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A spatiotemporal multi-scale computational model for FDG PET imaging at different stages of tumor growth and angiogenesis

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SCIENTIFIC REPORTS 2022年第1期12卷 1-16页

作者： Kashkooli, Farshad Moradi Abazari, Mohammad Amin Soltani, M. Ghazani, Mehran Akbarpour Rahmim, Arman Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Advanced Bioengineering Initiative Center Multidisciplinary International Complex K. N. Toosi University of Technology Tehran Iran Centre for Biotechnology and Bioengineering (CBB) University of Waterloo Waterloo ON Canada Faculty of Mechanical Engineering University of Tabriz Tabriz Iran Department of Integrative Oncology BC Cancer Research Institute Vancouver BC Canada Department of Physics and Astronomy University of British Columbia Vancouver BC Canada Department of Radiology University of British Columbia Vancouver BC Canada

A deeper understanding of the tumor microenvironment (TME) and its role in metabolic activity at different stages of vascularized tumors can provide useful insights into cancer progression and better support clinical assessments. In this study, a robust and comprehensive multi-scale computational model for spatiotemporal transport of F-18 fluorodeoxyglucose (FDG) is developed to incorporate important aspects of the TME, spanning subcellular-, cellular-, and tissue-level scales. Our mathematical model includes biophysiological details, such as radiopharmaceutical transport within interstitial space via convection and diffusion mechanisms, radiopharmaceutical exchange between intracellular and extracellular matrices by glucose transporters, cellular uptake of radiopharmaceutical, as well as its intracellular phosphorylation by the enzyme. Further, to examine the effects of tumor size by varying microvascular densities (MVDs) on FDG dynamics, four different capillary networks are generated by angiogenesis modeling. Results demonstrate that as tumor grows, its MVD increases, and hence, the spatiotemporal distribution of total FDG uptake by tumor tissue changes towards a more homogenous distribution. In addition, spatiotemporal distributions in tumor with lower MVD have relatively smaller magnitudes, due to the lower diffusion rate of FDG as well as lower local intravenous FDG release. Since mean standardized uptake value (SUVmean) differs at various stages of microvascular networks with different tumor sizes, it may be meaningful to normalize the measured values by tumor size and the MVD prior to routine clinical reporting. Overall, the present framework has the potential for more accurate investigation of biological phenomena within TME towards personalized medicine.

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Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

Neural network-based multiplexed and micro-structured virtua...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yijie Zhang Kevin de Haan Jingxi Li Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) University of California Los Angeles CA USA Department of Surgery David Geffen School of Medicine University of California Los Angeles CA USA

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network, which is fed with the autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining matrix. Users can indicate which stain to apply on each pixel by editing the digital-staining matrix and blend multiple virtual stains, creating entirely new stain combinations.

关键词： Multiplexing Deep learning Neural networks Optics Electrooptical waveguides Biomedical imaging

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Virtual stain transfer in histology via cascaded deep neural networks

arXiv

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arXiv 2022年

作者： Yang, Xilin Bai, Bijie Zhang, Yijie Li, Yuzhu de Haan, Kevin Liu, Tairan Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA United States Bioengineering Department University of California Los AngelesCA United States University of California Los AngelesCA United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA United States

Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlight various desired histological features. However, the destructive histochemical staining procedures are usually irreversible, making it very difficult to obtain multiple stains on the same tissue section. Here, we demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E) stained tissue images into other types of histological stains. Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs stain transfer from H&E to the domain of the other stain in a cascaded manner. This cascaded structure in the training phase allows the model to directly exploit histochemically stained image data on both H&E and the target special stain of interest. This advantage alleviates the challenge of paired data acquisition and improves the image quality and color accuracy of the virtual stain transfer from H&E to another stain. We validated the superior performance of this C-DNN approach using kidney needle core biopsy tissue sections and successfully transferred the H&E-stained tissue images into virtual PAS (periodic acid-Schiff) stain. This method provides high-quality virtual images of special stains using existing, histochemically stained slides and creates new opportunities in digital pathology by performing highly accurate stain-to-stain transformations. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A comprehensive review of solid-state lithium batteries: Fast Charging characteristics and in-operando diagnostics

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Nano Energy 2025年 142卷

作者： Ruoxu Shang Talyah Nelson Thanh Vy Nguyen Charlotte Nelson Harsha Antony Brian Abaoag Mihrimah Ozkan Cengiz S. Ozkan Materials Science and Engineering Program University of California Riverside CA USA Department of Mechanical Engineering University of California Riverside CA USA Department of Bioengineering University of California Riverside CA USA Department of Electrical and Computer Engineering University of California Riverside CA USA

