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Camera-based Motion Correction for PET/MR Brain Imaging

Camera-based Motion Correction for PET/MR Brain Imaging

2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Fisher, Jonathan Groll, Andrew Kim, Chris C. Innes, Derek Levin, Craig S. Stanford University Department of Electrical Engineering CA United States Molecular Imaging Program Stanford University Department of Radiology CA United States Stanford University Department of Computer Science CA United States Stanford University Physics Electrical Engineering and Bioengineering Department of Radiology CA United States

ISBN: (纸本)9781665488723

In this work, we evaluated the performance of a camera-based rigid body motion correction solution in PET studies. We compared the image quality obtained by reconstructing a static phantom scan to those obtained by reconstructing a moving phantom scan with and without applying motion correction. Our suggested solution uses a camera to track a 3D marker attached to the object to be imaged (in this work, a resolution phantom). This data was incorporated in the multiple acquisition frames (MAF) image reconstruction algorithm to compensate for motion. While we got multiple spatially shifted replicas of the resolution phantom in the motion study's reconstructed image when motion correction was not applied, the motion-corrected image was comparable to the static image, with some degradation in spatial resolution (3.2-4.2 vs. 2.8-3.2 mm) and lower peak to valley ratio (indicating lower contrast to noise ratio). The context of the results presented is in PET/MR imaging, but in principle, this motion compensation method could be used in any type of medical imaging modality. © 2022 IEEE.

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Dynamics and Perturbations of Overparameterized Linear Neural Networks

Dynamics and Perturbations of Overparameterized Linear Neura...

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IEEE Conference on Decision and Control

作者： Arthur Castello B. de Oliveira Milad Siami Eduardo D. Sontag Department of Electrical & Computer Engineering Northeastern University Boston MA USA Department of Bioengineering Northeastern University Boston MA USA

Recent research in neural networks and machine learning suggests that using many more parameters than strictly required by the initial complexity of a regression problem can result in more accurate or faster-converging models - contrary to classical statistical belief. This phenomenon, sometimes referred to as “benign overfitting”, raises questions regarding in what other ways might overparameterization affect the properties of a learning problem. In this work, we investigate the effects of overfitting on the robustness of gradient-descent training when subject to uncertainty on the gradient estimation, which arises naturally if the gradient is estimated from noisy data or directly measured. Our object of study is a linear neural network with a single, arbitrarily wide, hidden layer and an arbitrary number of inputs and outputs, which can be equivalently written as an overparameterized matrix factorization problem. In this paper we solve the problem for the case where the input and output of our neural network are one-dimensional, deriving sufficient conditions for the robustness of our system based on necessary and sufficient conditions for convergence in the undisturbed case. We then show that the general overparameterized formulation introduces a set of spurious equilibria that lay outside the set where the loss function is minimized, and discuss directions of future work that might extend our current results for more general settings.

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Computational imaging without a computer:seeing through random diffusers at the speed of light

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eLight 2022年第1期2卷 42-57页

作者： Yi Luo Yifan Zhao Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesLos AngelesCA 90095USA.

Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using ***,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of *** deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase *** the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase *** experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during *** digital methods,all-optical diffractive reconstructions do not require power except for the illumination *** diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others.

关键词： Imaging through diffusers Computational imaging Diffractive neural network Deep learning

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Hybrid MIMO in the Upper Mid-Band: Architectures, Processing, and Energy Efficiency

arXiv

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arXiv 2025年

作者： Mizmizi, Marouan Alkhateeb, Ahmed Spagnolini, Umberto Department of electronics Information and Bioengineering Politecnico di Milano Milano20133 Italy School of Electrical Computer and Energy Engineering Arizona State University United States

