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Full-field MRI Measurements of in-vivo Positional Brain Shift Reveal the Significance of Intra-cranial Geometry and Head Orientation for Stereotactic Surgery

Research Square

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Research Square 2021年

作者： Zappalà, Stefano Bennion, Nicholas J. Potts, Matthew R. Wu, Jing Kusmia, Slawomir Jones, Derek K. Evans, Sam L. Marshall, David School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Psychology Cardiff University Cardiff United Kingdom School of Engineering Cardiff University Cardiff United Kingdom Centre for Medical Image Computing University College London London United Kingdom MRI Unit Epilepsy Society Chalfont St Peter United Kingdom

Positional brain shift (PBS), the sagging of the brain under the effect of gravity, is comparable in magnitude to the margin of error for the success of stereotactic interventions (∼1 mm). This non-uniform shift due to slight differences in head orientation can lead to a significant discrepancy between the planned and the actual location of surgical targets. Accurate in-vivo measurements of this complex deformation are critical for the design and validation of an appropriate compensation to integrate into neuronavigational systems. PBS arising from prone-to-supine change of head orientation was measured with magnetic resonance imaging on 11 young adults. The full-field displacement was extracted on a voxel-basis via digital volume correlation and analysed in a standard reference space. Results showed the need for target-specific correction of surgical targets, as a significant displacement ranging from 0.52 mm to 0.77 mm was measured at surgically relevant structures. Strain analysis further revealed local variability in compressibility: anterior regions showed expansion (both volume and shape change), whereas posterior regions showed small compression, mostly dominated by shape change. Finally, analysis of correlation demonstrated the potential for further patient- and intervention-specific adjustments, as intra-cranial breadth and head tilt correlated with PBS reaching statistical significance. © 2021, CC BY.

关键词： Magnetic resonance imaging

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NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

arXiv

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arXiv 2021年

作者： Machado, Pedro Ferreira, João Filipe Oikonomou, Andreas McGinnity, T.M. Department of Computer Science School of Science and Technology Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Northlands Road Magee Campus LondonderryBT48 7JL United Kingdom

Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, which still represent complex challenges for modern computers. In vertebrates, the detection of object motion is performed by specialised retinal cells named Object Motion Sensitive Ganglion Cells (OMS-GC). OMS-GC process continuous visual signals and generate spike patterns that are post-processed by the Visual Cortex. Our previous Hybrid Sensitive Motion Detector (HSMD) algorithm was the first hybrid algorithm to enhance Background subtraction (BS) algorithms with a customised 3-layer Spiking Neural Network (SNN) that generates OMS-GC spiking-like responses. In this work, we present a Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) algorithm that accelerates our HSMD algorithm using Field-Programmable Gate Arrays (FPGAs). The NeuroHSMD was compared against the HSMD algorithm, using the same 2012 Change Detection (CDnet2012) and 2014 Change Detection (CDnet2014) benchmark datasets. When tested against the CDnet2012 and CDnet2014 datasets, NeuroHSMD performs object motion detection at 720 × 480 at 28.06 Frames Per Second (fps) and 720 × 480 at 28.71 fps, respectively, with no degradation of quality. Moreover, the NeuroHSMD proposed in this paper was completely implemented in Open computer Language (OpenCL) and therefore is easily replicated in other devices such as Graphical Processing Units (GPUs) and clusters of Central Processing Units (CPUs). © 2021, CC BY.

关键词： Neural networks

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Skyline Query Optimization for Preferable Product Selection and Recommendation System

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Wireless Personal Communications 2020年第4期117卷 3091-3108页

作者： Rajasoundaran Soundararajan S. Rakesh Kumar N. Gayathri Fadi Al-Turjman Department of Computer Science PSR Engineering College Sivakasi India School of Computing Science and Engineering Galgotias University Greater Noida India Artificial Intelligence Engineering Department Near East University Nicosia Turkey Research Centre for AI and IoT Near East University Nicosia Turkey

Many social marketing systems use decision-making strategies for implementing product dominance analysis. The objective of the proposed work is to classify on-time issues in observing highly preferable market products, which is newer in online market places. Existing researches are interested to be useful for customers to identify the best possible products groups from the vast product details. To deal with this main objective, different types of product instances are reviewed. In this case, the price of popular products and the product groups are evaluated. This proposed system analyses the need for online market growth using novel skyline query analysis. The proposed system monitor user-based ratings affect the sales of various products. After finding the desirable products, the market prices are predicted. Once products are predicted, the new packages are assigned with optimal prices and added to the package database. Moreover, the proposed Skyline Query Optimization and Security Management System (SQOSMS) approach is focused on authorized user ratings and ensures they are more secured. The review system validates each and every user identities with the user activities involved with in review system. This is considered as major objective of this proposed system. The implementation section shows that the proposed system provides 10–15% of reduced movie lists than other systems. This illustrates the proposed SQOSMS’s controlled performance over the selection of preferable products.

