StructuredIlluminationMicroscopy

Structured Illumination Microscopy (SIM) is a super-resolution technique that uses a patterned illumination to increase the resolution of the microscope. This package provides tools to simulate and reconstruct SIM data.

The package provides the following functionality:

1. Generate SIM illumination patterns
2. Simulate SIM data
3. Reconstruct SIM data

The package is designed to be flexible and can be used with different PSFs and illumination patterns (series of grating directions as well as two-dimensional excitation patterns). It is fairly optimized for speed and low memory requirements (a few more optimization such as sharing memory between arrays that are sequentially needed during reconstruction can still be done).

It does not yet support three-dimensional reconstructions, and is does not yet have any parameter estimation routines included. Therefor it is not ready for use on experimentally acquired data.

Speed

On a Laptop (11th generation Intel(R) Core(TM) i7 1180H @2.3 GHz) CPU: 512x512 raw data (1536x1536 result size), upsample 3, 5 phase/direction, 5 directions, 5 (both sides) orders total: 44ms per reconstruction

On the Laptop GPU (TX3050Ti): 512x512 raw data (1536x1536 result size), upsampling 3x, 5 phase/direction, 5 directions, 5 (both sides) order total: 6 ms per reconstruction

Current Memory Requirements

42 Mb (mostly preallocated) for the same configuration

Example

using StructuredIlluminationMicroscopy
using TestImages

lambda = 0.532; NA = 1.2; n = 1.52
pp = PSFParams(lambda, NA, n);  # 532 nm, NA 0.25 in Water n= 1.33
sampling = (0.06, 0.06, 0.1)  # 100 nm x 100 nm x 200 nm

# define the SIM illumination pattern
num_directions = 5  ; num_images =  5*num_directions; num_orders = 3
k_peak_pos, peak_phases, peak_strengths = generate_peaks(num_images, num_directions, num_orders, 0.48 / (num_orders-1))
num_photons = 0.0
sp = SIMParams(pp, sampling, num_photons, 100.0, k_peak_pos, peak_phases, peak_strengths)

obj = Float32.(testimage("resolution_test_512"))
obj[257, 257] = 2.0
sim_data = simulate_sim(obj, pp, sp);

upsample_factor = 3; wiener_eps = 0.00001; suppression_strength = 0.99
rp = ReconParams(wiener_eps, suppression_strength, upsample_factor)
do_preallocate = true
prep = recon_sim_prepare(sim_data, pp, sp, rp, do_preallocate); # do preallocate

recon = recon_sim(sim_data, prep, sp, rp);

References

1. Heintzmann, R., & Cremer, C. (1999). Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 3568, pp. 185–196). https://doi.org/10.1117/12.336826
2. Gustafsson, M. G. L. (2000). Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. Journal of Microscopy, 198(2), 82–87. https://doi.org/10.1046/j.1365-2818.2000.00710.x
3. Heintzmann, R., Jovin, T. M., & Cremer, C. (2002). Saturated patterned excitation microscopy—a concept for optical resolution improvement. Journal of the Optical Society of America A, 19(8), 1599. https://doi.org/10.1364/JOSAA.19.001599
4. Gustafsson, M. G. L., Shao, L., Carlton, P. M., Wang, C. J. R., Golubovskaya, I. N., Cande, W. Z., … Sedat, J. W. (2008). Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophysical Journal, 94(12), 4957–4970. https://doi.org/10.1529/biophysj.107.120345
5. Heintzmann, R., & Gustafsson, M. G. L. (2009). Subdiffraction resolution in continuous samples. Nature Photonics, 3(7), 362–364. https://doi.org/10.1038/nphoton.2009.96

Installation

using Pkg
Pkg.add("StructuredIlluminationMicroscopy")

Author(s)

• Rainer Heintzmann ( Friedrich-Schiller-Universität Jena, Institut für Angewandte Physik, Jena, Germany Leibniz-Institut für Photonische Technologien e.V., Jena, Germany )

ToDo

• Add example for two-dimensional excitation patterns
• Add code for parameter estimation
• sharing memory between pre-allocated arrays result_rft and [result, order, ftorder]
• Add proper 3D support (one linked OTF per order)
• Add support for automatic differentiation
• Add spatial (Labouesse Masterthesis DOI:10.13140/RG.2.2.25191.66727) reconstruction mode
• Add more examples
• Add more tests