3D particle-localization using in-line holography is a fundamental problem with important applications. It involves estimating the unknown positions of scatterers in a 3D volume from a single 2D hologram. We propose a deep learning based framework that is highly computationally efficient for large-scale 3D reconstructio and demonstrates accurate results for a wide variety of scattering scenarios.
The proposed approach incorporates physical scattering information into the result via 3D backpropagation of the hologram, followed by artifact removal with an end-to-end 3D deep neural network (DNN). To address the challenge of limited data availability, we train our DNN solely on simulated data, and show that it works accurately for experimental data as well. The results show that our DNN is able to accurately localize particles under various scattering scenarios with little computational overhead.
Quantitative 3D analysis of brain vasculature is a fundamental problem with important applications, for which vessel segmentation is a first step. Traditional segmentation methods based on parametric models have limited accuracy. More recent techniques based on machine learning have promising results but limited generalization capability. We present a deep-learning based segmentation method that overcomes limitations of existing systems and demonstrates the ability to generalize to various imaging setups, samples including both in-vivo/ex-vivo data, with state-of-the-art results. We achieve so by exploiting several novel methods in deep learning, such as semi-supervised learning. We believe that our work will be another step forward towards improved large-scale neurovascular analysis.
This analytical modeling-and-simulation paper presents a compact passive photonic filter using Mach-Zehnder Interferometer (MZI) on silicon-on-insulator (SOI) platform. MZI based wavelength filters have been demonstrated for broadband wavelength-division-multiplexing (WDM). The imbalance in MZI provides means to control the free spectral range (FSR) hence enabling designing of a photonic filter that can pass the required band and block the rest of the signal. Low loss Y-splitter/combiner are used with very small footprint to make compact size MZI. A 3D Finite-Difference Time-Domain (FDTD) simulation is performed on our design. FSR value of 50 nm was observed in the span of 100 nm wavelength range centered on 1.55 μm. The footprint of the designed filter is 15 μm x 20 μm.
We propose a novel compact structure with EIT-like spectrum by introducing two partial reflectors (silica holes) symmetrically placed within the microring resonator (MRR). The two Fabry-Pérot (FP) resonating cavities created by the silica holes, coupled with microring’s resonance modes, show EIT-like lineshapes spaced by free spectral range of MRR. The FWHM of EIT-like spectrum can be controlled by tuning the radii of the silica holes. Analytical results from the developed mathematical model and 3D FDTD simulations were found to be in good agreement with each other.
We introduce a computational framework that incorporates multiple scattering for large-scale three-dimensional (3-D) particle localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models that become inaccurate under high particle densities and large refractive index contrasts. Existing multiple scattering solvers become computationally prohibitive for large-scale problems, which comprise millions of voxels within the scattering volume. Our approach overcomes the computational bottleneck by slicewise computation of multiple scattering under an efficient recursive framework. In the forward model, each recursion estimates the next higher-order multiple scattered field among the object slices. In the inverse model, each order of scattering is recursively estimated by a nonlinear optimization procedure. This nonlinear inverse model is further supplemented by a sparsity promoting procedure that is particularly effective in localizing 3-D distributed particles. We show that our multiple-scattering model leads to significant improvement in the quality of 3-D localization compared to traditional methods based on single scattering approximation. Our experiments demonstrate robust inverse multiple scattering, allowing reconstruction of 100 million voxels from a single 1-megapixel hologram with a sparsity prior. The performance bound of our approach is quantified in simulation and validated experimentally. Our work promises utilization of multiple scattering for versatile large-scale applications.
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