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  Subjects -> ELECTRONICS (Total: 186 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 6)
Advances in Electronics     Open Access   (Followers: 90)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 35)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 331)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 14)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 29)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 289)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access  
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 117)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 97)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 100)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 55)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 205)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 99)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 80)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 49)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 72)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 71)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 58)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 42)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 76)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access  
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 52)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 70)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 15)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 11)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 32)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 172)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 29)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 26)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 41)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 56)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 78)
Solid State Electronics Letters     Open Access  
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Ural Radio Engineering Journal     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
Biomedical Engineering, IEEE Transactions on
Journal Prestige (SJR): 1.267
Citation Impact (citeScore): 5
Number of Followers: 38  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9294
Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • IEEE Engineering in Medicine and Biology Society
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • IEEE Transactions on Biomedical Engineering (T-BME)
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • IEEE Transactions on Biomedical Engineering Handling Editors
    • Abstract: Presents a listing of the EMBS society handling editors.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Reliable Label-Efficient Learning for Biomedical Image Recognition
    • Authors: Yun Gu;Mali Shen;Jie Yang;Guang-Zhong Yang;
      Pages: 2423 - 2432
      Abstract: The use of deep neural networks for biomedical image analysis requires a sufficient number of labeled datasets. To acquire accurate labels as the gold standard, multiple observers with specific expertise are required for both annotation and proofreading. This process can be time-consuming and labor-intensive, making high-quality, and large-annotated biomedical datasets difficult. To address this problem, we propose a deep active learning framework that enables the active selection of both informative queries and reliable experts. To measure the uncertainty of the unlabeled data, a dropout-based strategy is integrated with a similarity criterion for both data selection and random error elimination. To select the reliable labelers, we adopt an expertise estimator to learn the expertise levels of labelers via offline-testing and online consistency evaluation. The proposed method is applied to classification tasks on two types of medical images including confocal endomicroscopy images and gastrointestinal endoscopic images. The annotations are acquired from multiple labelers with diverse levels of expertise. The experiments demonstrate the efficiency and promising performance of the proposed method compared to a set of baseline methods.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Time-Varying Respiratory Mechanics as a Novel Mechanism Behind Frequency
           Dependence of Impedance: A Modeling Approach
    • Authors: Hamed Hanafi Alamdari;Kamal El-Sankary;Geoffrey N. Maksym;
      Pages: 2433 - 2446
      Abstract: Frequency dependence of respiratory mechanics is a well-established behavior of the respiratory system and is known to be an indicator of severity of obstructive disease, attributed to both tissue viscoelasticity and heterogeneity of airflow in the lung. Despite the fact that respiratory parameters are known to vary in time, often amplified in disease, all analysis methods assume stationarity or short-time stationarity in the parameters used to describe the respiratory system, and the effects of this assumption have not yet been examined in any detail. Here, using a generalized approach, we developed a theory for time-varying respiratory mechanics in time-frequency domain for analysis of linear time-varying systems, then, we analyzed the same respiratory system model with time-varying parameters in the time domain. Both time-frequency domain and time-domain derivations revealed a striking correlation between time-varying behavior of the respiratory system and frequency dependence of resistance. Remarkably, this phenomenon arose from the amplitude of time variations of the elastance. This links two mechanisms that are known to increase in obstructive disease: apparent low frequency increases in resistance and the time variations of reactance.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy
           Images of Human Cervical Tissue
    • Authors: Yutao Ma;Tao Xu;Xiaolei Huang;Xiaofang Wang;Canyu Li;Jason Jerwick;Yuan Ning;Xianxu Zeng;Baojin Wang;Yihong Wang;Zhan Zhang;Xiaoan Zhang;Chao Zhou;
