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Sample Motion for Structured Illumination Fluorescence Microscopy

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arXiv 2025年

作者： Cao, Ruiming Meng, Guanghan Waller, Laura Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States School of Optometry and Vision Science University of California Berkeley BerkeleyCA94720 United States Chan Zuckerberg Imaging Institute Redwood CityCA94605 United States

Structured illumination microscopy (SIM) uses a set of images captured with different illumination patterns to computationally reconstruct resolution beyond the diffraction limit. Here, we propose an alternative approach using a single speckle illumination pattern and relying on inherent sample motion to encode the super-resolved information in multiple raw images. From a set of raw fluorescence images captured as the sample moves, we jointly estimate both the sample motion and the super-resolved image. We demonstrate the feasibility of the proposed method both in simulation and in experiment. © 2025, CC BY.

关键词： Fluorescence microscopy

Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data

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arXiv 2024年

作者： Fanous, Michael John Costa, Paloma Casteleiro Işıl, Çağatay Huang, Luzhe Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los Angeles90095 CA United States Bioengineering Department University of California Los Angeles90095 CA United States University of California Los Angeles90095 CA United States Department of Surgery David Geffen School of Medicine University of California Los Angeles90095 CA United States

In recent years, the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g., cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed, critical for capturing fine dynamic biological processes. Additionally, this approach offers the prospect of simplifying hardware requirements and complexities, thereby making advanced imaging standards more accessible in terms of cost and/or size. This article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups, including the point spread function (PSF), signal-to-noise ratio (SNR), sampling density, and pixel resolution. By deliberately compromising these metrics, researchers aim to not only recuperate them through the application of deep learning networks, but also bolster in return other crucial parameters, such as the field-of-view (FOV), depth-of-field (DOF), and space-bandwidth product (SBP). Throughout this article, we discuss various biophotonic methods that have successfully employed this strategic approach. These techniques span a wide range of applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic data. Finally, by offering our perspectives on the exciting future possibilities of this rapidly evolving concept, we hope to motivate our readers from various disciplines to explore novel ways of balancing

关键词： Bioimaging

An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning

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arXiv 2024年

作者： Goncharov, Artem Gorocs, Zoltan Pradhan, Ridhi Ko, Brian Ajmal, Ajmal Rodriguez, Andres Baum, David Veszpremi, Marcell Yang, Xilin Pindrys, Maxime Zheng, Tianle Wang, Oliver Ramella-Roman, Jessica C. McShane, Michael J. Ozcan, Aydogan Electrical & Computer Engineering Department Bioengineering Department University of California Los AngelesCA90095 United States Department of Biomedical Engineering Texas A&M University College StationTX77843 United States Department of Biomedical Engineering Florida International University FL33199 United States Department of Physics University of Connecticut StorrsCT06269 United States Department of Computer Science University of California Los AngelesCA90095 United States Department of Materials Science and Engineering Texas A&M University College StationTX77843 United States Department of Surgery University of California Los AngelesCA90095 United States

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1-mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ~4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for re-alignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective phosphorescence lifetime imager makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers. Copyright © 2024, The Authors. All rights reserved.

关键词： Glucose

Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography with Hemispherical Measurement Geometries

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arXiv 2024年

作者： Chen, Panpan Park, Seonyeong Cam, Refik Mert Huang, Hsuan-Kai Oraevsky, Alexander A. Villa, Umberto Anastasio, Mark A. Department of Bioengineering University of Illinois Urbana–Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois Urbana–Champaign UrbanaIL61801 United States TomoWave Laboratories Inc. HoustonTX77054 United States Oden Institute The University of Texas at Austin AustinTX78712 United States

In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including in vivo breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisition. Data acquired with such measurement geometries are referred to as half-scan data, as only half of a complete spherical measurement aperture is employed. Although previous studies have demonstrated that half-scan data can uniquely and stably reconstruct the sought-after object, no closed-form reconstruction formula for use with half-scan data has been reported. To address this, a semi-analytic reconstruction method in the form of filtered backprojection (FBP), referred to as the half-scan FBP method, is developed in this work. Because the explicit form of the filtering operation in the half-scan FBP method is not currently known, a learning-based method is proposed to approximate it. The proposed method is systematically investigated by use of virtual imaging studies of 3D breast PACT that employ ensembles of numerical breast phantoms and a physics-based model of the data acquisition process. The method is subsequently applied to experimental data acquired in an in vivo breast PACT study. The results confirm that the half-scan FBP method can accurately reconstruct 3D images from half-scan data. Importantly, because the sought-after inverse mapping is well-posed, the reconstruction method remains accurate even when applied to data that differ considerably from those employed to learn the filtering operation. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

Broadband Unidirectional Visible Imaging Using Wafer-Scale Nano-Fabrication of Multi-Layer Diffractive Optical Processors

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arXiv 2024年

作者： Shen, Che-Yung Batoni, Paolo Yang, Xilin Li, Jingxi Liao, Kun Stack, Jared Gardner, Jeff Welch, Kevin Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Micro Optics Optical Systems Division Broadcom Inc. CharlotteNC28262 United States

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates the wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Our design can seamlessly integrate into conventional optical systems, broadening its applicability in fields such as security, defense, and telecommunication. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level massively scalable fabrication. Copyright © 2024, The Authors. All rights reserved.