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Deep learning models for triaging hospital head MRI examinations

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Medical Image Analysis 2022年 78卷 102391-102391页

作者： Wood, David A. Kafiabadi, Sina Busaidi, Ayisha Al Guilhem, Emily Montvila, Antanas Lynch, Jeremy Townend, Matthew Agarwal, Siddharth Mazumder, Asif Barker, Gareth J. Ourselin, Sebastien Cole, James H. Booth, Thomas C. School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom King's College Hospital NHS Foundation Trust United Kingdom Wrightington Wigan and Leigh NHSFT United Kingdom Guy's and St Thomas’ NHS Foundation Trust United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology & Neuroscience King's College London United Kingdom Dementia Research Centre Institute of Neurology University College London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London United Kingdom

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans in recent years. For many neurological conditions, this delay can result in poorer patient outcomes and inflated healthcare costs. Potentially, computer vision models could help reduce reporting times for abnormal examinations by flagging abnormalities at the time of imaging, allowing radiology departments to prioritise limited resources into reporting these scans first. To date, however, the difficulty of obtaining large, clinically-representative labelled datasets has been a bottleneck to model development. In this work, we present a deep learning framework, based on convolutional neural networks, for detecting clinically-relevant abnormalities in minimally processed, hospital-grade axial T2-weighted and axial diffusion-weighted head MRI scans. The models were trained at scale using a Transformer-based neuroradiology report classifier to generate a labelled dataset of 70,206 examinations from two large UK hospital networks, and demonstrate fast ( 0.9), and interpretable classification, with good generalisability between hospitals (ΔAUC ≤ 0.02). Through a simulation study we show that our best model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospital networks, demonstrating feasibility for use in a clinical triage environment. © 2022

关键词： Magnetic resonance imaging

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SMTrust: Proposing Trust-Based Secure Routing Protocol for RPL Attacks for IoT Applications

SMTrust: Proposing Trust-Based Secure Routing Protocol for R...

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Computational Intelligence (ICCI),International Conference on

作者： Syeda Mariam Muzammal Raja Kumar Murugesan Noor Zaman Jhanjhi Low Tang Jung School of Computer Science and Engineering Taylor’s University Subang Jaya Malaysia High Performance Computing Cloud Centre Universiti Teknologi PETRONAS Perak Malaysia

ISBN: (数字)9781728184081

ISBN: (纸本)9781728184104

With large scale generation and exchange of data between IoT devices and constrained IoT security to protect data communication, it becomes easy for attackers to compromise data routes. In IoT networks, IPv6 Routing Protocol is the de facto routing protocol for Low Power and Lossy Networks (RPL). RPL offers limited security against several RPL-specific and WSN-inherited attacks in IoT applications. Additionally, IoT devices are limited in memory, processing, and power to operate properly using the traditional Internet and routing security solutions. Several mitigation schemes for the security of IoT networks and routing, have been proposed including Machine Learning-based, IDS-based, and Trust-based approaches. In existing trust-based methods, mobility of nodes is not considered at all or its insufficient for mobile sink nodes, specifically for security against RPL attacks. This research work proposes a conceptual design, named SMTrust, for security of routing protocol in IoT, considering the mobility-based trust metrics. The proposed solution intends to provide defense against popular RPL attacks, for example, Blackhole, Greyhole, Rank, Version Number attacks, etc. We believe that SMTrust shall provide better network performance for attacks detection accuracy, mobility and scalability as compared to existing trust models, such as, DCTM-RPL and SecTrust-RPL. The novelty of our solution is that it considers the mobility metrics of the sensor nodes as well as the sink nodes, which has not been addressed by the existing models. This consideration makes it suitable for mobile IoT environment. The proposed design of SMTrust, as secure routing protocol, when embedded in RPL, shall ensure confidentiality, integrity, and availability among the sensor nodes during routing process in IoT communication and networks.

关键词： Measurement Wireless sensor networks Transportation Routing Routing protocols Internet of Things Security

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Preface

EAI/Springer Innovations in Communication and Computing

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EAI/Springer Innovations in Communication and computing 2022年 V-VI页

作者： Haldorai, Anandakumar Ramu, Arulmurugan Mohanram, Sudha Lu, Joan Department of Computer Science and Engineering Sri Eshwar College of Engineering TN Coimbatore India Department of Computer Science and Engineering Presidency University KA Bengaluru India Sri Eshwar College of Engineering TN Coimbatore India Department of Computer Science School of Computing and Engineering Centre for Planning Autonomy and Representation of Knowledge University of Huddersfield Huddersfield United Kingdom

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Distinguishing healthy ageing from dementia: A biomechanical simulation of brain atrophy using deep networks

arXiv

引用

arXiv 2021年

作者： da Silva, Mariana Sudre, Carole H. Garcia, Kara Bass, Cher Cardoso, M. Jorge Robinson, Emma C. School of Biomedical Engineering and Imaging Sciences King's College London MRC Unit for Lifelong Health and Ageing at UCL University College London Centre for Medical Image Computing Department of Computer Science University College London Department of Radiology and Imaging Sciences School of Medicine Indiana University Panakeia Technologies London United Kingdom

Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work, we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer's disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples. © 2021, CC BY.