      Pages: 2447 - 2456
      Abstract: Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. Methods: 497 high-resolution three-dimensional (3-D) OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information [e.g., age and human papillomavirus (HPV) results], and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task. Results: An 88.3 ± 4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task [low-risk (normal, ectropion, and LSIL) versus high-risk (HSIL and cancer)], the CADx method achieved an area-under-the-curve value of 0.959 with an 86.7 ± 11.4% sensitivity and 93.5 ± 3.8% specificity. Conclusion: The proposed deep-learning-based CADx method outperformed four human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations. Significance: Label-free OCM imaging, combined with deep-learning-based -ADx methods, holds a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources Based
           on Matrix Factorization
    • Authors: Ke Liu;Zhu Liang Yu;Wei Wu;Zhenghui Gu;Jun Zhang;Ling Cen;Srikantan Nagarajan;Yuanqing Li;
      Pages: 2457 - 2469
      Abstract: Accurate estimation of the locations and extents of neural sources from electroencephalography and magnetoencephalography (E/MEG) is challenging, especially for deep and highly correlated neural activities. In this study, we proposed a new fully data-driven source imaging method, source imaging based on spatio-temporal basis function (SI-STBF), which is built upon a Bayesian framework, to address this issue. The SI-STBF is based on the factorization of a source matrix as a product of a sparse coding matrix and a temporal basis function (TBF) matrix, which includes a few TBFs. The prior of the TBF is set in the empirical Bayesian manner. Similarly, for the spatial constraint, the SI-STBF assumes the prior covariance of the coding matrix as a weighted sum of several spatial covariance components. Both the TBFs and the coding matrix are learned from E/MEG simultaneously through variational Bayesian inference. To enable inference on high-resolution source space, we derived a scalable algorithm using convex analysis. The performance of the SI-STBF was assessed using both simulated and experimental E/MEG recordings. Compared with $L_2$-norm constrained methods, the SI-STBF is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the spatio-temporal factorization of source matrix, the SI-STBF also produces more accurate estimations than spatial-only constraint method for high correlated and deep sources.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Three-Dimensional Electrical Impedance Tomography With Multiplicative
    • Authors: Ke Zhang;Maokun Li;Fan Yang;Shenheng Xu;Aria Abubakar;
      Pages: 2470 - 2480
      Abstract: Objective: The multiplicative regularization scheme is applied to three-dimensional electrical impedance tomography (EIT) image reconstruction problem to alleviate its ill-posedness. Methods: A cost functional is constructed by multiplying the data misfit functional with the regularization functional. The regularization functional is based on a weighted $L^2$-norm with the edge-preserving characteristic. Gauss–Newton method is used to minimize the cost functional. A method based on the discrete exterior calculus (DEC) theory is introduced to formulate the discrete gradient and divergence operators related to the regularization on unstructured meshes. Results: Both numerical and experimental results show good reconstruction accuracy and anti-noise performance of the algorithm. The reconstruction results using human thoracic data show promising applications in thorax imaging. Conclusion: The multiplicative regularization can be applied to EIT image reconstruction with promising applications in thorax imaging. Significance: In the multiplicative regularization scheme, there is no need to set an artificial regularization parameter in the cost functional. This helps to reduce the workload related to choosing a regularization parameter which may require expertise and many numerical experiments. The DEC-based method provides a systematic and rigorous way to formulate operators on unstructured meshes. This may help EIT image reconstructions using regularizations imposing structural or spatial constraints.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Stimulation and Artifact-Suppression Techniques for In Vitro High-Density
           Microelectrode Array Systems
    • Authors: Amir Shadmani;Vijay Viswam;Yihui Chen;Raziyeh Bounik;Jelena Dragas;Milos Radivojevic;Sydney Geissler;Sergey Sitnikov;Jan Müller;Andreas Hierlemann;
      Pages: 2481 - 2490
      Abstract: We present novel voltage stimulation buffers with controlled output current, along with recording circuits featuring adjustable high-pass cut-off filtering to perform efficient stimulation while actively suppressing stimulation artifacts in high-density microelectrode arrays. Owing to the dense packing and close proximity of the electrodes in such systems, a stimulation through one electrode can cause large electrical artifacts on neighboring electrodes that easily saturate the corresponding recording amplifiers. To suppress such artifacts, the high-pass corner frequencies of all available 2048 recording channels can be raised from several Hz to several kHz by applying a “soft-reset” or pole-shifting technique. With the implemented artifact suppression technique, the saturation time of the recording circuits, connected to electrodes in immediate vicinity to the stimulation site, could be reduced to less than 150 μs. For the stimulation buffer, we developed a circuit, which can operate in two modes: either control of only the stimulation voltage or control of current and voltage during stimulation. The voltage-only controlled mode employs a local common-mode feedback operational transconductance amplifier with a near rail-to-rail input/output range, suitable for driving high-capacitive loads. The current/voltage controlled mode is based on a positive current conveyor generating adjustable output currents, whereas its upper and lower output voltages are limited by two feedback loops. The current/voltage controlled circuit can generate stimulation pulses up to 30 μA with less than ±0.1% linearity error in the low-current mode and up to 300 μA with less than ±0.2% linearity error in the high-current mode.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Fluidic Bypass Structures for Improving the Robustness of Liquid Scanning
