Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty

Ray, Deep; Murgoitio-Esandi, Javier; Dasgupta, Agnimitra; Oberai, Assad A.

doi:10.1016/j.cma.2023.116338

Cited by 6 publications

(2 citation statements)

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“…While the input to a GAN is typically a random vector (termed the latent vector), more recently developed conditional GANs (cGANs) also accept a single instance of another random vector (which can encode prior knowledge) and generate images that are conditioned on this vector [45]. For example, in ultrasound elastography, cGANs have been used to synthesize elasticity given prior knowledge of the sample structure and/or displacement [46–48]. The problem of spheroid elasticity estimation with micro-scale resolution is particularly suited to cGANs since, at this scale, spheroids can be represented parametrically (e.g., in terms of the number of cells, cell size, and nucleus elasticity), thus enabling the training set to be generated by simulation using FEA.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in ultrasound elastography, cGANs have been used to synthesize elasticity given prior knowledge of the sample structure and/or displacement [46][47][48]. The problem of spheroid elasticity estimation with micro-scale resolution is particularly suited to cGANs since, at this scale, spheroids can be represented parametrically (e.g., in terms of the number of cells, cell size, and nucleus elasticity), thus enabling the training set to be generated by simulation using FEA.…”

Section: Introductionmentioning

confidence: 99%

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Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Foo,

Shaddy,

Murgoitio-Esandi

et al. 2024

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Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. To train and test the cGAN, we constructed 30,000 elasticity and phase difference image pairs, where elasticity images were generated using a parametric model to simulate artificial samples, and phase difference images were computed using finite element analysis to simulate compression applied to the artificial samples. By applying both the cGAN and algebraic methods to simulated phase difference images, our results indicate the cGAN elasticity images exhibit better spatial resolution and sensitivity. We also evaluated the cGAN on experimental phase difference images of real spheroids embedded in hydrogels and compared the cGAN elasticity with the algebraic elasticity, OCM, and confocal fluorescence microscopy, and found the cGAN elasticity is often more robust to noise, especially within stiff nuclei.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%