynomials
  158. R=obj.PSFsize;
  159. ceffp=obj.PRstruct.Zernike_phase;
  160. ceffm=obj.PRstruct.Zernike_mag;
  161. pupil_phase=zeros(R,R);
  162. pupil_mag=zeros(R,R);
  163. N=numel(ceffp);
  164. for k = 1 : N
  165. pupil_phase = pupil_phase + obj.Z.ZM(:, :, k) .* ceffp(k);
  166. end
  168. for k = 1 : N
  169. pupil_mag = pupil_mag + obj.Z.ZM(:, :, k) .* ceffm(k);
  170. end
  172. % Normalize Zernike coefficients.
  173. % tmp = pupil_mag .* (1/R); changed by FX
  174. tmp = pupil_mag;
  175. normF = sqrt(sum(sum(tmp .* conj(tmp))));
  176. Ceffnorm = ceffm ./ normF;
  178. pupil_magnorm = zeros(R, R);
  179. for k = 1 : N
  180. pupil_magnorm = pupil_magnorm + obj.Z.ZM(:, :, k) .* Ceffnorm(k);
  181. end
  183. obj.Pupil.phase=pupil_phase;
  184. obj.Pupil.mag=pupil_magnorm;
  186. end
  188. function set2Pupil(obj,PRstruct1,PRstruct2) % set pupil function from reterived model
  189. obj.PRstruct1.Pupil.phase = PRstruct1.Pupil.phase;
  190. obj.PRstruct1.Pupil.mag = PRstruct1.Pupil.mag;
  191. obj.PRstruct2.Pupil.phase = PRstruct2.Pupil.phase;
  192. obj.PRstruct2.Pupil.mag = PRstruct2.Pupil.mag;
  194. obj.Pupila.phase = PRstruct1.Pupil.phase;
  195. obj.Pupila.mag = PRstruct1.Pupil.mag;
  197. obj.Pupilb.phase = PRstruct2.Pupil.phase;
  198. obj.Pupilb.mag = PRstruct2.Pupil.mag;
  199. end
  201. function pad2Pupil(obj)
  202. pad_size = (obj.PSFsize - size(obj.Pupila.phase,1)) / 2;
  204. obj.Pupila.phase = padarray(obj.Pupila.phase, [pad_size pad_size]);
  205. obj.Pupila.mag = padarray(obj.Pupila.mag, [pad_size pad_size]);
  206. obj.Pupilb.phase = padarray(obj.Pupilb.phase, [pad_size pad_size]);
  207. obj.Pupilb.mag = padarray(obj.Pupilb.mag, [pad_size pad_size]);
  209. end
  211. function addMisalign(obj,aber_misalign)
  212. R=obj.PSFsize;
  213. pupila_phase=zeros(R,R);
  214. pupilb_phase=zeros(R,R);
  215. N=numel(aber_misalign);
  216. for k = 1 : N
  217. if k == 3
  218. sign = 1;
  219. else
  220. sign = -1;
  221. end
  223. pupila_phase = pupila_phase + sign*obj.Z.ZM(:, :, k+1) .* aber_misalign(k);
  224. pupilb_phase = pupilb_phase + obj.Z.ZM(:, :, k+1) .* aber_misalign(k);
  225. end
  227. obj.Pupila.phase = obj.PRstruct1.Pupil.phase .* exp(1i*pupila_phase);
  228. obj.Pupilb.phase = obj.PRstruct2.Pupil.phase .* exp(1i*pupilb_phase);
  229. end
  231. function setUnifMag(obj) % set uniform magnitude for pupil function
  232. mag=obj.Pupila.mag;
  233. mag(mag>0)=1;
  234. mag = mag.^2;
  235. mag = mag./sum(sum(mag));
  236. mag = sqrt(mag);
  238. obj.Pupila.mag = mag;
  240. mag=obj.Pupilb.mag;
  241. mag(mag>0)=1;
  242. mag = mag.^2;
  243. mag = mag./sum(sum(mag));
  244. mag = sqrt(mag);
  246. obj.Pupilb.mag = mag;
  247. end
  250. function genPupil_4pi_2(obj)
  252. pupilA = obj.Pupila.mag.*obj.Pupila.phase;%top
  253. pupilB = obj.Pupilb.mag.*obj.Pupilb.phase.*exp(1i.*obj.Phi0).*obj.Iratio;%bottom
  255. kz = obj.k_z;
  256. zpos = obj.Zpos;
  258. phia = 0;% no effect on 4pi PSF
  259. phib = obj.Phasediff; % changed by FX
  260. N = numel(obj.Xpos);
  261. R = obj.PSFsize;
  262. obj.Pupil4pi.s1 = zeros(R,R,N);
  263. obj.Pupil4pi.s2 = zeros(R,R,N);
  264. obj.Pupil4pi.p1 = zeros(R,R,N);
  265. obj.Pupil4pi.p2 = zeros(R,R,N);
  266. obj.Pupil.top = zeros(R,R,N);
  267. obj.Pupil.bot = zeros(R,R,N);
  268. for ii=1:N
  269. defocusphaseA = exp(-2.*pi.*1i.*(zpos(ii)+obj.Zoffset).*kz);%top