    • Authors: David P. Taylor;Govind V. Kaigala;
      Pages: 2491 - 2498
      Abstract: Objective: We aim to improve operational robustness of liquid scanning probes. Two main failure modes to be addressed are an obstruction of the flow path of the processing liquid and a deviation from the desired gap distance between probe and sample. Methods: We introduce a multi-functional design element, a microfluidic bypass channel, which can be operated in dc and in ac mode, each preventing one of the two main failure modes. Results: In dc mode, the bypass channel is filled with liquid and exhibits resistive behavior, enabling the probe to passively react to an obstruction. In the case of an obstruction of the flow path, the processing liquid is passively diverted through the bypass to prevent its leakage and to limit the buildup of high pressure levels. In ac mode, the bypass is filled with gas and has capacitive characteristics, allowing the gap distance between the probe and the sample to be monitored by observing a phase shift in the motion of two gas–liquid interfaces. For a modulation of the input pressure at 4 Hz, significant changes of the phase shift were observed up to a gap distance of 25 μm. Conclusion: The presented passive design element counters both failure modes in a simple and highly compatible manner. Significance: Liquid scanning probes enabling targeted interfacing with biological surfaces are compatible with a wide range of workflows and bioanalytical applications. An improved operational robustness would facilitate rapid and widespread adoption of liquid scanning probes in research as well as in diagnostics.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Impedance-Based Gaussian Processes for Modeling Human Motor Behavior in
           Physical and Non-Physical Interaction
    • Authors: José R. Medina;Hendrik Börner;Satoshi Endo;Sandra Hirche;
      Pages: 2499 - 2511
      Abstract: Objective: Modeling of human motor intention plays an essential role in predictively controlling a robotic system in human–robot interaction tasks. In most machine learning techniques, human motor behavior is modeled as a generic stochastic process. However, the integration of a priori knowledge about underlying system structures can provide insights on otherwise unobservable intrinsic states that yield the superior prediction performance and increased generalization capabilities. Methods: We present a novel method for modeling human motor behavior that explicitly includes a neuroscientifically supported model of human motor control, in which the dynamics of the human arm are modeled by a mechanical impedance that tracks a latent desired trajectory. We adopt a Bayesian setting by defining Gaussian process (GP) priors for the impedance elements and the latent desired trajectory. This enables exploitation of a priori human arm impedance knowledge for regression of interaction forces through inference of a latent desired human trajectory. Results: The method is validated using simulated data, with particular focus on effects of GP prior parameterization and intention estimation capabilities. The superior prediction performance is shown with respect to a naive GP prior. An experiment with human participants evaluates generalization capabilities and effects of training data sparsity. Conclusion: We derive the correlations of an impedance-based GP model of human motor behavior that exploits a priori knowledge. Significance: The model effectively predicts interaction forces by inferring a latent desired human trajectory in previously observed as well as unobserved regions of the input space.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • A Novel Controllable Cell Array Printing Technique on Microfluidic Chips
    • Authors: Shengli Mi;Shuaitao Yang;Tiankun Liu;Zhichang Du;Yuanyuan Xu;Bohan Li;Wei Sun;
      Pages: 2512 - 2520
      Abstract: Goal: The construction of single-cell array is known as the challenging technology to manipulate cell position and number and accomplish cell analysis in biomedical engineering. Methods: We put forward a novel controllable cell printing technique for rapid, precise, convenient, high cell viability, multicellular, and high-throughput printing. We also proposed a novel microfluidic device to verify the effectiveness of the printing and study the migration ability and anti-cancer drug responses of cancer cell as important applications. Results: This technique offered a minimum process time of 5 min, a maximum positional accuracy of 10 μm, 0.1 nL liquid volume level per droplet, above 87% cell viability after seven days and the ability to print different multicellular arrays. We found that the cell compared to cell culture in petri dish after 48 h. In addition, there was a significant different inhibition on cancer cells migration ability and cell drug activities with different concentrations of paclitaxel. Conclusion: This novel controllable cell array printing technique on the microfluidic platforms provides a useful method with high-quality printing and cell viability for the applications of single-cell analysis and high-throughput drug screening. Significance: The controllable cell printing technique could apply in many biological processes and biomedical engineering applications, such as cell analysis, cancer development, and drug screening and metabolism. Combined with the microfluidic chips, tissue engineering, and sensors, this technique will be widely used for the construction and analysis of biological and biomedical model.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Visualizing Interactions of Circulating Tumor Cell and Dendritic Cell in
