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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Bai, Bijie Wei, Heming Yang, Xilin Gan, Tianyi Mengu, Deniz Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China University of California Los AngelesCA United States

We present an all-optical data-class-specific transformation framework that encrypts image data using separate transformations uniquely assigned to each data class. The presented method was experimentally tested under... 详细信息

ISBN: (纸本)9781957171296

关键词： Image encryption Light diffraction Near infrared Neural networks Numerical simulation Optical computing

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Sample motion for structured illumination fluorescence microscopy

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Optics Letters 2025年第12期50卷 4074-4077页

作者： Ruiming Cao Guanghan Meng Laura Waller Department of Bioengineering University of California Berkeley Berkeley California 94720 USA Chan Zuckerberg Imaging Institute Redwood City California 94605 USA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley California 94720 USA School of Optometry and Vision Science University of California Berkeley Berkeley California 94720 USA

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.

关键词： Diffraction limit Fourier transforms Imaging systems Spatial light modulators Speckle patterns Structured illumination microscopy

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Advancing Personalized Prostate Cancer Therapy Through Hormonal Treatment: Promising Findings

Advancing Personalized Prostate Cancer Therapy Through Hormo...

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IEEE International Symposium on Biomedical Imaging

作者： I. Abdelhalim A. Alksas H. M. Balaha M. Badawy M. Abou El-Ghar N. S. Alghamdi M. Ghazal S. Contractor E. V. Bogaert D. Gondim S. R. Silva F. Khalifa A. El-Baz Department of Bioengineering University of Louisville Louisville KY USA Radiology Department Urology and Nephrology Center Mansoura Egypt Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Radiology University of Louisville Louisville KY USA Department of Pathology and Laboratory Medicine University of Louisville Louisville KY USA Department of Radiation Oncology University of Louisville Louisville KY USA Electrical and Computer Engineering Department Morgan State University Baltimore MD USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Personalized prediction of hormonal therapy response in Prostate Cancer (PC) is crucial for planning effective treatment. In this paper, we propose a novel framework to combine MRI imaging, pathology, clinical, and demographic markers, aiming to develop a robust prediction system. The process involves sequential steps: preprocessing, prostate/tumor localization, feature extraction, and classification. Using the Multibranch Multimodality MRI Feature Extractor (M3FE), a deep learning technique, we extract salient information from MRI images. The final step employs a weighted sum fusion algorithm to combine MRI features with other markers. Testing on a dataset of 39 patients demonstrates that the framework effectively predicts hormonal therapy effects on PC with 97.5% sensitivity and 100% specificity. This highlights the potential of using radiomics, which involves the analysis of image features, along with other data sources for the precise prediction of hormonal therapy responses in PC.

关键词： Sensitivity Magnetic resonance imaging Soft sensors Medical treatment Feature extraction Prediction algorithms Data mining

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In-flow holographic tomography boosts lipid droplet quantification

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Opto-Electronic Advances 2023年第6期6卷 1-3页

作者： Michael John Fanous Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos Angeles 90095 CAUSA Bioengineering Department University of CaliforniaLos Angeles 90095 CAUSA California NanoSystems Institute(CNSI) University of CaliforniaLos Angeles 90095 CAUSA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos Angeles 90095 CAUSA

In their recently published paper in Opto-Electronic Ad-vances,Pietro Ferraro and his colleagues report on a new high-throughput tomographic phase instrument that precisely quantifies intracellular lipid droplets(LDs)*** are lipid storage organelles found in most cell types and play an active role in critical biological pro-cesses,including energy metabolism,membrane homeo-stasis.

关键词： holographic flow precisely

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MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zhao, Ruiyang Wang, Zepeng Anderson, Aaron Huesmann, Graham Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology United States University of Illinois Urbana-Champaign Department of Bioengineering Beckman Institute for Advanced Science and Technology United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology United States Carle Foundation Hospital United States University of Illinois Urbana-Champaign Department of Bioengineering Beckman Institute for Advanced Science and Technology IL61801 United States

We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes an explicit low-dimensional representation of the high-dimensional spatiospectral functions of interest for noise reduction, while the denoiser serves as a complementary spatiotemporal prior to constrain the subspace reconstruction. A self-supervised learning strategy was proposed to train a denoiser that can distinguish the spatio-temporally correlated signals from uncorrelated noise. An iterative reconstruction formalism was developed based on the Plug-and-Play (PnP)-ADMM framework to synergize the subspace constraint, plug-in denoiser and spatiospectral encoding model. We evaluated the proposed method using numerical simulations and in vivo data, demonstrating improved performance over state-of-the-art subspacebased methods. We also provided theoretical analysis on the utility of combining subspace projection and iterative denoising in terms of both algorithm convergence and performance. Our work demonstrated the potential of integrating self-supervised denoising priors and low-dimensional representations for high-dimensional imaging problems. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Smart Dura: A Monolithic Optoelectrical Surface Array for Neural Interfacing with Primate Cortex

Smart Dura: A Monolithic Optoelectrical Surface Array for Ne...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Sergio Montalvo Vargo Tiphaine Belloir Ibrahim Kimukin Zabir Ahmed Devon J. Griggs Noah Stanis Azadeh Yazdan-Shahmorad Maysamreza Chamanzar Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Department of Bioengineering University of Washington Seattle WA USA Washington National Primate Research Center Seattle WA USA Department of Electrical and Computer Engineering University of Washington Seattle WA USA Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Carnegie Mellon Neuroscience Institute Pittsburgh PA USA