As 6G networks evolve, the upper mid-band spectrum (7 GHz–24 GHz), or frequency range 3 (FR3), is emerging as a promising balance between the coverage offered by sub-6 GHz bands and the high-capacity of millimeter wave (mmWave) frequencies. This paper explores the structure of FR3 hybrid MIMO systems and proposes two architectural classes: Frequency Integrated (FI) and Frequency Partitioned (FP). FI architectures enhance spectral efficiency by exploiting multiple subbands parallelism, while FP architectures dynamically allocate sub-band access according to specific application requirements. Additionally, two approaches—fully digital (FD) and hybrid analog-digital (HAD)—are considered, comparing shared (SRF) versus dedicated RF (DRF) chain configurations. Herein signal processing solutions are investigated, particularly for an uplink multi-user scenario with power control optimization. Results demonstrate that SRF and DRF architectures achieve comparable spectral efficiency;however, SRF structures consume nearly half the power of DRF in the considered setup. While FD architectures provide higher spectral efficiency, they do so at the cost of increased power consumption compared to HAD. Additionally, FI architectures show slightly greater power consumption compared to FP;however, they provide a significant benefit in spectral efficiency (over 4×), emphasizing an important trade-off in FR3 engineering. © 2025, CC BY-SA.

关键词： Spectrum efficiency

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SSCAE: A Neuromorphic SNN Autoencoder for sc-RNA-seq Dimensionality Reduction

SSCAE: A Neuromorphic SNN Autoencoder for sc-RNA-seq Dimensi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tim Zhang Amirali Amirsoleimani Jason K. Eshraghian Mostafa Rahimi Azghadi Roman Genov Yu Xia Department of Bioengineering McGill University Montreal Canada Department of Electrical Engineering and Computer Science York University Toronto ON Canada Department of Electrical and Computer Engineering UC Santa Cruz CA United States College of Science and Engineering James Cook University Australia Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Single-cell RNA sequencing is an emerging technique in the field of biology that departs radically from the previous assumption of gene-expression homogeneity within a tissue. The large quantity of data generated by this technology enables discoveries of cellular biology and disease mechanics that were previously not possible, and calls for accurate, scalable, and efficient processing pipelines. In this work, we propose SSCAE (spiking single-cell autoencoder), a novel SNN-based autoencoder for sc-RNA-seq dimensionality reduction. We apply this architecture to a variety of datasets, and the results show that it can match and surpass the performance of current state-of-the-art techniques. Moreover, the potential of this technique lies in its ability to be scaled up and to take advantage of neuromorphic hardware, circumventing the memory bottleneck that currently limits the size of sequencing datasets that can be processed.

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Toward smartphone-based blood pressure monitoring

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Journal of Electrocardiology 2023年 78卷 9-10页

作者： Landry, Cederick, PhD Freithaler, Mark, MSc Daher, Hadi Dhamotharan, Vishaal, MEng Chandrasekhar, Anand, PhD Shroff, Sanjeev G., PhD Mukkamala, Ramakrishna, PhD Department of Bioengineering University of Pittsburgh Pittsburgh PA United States Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Cambridge MA United States

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Huygens Principle-based Microwave Brain Imaging through Finite Difference Time Domain

Huygens Principle-based Microwave Brain Imaging through Fini...

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2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023

作者： Movafagh, Moein Ghavami, Navid Tiberi, Gianluigi Dudley, Sandra Ochiai, Hideki Ghavami, Mohammad Ubt Uk Division LondonW1B 3HH United Kingdom London South Bank University School of Engineering London United Kingdom Ubt - Umbria Bioengineering Technologies Perugia Italy Yokohama National University Department of Electrical and Computer Engineering Japan

ISBN: (纸本)9798350323047

This paper investigates the application of Huygens principle (HP) in brain imaging. We employ simulation techniques, specifically the Finite Difference Time Domain (FDTD) method, in order to comprehensively evaluate the efficacy of this methodology in detecting strokes within the brain. The FDTD method is particularly noteworthy due to its straightforward implementation and high level of accuracy. Moreover, its compatibility with time domain analysis allows us to effectively incorporate time-varying sources. In order to demonstrate the effectiveness of HP procedure, we examine various scenarios in our simulations. The results provide compelling evidence of the considerable potential of HP imaging for the brain, highlighting its adaptability for a wide range of applications. These research findings yield valuable insights into the possibilities of using HP for brain imaging, laying the foundation for future advancements and innovation in the field of brain imaging for stroke detection. © 2023 IEEE.