           the Blood Circulation Using In Vivo Imaging Flow Cytometry
    • Authors: Dan Wei;Xuejiao Zeng;Zhangru Yang;Quanyu Zhou;Xiaofu Weng;Hao He;Wenyuan Gao;Zhengqin Gu;Xunbin Wei;
      Pages: 2521 - 2526
      Abstract: Objective: Visualizing cell interactions in blood circulation is of great importance in studies of anticancer immunotherapy or drugs. However, the lack of a suitable imaging system hampers progress in this field. Methods: In this work, we built a dual-channel in vivo imaging flow cytometer to visualize the interactions of circulating tumor cells (CTCs) and dendritic cells (DCs) simultaneously in the bloodstream. Two artificial neural networks were trained to identify blood vessels and cells in the acquired images. Results and Conclusion: Using this technique, single CTCs and CTC clusters were readily distinguished by their morphology. Interactions of CTCs and DCs were identified, while their moving velocities were analyzed. The CTC-DC clusters moved at a slower velocity than that of single CTCs or DCs. This may provide new insights into tumor metastasis and blood rheology. Significance: This in vivo imaging flow cytometry system holds great potential for assessing the efficiency of targeting CTCs with anticancer immune cells or drugs.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Robotic Transrectal Ultrasound Guided Prostate Biopsy
    • Authors: Sunghwan Lim;Changhan Jun;Doyoung Chang;Doru Petrisor;Misop Han;Dan Stoianovici;
      Pages: 2527 - 2537
      Abstract: We present a robot-assisted approach for transrectal ultrasound (TRUS) guided prostate biopsy. The robot is a hands-free probe manipulator that moves the probe with the same 4 DoF that are used manually. Software was developed for three-dimensional (3-D) imaging, biopsy planning, robot control, and navigation. Methods to minimize the deformation of the prostate caused by the probe at 3-D imaging and needle targeting were developed to reduce biopsy targeting errors. We also present a prostate coordinate system (PCS). The PCS helps defining a systematic biopsy plan without the need for prostate segmentation. Comprehensive tests were performed, including two bench tests, one imaging test, two in vitro targeting tests, and an IRB-approved clinical trial on five patients. Preclinical tests showed that image-based needle targeting can be accomplished with accuracy on the order of 1 mm. Prostate biopsy can be accomplished with minimal TRUS pressure on the gland and submillimetric prostate deformations. All five clinical cases were successful with an average procedure time of 13 min and millimeter targeting accuracy. Hands-free TRUS operation, transrectal TRUS guided prostate biopsy with minimal prostate deformations, and the PCS-based biopsy plan are novel methods. Robot-assisted prostate biopsy is safe and feasible. Accurate needle targeting has the potential to increase the detection of clinically significant prostate cancer.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Long-Term Developmental Process of the Human Cortex Revealed In Vitro by
           Axon-Targeted Recording Using a Microtunnel-Augmented Microelectrode Array
    • Authors: Kenta Shimba;Koji Sakai;Shoko Iida;Kiyoshi Kotani;Yasuhiko Jimbo;
      Pages: 2538 - 2545
      Abstract: Objective: We aimed to develop a method for evaluating developmental changes in the synchronized activity of human induced pluripotent stem cell (hiPSC)-derived neurons without extrinsic signals from feeder astrocytes. Methods: Microelectrode arrays (MEAs) and microtunnels were fabricated with photolithography and soft lithography. hiPSCs were induced to differentiate into cortical neurons, and seeded to conventional and microtunnel MEAs. Spontaneous activity was recorded every ten days, and spiking and bursting activities were elucidated. Results: First, hiPSC-derived neurons were cultured on conventional MEAs. They formed aggregates and subsequently detached from the culture substrate. Hence, no MEAs showed spontaneous synchronized activity beyond 300 days post-induction. Next, we applied a microtunnel structure designed to keep the axons on the array. Synchronized activity was then recorded from all microtunnel MEAs by 450 days post-induction. The proportion of electrodes showing neural activity was greater than that in conventional MEAs. The activity pattern reached a steady state after approximately 330 days, which may be the maturation time of the human neuronal network. Conclusion: The use of a microtunnel MEA enables the monitoring of the long-term development of human neuronal networks of cell populations that are relatively natural given their lack of astrocyte feeders. Significance: We report a more accurate method for culturing cortical neurons differentiated from hiPSCs, validating their use in elucidating cortical development and pathogenic mechanisms in humans.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Dominant-Current Deep Learning Scheme for Electrical Impedance Tomography
    • Authors: Zhun Wei;Dong Liu;Xudong Chen;
      Pages: 2546 - 2555