Chronic interfacing with the brain to record and stimulate neural activity with high spatiotemporal resolution is highly desirable to advance the understanding of neural circuits and design of novel therapeutics. Traditional neural interfaces mainly rely on electrical recording and stimulation, which have inherent limitations such as lack of specificity and a large volume of tissue activation. With the advent of optogenetics, hybrid opto-electrical platforms have emerged as new bidirectional interfaces. In this paper, we introduce our groundwork towards the Smart Dura, a novel device that will ultimately integrate high density recording electrodes and light sources in the form of a chronic implant that can cover large cortical areas in the primate brain (300 mm 2 ) for adaptive recording and stimulation of neural activity in a closed loop. Smart Dura has two main components, the electrical dura for recording and the optical dura for optogenetic stimulation. As the first step to implement this platform, we have implemented the electrical dura as a high density, transparent and flexible $\mu \text{ECoG}$ array, as well as the optical dura, which is a high-density LED array, independently. Both the electrical and optical dura are implemented in flexible polymer substrates that provide mechanical compliance with the brain tissue and can be integrated together in a vertical stack. Here we present the fabrication, bench top characterization and acute in vivo validation of both the electrical and optical dura. This work paves the way towards the ultimate Smart Dura, a functional device that can replace the native dura as a chronic, large-scale bidirectional interface to study cortical brain activity in non-human primates (NHPs), with the potential for translation to humans.

关键词： Integrated optics Electrodes Stimulated emission Optical recording Optical device fabrication Neural activity Optogenetics

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Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

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Advanced Photonics Nexus 2024年第1期3卷 76-85页

作者： Xilin Yang Md Sadman Sakib Rahman Bijie Bai Jingxi Li Aydogan Ozcan University of California Los AngelesElectrical and Computer Engineering DepartmentLos AngelesCaliforniaUnited States University of California Los AngelesBioengineering DepartmentLos AngelesCaliforniaUnited States University of California Los AngelesCalifornia NanoSystems Institute(CNSI)Los AngelesCaliforniaUnited States

As an optical processor,a diffractive deep neural network(D2NN)utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing,completing its tasks at the speed of light propagation through thin optical *** sufficient degrees of freedom,D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent ***,D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination;however,under spatially incoherent light,these transformations are nonnegative,acting on diffraction-limited optical intensity patterns at the input field of ***,we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent *** simulations,we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products,a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent *** findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

关键词： optical computing optical networks machine learning diffractive optical networks diffractive neural networks image encryption

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Time-Domain Terahertz Video Captured with a Plasmonic Photoconductive Focal-Plane Array

Time-Domain Terahertz Video Captured with a Plasmonic Photoc...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Xurong Li Deniz Mengu Aydogan Ozcan Mona Jarrahi Electrical and Computer Engineering Department University of California Los Angeles California USA California NanoSystems Institute University of California Los Angeles California USA Department of Bioengineering University of California Los Angeles California USA

We demonstrate a time-domain terahertz video captured at 16 fps using a plasmonic photoconductive terahertz focal-plane array that provides hyperspectral and spatial amplitude and phase information of the imaged objec... 详细信息

ISBN: (纸本)9781957171258

关键词： Holographic techniques Hyperspectral imaging Imaging systems Photoconductivity Terahertz imaging Video

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Error detection using pneumatic logic

arXiv

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arXiv 2024年

作者： Hoang, Shane Shehada, Mabel Patel, Zinal Tran, Minh-Huy Karydis, Konstantinos Brisk, Philip Grover, William H. Department of Bioengineering University of California Riverside 900 University Ave. RiversideCA92521 United States Department of Electrical and Computer Engineering University of California Riverside 900 University Ave. RiversideCA92521 United States Department of Computer Science and Engineering University of California Riverside 900 University Ave. RiversideCA92521 United States

Pneumatic systems are common in manufacturing, healthcare, transportation, robotics, and many other fields. Failures in these systems can have very serious consequences, particularly if they go undetected. In this work, we present an air-powered error detector device that can detect and respond to failures in pneumatically actuated systems. The device contains 21 monolithic membrane valves that act like transistors in a pneumatic logic "circuit" that uses vacuum to represent TRUE and atmospheric pressure as FALSE. Three pneumatic exclusive-OR (XOR) gates are used to calculate the parity bit corresponding to the values of several control bits. If the calculated value of the parity bit differs from the expected value, then an error (like a leak or a blocked air line) has been detected and the device outputs a pneumatic error signal which can in turn be used to alert a user, shut down the system, or take some other action. As a proof-of-concept, we used our pneumatic error detector to monitor the operation of a medical device, an intermittent pneumatic compression (IPC) device commonly used to prevent the formation of life-threatening blood clots in the wearer’s legs. Experiments confirm that when the IPC device was damaged, the pneumatic error detector immediately recognized the error (a leak) and alerted the wearer using sound. By providing a simple and low-cost way to add fault detection to pneumatic actuation systems without using sensors, our pneumatic error detector can promote safety and reliability across the wide range of pneumatic systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Fault detection

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Super-Resolution Terahertz Imaging Through a Plasmonic Photoconductive Focal-Plane Array

Super-Resolution Terahertz Imaging Through a Plasmonic Photo...

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Conference on Lasers and Electro-Optics (CLEO)

We utilize hyperspectral amplitude and phase information of imaged objects with a plasmonic photoconductive focal-plane array to resolve their structural information with an effective number of pixels exceeding 1-kilo... 详细信息

ISBN: (纸本)9781957171258

关键词： Image processing Imaging systems Medical imaging Neural networks Scanning electron microscopy Terahertz imaging

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