Fluorescence microscopy three-dimensional depth variant point spread function interpolation using Zernike moments

Maalouf, Elie; Colicchio, Bruno; Dieterlen, Alain

doi:10.1364/josaa.28.001864

Cited by 32 publications

(22 citation statements)

References 29 publications

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“…It drastically improves the performance of algorithms. For instance, in 3D fluorescence microscopy, the authors of [39,32,4,50] proposed to approximate PSFs by anisotropic Gaussians and assumed that the Gaussian variances only vary along one direction (e.g., the direction of light propagation). The separability assumption implies that both the PSF and its variations are separable.…”

Section: Existing Approachesmentioning

confidence: 99%

Sparse Wavelet Representations of Spatially Varying Blurring Operators

Escande¹,

Weiss²

2015

SIAM J. Imaging Sci.

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Restoring images degraded by spatially varying blur is a problem encountered in many disciplines such as astrophysics, computer vision or biomedical imaging. One of the main challenges to perform this task is to design efficient numerical algorithms to approximate integral operators.We introduce a new method based on a sparse approximation of the blurring operator in the wavelet domain. This method requires O N −d/M operations to provide -approximations, where N is the number of pixels of a d-dimensional image and M ≥ 1 is a scalar describing the regularity of the blur kernel. In addition, we propose original methods to define sparsity patterns when only the operators regularity is known.Numerical experiments reveal that our algorithm provides a significant improvement compared to standard methods based on windowed convolutions.

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Section: Existing Approachesmentioning

confidence: 99%

Sparse Wavelet Representations of Spatially Varying Blurring Operators

Escande¹,

Weiss²

2015

SIAM J. Imaging Sci.

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“…The fluorescence microscopy analysis was performed using an OLYMPUS BX51 wide field microscope, which was modified to acquire 3D-images using computational optical sectioning, as described by previous users (Maalouf et al 2011). Acquisitions are possible in either white-light or epifluorescence mode.…”

Section: Fluorescence Microscope Specificationsmentioning

confidence: 99%

Fluorescence Microscopy Analysis of Particulate Matter from Biomass Burning: Polyaromatic Hydrocarbons as Main Contributors

Garra¹,

Maschowski

Liaud³

et al. 2015

Aerosol Science and Technology

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New efficient approaches to the characterization of fly ash and particulate matter (PM) have to be developed in order to better understand their impacts on environment and health. Polycyclic aromatic hydrocarbons (PAH) contained in PM from biomass burning have been identified as genotoxic and cytotoxic, and some tools already exist to quantify their contribution to PM. Optical fluorescence microscopy is proposed as a rapid and relatively economical method to allow the quantification of PAH in different particles emitted from biomass combustion. In this study samples were collected in the flue gas of biomasscombustion facilities with nominal output ranging from 40 kW to 17.3 MW. The fly ash samples were collected with various flue gas treatment devices (multicyclone, baghouse filter, electrostatic precipitator); PM samples were fractionated from the flue gas with a DEKATI Ò DGI impactor. A method using fluorescence observations (at 470 nm), white-light observations and image processing has been developed with the aim of quantifying fluorescence per sample. Organic components of PM and fly ash, such as PAH, humic-like substances (HULIS) and water-soluble organic carbon (WSOC) were also quantified. Fluorescence microscopy analysis method assessment was first realized with fly ash that was artificially coated with PAH and HULIS. Total amounts of PAH in the three size fractions of actual PM from biomass burning strongly correlated with the intensities of fluorescence. These encouraging results contribute to the development of a faster and cheaper method of quantifying particle-bound PAH.

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“…Several studies have demonstrated improved techniques for large field‐of‐view, three‐dimensional (3D) imaging of in vivo specimens at a great depth (Kobat et al ., ; Schroeder et al ., ; Crosignani et al ., ; Zong et al ., ; Glancy et al ., ). However, achieving high spatial fidelity in the obtained biological structures is still a challenge due to the distortion of the point‐spread function (PSF) of the microscope, the inhomogeneity of tissue and immersion media (Boutet de Monvel et al ., ; de Monvel et al ., ; Von Tiedemann et al ., ), and most importantly, spatially varying distortion resulting from different specimen structures across the imaging field (Booth et al ., ; Dong et al ., ; Arigovindan et al ., ; Maalouf et al ., ; Temerinac‐Ott et al ., ).…”

Section: Introductionmentioning

confidence: 97%

“…Several studies have demonstrated improved techniques for large field-of-view, three-dimensional (3D) imaging of in vivo spec-Correspondence to: Li-Yueh Hsu, DSc, National Heart, Lung and Blood Institute, National Institutes of Health Bldg 10, Rm B1D416, MSC 1061, 10 Center Drive, Bethesda, MD 20892-1061, U.S.A. Tel: (301) 435-5839; fax: (301) 402-2389; e-mail: lyhsu@mail.nih.gov imens at a great depth (Kobat et al, 2009;Schroeder et al, 2011;Crosignani et al, 2012;Zong et al, 2014;Glancy et al, 2014). However, achieving high spatial fidelity in the obtained biological structures is still a challenge due to the distortion of the point-spread function (PSF) of the microscope, the inhomogeneity of tissue and immersion media (Boutet de Monvel et al, 2001;de Monvel et al, 2003;Von Tiedemann et al, 2006), and most importantly, spatially varying distortion resulting from different specimen structures across the imaging field (Booth et al, 2002;Dong et al, 2003;Arigovindan et al, 2010;Maalouf et al, 2011;Temerinac-Ott et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A Model‐based approach for microvasculature structure distortion correction in two‐photon fluorescence microscopy images

Dao

Lucotte

Chang

et al. 2015

Journal of Microscopy

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SUMMARY This paper investigates a post-processing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modeling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to sub volumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images.

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Fluorescence microscopy three-dimensional depth variant point spread function interpolation using Zernike moments

Cited by 32 publications

References 29 publications

Sparse Wavelet Representations of Spatially Varying Blurring Operators

Sparse Wavelet Representations of Spatially Varying Blurring Operators

Fluorescence Microscopy Analysis of Particulate Matter from Biomass Burning: Polyaromatic Hydrocarbons as Main Contributors

A Model‐based approach for microvasculature structure distortion correction in two‐photon fluorescence microscopy images

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