      Abstract: Objective: Deep learning has recently been applied to electrical impedance tomography (EIT) imaging. Nevertheless, there are still many challenges that this approach has to face, e.g., targets with sharp corners or edges cannot be well recovered when using circular inclusion training data. This paper proposes an iterative-based inversion method and a convolutional neural network (CNN) based inversion method to recover some challenging inclusions such as triangular, rectangular, or lung shapes, where the CNN-based method uses only random circle or ellipse training data. Methods: First, the iterative method, i.e., bases-expansion subspace optimization method (BE-SOM), is proposed based on a concept of induced contrast current (ICC) with total variation regularization. Second, the theoretical analysis of BE-SOM and the physical concepts introduced there motivate us to propose a dominant-current deep learning scheme for EIT imaging, in which dominant parts of ICC are utilized to generate multi-channel inputs of CNN. Results: The proposed methods are tested with both numerical and experimental data, where several realistic phantoms including simulated pneumothorax and pleural effusion pathologies are also considered. Conclusions and Significance: Significant performance improvements of the proposed methods are shown in reconstructing targets with sharp corners or edges. It is also demonstrated that the proposed methods are capable of fast, stable, and high-quality EIT imaging, which is promising in providing quantitative images for potential clinical applications.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Evolving Gaussian Process Autoregression Based Learning of Human Motion
           Intent Using Improved Energy Kernel Method of EMG
    • Authors: Yan Zeng;JianTao Yang;Cheng Peng;Yuehong Yin;
      Pages: 2556 - 2565
      Abstract: Continuous human motion intent learning may be modeled using a Gaussian process (GP) autoregression based evolving system to cope with the unspecified and time-varying motion patterns. Electromyography (EMG) signals are the primary input. GP is used as a mathematical foundation to model human kinematics by adopting the nonlinear autoregressive with exogenous inputs (NARX) framework, and an evolving system is applied to learn the irregular and unspecified dynamic features. The statistical nature of the GP offers superior flexibility for learning human kinematics and is capable of giving credibility to motion intent prediction, which also enables risk-based control. As an important neuromuscular signal, EMG is processed with a novel method, the energy kernel method, to extract the activation level of muscle and feature out muscular force and motion intent. Without losing robustness, the high signal-to-noise ratio or the linearity level with muscular force, huge improvement has been made in computational efficiency to meet the requirements of real-time applications. Experimental works concerning the validity and application of this method are also presented.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • 3-D Microwave Tomography Using the Soft Prior Regularization Technique:
           Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms
    • Authors: Amir H. Golnabi;Paul M. Meaney;Shireen D. Geimer;Keith D. Paulsen;
      Pages: 2566 - 2575
      Abstract: Objective: Fusion of magnetic resonance imaging (MRI) breast images with microwave tomography is accomplished through a soft prior technique, which incorporates spatial information (from MRI), i.e., accurate boundary location of different regions of interest, into the regularization process of the microwave image reconstruction algorithm. Methods: Numerical experiments were completed on a set of three-dimensional (3-D) breast geometries derived from MR breast data with different parenchymal densities, as well as a simulated tumor to evaluate the performance over a range of breast shapes, sizes, and property distributions. Results: When the soft prior regularization technique was applied, both permittivity and conductivity relative root mean square error values decreased by more than 87% across all breast densities, except in two cases where the error decrease was only 55% and 78%. In addition, the incorporation of structural priors increased contrast between tumor and fibroglandular tissue by 59% in permittivity and 192% in conductivity. Conclusion: This study confirmed that the soft prior algorithm is robust in 3-D and can function successfully across a range of complex geometries and tissue property distributions. Significance: This study demonstrates that our microwave tomography is capable of recovering accurate tissue property distributions when spatial information from MRI is incorporated through soft prior regularization.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Combining Metrics From Clinical Simulators and Sensorimotor Tasks Can
           Reveal the Training Background of Surgeons
    • Authors: Felix C. Huang;Hossein Mohamadipanah;Ferdinando A. Mussa-Ivaldi;Carla M. Pugh;
      Pages: 2576 - 2584
      Abstract: Background: Skill assessment in surgery traditionally has relied on the expert observation and qualitative scoring. Our novel study design demonstrates how analysis of performance in sensorimotor tasks and bench-top surgical simulators can provide inferences about the technical proficiency as well as the training history of surgeons. Methods: We examined metrics for basic sensorimotor tasks in a virtual reality interface as well as motion metrics in clinical scenario simulations. As indicators of the training level, we considered survey responses from surgery residents, including the number of postgraduation years (PGY, four levels), research years (RY, three levels), and clinical years (CY, three levels). Next, we performed a linear discriminant analysis with cross-validation (90% training, 10% testing) to relate the training levels to the selected metrics. Results: Using combined metrics from all stations, we found greater than chance predictions for each survey category, with an overall accuracy of 43.4 ± 2.9% for identifying the level for post-graduate years, 79.1 ± 1.0% accuracy for research training years, and 64.2 ± 1.0% for clinical training years. Our main finding was that combining metrics from all stations resulted in more accurate predictions than using only sensorimotor or clinical scenario tasks. In addition, we found that metrics related to the ability to cope with changes in the task environment were the most important predictors of training level. Conclusions: These results suggest that each simulator-type provided crucial information for evaluating surgical proficiency. The methods developed in this paper could improve evaluations of a surgeon's clinical proficiency as well as training potential in terms of basic-sensorimotor ability.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Sparse Deconvolution of Electrodermal Activity via Continuous-Time System
