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

作者： E. L. Santos M. C. Santos E. Krueger G. N. Nogueira-Neto P. Nohama Biomedical Engineering at the Graduate Program in Electrical and Computer Engineering Federal Technological University of Paraná - (CPGEI-UTFPR) Curitiba/PR Brazil Biomedical Engineering at the Graduate Program in Biomedical Engineering Federal Technological University of Paraná - (PPGEB-UTFPR) Biomedical Engineering UTFPR Curitiba PR Brazil Anatomy department Londrina State University Electrical Engineering Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil Electrical Engineering UTFPR and PUCPR Curitiba PR Brazil

ISBN: (纸本)9781457702198

The modified Ashworth scale (MAS) is the most widely used measurement technique to assess levels of spasticity. In MAS, the evaluator graduates spasticity considering his/her subjective analysis of the muscular endurance during passive stretching. Therefore, it is a subjective scale. Mechanomyography (MMG) allows registering the vibrations generated by muscle contraction and stretching events that propagate through the tissue until the surface of the skin. With this in mind, this study aimed to investigate possible correlations between MMG signal and muscle spasticity levels determined by MAS. We evaluated 34 limbs considered spastic by MAS, including upper and lower limbs of 22 individuals of both sexes. Simultaneously, the MMG signals of the spastic muscle group (agonists) were acquired. The features investigated involved, in the time domain, the median energy (MMGME) of the MMG Z-axis (perpendicular to the muscle fibers) and, in the frequency domain, the median frequency (MMG mf ). The Kruskal-Wallis test (pme. There was a high linear correlation between the MMG me and MAS (R 2 =0.9557) and also a high correlation as indicated by Spearman test (ρ=0.9856; pmf did not present significant differences between MAS spasticity levels. There was moderate linear correlation between MAS and MMG mf (R 2 =0.4883 and Spearman test [ρ = 0.4590; p

关键词： Muscles Correlation Skin Spectral analysis biomedical engineering Elbow Vibrations

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Multistatic Microwave Holography: Initial Results on Anthropomorphic Phantoms

Multistatic Microwave Holography: Initial Results on Anthrop...

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European Conference on Antennas and Propagation

作者： Daniel Flores Tapia Mario Solis Nepote Diego Rodriguez Herrera Lei Fu Yonsheng Gui Valerie Benyon Can-Min Hu Stephen Pistorius Saeed Latif Department of Physics and Astronomy Biomedical Engineering Program Department of Electrical and Computer Engineering University of Manitoba Winnipeg Manitoba Canada Department of Electrical and Computer Engineering University of South Alabama Mobile Alabama USA

ISBN: (纸本)9781509022342

Ultra Wide Band Radar is a novel technology used for breast cancer detection. This imaging modality uses ultra wide band waveforms to form a reflectivity map of the breast region in which tumors can potentially been clearly distinguished. In this study, the authors assess the use of a novel Breast UWBR reconstruction approach, microwave holography, on anthropomorphic phantoms. These phantoms mimic both the shape and dielectric properties of human breast. The images formed using microwave holography are consistent with the location of the synthetic breast structures and show signal to clutter level above 3dB.

关键词： UWBR Multistatic Microwave

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An Automated Approach for Identifying Geometric Distortions in Gamma Cameras

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MEDICAL PHYSICS 2016年第6期43卷 3773-3774页

作者： Mann, S. Nelson, J. Samei, E. Clinical Imaging Physics Group and Carl E. Ravin Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham North Carolina 27705 and Medical Physics Graduate Program Duke University Durham North Carolina 27705 Clinical Imaging Physics Group and Carl E. Ravin Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham North Carolina 27705 and Departments of Physics Electrical and Computer Engineering and Biomedical Engineering and Medical Physics Graduate Program Duke University Durham North Carolina 27705

Purpose: To develop a clinically-deployable, automated process for detecting artifacts in routine nuclear medicine (NM) quality assurance (QA) bar phantom images. Methods: An artifact detection algorithm was created to analyze bar phantom images as part of an ongoing QA program. A low noise, high resolution reference image was acquired from an x-ray of the bar phantom with a Philips Digital Diagnost system utilizing image stitching. NM bar images, acquired for 5 million counts over a 512×512 matrix, were registered to the template image by maximizing mutual information (MI). The MI index was used as an initial test for artifacts; low values indicate an overall presence of distortions regardless of their spatial location. Images with low MI scores were further analyzed for bar linearity, periodicity, alignment, and compression to locate differences with respect to the template. Findings from each test were spatially correlated and locations failing multiple tests were flagged as potential artifacts requiring additional visual analysis. The algorithm was initially deployed for GE Discovery 670 and Infinia Hawkeye gamma cameras. Results: The algorithm successfully identified clinically relevant artifacts from both systems previously unnoticed by technologists performing the QA. Average MI indices for artifact-free images are 0.55. Images with MI indices < 0.50 have shown 100% sensitivity and specificity for artifact detection when compared with a thorough visual analysis. Correlation of geometric tests confirms the ability to spatially locate the most likely image regions containing an artifact regardless of initial phantom orientation. Conclusion: The algorithm shows the potential to detect gamma camera artifacts that may be missed by routine technologist inspections. Detection and subsequent correction of artifacts ensures maximum image quality and may help to identify failing hardware before it impacts clinical workflow. Going forward, the algorithm is being deploye