关键词： Finite difference time domain method

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Tactile Displays Driven by Projected Light

arXiv

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arXiv 2024年

作者： Linnander, Max Goetz, Dustin Reardon, Gregory Kumar, Vijay Hawkes, Elliot Visell, Yon Department of Mechanical Engineering United States Department of Bioengineering United States Media Arts & Technology Program United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Tactile displays that lend tangible form to digital content could transform computing interactions. However, achieving the resolution, speed, and dynamic range needed for perceptual fidelity remains challenging. We present a tactile display that directly converts projected light into visible tactile patterns via a photomechanical surface populated with millimeter-scale optotactile pixels. The pixels transduce incident light into mechanical displacements through photostimulated thermal gas expansion, yielding millimeter scale displacements with response times of 2 to 100 milliseconds. Employing projected light for power transmission and addressing renders these displays highly scalable. We demonstrate optically driven displays with up to 1,511 addressable pixels – several times more pixels than any prior tactile display attaining comparable performance. Perceptual studies confirm that these displays can reproduce diverse spatiotemporal tactile patterns with high fidelity. This research establishes a foundation for practical, versatile high-resolution tactile displays driven by light. Copyright © 2024, The Authors. All rights reserved.

关键词： Light transmission

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A PAPER-BASED MULTIPLEXED SEROLOGICAL TEST TO MONITOR IMMUNITY AGAINST SARS-COV-2 USING MACHINE LEARNING

arXiv

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arXiv 2024年

作者： Eryilmaz, Merve Goncharov, Artem Han, Gyeo-Re Joung, Hyou-Arm Ballard, Zachary S. Ghosh, Rajesh Zhang, Yijie Di Carlo, Dino Ozcan, Aydogan Electrical & Computer Engineering Department United States Bioengineering Department United States United States Department of Surgery University of California Los AngelesCA90095 United States

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 µL of human serum sample in Copyright © 2024, The Authors. All rights reserved.

关键词： COVID-19

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Stability Assessment of Ultramicroelectrode Arrays in Neural Stimulation: an Electrochemical Impedance Spectroscopy Analysis

Stability Assessment of Ultramicroelectrode Arrays in Neural...

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

作者： Qiwei Dong Cynthia Ezeh Yupeng Wu Jamille F. Hetke Stuart Cogan Mark E. Orazem Kevin J. Otto Department of Electrical & Computer Engineering University of Florida Department of Chemical Engineering University of Florida Department of Bioengineering and Department of Material Sciences & Engineering The University of Texas at Dallas NeuroNexus J. Crayton Pruitt Family Department of Biomedical Engineering University of Florida

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The overall focus of this research program is to investigate the viability and safety of microelectrode arrays (MEAs) and ultramicroelectrode arrays (UMEAs) for microstimulation (µStim) in the brain. We aim to assess their potential to deliver adequate electrical stimulation for neural activation without causing electrode or tissue damage. To this end, here we conducted galvanostatic Electrochemical Impedance Spectroscopy (EIS) to assess the behavior of electrodes of various diameters both pre- and post-stimulation in a rat model. Our first-generation electrode arrays (G1) were designed with electrode diameters spanning 3 orders of magnitude and including subcellular dimensions. Using these devices, we delivered 0.4 nC/phase stimulation in vivo. We conducted in vitro EIS measurements before and after the current pulsing. Our findings show that these electrode arrays, including UMEAs, exhibit stable electrochemical behavior following a 0.4 nC/phase current pulse. To validate the EIS frequency range used for the G1 measurements, we designed a second-generation electrode array (G2) that included a terminating lead and a looping lead. This array provided the ability to assess the accuracy contour plot of our EIS system and electrode array platform. This exploratory research contributes to the ongoing knowledge of UMEAs in µStim applications. These developments may result in high-fidelity, non-damaging multichannel µStim, and improved neuromodulation technology for the treatment of neural diseases and injuries.

关键词： Electrodes Microelectrodes Tissue damage Current measurement Lead Stability analysis Frequency measurement Safety Neuromodulation Electrochemical impedance spectroscopy

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