This comprehensive review article delves into the evolving landscape of solid-state batteries (SSBs), presenting a critical evaluation beyond the conventional lithium-ion technology. It meticulously explores the performance analysis, fast-charging capabilities, and in-operando characterization of SSBs. Amid growing demands for safer, higher energy density, and more sustainable energy storage solutions, SSBs emerge as a compelling alternative, promising to revolutionize applications from portable electronics to electric vehicles. This review begins with a detailed examination of the fundamental principles underpinning SSBs, including their unique architecture and the roles of solid electrolytes in enhancing safety and energy density. It then shifts focus to the pivotal aspect of fast charging – a critical parameter for consumer acceptance and market penetration of electric vehicles. The analysis extends to the intricacies of in-operando characterization techniques, which are essential for understanding the dynamic processes within SSBs during operation, thus offering insights into optimizing performance and longevity. By juxtaposing SSBs with conventional lithium-ion batteries, the review highlights the technological strides and hurdles ahead. Furthermore, it discusses the latest advancements in materials science, engineering strategies, and characterization methods that potentially address the current limitations of SSBs. Conclusively, this review not only underscores the significant progress in SSB technology but also outlines the roadmap and future directions for researchers and industry stakeholders aiming to harness the full potential of SSBs in next-generation energy storage systems.

关键词： Solid-state batteries (SSBs) Fast charging Operando Lithium-ion Batteries

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Participant Modeling: The Use of a Guided Master in the Modern World of Virtual Reality Exposure Therapy Targeting Fear of Heights 1

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2nd EAI International Conference on Wearables in Healthcare, HealthWear 2020

作者： Caravas, Pamela Kritikos, Jacob Alevizopoulos, Giorgos Koutsouris, Dimitris Member of Institute of Coaching McLean Hospital Harvard Medical School Affiliate BostonMA United States Department of Bioengineering Imperial College London South Kensington Campus London United Kingdom Head of Psychiatry Department of the General and Oncological Hospital Athens14564 Greece National Kapodistrian University of Athens Athens15784 Greece Head of Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

ISBN: (数字)9783030760663

ISBN: (纸本)9783030760656

With the percentage of mental health disorders on the rise and the cost for their treatment reaching astounding proportions, research in their treatment has also become quite extensive. Individuals suffering from the effects of their disorder constituting them incapable at various levels to lead a normal life, the need for a more effective treatment has been well established. We have focused on anxiety disorders specifically, which have mainly fear as their common denominator, and using this we decided to look into the role of the clinician in live ET sessions so as to examine whether this role can be replicated in a VRET simulation with similar or better outcomes for the patient, i.e. a more effective treatment. Our hypothesis was tested in an outpatient setting with patients being separated into two groups. We examined whether the presence of a virtual guided master using participant modeling in a virtual environment was as effective or more effective than the Standard ET method. Our VR system is based on the Full Body Immersive Virtual Reality System with Motion Recognition Camera created by Jacob Kritikos. The outcomes were gathered via the Session Rating Scale by Miller which led to the conclusion that participant modeling within a VRET approach can lead to a better treatment quality. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Cameras

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Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer’s Disease Classification

arXiv

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arXiv 2023年

作者： Coluzzi, Davide Bordin, Valentina Rivolta, Massimo Walter Fortel, Igor Zhang, Liang Leow, Alex Baselli, Giuseppe Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Dipartimento di Informatica Università degli Studi di Milano Milan Italy Department of Biomedical Engineering University of Illinois Chicago ChicagoIL United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Psychiatry University of Illinois Chicago ChicagoIL United States Politecnico di Milano Department of Electronics Information and Bioengineering Via Camillo Golgi 39 Milan20133 Italy

Alzheimer’s Disease (AD) is the world leading cause of dementia, a progressively impairing condition leading to high hospitalization rates and mortality. To optimize the diagnostic process, numerous efforts have been directed towards the development of deep learning approaches (DL) for the automatic AD classification. However, their typical black box outline has led to low trust and scarce usage within clinical frameworks. In this work, we propose two state-of-the art DL models, trained respectively on structural MRI (ResNet18) and brain connectivity matrixes (BC-GCN-SE) derived from diffusion data. The models were initially evaluated in terms of classification accuracy. Then, results were analyzed using an Explainable Artificial Intelligence (XAI) approach (Grad-CAM) to measure the level of interpretability of both models. The XAI assessment was conducted across 132 brain parcels, extracted from a combination of the Harvard-Oxford and AAL brain atlases, and compared to well-known pathological regions to measure adherence to domain knowledge. Results highlighted acceptable classification performance as compared to the existing literature (ResNet18: TPRmedian = 0.817, TNRmedian = 0.816;BC-GCN-SE: TPRmedian = 0.703, TNRmedian = 0.738). As evaluated through a statistical test (p © 2023, CC BY.

