Joint Reconstruction Strategy for Structured Illumination Microscopy With Unknown Illuminations

Labouesse, Simon; Negash, Awoke; Idier, Jérôme; Bourguignon, Sébastien; Mangeat, Thomas; Liu, Penghuan; Sentenac, Anne; Allain, Marc

doi:10.1109/tip.2017.2675200

Cited by 37 publications

(34 citation statements)

References 53 publications

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“…The development of fluorophores that emit at wavelengths 433 much larger than the excitation should alleviate the deformation of the observation point spread function to 434 probe even deeper into the sample. There is also the possibility to modify the nature of the speckle and of the 435 excitation to further improve the resolution(Labouesse et al, 2017, Negash et al 2018.436To conclude, we believe that RIM will fill the expectations of cell biology laboratories, in line with the 437 growing need for simple, fast, super-resolved functional imaging of live-cells within normal or pathological 438 tissues or model organisms. It is worth noting that the mathematical concepts of RIM applies to all imaging 439 techniques in which the recorded data are linearly linked to the sought parameter times an excitation field.440Ultrasound imaging, diffraction microscopy, microwave scanning, photo-acoustic imaging among others, 441 could benefit from the philosophy of this novel approach.…”

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confidence: 93%

Super-resolved live-cell imaging using Random Illumination Microscopy

Mangeat

Labouesse

Allain

et al. 2020

Preprint

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25Super-resolution fluorescence microscopy has been instrumental to progress in biology. Yet, the photo-26 induced toxicity, the loss of resolution into scattering samples or the complexity of the experimental setups 27 curtail its general use for functional cell imaging. Here, we describe a new technology for tissue imaging 28 reaching a 114nm/8Hz resolution at 30 µm depth. Random Illumination Microscopy (RIM) consists in 29 shining the sample with uncontrolled speckles and extracting a high-fidelity super-resolved image from the 30 variance of the data using a reconstruction scheme accounting for the spatial correlation of the illuminations. 31 Super-resolution unaffected by optical aberrations, undetectable phototoxicity, fast image acquisition rate and 32 ease of use, altogether, make RIM ideally suited for functional live cell imaging in situ. RIM ability to image 33 molecular and cellular processes in three dimensions and at high resolution is demonstrated in a wide range 34 of biological situations such as the motion of Myosin II minifilaments in Drosophila. 35 36 37 38

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confidence: 93%

Super-resolved live-cell imaging using Random Illumination Microscopy

Mangeat

Labouesse

Allain

et al. 2020

Preprint

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“…Our method is related to blind SIM [46]; however, instead of using many random speckle patterns (which restricts resolution gain to ∼1.8×), we translate the speckle laterally, enabling resolution gains beyond that of previous methods [46][47][48][49][50][51][52] (see Appendix D). Previous works also use high-cost spatial-light-modulators (SLM) [53] or galvonemeter/MEMs mirrors [41,54] for precise illumination, as well as expensive objective lenses for aberration correction.…”

Section: Introductionmentioning

confidence: 99%

Computational structured illumination for high-content fluorescence and phase microscopy

Yeh

Chowdhury

Waller

2019

Biomed. Opt. Express

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High-content biological microscopy targets high-resolution imaging across large fieldsof-view (FOVs). Recent works have demonstrated that computational imaging can provide efficient solutions for high-content microscopy. Here, we use speckle structured illumination microscopy (SIM) as a robust and cost-effective solution for highcontent fluorescence microscopy with simultaneous high-content quantitative phase (QP). This multi-modal compatibility is essential for studies requiring cross-correlative biological analysis. Our method uses laterally-translated Scotch tape to generate high-resolution speckle illumination patterns across a large FOV. Custom optimization algorithms then jointly reconstruct the sample's super-resolution fluorescent (incoherent) and QP (coherent) distributions, while digitally correcting for system imperfections such as unknown speckle illumination patterns, system aberrations and pattern translations. Beyond previous linear SIM works, we achieve resolution gains of 4× the objective's diffraction-limited native resolution, resulting in 700 nm fluorescence and 1.2 µm QP resolution, across a FOV of 2 × 2.7 mm 2 , giving a spacebandwidth product (SBP) of 60 megapixels.

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“…The proposed inversion schemes take advantage of the nonnegativity of the sample ρ, and on statistical information on ρ and/or the illuminations E m . In particular, some of them introduce sparsity information on ρ [14], or on the products ρE m [17]. Generally speaking, the stack of low resolution speckle data yielded reconstructed images with significantly better resolution than that provided by a standard imager using homogeneous illumination.…”

Section: Introductionmentioning

confidence: 99%

On the Superresolution Capacity of Imagers Using Unknown Speckle Illuminations

Idier

Labouesse

Allain

et al. 2018

IEEE Trans. Comput. Imaging

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Speckle based imaging consists of forming a superresolved reconstruction of an unknown sample from lowresolution images obtained under random inhomogeneous illuminations (speckles). In a blind context where the illuminations are unknown, we study the intrinsic capacity of speckle-based imagers to recover spatial frequencies outside the frequency support of the data, with minimal assumptions about the sample.We demonstrate that, under physically realistic conditions, the covariance of the data has a super-resolution power corresponding to the squared magnitude of the imager point spread function. This theoretical result is important for many practical imaging systems such as acoustic and electromagnetic tomographs, fluorescence and photoacoustic microscopes, or synthetic aperture radar imaging. A numerical validation is presented in the case of fluorescence microscopy.

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Joint Reconstruction Strategy for Structured Illumination Microscopy With Unknown Illuminations

Cited by 37 publications

References 53 publications

Super-resolved live-cell imaging using Random Illumination Microscopy

Super-resolved live-cell imaging using Random Illumination Microscopy

Computational structured illumination for high-content fluorescence and phase microscopy

On the Superresolution Capacity of Imagers Using Unknown Speckle Illuminations

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