A Joint Richardson-Lucy Deconvolution Algorithm for the Reconstruction of Multifocal Structured Illumination Microscopy Data

Abstract

We demonstrate the reconstruction of images obtained by multifocal structured illumination microscopy, MSIM, using a joint Richardson-Lucy, jRL-MSIM, deconvolution algorithm, which is based on an underlying widefield image-formation model. The method is efficient in the suppression of out-of-focus light and greatly improves image contrast and resolution. Furthermore, it is particularly well suited for the processing of noise corrupted data. The principle is verified on simulated as well as experimental data and a comparison of the jRL-MSIM approach with the standard reconstruction procedure, which is based on image scanning microscopy, ISM, is made. Our algorithm is efficient and freely available in a user friendly software package.

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

“…Reconstruction algorithms which use the standard Wiener filter approach will simply amplify the noise in the SIM passbands and yield an image which is essentially a standard TIRF image overlaid with hexagonal (or "honeycomb") ringing artifacts ( Figure 6A, right panel). A possible enhancement might be the use of iterative 29,30 or blind reconstruction algorithms 31,32 to reduce these artifacts depending on the type of sample. We recommend the use of the ImageJ plugin SIMcheck to check the quality of SIM data before and after reconstruction .…”

“…The excitation foci can then be translated by shifting the lattice pattern on the SLM and this is repeated multiple times in order to illuminate the entire field of view. Images are acquired for each translated pattern position and the stack is post-processed to yield a reconstructed image with improved resolution of up to a factor of and reduced out-of-focus light compared to the equivalent widefield image 30 . This modality can be useful for imaging thick, dense samples for which standard SIM is unsuited, for example low contrast structures such as stained red blood cells (Figure 7C), although the acquisition time is increased due to the large number of raw frames required per field of view (in this case N = 168).…”

Image scanning microscopy: an overview

“…This has proven problematic in practical applications of ISM, where the pattern must either be determined by regularly calibrating the system using a test slide (York et al 2012;Schulz et al 2013) or by computationally identifying the excitation pattern post-acquisition. (Ströhl & Kaminski 2015;McGregor et al 2015) Both of these techniques have their limitations: calibrating the system is a time-consuming step, and is ineffective if the sample has greatly different optical properties to the test slide; determining the pattern post-acquisition adds another step to an already computationally intensive technique; and, depending on the algorithm used, reconstruction can break down in patterned or sparsely fluorescing samples. affords.…”

Structured illumination microscopy with unknown patterns and a statistical prior

“…The reconstruction algorithm for SIM combines demodulation process which brings the high spatial frequency information back to its original position and synthetic aperture that extends the support of the effective OTF. Various structured patterns have been used to realize SIM: periodic gratings [1][2][3][4], a single focal spot (confocal microscope) [8,9], multifocal spots [10][11][12][13] and random speckles [13][14][15][16][17][18][19][20][21][22]. When the illumination patterns themselves are diffraction-limited, linear SIM is restricted to 2× the bandwidth of a widefield microscope [4], allowing up to ∼ 2.4× resolution enhancement (metrics explained in Sec.…”