关键词： Fabrication

Scalable Anatomically-Tunable Fully In-Ear Dry-Electrode Array for User-Generic Unobtrusive Electrophysiology

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Scalable Anatomically-Tunable Fully In-Ear Dry-Electrode Arr...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Min Suk Lee Akshay Paul Tae Houn Joung Yuchen Xu Jiajia Wu W. David Hairston Gert Cauwenberghs Department of Bioengineering Jacobs School of Engineering and Institute for Neural Computation UC San Diego La Jolla CA USA Department of Electrical and Computer Engineering Jacobs School of Engineering and Institute for Neural Computation UC San Diego La Jolla CA USA Humans in Complex Systems DEVCOM Army Research Lab Aberdeen Proving Ground MD USA

Traditional scalp EEG instrumentation is bulky and arduous to set up, requiring wires that constrain the subject’s movement, conductive wet gels that dry over time which limits long-term recording, and/or is socially stigmatized. Therefore, there is growing research in in-ear EEG to increase user wearability, ease of use, and concealability. However, the fabrication of in-ear EEG sensors utilizes complex equipment and materials to capture the intricate geometry of the ear and to fabricate custom earpieces and electrodes. This work aims to lower the barrier of entry by decreasing the fabrication complexity by using PCB components with versatile, user-generic designs. Measured results on the assembled earpiece demonstrate that it viably captures eye blinks, jaw clench, auditory steady-state response (ASSR), and alpha modulation. Additionally, electrochemical impedance spectroscopy (EIS) experiments show reliable electrode-skin contact with impedance comparable to conventional dry-electrode designs at substantially greater channel density.

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Biopsy-free in vivo virtual histology of skin using deep learning

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Light(Science & Applications) 2021年第12期10卷 2421-2442页

作者： Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O.Scumpia Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA Dermatology and Laser Centre Studio CityCA 91604USA Computer Science Department University of CaliforniaLos AngelesCA 90095USA Division of Dermatology University of CaliforniaLos AngelesCA 90095USA Department of Dermatology Veterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCA 90073USA Department of Surgery University of CaliforniaLos AngelesCA 90095USA

An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin *** process is cumbersome and time-consuming,often leading to unnecessary biopsies and *** noninvasive optical technologies such as reflectance confocal microscopy(RCM)can provide label-free,cellular-level resolution,in vivo images of skin without performing a *** RCM is a useful diagnostic tool,it requires specialized training because the acquired images are grayscale,lack nuclear features,and are difficult to correlate with tissue ***,we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution,enabling visualization of the epidermis,dermal-epidermal junction,and superficial dermis *** network was trained under an adversarial learning scheme,which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground *** show that this trained neural network can be used to rapidly perform virtual histology of in vivo,label-free RCM images of normal skin structure,basal cell carcinoma,and melanocytic nevi with pigmented melanocytes,demonstrating similar histological features to traditional histology from the same excised *** application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

关键词： network vivo virtual

Combining linear and nonlinear polarization-sensitive imaging modalities for enhanced characterizations of collagen

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Combining linear and nonlinear polarization-sensitive imagin...

Microscopy Histopathology and Analytics, Microscopy 2022

作者： Yang, Lingxiao Iyer, Rishyashring R. Sorrells, Janet E. Chaney, Eric J. Boppart, Stephen A. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Here we propose a custom multimodal system with simultaneous and spatially co-registered polarization-sensitive optical coherence microscopy and polarization-sensitive second harmonic generation microscopy to enable q... 详细信息

ISBN: (纸本)9781557528209

关键词： Collagen

In vivo Detection of NO using Photonic Microring Resonators

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In vivo Detection of NO using Photonic Microring Resonators

Conference on Lasers and Electro-Optics (CLEO)

作者： Sakib Hassan Xuan Zhao Christian C. Schreib Guillaume Duret Omid Veiseh Jacob T. Robinson Electrical & Computer Engineering Rice University Houston USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston USA Department of Bioengineering Rice University Houston USA

Here we show how photonic microring resonators enable real-time detection of Nitric Oxide (NO) in vivo, which is critical for physiological monitoring particularly during the wound healing process.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

Here we show how photonic microring resonators enable real-time detection of Nitric Oxide (NO) in vivo, which is critical for physiological monitoring particularly during the wound healing process.

关键词： Integrated optics In vivo Optical resonators Wounds Laser modes Lab-on-a-chip Real-time systems