关键词： Neurodegenerative diseases

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Hardware acceleration of sparse and irregular tensor computations of ML models: A survey and insights

arXiv

引用

arXiv 2020年

作者： Dave, Shail Baghdadi, Riyadh Nowatzki, Tony Avancha, Sasikanth Shrivastava, Aviral Li, Baoxin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States School of Computer Science University of California Los AngelesCA90095 United States Parallel Computing Lab at Intel Labs Bangalore India

Machine learning (ML) models are widely used in many important domains. For efficiently processing these computational- and memory-intensive applications, tensors of these over-parameterized models are compressed by leveraging sparsity, size reduction, and quantization of tensors. Unstructured sparsity and tensors with varying dimensions yield irregular computation, communication, and memory access patterns;processing them on hardware accelerators in a conventional manner does not inherently leverage acceleration opportunities. This paper provides a comprehensive survey on the efficient execution of sparse and irregular tensor computations of ML models on hardware accelerators. In particular, it discusses enhancement modules in the architecture design and the software support;categorizes different hardware designs and acceleration techniques and analyzes them in terms of hardware and execution costs;analyzes achievable accelerations for recent DNNs;highlights further opportunities in terms of hardware/software/model co-design optimizations (inter/intra-module). The takeaways from this paper include: understanding the key challenges in accelerating sparse, irregular-shaped, and quantized tensors;understanding enhancements in accelerator systems for supporting their efficient computations;analyzing trade-offs in opting for a specific design choice for encoding, storing, extracting, communicating, computing, and load-balancing the non-zeros;understanding how structured sparsity can improve storage efficiency and balance computations;understanding how to compile and map models with sparse tensors on the accelerators;understanding recent design trends for efficient accelerations and further opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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computing Electronic Correlation Energies using Linear Depth Quantum Circuits

arXiv

引用

arXiv 2022年

作者： Chee, Chong Hian Mak, Adrian M. Leykam, Daniel Barkoutsos, Panagiotis Kl. Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore Ibm Quantum Ibm Research Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece AngelQ Quantum Computing 531A Upper Cross Street #04-95 Hong Lim Complex Singapore051531 Singapore

Efficient computation of molecular energies is an exciting application of quantum computing for quantum chemistry, but current noisy intermediate-scale quantum (NISQ) devices can only execute shallow circuits, limiting existing variational quantum algorithms, which require deep entangling quantum circuit ansatzes to capture correlations, to small molecules. Here we demonstrate a variational NISQ-friendly algorithm that generates a set of mean-field Hartree-Fock (HF) ansatzes using multiple shallow circuits with depth linear in the number of qubits to estimate electronic correlation energies via perturbation theory up to the second order. We tested the algorithm on several small molecules, both with classical simulations including noise models and on cloud quantum processors, showing that it not only reproduces the equilibrium molecular energies but it also captures the perturbative electronic correlation effects at longer bond distances. As fidelities of quantum processors continue to improve our algorithm will enable the study of larger molecules compared to other approaches requiring higher-order polynomial circuit depth. © 2022, CC BY.

关键词： Quantum chemistry

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Augmenting photoluminescence of monolayer MoS_(2) using high order modes in a metal dimer-on-film nanocavity

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Photonics Research 2021年第4期9卷 501-506页

作者： Shiyin Cao Liping Hou Qifa Wang Chenyang Li Weixing Yu Xuetao Gan Kaihui Liu Malin Premaratne Fajun Xiao Jianlin Zhao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an 710129China Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi’an 710119China State Key Laboratory for Mesoscopic Physics Collaborative Innovation Centre of Quantum MatterSchool of PhysicsPeking UniversityBeijing 100871China Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonVIC 3800Australia

Plasmonic particle-on-film nanocavities,supporting gap modes with ultra-small volume,provide a great solution to boost light–matter interactions at the *** this work,we report on the photoluminescence(PL)enhancement of monolayer MoS_(2) using high order modes of an Au nanosphere dimer-on-film nanocavity(DoFN).The high order plasmon modes,consisting of two bonding quadrupoles in the dimer and their images in the Au film,are revealed by combining the polarization-resolved scattering spectra with the numerical *** integrating the monolayer MoS_(2) into the DoFN,these high order modes are used to enhance PL intensity through simultaneously boosting the absorption and emission processes,producing a 1350-fold enhancement *** opens an avenue to enhance the light–matter interaction with high order plasmon modes and may find applications in future optoelectronics and nanophotonics devices.

关键词： cavity high integrating

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