    • Authors: Md. Rafiul Amin;Rose T. Faghih;
      Pages: 2585 - 2595
      Abstract: Objective: Electrodermal activity (EDA) indicates different eccrine sweat gland activity caused by the stimulation of the autonomic nervous system. Recovering the number, timings, and amplitudes of underlying neural stimuli and physiological system parameters from the EDA is a challenging problem. One of the challenges with the existing methods is the non-convexity of the optimization formulations for estimating the parameters given the stimuli. Methods: We solve this parameter estimation problem using the following continuous-time system identification framework: 1) we specifically use the Hartley modulating function (HMF) for parameter estimation so that the optimization formulation for estimating the parameters given the stimuli is convex; and 2) we use Kaiser windows with different shape parameters to put more emphasis on the significant spectral components so that there is a balance between filtering out the noise and capturing the data. We apply this algorithm to skin conductance (SC) data, a measure of EDA, collected during cognitive stress experiments. Results: Under a sparsity constraint, in the HMF domain, we successfully deconvolve the SC signal. We obtain number, timings, and amplitudes of the underlying neural stimuli along with the system parameters with $R^2$ above 0.915. Moreover, using simulated data, we illustrate that our approach outperforms the existing EDA data analysis methods, in recovering underlying stimuli. Conclusion: We develop a novel approach for deconvolution of SC by employing the HMF method and capturing the significant spectral components of SC data. Significance: Recovering the underlying neural stimuli more accurately using this approach will potentially improve tracking emotional states in affective computing.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Lower Limb Pulse Rise Time as a Marker of Peripheral Arterial Disease
    • Authors: Mikko Peltokangas;Damir Vakhitov;Velipekka Suominen;Janne Korhonen;Matti Huotari;Jarmo Verho;Juha Röning;Ville M. Mattila;Pekka Romsi;Niku Oksala;Antti Vehkaoja;
      Pages: 2596 - 2603
      Abstract: Objective: The aim of the study was to show if pulse rise times (PRTs) extracted from photoplethysmographic (PPG) pulse waves (PWs) have an association with peripheral arterial disease (PAD) or its endovascular treatment, percutanoeus transluminal angioplasty (PTA) of the superficial femoral artery. Methods: Lower and upper limb PPG PWs were recorded and analyzed from 24 patients who suffered from PAD. The measurements were conducted before and after the treatment, and one month later by using transmission-mode PPG-probes placed in the index finger and second toe. Ankle-to-brachial pressure index and toe pressures were used as references in clinical patient measurements. PRTs, i.e., the time from the foot point to the peak point of the PW, were extracted from the PWs and compared bilaterally. The results from the PAD patients were also compared with 31 same-aged and 34 younger control subjects. Results: Statistically significant differences were found between the pretreatment PRTs of the treated limb of the PAD patients and the same-aged control subjects ($p< 10^{-9}$, Mann–Whitney U-test). The changes in the PRT of the treated lower limb were observed immediately after the PTA ($p< 0.001$, Student's t-test), and after one month ($p< 0.0005$), whereas the PRTs of the non-treated lower limb and upper limb did not indicate changes between different examinations. Conclusion: Results show that a PRT greater than 240 ms indicates PAD-lesions in the lower limb. Significance: This proof-of-concept study suggests that the PRT could be an effective and easy-to-use indicator for PAD a-d monitoring the effectiveness of its treatment.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image
    • Authors: Jaya Prakash;Subhamoy Mandal;Daniel Razansky;Vasilis Ntziachristos;
      Pages: 2604 - 2616
      Abstract: Objective: Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise, and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of this work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging. Methods: We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior-based fluence correction. Results: We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms, and in-vivo samples. Conclusion: The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures. Significance: Our method can enable quantitative optoacoustic imaging, and has the potential to improve preclinical and translational imaging applications.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Auto-Regressive Discrete Acquisition Points Transformation for Diffusion
           Weighted MRI Data
    • Authors: Emma Metcalfe-Smith;Emma M. Meeus;Jan Novak;Hamid Dehghani;Andrew C. Peet;Niloufar Zarinabad;
      Pages: 2617 - 2628
      Abstract: Objective: A new method for fitting diffusion-weighted magnetic resonance imaging (DW-MRI) data composed of an unknown number of multi-exponential components is presented and evaluated. Methods: The auto-regressive discrete acquisition points transformation (ADAPT) method is an adaption of the auto-regressive moving average system, which allows for the modeling of multi-exponential data and enables the estimation of the number of exponential components without prior assumptions. ADAPT was evaluated on simulated DW-MRI data. The optimum ADAPT fit was then applied to human brain DWI data and the correlation between the ADAPT coefficients and the parameters of the commonly used bi-exponential intravoxel incoherent motion (IVIM) method were investigated. Results: The ADAPT method can correctly identify the number of components and model the exponential data. The ADAPT coefficients were found to have strong correlations with the IVIM parameters. ADAPT(1,1)-β0 correlated with IVIM-D: ρ = 0.708, P < 0.001. ADAPT(1,1)-α1 correlated with IVIM-f: ρ = 0.667, P < 0.001. ADAPT(1,1)-β1 correlated with IVIM-D*: ρ = 0.741, P < 0.001). Conclusion: ADAPT provides a method that can identify the number of exponential components in DWI data without prior assumptions, and determine potential complex diffusion biomarkers. Significance: ADAPT has the potential to provide a generalized fitting method for discrete multi-exponential data, and determine meaningful coefficients without prior information.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler
