19 citations · 41 across the 5 of their papers we have counts for
6 papers
Predicting nonlinear optical scattering with physics-driven neural networks
Carlo Gigli, Amirhossein Saba, Ahmed Bassam Ayoub +1
Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and co…
Physics-informed neural networks for diffraction tomography
Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub +1
We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmh…
Single-cell phase-contrast tomograms data encoded by 3D Zernike descriptors
Pasquale Memmolo, Daniele Pirone, Daniele G. Sirico +5
Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and managem…
Optical Diffraction Tomography based on 3D Physics-Inspired Neural Network (PINN)
Ahmed B. Ayoub, Amirhossein Saba, Carlo Gigli +1
Optical diffraction tomography (ODT) is an emerging 3D imaging technique that is used for the 3D reconstruction of the refractive index (RI) for semi-transparent samples. Various i…
Three-Dimensional Optical Diffraction Tomography with Lippmann-Schwinger Model
Thanh-an Pham, Emmanuel Soubies, Ahmed Ayoub +3
A broad class of imaging modalities involve the resolution of an inverse-scattering problem. Among them, three-dimensional optical diffraction tomography (ODT) comes with its own c…
Method for Assessing the Fidelity of Optical Diffraction Tomography Reconstruction Methods
Ahmed B. Ayoub, Thanh-An Pham, Joowon Lim +2
We use a spatial light modulator in a diffraction tomographic system to assess the accuracy of different refractive index reconstruction algorithms. Optical phase conjugation princ…