Dual fluorescence-absorption deconvolution applied to extended-depth-of-field microscopy

Shain, William J.; Vickers, Nicholas A.; Negash, Awoke; Bifano, Thomas G.; Sentenac, Anne; Mertz, Jérôme

doi:10.1364/ol.42.004183

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…In addition to the 343 super-resolved reconstructions, classical widefield images were obtained by simply summing the speckle 344 frames as proposed in (Mudry et al, 2012). The widefield images, which resemble transmission-microscopy 345 images thanks to the out-of-focus fluorescence (Shain et al, 2017), permit to locate the migrating cells in its 346 complex environment ( Figure 7A). The migration process was clearly unaffected by the 200,000 speckles 347 illumination.…”

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confidence: 99%

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: 99%

Super-resolved live-cell imaging using Random Illumination Microscopy

Mangeat

Labouesse

Allain

et al. 2020

Preprint

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“…However, this assumption is often invalid when the sample is thicker than the imaging volume and signals can originate from outside V I , i.e., far-out-of-focus fluorescence. Strategies have been reported to account for such contributions, such as assuming constant background 34 or using 3D deconvolution applied to 2D images 35,36 .…”

Section: Contrast Enhancement With Extended-volume 3d Deconvolutionmentioning

confidence: 99%

High-contrast multifocus microscopy with a single camera and z-splitter prism

Xiao¹,

Gritton²,

Tseng³

et al. 2020

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We present a multifocus imaging strategy based on the use of a simple z-splitter prism that can be assembled from off-the-shelf components. Our technique enables a widefield image stack to be distributed onto a single camera and recorded simultaneously. We exploit the volumetric nature of our image acquisition by further introducing a novel extended-volume 3D deconvolution strategy to suppress far-out-of-focus fluorescence background to significantly improve the contrast of our recorded images, conferring to our system a capacity for quasi optical sectioning. By swapping in different z-splitter configurations, we can prioritize high speed or large 3D field-of-view imaging depending on the application of interest. Moreover, our system can be readily applied to a variety of imaging modalities in addition to fluorescence, such as phase-contrast and darkfield imaging. Because of its simplicity, versatility, and performance, we believe our system will be a useful tool for general biological or biomedical imaging applications.

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“…Those algorithms work by applying constraints such as total variation (

| ▿ u |

), sparsity (

| u |

) (where u is the reconstructed image) or other constraints to suppress noise amplification during the deconvolution process. When the structures within the whole field of view are similar to each other, those algorithms perform very well (Lefkimmiatis et al ., ; Wu et al ., ; Chu et al ., ; Chouzenoux et al ., ; Proag et al ., ; Masuzzo et al ., ; Aymard et al ., ; Shain et al ., ; Fortun et al ., ). However, when structures of vastly different sizes are imaged simultaneously, they often fail to reconstruct as demonstrated by the examples shown later in this paper.…”

Section: Introductionmentioning

confidence: 97%

Simultaneous recovery of both bright and dim structures from noisy fluorescence microscopy images using a modified TV constraint

Xiao

Smith

Chu

2019

Journal of Microscopy

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Summary The quality and information content of biological images can be significantly enhanced by postacquisition processing using deconvolution and denoising. However, when imaging complex biological samples, such as neurons, stained with fluorescence labels, the signal level of different structures can differ by several orders of magnitude. This poses a challenge as current image reconstruction algorithms are focused on recovering low signals and generally have sample‐dependent performance, requiring tedious manual tuning. This is one of the main hurdles for their wide adoption by nonspecialists. In this work, we modify the general constrained reconstruction method (in our case utilizing a total variation constraint) so that both bright and dim structures can drive the deconvolution with equal force. In this way, we can simultaneously obtain high‐quality reconstruction across a wide range of signals within a single image or image sequence. The algorithm is tested on both simulated and experimental data. When compared with current state‐of‐art algorithms, our algorithm outperforms others in terms of maintaining the resolution in the high‐signal areas and reducing artefacts in the low‐signal areas. The algorithm was also tested on image sequences where one set of parameters are used to reconstruct all images, with blind evaluation by a group of biologists demonstrating a marked preference for the images produced by our method. This means that our method is suitable for batch processing of image sequences obtained from either spatial or temporal scanning. Lay Description Fluorescence microscopy images of complex biological samples contain a wide range of signal levels. This signal variation leads current reconstruction algorithms, which aim to enhance the quality of the raw images, to have sample‐dependent performance. In this work, we design a new optimization that allows the reconstruction to “pay equal eqattention to” both bright and dim structures. In this way, we can simultaneously recover both bright and dim structures within a single image or image sequence, as validated when the algorithm was quantitatively tested on both simulated and experimental data. When our method was evaluated alongside current state of art algorithms by a group of biologists, our algorithm was considered qualitatively superior.

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Dual fluorescence-absorption deconvolution applied to extended-depth-of-field microscopy

Cited by 9 publications

References 12 publications

Super-resolved live-cell imaging using Random Illumination Microscopy

Super-resolved live-cell imaging using Random Illumination Microscopy

High-contrast multifocus microscopy with a single camera and z-splitter prism

Simultaneous recovery of both bright and dim structures from noisy fluorescence microscopy images using a modified TV constraint

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