  270. defocusphaseB = exp(2.*pi.*1i.*zpos(ii).*kz); %bottom
  271. obj.Pupil4pi.s1(:,:,ii) = pupilA.*exp(1i*pi).*exp(1i*phia).*defocusphaseA+pupilB.*exp(1i*phib).*defocusphaseB;
  272. obj.Pupil4pi.s2(:,:,ii) = pupilA.*exp(1i*phia).*defocusphaseA+pupilB.*exp(1i*phib).*defocusphaseB;
  273. obj.Pupil4pi.p1(:,:,ii) = pupilA.*exp(1i*pi).*defocusphaseA+pupilB.*defocusphaseB; %pi delay in ha
  274. obj.Pupil4pi.p2(:,:,ii) = pupilA.*defocusphaseA+pupilB.*defocusphaseB;
  276. obj.Pupil.top(:,:,ii) = pupilA.*defocusphaseA;
  277. obj.Pupil.bot(:,:,ii) = pupilB.*defocusphaseB;
  278. end
  279. end
  282. function psfs=genPSF(obj,pupil)
  283. % genPSF - generate PSFs from the given pupil function.
  284. % The PSFs are directly calculated from the Fourier transform
  285. % of pupil functions modified by shift phase in x, y and
  286. % defocus phase in z. The out put is 'PSFs'
  287. N=numel(obj.Xpos);
  288. R=obj.PSFsize;
  289. Ri=obj.Boxsize;
  290. psfs=zeros(Ri,Ri,N);
  291. for ii=1:N
  292. shiftphase=-obj.k_r.*cos(obj.Phi).*obj.Xpos(ii).*obj.Pixelsize-obj.k_r.*sin(obj.Phi).*obj.Ypos(ii).*obj.Pixelsize;
  293. shiftphaseE=exp(-1i.*2.*pi.*shiftphase);
  294. defocusphaseDual = exp(2.*pi.*1i.*obj.PlaneDis.*obj.k_z);
  295. if nargin>1
  296. pupil_complex=pupil(:,:,ii).*shiftphaseE.*defocusphaseDual;
  297. else
  298. defocusphaseE=exp(2.*pi.*1i.*obj.Zpos(ii).*obj.k_z);
  299. pupil_complex=obj.Pupil.mag.*exp(obj.Pupil.phase.*1i).*shiftphaseE.*defocusphaseE;
  300. end
  301. psfA=abs(fftshift(fft2(pupil_complex)));
  302. Fig2=psfA.^2;
  303. realsize0=floor(Ri/2);
  304. realsize1=ceil(Ri/2);
  305. startx=-realsize0+R/2+1;endx=realsize1+R/2;
  306. starty=-realsize0+R/2+1;endy=realsize1+R/2;
  307. psfs(:,:,ii)=Fig2(startx:endx,starty:endy)./R^2;
  308. end
  309. obj.PSFs=psfs;
  310. end
  313. function genPSF_4pi(obj)
  314. obj.PSF4pi.s1=obj.genPSF(obj.Pupil4pi.s1)./4;
  315. obj.PSF4pi.s2=obj.genPSF(obj.Pupil4pi.s2)./4;
  316. obj.PSF4pi.p1=obj.genPSF(obj.Pupil4pi.p1)./4;
  317. obj.PSF4pi.p2=obj.genPSF(obj.Pupil4pi.p2)./4;
  318. end
  321. function genPSF_4pi_md(obj)
  323. obj.PSF4pi.s1 = obj.ModulationDepth.*obj.genPSF(obj.Pupil4pi.s1)./4 + (1-obj.ModulationDepth).*(obj.genPSF(obj.Pupil.top)+obj.genPSF(obj.Pupil.bot))./4;
  324. obj.PSF4pi.s2 = obj.ModulationDepth.*obj.genPSF(obj.Pupil4pi.s2)./4 + (1-obj.ModulationDepth).*(obj.genPSF(obj.Pupil.top)+obj.genPSF(obj.Pupil.bot))./4;
  325. obj.PSF4pi.p1 = obj.ModulationDepth.*obj.genPSF(obj.Pupil4pi.p1)./4 + (1-obj.ModulationDepth).*(obj.genPSF(obj.Pupil.top)+obj.genPSF(obj.Pupil.bot))./4;
  326. obj.PSF4pi.p2 = obj.ModulationDepth.*obj.genPSF(obj.Pupil4pi.p2)./4 + (1-obj.ModulationDepth).*(obj.genPSF(obj.Pupil.top)+obj.genPSF(obj.Pupil.bot))./4;
  328. % FX
  329. obj.PSF4pi.top = obj.genPSF(obj.Pupil.top);
  330. obj.PSF4pi.bot = obj.genPSF(obj.Pupil.bot);
  331. end
  334. function scalePSF(obj)
  335. % scalePSF - generate OTF rescaled PSFs
  336. % It operates 'PSFs' using the OTFrescale class. The OTF
  337. % rescale acts as a 2D Gaussian filter, the resulting PSFs
  338. % are smoother than the orignal PSFs.
  339. %
  340. % see also OTFrescale
  341. otfobj=OTFrescale;
  342. otfobj.SigmaX=obj.PRstruct.SigmaX;
  343. otfobj.SigmaY=obj.PRstruct.SigmaY;
  344. otfobj.Pixelsize=obj.Pixelsize;
  345. otfobj.PSFs=obj.PSFs;
  346. otfobj.scaleRspace();
  347. obj.ScaledPSFs=otfobj.Modpsfs;
  348. end
  351. end
  356. end