关键词： Medical X-ray imaging Medical image artifacts Cameras Image analysis Nuclear medicine imaging Spatial analysis Quality assurance Medical image noise

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Distributed multi-robot coordination for dynamic perimeter surveillance in uncertain environments

Distributed multi-robot coordination for dynamic perimeter s...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Jahn Reza Javanmard Alitappeh David Saldaña Luciano C. A. Pimenta Andre G. Santos Mario F. M. Campos Graduate Program in Electrical Engineering (PPGEE) National Institute of Science and Technology for Cooperative Autonomous Systems Applied to Security and Environment Departamento de Informatica Universidade Federal de Vicosa Computer Science Department Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais Belo Horizonte MG BR

In this work, multiple robots circulate around the boundary of a desired region in order to create a virtual fence. The aim of the this fence is to avoid internal or external agents crossing through the delimited area. In this paper, we propose a distributed technique that allows a team of robots to plan the deformation of the boundary shape in order to escort the safe region from one place to a goal. Our proposal is composed of two parts. First, we present a distributed planning method for the dynamic boundary. We model the resulting plan as a twice differentiable function. Second, we use the obtained function to guide the robot team, where every member uses only local information for the controller. The robots distribute themselves along the time-varying perimeter and patrol around it. We show in simulation how the robots behave in partially/totally unknown environments with static obstacles.

关键词： Shape Robot kinematics Trajectory Planning Robot sensing systems Dynamics

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Improving high-resistance state uniformity and leakage current for polyimide-based resistive switching memory by rubbing post-treatment

Improving high-resistance state uniformity and leakage curre...

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作者： Hsiao, Yu-Ping Yang, Wen-Luh Wu, Chi-Chang Lin, Li-Min Chin, Fun-Tat Lin, Yu-Hsien Yang, Ke-Luen Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung 407 Taiwan Department of Electronic Engineering Feng Chia University Taichung 407 Taiwan Graduate Institute of Nanomedicine College of Biomedical Engineering Taipei Medical University Taipei 110 Taiwan Department of Electronic Engineering National United University Miaoli 360 Taiwan Daxin Materials Corporation Taichung 407 Taiwan

In this study, a polyimide (PI) thin film is synthesized as a resistive switching layer for resistive random access memory (ReRAM) applications. The experimental results on polyimide thickness show that the Schottky effect between the interface of polyimide and metal thin films is the dominant mechanism in the high-resistance state (HRS). We, therefore, propose a rubbing post-treatment to improve the device performance. Results show that the uniformity and leakage of the memory in the HRS, as well as the power consumption in the low-resistance state (LRS), are improved. The power density of the set process is less than half after the rubbing post-treatment. Moreover, the power density of the reset process can be markedly decreased by about two orders of magnitude. In addition, the rubbed ReRAM exhibits a stable storage capability with seven orders of magnitude ION/IOFF current ratio at 85 °C. © 2016 The Japan Society of Applied Physics.

关键词： Polyimides

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Fast Saliency Detection Using Sparse Random Color Samples and Joint Upsampling

Fast Saliency Detection Using Sparse Random Color Samples an...

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Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Maiko Min Ian Lie Gustavo Benvenutti Borba Hugo Vieira Neto Humberto Remigio Gamba Grad. Program in Electr. & Comput. Eng. Fed. Univ. of Technol.-Parana Curitiba Brazil Graduate Program in Biomedical Engineering Graduate Program in Electrical and Computer Engineering

ISBN: (纸本)9781509035694;9781509035687

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime.

关键词： Image color analysis Runtime Visualization Image edge detection Smoothing methods Computational modeling Noise measurement

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Applying the MVC EA-IRPM to reporting-guidelines in medicine: A strategy that is a web system

Applying the MVC EA-IRPM to reporting-guidelines in medicine...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Patrick Letouze J. I. M. de Souza Valéria Martins Silva Glaubos Climaco F. B. Chagas Joao Y. Ishihara Faculty of Computer Science Universidade Federal do Tocantins Palmas Brazil Universidade Federal do Tocantins Palmas TO BR Graduate Program of Systems' Engineering and Computation Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Department of Informatics Universidade Federal do Maranh/ ao - UFMA Faculty of Computer Science Faculdade Pitagoras Imperatriz Imperatriz Brazil Faculty of Electrical Engineering Universidade de Brasília Brasília Brazil

