The recent development of tomographic phase imaging flow cytometry has unlocked the possibility to achieve data throughput comparable to the state-of-the-art imaging flow cytometry systems, but with the great advantages to be fully label-free and 3D. On the other hand, the huge amount of data to manage becomes one of the main computational problems to face with. Here we show that by using the 3D version of Zernike polynomials it is possible to efficiently encode single-cell phase-contrast tomograms, demonstrating high data compression capability with negligible information loss. A full simulative analysis is reported also quantifying the trade-off between compression factor and representation accuracy.
Deep neural network trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell’s equations and compute gradients of output fields with respect to material properties in millisecond times which makes them very attractive for inverse design or inverse scattering applications. Here we demonstrate a neural network able to compute light scattering from inhomogeneous media in the presence of the optical Kerr effect from glass diffusers with a size comparable with the incident wavelength. The weights of the network are dynamically adjusted to take into account the intensity dependent refractive index of the material.
We propose a physics-informed neural network (PINN) as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our PINNs can be generalized for any forward and inverse scattering problem.
We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are introduced on reconstructions obtained by the Wolf transform inversion method due to the ill-posed measurements acquired from the limited numerical apertures (NAs) of the optical system. Despite the recent success of DNNs in solving ill-posed inverse problems, the application to 3-D optical imaging is particularly challenging due to the lack of the ground truth. We overcome this limitation by generating digital phantoms that serve as samples for the discrete dipole approximation (DDA) to generate multiple 2-D projection maps for a limited range of illumination angles. The presented samples are red blood cells (RBCs), which are highly affected by the ill-posed problems due to their morphology. The trained network using synthetic measurements from the digital phantoms successfully eliminates the introduced distortions. Most importantly, we obtain high fidelity reconstructions from experimentally recorded projections of real RBC sample using the network that was trained on digitally generated RBC phantoms. Finally, we confirm the reconstruction accuracy using the DDA to calculate the 2-D projections of the 3-D reconstructions and compare them to the experimentally recorded projections.
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