PSF_4pi.m at commit 0d9e7e7, under other · at the source

Overview

Authors: Hao-Cheng Gao1, Fan Xu1,2,3, Xi Cheng4, Tailong Chen2,3, Cheng Bi1, Yue Zheng1, Yilun Li1, Yumian Li2,3, Alexander A Chubykin4, Fang Huang1,5,6
ORCID iDs: Hao-Cheng Gao, Fan Xu, Xi Cheng, Tailong Chen, Cheng Bi, Yue Zheng, Yilun Li, Alexander A Chubykin, Fang Huang
  1. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN USA
  2. School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
  3. School of Medical Engineering, Beijing Institute of Technology, Zhuhai, China
  4. Department of Biological Science, Purdue University, West Lafayette, IN USA
  5. Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN USA
  6. Institute for Cancer Research, Purdue University, West Lafayette, IN USA
Institutions: Purdue University West Lafayette (United States); Beijing Institute of Technology (China); Purdue University Institute for Cancer Research
Journal: Nature communications, volume 17, issue 1, article 5550
Dates: received 27 March 2025; accepted 26 March 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71614-6 · PMID 42020380 · PMCID PMC13287808 · OpenAlex W4407208178
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.02.03.636258
Status: code verified
Categories: histology / microscopy (modality), mouse (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: Super-resolution microscopy, Applied optics, Neuroscience
MeSH: Brain*, Imaging, Three-Dimensional*, Interferometry*, Single Molecule Imaging*, Animals, Dendritic Spines, Mice (* major topic)
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (MH123401, MH116500); U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (MH123401, MH116500); U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) (GM119785); U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (GM119785)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

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HuanglabPurdue/4Pi-BRAINSPOT

License: other
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 0d9e7e78021da4b143614701ec8a9430554c8dbf, 18 December 2025
Languages: MATLAB (77)
Size: 99 files, 77 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (11 files), Statistics and Machine Learning Toolbox (5 files), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
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79 files
At the source: github.com/HuanglabPurdue/4Pi-BRAINSPOT

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  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

The paper has a 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/s41467-026-71614-6.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 7 MeSH terms, 4 funders, 67 references.

Cite

This paper

Gao, H.-C., Xu, F., Cheng, X., Chen, T., Bi, C., Zheng, Y., Li, Y., Li, Y., Chubykin, A. A., & Huang, F. (2026). Interferometric ultra-high resolution 3D imaging through brain sections. Nature communications, 17(1), 5550. https://doi.org/10.1038/s41467-026-71614-6

BibTeX

@article{gao2026interferometric,
author = {Gao, Hao-Cheng and Xu, Fan and Cheng, Xi and Chen, Tailong and Bi, Cheng and Zheng, Yue and Li, Yilun and Li, Yumian and Chubykin, Alexander A and Huang, Fang},
title = {{Interferometric ultra-high resolution 3D imaging through brain sections}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5550},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71614-6},
url = {https://doi.org/10.1038/s41467-026-71614-6},
pmid = {42020380},
pmcid = {PMC13287808}
}

RIS

TY - JOUR
AU - Gao, Hao-Cheng
AU - Xu, Fan
AU - Cheng, Xi
AU - Chen, Tailong
AU - Bi, Cheng
AU - Zheng, Yue
AU - Li, Yilun
AU - Li, Yumian
AU - Chubykin, Alexander A
AU - Huang, Fang
TI - Interferometric ultra-high resolution 3D imaging through brain sections
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/22
VL - 17
IS - 1
SP - 5550
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71614-6
UR - https://doi.org/10.1038/s41467-026-71614-6
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-71614-6",
"type": "article-journal",
"title": "Interferometric ultra-high resolution 3D imaging through brain sections",
"container-title": "Nature communications",
"author": [
{
"family": "Gao",
"given": "Hao-Cheng"
},
{
"family": "Xu",
"given": "Fan"
},
{
"family": "Cheng",
"given": "Xi"
},
{
"family": "Chen",
"given": "Tailong"
},
{
"family": "Bi",
"given": "Cheng"
},
{
"family": "Zheng",
"given": "Yue"
},
{
"family": "Li",
"given": "Yilun"
},
{
"family": "Li",
"given": "Yumian"
},
{
"family": "Chubykin",
"given": "Alexander A"
},
{
"family": "Huang",
"given": "Fang"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5550",
"DOI": "10.1038/s41467-026-71614-6",
"PMID": "42020380",
"PMCID": "PMC13287808",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71614-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
22
]
]
}
}

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