ISBN: (纸本)9781509041800

Reporting-Guidelines in Medicine play an important role in promoting the quality of reports in health-related research. For instance, a poorly reported research may induce misinterpretation and inappropriate clinical settings. Basically, three major issues regarding Reporting-Guidelines in Medicine has to be addressed: (1) the existence of lots of reporting guidelines covering a broad spectrum of medical research increases the difficulties when reporting a medical research because it may not be clear which guidelines are applicable, or if all related guidelines are considered; (2) most guideline developers have not described how their guidance was developed, consequently tracking guidelines and features for evaluation, or even for correcting, may be not possible; (3) few guideline developers have evaluated the effectiveness of their guidance, then the real value that the guideline brings to reporting the research is not assured. Hence, we propose a solution to these three issues based on the Model-View-Controller Evolutionary Acquisition Interdisciplinary Research Project Management (MVC EA-IRPM) that is simultaneously a strategy and a web system for the development and management of reporting guidelines.

关键词： Guidelines Project management Planning Adaptation models Phase measurement Systems architecture Systematics

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Author Correction: lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements

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Nature protocols 2021年第7期16卷 3736页

作者： M Grace Gordon Fumitaka Inoue Beth Martin Max Schubach Vikram Agarwal Sean Whalen Shiyun Feng Jingjing Zhao Tal Ashuach Ryan Ziffra Anat Kreimer Ilias Georgakopoulos-Soares Nir Yosef Chun Jimmie Ye Katherine S Pollard Jay Shendure Martin Kircher Nadav Ahituv Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. Institute for Human Genetics University of California San Francisco San Francisco CA USA. Biological and Medical Informatics Graduate Program University of California San Francisco San Francisco CA USA. Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. fumitaka.inoue@ucsf.edu. Institute for Human Genetics University of California San Francisco San Francisco CA USA. fumitaka.inoue@ucsf.edu. Berlin Institute of Health (BIH) Berlin Germany. Department of Genome Sciences University of Washington Seattle WA USA. Calico Life Sciences LLC South San Francisco CA USA. Gladstone Institutes San Francisco CA USA. Department of Electrical Engineering and Computer Sciences and Center for Computational Biology University of California Berkeley Berkeley CA USA. Chan-Zuckerberg Biohub San Francisco CA USA. Division of Rheumatology Department of Medicine University of California San Francisco San Francisco CA USA. Institute for Computational Health Sciences University of California San Francisco San Francisco California USA. Department of Epidemiology and Biostatistics and Institute of Computational Health Sciences University of California San Francisco San Francisco CA USA. Department of Genome Sciences University of Washington Seattle WA USA. shendure@uw.edu. Howard Hughes Medical Institute Seattle WA USA. shendure@uw.edu. Brotman Baty Institute for Precision Medicine University of Washington Seattle WA USA. shendure@uw.edu. Berlin Institute of Health (BIH) Berlin Germany. martin.kircher@bihealth.de. Charité-Universitätsmedizin Berlin Berlin Germany. martin.kircher@bihealth.de. Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. nadav.ahituv@ucsf.edu. Institute for Human Genetics University of California San

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Potential Fluctuations at Low Temperatures in Mesoscopic-Scale SmTiO3/SrTiO3/SmTiO3 Quantum Well Structures

arXiv

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arXiv 2017年

作者： Hardy, Will J. Isaac, Brandon Marshall, Patrick Mikheev, Evgeny Zhou, Panpan Stemmer, Susanne Natelson, Douglas Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Materials Department University of California Santa BarbaraCA United States Department of Physics and Astronomy Rice University HoustonTX United States Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Materials Science and Nanoengineering Rice University HoustonTX United States

Heterointerfaces of SrTiO3 with other transition metal oxides make up an intriguing family of systems with a bounty of coexisting and competing physical orders. Some examples, such as LaAlO3/SrTiO3, support a high carrier density electron gas at the interface whose electronic properties are determined by a combination of lattice distortions, spin-orbit coupling, defects, and various regimes of magnetic and charge ordering. Here, we study electronic transport in mesoscale devices made with heterostructures of SrTiO3 sandwiched between layers of SmTiO3, in which the transport properties can be tuned from a regime of Fermi-liquid like resistivity (ρ ∝ T2) to a non-Fermi liquid (ρ ∝ T5/3) by controlling the SrTiO3 thickness. In mesoscale devices at low temperatures, we find unexpected voltage fluctuations that grow in magnitude as T is decreased below 20 K, are suppressed with increasing contact electrode size, and are independent of the drive current and contact spacing distance. Magnetoresistance fluctuations are also observed, which are reminiscent of universal conductance fluctuations but not entirely consistent with their conventional properties. Candidate explanations are considered, and a mechanism is suggested based on mesoscopic temporal fluctuations of the Seebeck coefficient. An improved understanding of charge transport in these model systems, especially their quantum coherent properties, may lead to insights into the nature of transport in strongly correlated materials that deviate from Fermi liquid theory. Copyright © 2017, The Authors. All rights reserved.

关键词： Rare earths

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