esoubies / flexsim

A flexible SIM reconstruction method capable to handle difficult data prone to reconstruction artifacts.

FlexSIM

FlexSIM, for flexible SIM reconstruction, aims at providing reliable SIM reconstructions for a variety of SIM data, going from ``ideal'' ones acquired under standardized protocols and configurations, to ones obtained under more challenging settings and subject to reconstruction artifacts. More details can be found in the following paper:

Surpassing Light Inhomogeneities in Structured-Illumination Microscopy with FlexSIM.,
Journal of Microscopy (2024),
E. Soubies, A. Nogueron, F. Pelletier, T. Mangeat, C. Leterrier, M. Unser, and D. Sage.

News

• March 2024: New parameters to ease the handling of temporal stacks.
• Feb 2024: Integration of a pure Matlab version of the VMLMB optimization method in GlobalBioIm which is used in FlexSIM. As such, no need anymore to compile mex files.

Getting started

Download or clone this repository and run the script InstallFlexSIM.m which will download the required GlobalBioIm library v1.2 (or more recent releases) and make neceesary changes in your Matlab path.

Repository content

The repository is organized as follows.

• File InstallFlexSIM.m: FlexSIM installation script
• File FlexSIM.m: Main function of FlexSIM, the one that needs to be run with parameters as input (see provided examples)
• Folder src: Matlab source files of FlexSIM
• Folder Examples: Scripts to download and reconstruct the 20 open datasets described in Table S1 of [1] (see Examples below).

FlexSIM parameters

General parameters

Parameter	Description
DataPath	Path to the SIM raw stack.
pathToFlexSIM	Path to FlexSIM root folder.
displ	From 0 to 2 with increasing number of display.
verbose	From 0 to 2 with increasing text displays.
sav	Boolean on whether to save the reconstructed image and estimated patterns.
GPU	Boolean on whether to use GPU or not
parallelProcess	Boolean on whether to use parallel computing. Requires the parallel computing toolbox.

Physical parameters and pre-processing

Parameter	Description
	---- Reconstruct on ROI / specific frames
SzRoi	Size (px) of the ROI of the data considered for reconstruction
posRoi	Position of the top-left corner of the ROI.
frameRange	To treat only a subset of temporal frames of the stack (e.g., [1;5]).
	---- Patterns
StackOrder	Stack order: ap, pa, apw, paw, wap, wpa, axp, pxa (with a=angles, p=phases, w=widefield). The last two correspond to 9x9 montage with angles (resp phases) in row axp (resp pxa).
nbOr	Number of orientations.
nbPh	Number of phases.
	---- OTF Approximation
lamb	Emission wavelength (nm).
res	Pixel size (nm).
Na	Objective numerical aperture.
damp	damping parameter in [0,1] (1= no damping) to attenuate middle freq in the approx OTF.
	---- Background estimation
SzRoiBack	Size (px) of the ROI for background estimation (position automatically detected to minimize the intensity within the ROI).

Patterns estimation

Parameter	Description
SzRoiPatt	Size (px) of the ROI for pattern estimation.
posRoiPatt	Position of the top-left corner of the ROI for pattern estimation. (if empty automatically detected to maximize the intensity within the ROI)
maskWF	Radius (as a factor of the cutoff freq.) of the disk used to mask central Fourier frequencies.
ringRegionSearch	Lower and upper limits of Fourier ring region to search peaks (given as factor of the cutoff freq.).
eqPh	Boolean, if true equally-spaced phases are assumed.
estiPattLowFreq	Boolean, if true, estimate the low-freq. component of the patterns.
doRefinement	If false, do not performs the refinement step
pattAmp	Amplitude a of the pattern (to be adjusted manually).
cstTimePatt	If true, common patterns are used for all frames.
framePattEsti	To use only a subset of frames for estimating a common pattern to all frames (empty to use all frames). Only used when cstTimePatt =1.

Image reconstruction

Parameter	Description
	---- OTF Attenuation
OTFAttStr	Strength of the OTF attenuation (in [0,1]). If 0 no OTF attenuation.
OTFAttwdth	Width of the OTF attenuation (>0). If 0 no OTF attenuation.
	---- Cost function
apodize	Boolean on whether to use apodization on boundaries.
sepOrr	Boolean on whether to treat each orientation separately.
padSz	Padding size (px) used in the forward operator.
mu	Regularization parameter.
regType	Regularizer: 1 - 1st-order Tikhonov, 2 - Total Variation, 3 - Good roughness.
	---- Optimization
maxIt	Maximum number of iterations (stopping criteria).
stepTol	elative error tolerance between two iterates (stopping criteria).

Examples

This folder contains scripts to download and reconstruct the 20 open 2D-SIM datasets described in Table S1 of [1]. Each script is made of the following two steps

• download raw data and set FlexSIM parameters
• run FlexSIM.m function

The 20 open 2D-SIM datasets are sourced from 7 publications including FairSIM [1], OpenSIM [2], HiFi-SIM [3], ML-SIM [4], JSFR-SIM [5], Direct-SIM [6], and PCA-SIM [7]. This corresponds to a collection of SIM data acquired with a diversity of SIM systems and configurations. Each subfolder of the Example folder corresponds to one dataset with the following naming convention

• Reference_SIM-type_Bio-structure

[1] FairSIM, M. Müller, V. Mönkemöller, S. Hennig, W. Hübner, and T. Huser, Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ, Nat. Commun., vol. 7, no. 1, no. 1, Mar. 2016.

[2] OpenSIM, A. Lal, C. Shan, and P. Xi, Structured Illumination Microscopy Image Reconstruction Algorithm, IEEE Journal of Selected Topics in Quantum Electronics, vol. 22, no. 4, Jul. 2016.

[3] HiFi-SIM, G. Wen et al., High-fidelity structured illumination microscopy by point-spread-function engineering, Light Sci Appl, vol. 10, no. 1, no. 1, Apr. 2021.

[4] ML-SIM, C. N. Christensen, E. N. Ward, P. Lio, and C. F. Kaminski, ML-SIM: A deep neural network for reconstruction of structured illumination microscopy images, Biomed. Opt. Express, vol. 12, no. 5, May 2021.

[5] JSFR-SIM, Z. Wang et al., High-speed image reconstruction for optically sectioned, super-resolution structured illumination microscopy, Advanced Photonics, vol. 4, no. 2, Mar. 2022.

[6] Direct-SIM, G. Wen et al., Spectrum-optimized direct image reconstruction of super-resolution structured illumination microscopy, PhotoniX, vol. 4, no. 1, June 2023.

[7] Direct-SIM, J. Qian, et al., Structured illumination microscopy based on principal component analysis, eLight, vol. 3, no. 1, Feb. 2023.

Conditions of use

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/ .

Whenever you present or publish results that are based on this repository, please cite:

E. Soubies, A. Nogueron, F. Pelletier, T. Mangeat, C. Leterrier, M. Unser, and D. Sage. Surpassing Light Inhomogeneities in Structured-Illumination Microscopy with FlexSIM. Journal of Microscopy, 2024.