Generalized method to design phase masks for 3D super-resolution microscopy

Wang, Wenxiao; Ye, Fan; Shen, Hao; Moringo, Nicholas A.; Dutta, Chayan; Robinson, Jacob T.; Landes, Christy F.

doi:10.1364/oe.27.003799

Cited by 16 publications

(14 citation statements)

References 41 publications

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“…in the typical case of PR from a z-stack of a bead on the coverslip surface, these correspond to different values of . Common examples of cost functions include the L1 [34] and L2 [24] norms. Other cost functions employ statistical estimation, e.g.…”

Section: Cost Functionsmentioning

confidence: 99%

“…Shows the average time required to calculate a single gradient as a function of number of pixels (binning) in the phase mask. The algorithm is orders of magnitudes faster than numerical pixel-wise gradient calculation [24], and much faster than fitting Zernike polynomials, which can take ~30 minutes when implemented using the MATLAB function fminunc [34]. The results were timed on a laptop with INTEL i7-8750H CPU and NVIDIA GeForce RTX 2070 GPU.…”

Section: Phase Mask Optimization Workflowmentioning

confidence: 99%

“…Any employed algorithm requires the nonobvious selection of a penalty function, and must overcome degeneracies in the solution, unstable derivatives, non-convex optimization, and more [18]. So far, PR methods have made use of the Iterative Gerchberg-Saxton(GS) Algorithm [19] and its variations [20], estimation over the Zernike basis of aberrations [21][22][23], and pixel-wise numerical gradient calculations [24]. The first consideration of these algorithms is the tradeoff between computational efficiency and model accuracy; namely, a limited number of parameters must be chosen to describe the phase mask and various model approximations are employed.…”

Section: Introductionmentioning

confidence: 99%

“…VIPR (blue), Scalar PR (using our method with a scalar model-red) and adding Zernike aberration to the vectorial model (green), using the prior knowledge of the designed phase mask. (c) Precision error on the full axial range as a function of added noise and (d) Precision error in each z position at a single noise multiplier(24) of (e) a full axial range acquisition using each of the retrieved phase masks. The orange outline shows a single PSF and MLE fits using each of the phase masks.…”

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confidence: 99%

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VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

Ferdman

Nehme

Weiss

et al. 2020

Preprint

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In microscopy, proper modeling of the image formation has a substantial effect on the precision and accuracy in localization experiments and facilitates the correction of aberrations in adaptive optics experiments. The observed images are subject to polarization effects, refractive index variations and system specific constraints. Previously reported techniques have addressed these challenges by using complicated calibration samples, computationally heavy numerical algorithms, and various mathematical simplifications. In this work, we present a phase retrieval approach based on an analytical derivation of the vectorial diffraction model. Our method produces an accurate estimate of the system phase information (without any prior knowledge) in under a minute.

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Section: Cost Functionsmentioning

confidence: 99%

Section: Phase Mask Optimization Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

Ferdman

Nehme

Weiss

et al. 2020

Preprint

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“…We chose to create this PSF using a phase mask ( Fig. 1b), because they are capable of producing a wide variety of PSFs [33][34][35][36][37][38] and allow higher light throughput compared to amplitude masks (which block some of the incoming light) 21 . The second major innovation in FlatScope 2.0 is a compact light delivery and filtering system that enables epifluorescence imaging.…”

Section: Introductionmentioning

confidence: 99%

In vivofluorescence imaging with a flat, lensless microscope

Adams

Boominathan

Gao

et al. 2020

Preprint

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Fluorescence imaging over large areas of the brain in freely behaving animals would allow researchers to better understand the relationship between brain activity and behavior; however, traditional microscopes capable of high spatial resolution and large fields of view (FOVs) require large and heavy lenses that restrict animal movement. While lensless imaging has the potential to achieve both high spatial resolution and large FOV with a thin lightweight device, lensless imaging has yet to be achieved in vivo due to two principal challenges: (a) biological tissue typically has lower contrast than resolution targets, and (b) illumination and filtering must be integrated into this non-traditional device architecture. Here, we show that in vivo fluorescence imaging is possible with a thin lensless microscope by optimizing the phase mask and computational reconstruction algorithms, and integrating fiber optic illumination and thin-film color filters. The result is a flat, lensless imager that achieves better than 10 μm spatial resolution and a FOV that is 30× larger than other cellular resolution miniature microscopes.

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Introduction to Fluorescence Microscopy

Macháň

2022

Springer Series on Fluorescence

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Generalized method to design phase masks for 3D super-resolution microscopy

Cited by 16 publications

References 41 publications

VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

VIPR: Vectorial Implementation of Phase Retrieval for fast and accurate microscopic pixel-wise pupil estimation

In vivofluorescence imaging with a flat, lensless microscope

Introduction to Fluorescence Microscopy

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