Holographic tomography with scanning of illumination: space-domain reconstruction for spatially invariant accuracy

Kostencka, Julianna; Kozacki, Tomasz; Kuś, Arkadiusz; Kemper, Björn; Kujawińska, Małgorzata

doi:10.1364/boe.7.004086

Cited by 55 publications

(24 citation statements)

References 30 publications

(62 reference statements)

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“…Two 4.45-μm beads are placed in a row so that they overlap in the z axis. In the case of single-scattering tomography which is based on Rytov, the location of the image plane is very important [29]. Figure 11(c) confirms this fact.…”

Section: Two Beadsmentioning

confidence: 61%

“…It comes from the fact that the singlescattering propagation model based on Rytov causes distortions moving away from the focal plane, as reported in Ref. [29]. As shown in Figs describes the propagated field itself through the sample, it is not only unaffected by the location of the image plane but also able to handle multiple scattering.…”

Section: Two Beadsmentioning

confidence: 89%

“…In terms of computational implementation, this mapping, Eq. (6), requires interpolation in the Fourier domain, and it can cause pixelation errors [28,29]. To avoid such errors, we directly define the single-scattering model in the spatial domain using Eq.…”

Section: Theory a Optical Diffraction Tomographymentioning

confidence: 99%

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Learning Tomography Assessed Using Mie Theory

et al. 2018

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In optical diffraction tomography, the multiply scattered field is a nonlinear function of the refractive index (RI) of the object. The Rytov method relies on a single-scattering propagation model and is commonly used to reconstruct images. Recently, a reconstruction model was introduced based on the beam propagation method that takes multiple scattering into account. We refer to this method as learning tomography (LT). We carry out simulations and experiments in order to assess the performance of LT over the iterative single-scattering propagation method. Each algorithm is rigorously assessed for spherical and cylinderical objects, with synthetic data generated using Mie theory. By varying the RI contrast and the size of the objects, we show that the LT reconstruction is more accurate and robust than the reconstruction based on the single-scattering propagation model. In addition, we show that LT is able to correct distortions that are evident in the Rytov-approximation-based reconstructions due to limitations in phase unwrapping. More importantly, the ability of LT to handle multiple scattering is demonstrated by simulations of multiple cylinders using Mie theory and is confirmed by experiment.

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Section: Two Beadsmentioning

confidence: 61%

Section: Two Beadsmentioning

confidence: 89%

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Learning Tomography Assessed Using Mie Theory

et al. 2018

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“…Initially reconstructed tomograms of thick tissues suffered from weak image contrast and distortion due to sampleinduced aberrations. Considering that samples nearest the optical focal plane display sharper image contrast when reconstructed with the Rytov approximation, we exploited holographic refocusing to reconstruct 3D RI maps with higher image contrast 28,29 (Figs. 4a-c).…”

Section: Reconstruction Of Refractive Index Distributionsmentioning

confidence: 99%

Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution

Hugonnet

Kim

Shin

et al. 2020

Preprint

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Histopathology relies upon the staining and sectioning of biological tissues, which can be laborious and may cause artefacts and distort tissues. Here, we demonstrate label-free volumetric imaging of thick-tissue slides, exploiting refractive index distributions as intrinsic imaging contrast. The present method systematically exploits label-free quantitative phase imaging techniques, volumetric reconstruction of intrinsic refractive index distributions in tissues, and numerical algorithms for the seamless stitching of multiple 3D tomograms and for reducing scattering-induced image distortion. We demonstrate demonstrated label-free volumetric imaging of thick tissues with the field of view of 2 mm × 1.75 mm × 0.2 mm with a spatial resolution of 170 nm × 170 nm × 1200 nm. The number of optical modes, calculated as the reconstructed volume divided by the size of the point spread function, was approximately 20 Giga voxels. We have also demonstrated that different tumour types, and a variety of precursor lesions and pathologies can be visualized with the present method.Abstract Figure

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“…It should be noted that the presented approach is not the only solution to anisotropic resolution in tomographic reconstructions that is associated with the limited DOF of an optical setup. Another approach is a purely numerical solution in which each projection is rigorously propagated to multiple planes that cover the range of the entire sample . In the next step, complex phases,

Ψ_{normals} false(boldr false)

(see Section ), are calculated from the propagated fields and the final reconstruction is obtained.…”

Section: Softwarementioning

confidence: 99%

Holographic tomography: hardware and software solutions for 3D quantitative biomedical imaging (Invited paper)

et al. 2019

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In this paper, we demonstrate the current concepts in holographic tomography (HT) realized within limited angular range with illumination scanning. The presented solutions are based on the work performed at Warsaw University of Technology in Poland and put in context with the state of the art in HT. Along with the theoretical framework for HT, the optimum reconstruction process and data visualization are described in detail. The paper is concluded with the description of hardware configuration and the visualization of tomographic reconstruction, which is calculated using a provided processing path.

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Holographic tomography with scanning of illumination: space-domain reconstruction for spatially invariant accuracy

Cited by 55 publications

References 30 publications

Learning Tomography Assessed Using Mie Theory

Learning Tomography Assessed Using Mie Theory

Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution

Holographic tomography: hardware and software solutions for 3D quantitative biomedical imaging (Invited paper)

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