关键词： Diagnosis

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Quantum Diamond Microscope for Dynamic Imaging of Magnetic Fields

arXiv

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arXiv 2023年

作者： Tang, Jiashen Yin, Zechuan Hart, Connor A. Blanchard, John W. Oon, Jner Tzern Bhalerao, Smriti Schloss, Jennifer M. Turner, Matthew J. Walsworth, Ronald L. Department of Physics University of Maryland College ParkMD20742 United States Quantum Technology Center University of Maryland College ParkMD20742 United States Department of Electrical Engineering and Computer Science University of Maryland College ParkMD20742 United States Fischell Department of Bioengineering University of Maryland College ParkMD20742 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States

Wide-field imaging of magnetic signals using ensembles of nitrogen-vacancy (NV) centers in diamond has garnered increasing interest due to its combination of micron-scale resolution, millimeter-scale field of view, and compatibility with diverse samples from across the physical and life sciences. Recently, wide-field NV magnetic imaging based on the Ramsey protocol has achieved uniform and enhanced sensitivity compared to conventional measurements. Here, we integrate the Ramsey-based protocol with spin-bath driving to extend the NV spin dephasing time and improve magnetic sensitivity. We also employ a high-speed camera to enable dynamic wide-field magnetic imaging. We benchmark the utility of this quantum diamond microscope (QDM) by imaging magnetic fields produced from a fabricated wire phantom. Over a 270 × 270 µm2 field of view, a median per-pixel magnetic sensitivity of 4.1(1) nT/√Hz is realized with a spatial resolution . 10 µm and sub-millisecond temporal resolution. Importantly, the spatial magnetic noise floor can be reduced to the picotesla scale by time-averaging and signal modulation, which enables imaging of a magnetic-field pattern with a peak-to-peak amplitude difference of about 300 pT. Finally, we discuss potential new applications of this dynamic QDM in studying biomineralization and electrically-active cells. © 2023, CC BY.

关键词： Magnetic fields

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A graph signal processing model of the cochlea with application to cochlear implants

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AIP Conference Proceedings 2024年第1期3062卷

作者： Melia E. Bonomo Santiago Segarra Robert M. Raphael Department of Bioengineering Rice University 6100 Main St Houston Texas 77005 USA Department of Electrical and Computer Engineering Rice University 6100 Main St Houston Texas 77005 USA

We take a novel graph approach to study the complex cochlear mechanics during music encoding for patients with normal hearing and hearing loss. Audiograms from AudGenDB, a pediatric clinical database, are used in tandem with the UR Ear 2020b simulation of inner hair cell voltage responses to different musical stimuli. The stimuli that we use consist of single pitches, single timbres, pitch combinations (chords), timbre combinations, and song excerpts. Graph signal processing is used to model the inner hair cell responses of each patient as a new data structure that we term a “cochlea graph.” By calculating various graph-theoretic properties, we show that these cochlea graphs carry distinguishing information based on a patient’s hearing loss diagnosis. Our results are poised for application to improving cochlear implant signal processing of music, where these music-based cochlea graphs can be used to develop individualized solutions based on a user’s underlying hearing health. Furthermore, the graph framework that we introduce here may be generalized to study other characteristics of auditory signal processing from a unique new lens.

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Phased Array Beamforming Methods for Powering Biomedical Ultrasonic Implants

arXiv

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arXiv 2022年

作者： Benedict, Braeden C. Ghanbari, Mohammad Meraj Muller, Rikky UC Berkeley UC San Francisco Joint Program in Bioengineering BerkeleyCA94720 United States The Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States The Chan Zuckerberg Biohub San FranciscoCA94158 United States

Millimeter-scale implants using ultrasound for power and communication have been proposed for a range of deep-tissue applications, including neural recording and stimulation. However, published implementations have shown high sensitivity to misalignment with the external ultrasound transducer. Ultrasonic beamforming using a phased array to these implants can improve tolerance to misalignment, reduce implant volume, and allow multiple implants to be operated simultaneously in different locations. This paper details the design of a custom planar phased array ultrasound system, which is capable of steering and focusing ultrasound power within a 3D volume. Analysis and simulation is performed to determine the choice of array element pitch, with special attention given to maximizing the power available at the implant while meeting FDA limits for diagnostic ultrasound. Time reversal is proposed as a computationally simple approach to beamforming that is robust despite scattering and inhomogeneity of the acoustic medium. This technique is demonstrated both in active drive and pulse-echo modes, and it is experimentally compared with other beamforming techniques by measuring energy transfer efficiency. Simultaneous power delivery to multiple implants is also demonstrated. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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OpenSTED: Inexpensive and open-source implementation of dynamic intensity minimum (DyMIN) for stimulated emission depletion (STED) microscopy

OpenSTED: Inexpensive and open-source implementation of dyna...

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Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Meyer, Stephanie A. Siemens, Mark E. Gopinath, Juliet T. Restrepo, Diego Gibson, Emily A. Department of Bioengineering University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Physics and Astronomy University of Denver DenverCO80210 United States Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Cell and Developmental Biology University of Colorado Anschutz Medical Campus AuroraCO80045 United States

The DyMIN method reduces photobleaching, a problem in STED microscopy. Labs implementing custom-built STED microscopes would greatly benefit from DyMIN capabilities. We present an inexpensive, open-source version util... 详细信息

ISBN: (纸本)9781557528209

关键词： Photobleaching

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