    • Authors: Ann-Kathrin Seifert;Moeness G. Amin;Abdelhak M. Zoubir;
      Pages: 2629 - 2640
      Abstract: Objective: In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation, and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this paper is concerned with solving the intra motion category classification problem of gait recognition. Methods: New gait classification approaches utilizing physical features, subspace features, and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological, and assisted walks. Results: The proposed approaches are shown to outperform existing methods for radar-based gait recognition, which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual, a classification accuracy on the order of 80% can be expected. Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Significance: Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Liver Extraction Using Residual Convolution Neural Networks From Low-Dose
           CT Images
    • Authors: Muhammad Nadeem Cheema;Anam Nazir;Bin Sheng;Ping Li;Jing Qin;David Dagan Feng;
      Pages: 2641 - 2650
      Abstract: An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT examination, low-dose CT (LDCT) is an effective solution for medical imaging. However, inhomogeneous appearances and indistinct boundaries due to additional noise and streaks artifacts in LDCT images often make it a challenging task. This study aims to extract a liver model from LDCT images for facilitating medical expert in surgical planning and post-operative assessment along with low radiation risk to the patient. Our method carried out liver extraction by employing residual convolutional neural networks (LER-CN), which is further refined by noise removal and structure preservation components. After patch-based training, our LER-CN shows a competitive performance relative to state-of-the-art methods for both clinical and publicly available MICCAI Sliver07 datasets. We have proposed training and learning algorithms for LER-CN based on back propagation gradient descent. We have evaluated our method on 150 abdominal CT scans for liver extraction. LER-CN achieves dice similarity coefficient up to 96.5$pm text{1.8}%$, decreased volumetric overlap error up to 4.30$pm text{0.58}%$, and average symmetric surface distance less than 1.4 $pm text{0.5mm}$. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Noninvasive Imaging of Epicardial and Endocardial Potentials With Low Rank
           and Sparsity Constraints
    • Authors: Lin Fang;Jingjia Xu;Hongjie Hu;Yunmei Chen;Pengcheng Shi;Linwei Wang;Huafeng Liu;
      Pages: 2651 - 2662
      Abstract: In this study, we explore the use of low rank and sparse constraints for the noninvasive estimation of epicardial and endocardial extracellular potentials from body-surface electrocardiographic data to locate the focus of premature ventricular contractions (PVCs). The proposed strategy formulates the dynamic spatiotemporal distribution of cardiac potentials by means of low rank and sparse decomposition, where the low rank term represents the smooth background and the anomalous potentials are extracted in the sparse matrix. Compared to the most previous potential-based approaches, the proposed low rank and sparse constraints are batch spatiotemporal constraints that capture the underlying relationship of dynamic potentials. The resulting optimization problem is solved using alternating direction method of multipliers. Three sets of simulation experiments with eight different ventricular pacing sites demonstrate that the proposed model outperforms the existing Tikhonov regularization (zero-order, second-order) and L1-norm based method at accurately reconstructing the potentials and locating the ventricular pacing sites. Experiments on a total of 39 cases of real PVC data also validate the ability of the proposed method to correctly locate ectopic pacing sites.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • A New RF Heating Strategy for Thermal Treatment of Atherosclerosis
    • Authors: Shiqing Zhao;Jincheng Zou;Aili Zhang;Lisa X. Xu;
      Pages: 2663 - 2670
      Abstract: Objectives: Restenosis remains a challenge for the treatment of atherosclerosis due to the damage of the endothelial layer and induced proliferation of the smooth muscle cell. Methods: A new RF heating strategy was proposed to selectively ablate the atherosclerosis plaque, and to thermally inhibit the proliferation of smooth muscle cells, while keeping the endothelial cells intact. To achieve the goal, an internal cooling agent and distributed electrodes have been integrated in the new designed balloon catheter to focus the shape conformal energy onto the plaque shape. A three-dimensional (3-D) model with experimentally fitted parameters has been established to demonstrate the heating ability of the design and evaluate the microelectrodes configurations for different plaque geometries. Results: The 3-D shape of the lesions resulting from different electrodes settings is obtained. It is found that by individual control of the micro-electrodes, special shapes of the lesions can be formed, which can match the eccentric crescent plaques. Besides, through changing of the polarity of the electrodes, separate lesions can be reached. This suggests the possibility for treatment of disconnected plaques in situ. Conclusion: By the control of RF heating and convection coefficient of the internal cooling agent, a targeted heating region away from the inner surface of the blood vessel can be realized. Significance: This study has illustrated the possibility of achieving a precision thermal treatment of atherosclerosis in favor of inhibiting further restenosis.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Electroporation-Induced Stress Response and Its Effect on Gene
