Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation

Lozano-Pérez, Tomás; Křížek, Pavel; Švindrych, Zdeněk; Benda, Jakub; Ovesný, Martin; Fliegel, Karel; Klíma, Miloš; Hagen, Guy M.

doi:10.1364/oe.22.029805

Cited by 60 publications

(63 citation statements)

References 29 publications

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“…Our method includes detailed information about the shape of the PSF of the imaging system. Information about the shape of the PSF can be important for PSF fitting photometry in astronomy [47], for deconvolution algorithms in microscopy [48,49] and for other applications. Our model of the UWFOV system was successfully used by Fliegel [50] for a comparison of the deconvolution methods.…”

Section: Experimental Laboratory Resultsmentioning

confidence: 99%

PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems

et al. 2017

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Ultra-wide-field of view (UWFOV) imaging systems are affected by various aberrations, most of which are highly angle-dependent. A description of UWFOV imaging systems, such as microscopy optics, security camera systems and other special space-variant imaging systems, is a difficult task that can be achieved by estimating the Point Spread Function (PSF) of the system. This paper proposes a novel method for modeling the space-variant PSF of an imaging system using the Zernike polynomials wavefront description. The PSF estimation algorithm is based on obtaining field-dependent expansion coefficients of the Zernike polynomials by fitting real image data of the analyzed imaging system using an iterative approach in an initial estimate of the fitting parameters to ensure convergence robustness. The method is promising as an alternative to the standard approach based on Shack-Hartmann interferometry, since the estimate of the aberration coefficients is processed directly in the image plane. This approach is tested on simulated and laboratory-acquired image data that generally show good agreement. The resulting data are compared with the results of other modeling methods. The proposed PSF estimation method provides around 5% accuracy of the optical system model.

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Section: Experimental Laboratory Resultsmentioning

confidence: 99%

PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems

et al. 2017

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“…Briefly, the SIM system uses a ferroelectric liquid crystal on silicon (LCOS) microdisplay (type SXGA-3DM, Forth Dimension Displays). This device has been used previously in SIM and related methods in fluorescence microscopy [5,10,15,33,[43][44][45][46][47] and allows one to produce patterns of illumination on the sample which can be reconfigured at will by changing the image displayed on the device. The light source (Lumencor Spectra-X) is toggled off between SIM patterns and during camera readout.…”

Section: Microscope Setup and Data Acquisitionmentioning

confidence: 99%

“…SIM reconstructions were performed using SIMToolbox, an open-source and freely available program that our group developed for processing SIM data [33]. We generated optically sectioned, enhanced resolution images using a Bayesian estimation method, maximum a posteriori probability SIM (MAP-SIM) [15]. MAP-SIM works using maximum a posteriori probability methods, which are well known in microscopy applications [48,49], to enhance high spatial frequency image information.…”

Section: Sim Data Processingmentioning

confidence: 99%

“…MAP-SIM works using maximum a posteriori probability methods, which are well known in microscopy applications [48,49], to enhance high spatial frequency image information. We then combine this information, in the frequency domain, with low spatial frequency image information obtained by OS-SIM methods, then produce the final image by an inverse Fourier transform [15]. We typically measure the final resolution obtained by analyzing the frequency spectrum of the resulting image, as is discussed below.…”

Section: Sim Data Processingmentioning

confidence: 99%

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Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction

Johnson

Hagen

2019

Preprint

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AbstractBackgroundStructured illumination microscopy (SIM) is a method which can be used to image biological samples and can achieve both optical sectioning and super-resolution effects. Optimization of the imaging setup and data processing methods results in high quality images without artifacts due to mosaicking or due to the use of SIM methods. Reconstruction methods based on Bayesian estimation can be used to produce images with a resolution beyond that dictated by the optical system.FindingsFive complete datasets are presented including large panoramic SIM images of human tissues in pathophysiological conditions. Cancers of the prostate, skin, ovary, and breast, as well as tuberculosis of the lung, were imaged using SIM. The samples are available commercially and are standard histological preparations stained with hematoxylin and eosin.ConclusionThe use of fluorescence microscopy is increasing in histopathology. There is a need for methods which reduce artifacts when employing image stitching methods or optical sectioning methods such as SIM. Stitched SIM images produce results which may be useful for intraoperative histology. Releasing high quality, full slide images and related data will aid researchers in furthering the field of fluorescent histopathology.

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“…In addition to new illumination schemes, alternative data processing methods have also been introduced [27][28][29][30][31][32][33]. For example, Orieux et al suggested a 2D method for SIM reconstruction based on Bayesian estimation [28], and our group showed that Bayesian reconstruction methods in SIM have several potential advantages and can achieve a performance comparable to traditional SIM methods [29].…”

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confidence: 99%

Imaging tissues and cells beyond the diffraction limit with structured illumination microscopy and Bayesian image reconstruction

Pospíšil

Lozano-Pérez

Bendesky

et al. 2018

Preprint

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Background Structured illumination microscopy (SIM) is a family of methods in optical fluorescence microscopy that can achieve both optical sectioning and super-resolution effects. SIM is a valuable method for high resolution imaging of fixed cells or tissues labeled with conventional fluorophores, as well as for imaging the dynamics of live cells expressing fluorescent protein constructs. In SIM, one acquires a set of images with shifting illumination patterns. This set of images is subsequently treated with image analysis algorithms to produce an image with reduced out-of-focus light (optical sectioning) and/or with improved resolution (super-resolution).Findings Five complete and freely available SIM datasets are presented including raw and analyzed data. We report methods for image acquisition and analysis using open source software along with examples of the resulting images when processed with different methods. We processed the data using established optical sectioning SIM and superresolution SIM methods, and with newer Bayesian restoration approaches which we are developing. ConclusionVarious methods for SIM data acquisition and processing are actively being developed, but complete raw data from SIM experiments is not typically published. Publicly available, high quality raw data with examples of processed results will aid researchers when developing new methods in SIM. Biologists will also find interest in the high-resolution images of animal tissues and cells we acquired. All of the data was processed with SIMToolbox, an open source and freely available software solution for SIM.

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Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation

Cited by 60 publications

References 29 publications

PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems

PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems

Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction

Imaging tissues and cells beyond the diffraction limit with structured illumination microscopy and Bayesian image reconstruction

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