Compressed sensing in fluorescence microscopy

Calisesi, Gianmaria; Ghezzi, Alberto; Ancora, Daniele; D’Andrea, Cosimo; Valentini, Gianluca; Farina, Andrea; Bassi, Andrea

doi:10.1016/j.pbiomolbio.2021.06.004

Cited by 49 publications

(34 citation statements)

References 154 publications

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“…The specimen translation is, in fact, of great importance when dealing with water-cleared or porous samples that might be damaged by motion, or eventually, misaligned due to their translation. Furthermore, the projection of different illumination patterns might be paired with compressed reconstruction routines, as compressive sensing, remarkably decreasing the total acquisition time (Calisesi et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…This method can be implemented with incoherent light sources, such as Light Emitting Diodes (LED), making the system attractive for low-cost, shadowing free, volumetric microscopy applications. Furthermore, smSVIM can be combined with compressed sensing strategies (Candes & Wakin, 2008; Calisesi et al, 2021), reducing the number of acquisitions needed for a 3D reconstruction (Woringer et al, 2017; Calisesi et al, 2019). This can be beneficial for high-speed imaging and to lower the photodamage of the sample during the measurement.…”

Section: Introductionmentioning

confidence: 99%

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Enlarged Field of View in Spatially Modulated Selective Volume Illumination Microscopy

Calisesi

Ancora

Tacconi

et al. 2022

Microscopy and Microanalysis

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Three-dimensional fluorescence microscopy is a key technology for inspecting biological samples, ranging from single cells to entire organisms. We recently proposed a novel approach called spatially modulated Selective Volume Illumination Microscopy (smSVIM) to suppress illumination artifacts and to reduce the required number of measurements using an LED source. Here, we discuss a new strategy based on smSVIM for imaging large transparent specimens or voluminous chemically cleared tissues. The strategy permits steady mounting of the sample, achieving uniform resolution over a large field of view thanks to the synchronized motion of the illumination lens and the camera rolling shutter. Aided by a tailored deconvolution method for image reconstruction, we demonstrate significant improvement of the resolution at different magnification using samples of varying sizes and spatial features.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enlarged Field of View in Spatially Modulated Selective Volume Illumination Microscopy

Calisesi

Ancora

Tacconi

et al. 2022

Microscopy and Microanalysis

Self Cite

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“…The performance of compressed imaging depends on the relationship between the sample sparsity and the illumination [11]. In many imaging applications, including fluorescence imaging, the object distribution is sparse in the Cartesian object space [12]. The optimal illumination basis should then be maximally incoherent in this space.…”

Section: Discussionmentioning

confidence: 99%

Compressed imaging with focused light

Pathak

Booth

Turcotte

2022

J. Opt.

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Compressed sensing applied to optical microscopy enables imaging with a number of measurements below the Nyquist criterion. The illumination basis selected, often unstructured for randomness considerations, influences the performance of image reconstruction algorithms. Here, we show through modelling based on multimode fiber imaging that an illumination basis composed of a series of uniformly spaced foci provides improved robustness to noise, increased volumetric imaging performance, and greater resilience to external perturbation when compared to a speckle illumination basis. These observations have broad implications for computational super-resolution imaging, endo-microscopy, and post-processing of images acquired with any point-scanning imaging system.

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“…We studied the performance of the reconstruction algorithms for each of the combinations of different normalized spatial frequencies ν = [0.1, 0.2, 0.3, 0.5, 0.7] and number of measurements m = [10,40,70,100,200,300,400,500,750]. Each correlation coefficient between the reconstruction result and the ground truth is averaged over the digits of each kind (0 − 9) randomly selected from the testing dataset.…”

Section: Simulationsmentioning

confidence: 99%

“…Compressive imaging through a MMF improves both spatial resolution and imaging speed [7][8][9]. However, practical application of compressive imaging has been restricted by the strong assumption of sample sparsity and the demand to adjust the approach to different experimental conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial network for super-resolution imaging through a fiber

Li,

Abrashitova,

Osnabrugge

et al. 2022

Preprint

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A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes.Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement a generative adversarial network for image reconstruction without relying on a sample sparsity constraint. The proposed method outperforms the conventional compressive imaging algorithms in terms of image quality and noise robustness. We experimentally demonstrate speckle-based imaging below the diffraction limit at a sub-Nyquist speed through a multimode fiber.

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Compressed sensing in fluorescence microscopy

Cited by 49 publications

References 154 publications

Enlarged Field of View in Spatially Modulated Selective Volume Illumination Microscopy

Enlarged Field of View in Spatially Modulated Selective Volume Illumination Microscopy

Compressed imaging with focused light

Generative adversarial network for super-resolution imaging through a fiber

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