           Electrotransfer Efficacy: In Vivo Imaging and Numerical Modeling
    • Authors: Tadeja Forjanič;Boštjan Markelc;Marija Marčan;Elisabeth Bellard;Franck Couillaud;Muriel Golzio;Damijan Miklavčič;
      Pages: 2671 - 2683
      Abstract: Objective: Skin is an attractive target tissue for gene transfer due to its size, accessibility, and its immune competence. One of the promising delivery methods is gene delivery by means of electroporation (EP), i.e., gene electrotransfer (GET). To assess the importance of different effects of electroporation for successful GET we investigated: stress response and transfection efficacy upon different pulse protocols. Moreover, numerical modeling was used to explain experimental results and to test the agreement of experimental results with current knowledge about GET. Methods: Double transgenic mice Hspa1b-LucF (+/+) Hspa1b-mPlum (+/+) were used to determine the level of stress sensed by the cell in the tissue in vivo that was exposed to EP. The effect of five different pulse protocols on stress levels sensed by the exposed cells and their efficacy for gene electrotransfer for two plasmids pEGFP-C1 (EGFP) and pCMV-tdTomato was tested. Results: Quantification of the bioluminescence signal intensity shows that EP, regardless of the electric pulse parameters used, increased mean bioluminescence compared to the baseline bioluminescence signal of the non-exposed skin. The results of numerical modeling indicate that thermal stress alone is not sufficient to explain the measured bioluminescence signal. Of the tested pulse protocols, the highest expression of EGFP and tdTomato was achieved with HV-MV (high voltage – medium voltage) protocols, which agrees also with numerical model. Significance: Although EP is widely used as a method for gene delivery, we show that the field could benefit from the use of mathematical modeling by introducing additional parameters such as EP induced stress and electrophoretic movement of plasmids.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Bayesian Inference Identifies Combination Therapeutic Targets in Breast
    • Authors: Haswanth Vundavilli;Aniruddha Datta;Chao Sima;Jianping Hua;Rosana Lopes;Michael Bittner;
      Pages: 2684 - 2692
      Abstract: Objective: Breast cancer is the second leading cause of cancer death among US women; hence, identifying potential drug targets is an ever increasing need. In this paper, we integrate existing biological information with graphical models to deduce the significant nodes in the breast cancer signaling pathway. Methods: We make use of biological information from the literature to develop a Bayesian network. Using the relevant gene expression data we estimate the parameters of this network. Then, using a message passing algorithm, we infer the network. The inferred network is used to quantitatively rank different interventions for achieving a desired phenotypic outcome. The particular phenotype considered here is the induction of apoptosis. Results: Theoretical analysis pinpoints to the role of Cryptotanshinone, a compound found in traditional Chinese herbs, as a potent modulator for bringing about cell death in the treatment of cancer. Conclusion: Using a mathematical framework, we showed that the combination therapy of mTOR and STAT3 genes yields the best apoptosis in breast cancer. Significance: The computational results we arrived at are consistent with the experimental results that we obtained using Cryptotanshinone on MCF-7 breast cancer cell lines and also by the past results of others from the literature, thereby demonstrating the effectiveness of our model.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
  • Gradient Field Deviation (GFD) Correction Using a Hybrid-Norm Approach
           With Wavelet Sub-Band Dependent Regularization: Implementation for Radial
           MRI at 9.4 T
    • Authors: Shanshan Shan;Mingyan Li;Fangfang Tang;Huan Ma;Feng Liu;Stuart Crozier;
      Pages: 2693 - 2701
      Abstract: In magnetic resonance imaging (MRI), system imperfections and eddy currents can cause gradient field deviation (GFD), leading to various image distortions, such as increased noise, ghosting artifacts, and geometric deformation. These distortions can degrade the clinical value of MR images. Generally, non-Cartesian image sequences, such as radial sampling, produce larger gradient deviations than Cartesian sampling, as a result of stronger eddy current-induced gradient delays and phase errors. In this paper, we developed a GFD encoding method to reduce image noise and artifacts for radial MRI. In the proposed method, a hybrid norm (combination of L2 and L1 norms) optimization problem was formed, which incorporated a wavelet sub-band adaptive regularization mechanism. The new approach seeks a regularized solution not only offering corrected images with reduced artifacts and geometric deformation, but also good preservation of anatomical structural details. The new method was evaluated with simulation and experiment at 9.4 T MRI. The results demonstrated that the proposed method can provide over 50% noise reduction and 15% artifact reduction compared with the traditional regridding method, suggesting substantially reduced GFD-induced distortions and improved image quality.
      PubDate: Sept. 2019
      Issue No: Vol. 66, No. 9 (2019)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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