DIC image reconstruction using an energy minimization framework to visualize optical path length distribution

Koós, Krisztián; Molnár, J.; Kelemen, Lóránd; Tamás, Gábor; Horváth, Péter

doi:10.1038/srep30420

Cited by 19 publications

(19 citation statements)

References 31 publications

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“…DIC images are often treated as directional intensity difference images. This supposes that the image formation model is linear, which was previously shown to be highly correlated to advanced image formation models [15] in typical cases where the amount of diffraction is low. The direction is defined by the imaging device and given a priori.…”

Section: Dic Reconstruction Taskmentioning

confidence: 81%

“…To apply the gradient descent algorithm, the Euler-Lagrangian (EL) of the two terms in (5) and (6) have to be calculated. The complete derivation of the EL equations can be found in [14] and [15], but here we only describe and explain the final equations. The EL equation of the data term is very complex because of the local integrals introduced by the kernel function.…”

Section: Reconstruction Using a Variational Frameworkmentioning

confidence: 99%

“…In our recent paper [15], we described and compared the most relevant DIC reconstruction algorithms. These algorithms can be organized into groups based on their computational principles.…”

Section: Other Reconstruction Algorithmsmentioning

confidence: 99%

“…Figure 3 shows 3 sample image series to compare the above mentioned algorithms. The input simulated DIC images are computed from the ground truth images by convolving them with a PSF, assuming a linear image formation model, which is known to be very close to the correct image formation model [15]. The difference in the output quality of the algorithms is visible.…”

Section: Other Reconstruction Algorithmsmentioning

confidence: 99%

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Phase Measurement Using DIC Microscopy

2017

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The development of fluorescent probes and proteins has helped make light microscopy more popular by allowing the visualization of specific subcellular components, location and dynamics of biomolecules. However, it is not always feasible to label the cells as it may be phototoxic or perturb their functionalities. Label-free microscopy techniques allow us to work with live cells without perturbation and to evaluate morphological differences, which in turn can provide useful information for high-throughput assays. In this study, we use one of the most popular label-free techniques called differential interference contrast (DIC) microscopy to estimate the phase of cells and other nearly transparent objects and instantly estimate their height. DIC images provide detailed information about the optical path length (OPL) differences in the sample and they are visually similar to a gradient image. Our previous DIC construction algorithm outputs an image where the values are proportional to the OPL (or implicitly the phase) of the sample. Although the reconstructed images are capable of describing cellular morphology and to a certain extent turn DIC into a quantitative technique, the actual OPL has to be computed from the input DIC image and the microscope calibration settings. Here we propose a computational method to measure the phase and approximate height of cells after microscope calibration, assuming a linear formation model. After a calibration step the phase of further samples can be determined when the refractive indices of the sample and the surrounding medium is known. The precision of the method is demonstrated on reconstructing the thickness of known objects and real cellular samples.

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Section: Dic Reconstruction Taskmentioning

confidence: 81%

Section: Reconstruction Using a Variational Frameworkmentioning

confidence: 99%

“…In our recent paper [15], we described and compared the most relevant DIC reconstruction algorithms. These algorithms can be organized into groups based on their computational principles.…”

Section: Other Reconstruction Algorithmsmentioning

confidence: 99%

Section: Other Reconstruction Algorithmsmentioning

confidence: 99%

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Phase Measurement Using DIC Microscopy

2017

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“…Although it has the same optical limitation as a conventional WFM, it provides volumetric quantification that is not available in conventional WFM, albeit at the low imaging speed. Holographic QPM [24][25][26] , on the other hand, directly records the phase of the object and uses a non-focussed laser light.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Embolism: Label‐Free Volumetric Mapping of Thrombus Structure and Kinesis in a Microfluidic System with Optical Holography

Montague

et al. 2018

Adv. Biosys.

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Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

Wu,

Daneker,

Turner

et al. 2024

Small Methods

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The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

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DIC image reconstruction using an energy minimization framework to visualize optical path length distribution

Cited by 19 publications

References 31 publications

Phase Measurement Using DIC Microscopy

Phase Measurement Using DIC Microscopy

Quantifying Embolism: Label‐Free Volumetric Mapping of Thrombus Structure and Kinesis in a Microfluidic System with Optical Holography

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

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