arXiv subjects

Joowon Lim

Publications and source records attributed to Joowon Lim.

5 recordsLinked to original sources

arXiv2026

Refractive-index tomography of opaque tissue from its own backscattered light

The refractive index (RI) is an intrinsic, label-free marker of a living cell's dry mass and subcellular morphology, and hence of its physiological state. Its three-dimensional (3D) reconstruction has become a powerful way to study cells and tissues in their native state, spanning cell growth, drug response and disease diagnosis. Yet this capability rests on a fundamental constraint: the RI can be recovered only from light transmitted through the specimen, which demands optical access to both sides. The cells that matter most -- those within thick tissues, intact organs and living animals -- are therefore out of reach. A tissue, however, can illuminate its own cells from behind: light backscattered by intrinsic tissue structures beneath a cell carries the same transmission information a microscope would collect from the far side. Here we develop a divide-and-conquer inverse-scattering framework that recovers this transmission from the backscattering and reconstructs a cell's 3D RI. We demonstrate label-free, quantitative imaging of cells within an engineered tissue, and a living mouse through its intact skull, where we further quantify the dry mass of individual osteocytes in vivo. By removing the need for two-sided access, this reflection-only approach extends RI tomography into living tissue, enabling non-destructive, longitudinal imaging of cells in their native environment.

physics.optics↗

arXiv2023

Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning

Retrieving images transmitted through multi-mode fibers is of growing interest, thanks to their ability to confine and transport light efficiently in a compact system. Here, we demonstrate machine-learning-based decoding of large-scale digital images (pages), maximizing page capacity for optical storage applications. Using a millimeter-sized square cross-section waveguide, we image an 8-bit spatial light modulator, presenting data as a matrix of symbols. Normally, decoders will incur a prohibitive O(n^2) computational scaling to decode n symbols in spatially scrambled data. However, by combining a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using efficient convolutional operations only. We compare trainable ray-tracing-based with eigenmode-based twins and show the former to be superior thanks to its ability to overcome the simulation-to-experiment gap by adjusting to optical imperfections. We train the pipeline end-to-end using a differentiable mutual-information estimator based on the von-Mises distribution, generally applicable to phase-coding channels.

physics.optics↗

arXiv2021

MaxwellNet: Physics-driven deep neural network training based on Maxwell's equations

Maxwell's equations govern light propagation and its interaction with matter. Therefore, the solution of Maxwell's equations using computational electromagnetic simulations plays a critical role in understanding light-matter interaction and designing optical elements. Such simulations are often time-consuming and recent activities have been described to replace or supplement them with trained deep neural networks (DNNs). Such DNNs typically require extensive, computationally demanding simulations using conventional electromagnetic solvers to compose the training dataset. In this paper, we present a novel scheme to train a DNN that solves Maxwell's equations speedily and accurately without relying on other computational electromagnetic solvers. Our approach is to train a DNN using the residual of Maxwell's equations as the physics-driven loss function for a network that finds the electric field given the spatial distribution of the material property. We demonstrate it by training a single network that simultaneously finds multiple solutions of various aspheric micro-lenses. Furthermore, we exploit the speed of this network in a novel inverse design scheme to design a micro-lens that maximizes a desired merit function. We believe that our approach opens up a novel way for light simulation and optical design of photonics devices.

physics.optics↗

arXiv2019

Three-Dimensional Optical Diffraction Tomography with Lippmann-Schwinger Model

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 challenges. These include a limited range of views, a large size of the sample with respect to the illumination wavelength, and optical aberrations that are inherent to the system itself. In this work, we present an accurate and efficient implementation of the forward model. It relies on the exact (nonlinear) Lippmann-Schwinger equation. We address several crucial issues such as the discretization of the Green function, the computation of the far field, and the estimation of the incident field. We then deploy this model in a regularized variational-reconstruction framework and show on both simulated and real data that it leads to substantially better reconstructions than the approximate models that are traditionally used in ODT.

eess.IV↗

arXiv2019

Method for Assessing the Fidelity of Optical Diffraction Tomography Reconstruction Methods

We use a spatial light modulator in a diffraction tomographic system to assess the accuracy of different refractive index reconstruction algorithms. Optical phase conjugation principles through complex media, allows us to quantify the error for different refractive index reconstruction algorithms without access to the ground truth. To our knowledge, this is the first assessment technique that uses structured illumination experimentally to test the accuracy of different reconstruction schemes.

physics.optics↗