Unified modeling of photothermal and photochemical damage.

Correlating damage outcomes to a retinal laser exposure is critical for diagnosis and choosing appropriate treatment modalities. Therefore, it is important to understand the causal relationships between laser parameters, such as wavelength, power density, and length of exposure, and any resulting injury. Differentiating photothermal from photochemical processes in an in vitro retinal model using cultured retinal pigment epithelial cells would be a first step in achieving this goal. The first-order rate constant of Arrhenius has been used for decades to approximate cellular thermal damage. A modification of this equation, called the damage integral (Ω), has been used extensively to predict the accumulation of laser damage from photothermal inactivation of critical cellular proteins. Damage from photochemical processes is less well studied and most models have not been verified because they require quantification of one or more uncharacterized chemical species. Additionally, few reports on photochemical damage report temperature history, measured or simulated. We used simulated threshold temperatures from a previous in vitro study to distinguish between photothermal and photochemical processes. Assuming purely photochemical processes also inactivate critical cellular proteins, we report the use of a photothermal Ω and a photochemical Ω that work in tandem to indicate overall damage accumulation. The combined damage integral (ΩCDI) applies a mathematical switch designed to describe photochemical damage relative to wavelength and rate of photon delivery. Although only tested in an in vitro model, this approach may transition to predict damage at the mammalian retina.

Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks.

Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and numerical methods for the EIT inverse problem. Despite the success, numerical instability is still a major hurdle due to many factors, including the discretization error of the problem. Furthermore, most algorithms with good performance are relatively time consuming and do not allow real-time applications. In our approach, the goal is to separate the unknown conductivity into two regions, namely the region of homogeneous background conductivity and the region of non-homogeneous conductivity. Therefore, we pose and solve the problem of shape reconstruction using machine learning. We propose a novel and simple jet intriguing neural network architecture capable of solving the EIT inverse problem. It addresses previous difficulties, including instability, and is easily adaptable to other ill-posed coefficient inverse problems. That is, the proposed model estimates the probability for a point of whether the conductivity belongs to the background region or to the non-homogeneous region on the continuous space Rd∩Ω with d∈{2,3}. The proposed model does not make assumptions about the forward model and allows for solving the inverse problem in real time. The proposed machine learning approach for shape reconstruction is also used to improve gradient-based methods for estimating the unknown conductivity. In this paper, we propose a piece-wise constant reconstruction method that is novel in the inverse problem setting but inspired by recent approaches from the 3D vision community. We also extend this method into a novel constrained reconstruction method. We present extensive numerical experiments to show the performance of the architecture and compare the proposed method with previous analytic algorithms, mainly the monotonicity-based shape reconstruction algorithm and iteratively regularized Gauss-Newton method.

Hyperchromatic structural color for perceptually enhanced sensing by the naked eye.

Colorimetric sensors offer the prospect for on-demand sensing diagnostics in simple and low-cost form factors, enabling rapid spatiotemporal inspection by digital cameras or the naked eye. However, realizing strong dynamic color variations in response to small changes in sample properties has remained a considerable challenge, which is often pursued through the use of highly responsive materials under broadband illumination. In this work, we demonstrate a general colorimetric sensing technique that overcomes the performance limitations of existing chromatic and luminance-based sensing techniques. Our approach combines structural color optical filters as sensing elements alongside a multichromatic laser illuminant. We experimentally demonstrate our approach in the context of label-free biosensing and achieve ultrasensitive and perceptually enhanced chromatic color changes in response to refractive index changes and small molecule surface attachment. Using structurally enabled chromaticity variations, the human eye is able to resolve ∼0.1-nm spectral shifts with low-quality factor (e.g., Q ∼ 15) structural filters. This enables spatially resolved biosensing in large area (approximately centimeters squared) lithography-free sensing films with a naked eye limit of detection of ∼3 pg/mm2, lower than industry standard sensors based on surface plasmon resonance that require spectral or angular interrogation. This work highlights the key roles played by both the choice of illuminant and design of structural color filter, and it offers a promising pathway for colorimetric devices to meet the strong demand for high-performance, rapid, and portable (or point-of-care) diagnostic sensors in applications spanning from biomedicine to environmental/structural monitoring.

Absorption and scattering images of heterogeneous scattering media can be simultaneously reconstructed by use of dc data.

We present a carefully designed phantom experimental study aimed to provide solid evidence that both absorption and scattering images of heterogeneous scattering media can be reconstructed independently from dc data. We also study the important absorption-scattering cross-talk issue. In this regard, we develop a simple normalizing scheme that is incorporated into our nonlinear finite-element-based reconstruction algorithm. Our results from the controlled phantom experiments show that the cross talk of an absorption object appearing in scattering images can be eliminated and that the cross talk of a scattering object appearing in absorption images can be reduced considerably. In addition, these carefully designed phantom experiments clearly suggest that both absorption and scattering images can be simultaneously recovered and quantitatively separated in highly scattering media by use of dc measurements. Finally, we discuss our results in light of recent theoretical findings on nonuniqueness for dc image reconstruction.