Subjects -> ENGINEERING (Total: 2844 journals)
    - CHEMICAL ENGINEERING (259 journals)
    - CIVIL ENGINEERING (255 journals)
    - ELECTRICAL ENGINEERING (182 journals)
    - ENGINEERING (1420 journals)
    - ENGINEERING MECHANICS AND MATERIALS (454 journals)
    - HYDRAULIC ENGINEERING (60 journals)
    - INDUSTRIAL ENGINEERING (101 journals)
    - MECHANICAL ENGINEERING (113 journals)

ENGINEERING (1420 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 9)
3D Research     Hybrid Journal   (Followers: 22)
AAPG Bulletin     Hybrid Journal   (Followers: 11)
Abstract and Applied Analysis     Open Access   (Followers: 4)
Aceh International Journal of Science and Technology     Open Access   (Followers: 9)
ACS Nano     Hybrid Journal   (Followers: 452)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 10)
Acta Nova     Open Access   (Followers: 1)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 4)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access   (Followers: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 9)
Adsorption     Hybrid Journal   (Followers: 5)
Advanced Energy and Sustainability Research     Open Access   (Followers: 8)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 14)
Advanced Engineering Research     Open Access  
Advanced Journal of Graduate Research     Open Access   (Followers: 4)
Advanced Quantum Technologies     Hybrid Journal   (Followers: 1)
Advanced Science     Open Access   (Followers: 13)
Advanced Science Focus     Free   (Followers: 7)
Advanced Science Letters     Full-text available via subscription   (Followers: 13)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 11)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 5)
Advances in Catalysis     Full-text available via subscription   (Followers: 8)
Advances in Complex Systems     Hybrid Journal   (Followers: 12)
Advances in Engineering Software     Hybrid Journal   (Followers: 31)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 20)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 22)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 30)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 10)
Advances in Natural Sciences : Nanoscience and Nanotechnology     Open Access   (Followers: 36)
Advances in Operations Research     Open Access   (Followers: 14)
Advances in OptoElectronics     Open Access   (Followers: 6)
Advances in Physics Theories and Applications     Open Access   (Followers: 21)
Advances in Polymer Science     Hybrid Journal   (Followers: 54)
Advances in Porous Media     Full-text available via subscription   (Followers: 6)
Advances in Remote Sensing     Open Access   (Followers: 58)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aerobiologia     Hybrid Journal   (Followers: 4)
Aerospace Systems     Hybrid Journal   (Followers: 10)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 8)
AIChE Journal     Hybrid Journal   (Followers: 38)
Ain Shams Engineering Journal     Open Access   (Followers: 7)
Al-Nahrain Journal for Engineering Sciences     Open Access  
Al-Qadisiya Journal for Engineering Sciences     Open Access   (Followers: 2)
AL-Rafdain Engineering Journal     Open Access   (Followers: 3)
Alexandria Engineering Journal     Open Access   (Followers: 3)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 27)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 12)
American Journal of Engineering Education     Open Access   (Followers: 20)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 31)
Annals of Civil and Environmental Engineering     Open Access   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 6)
Annals of Regional Science     Hybrid Journal   (Followers: 10)
Annals of Science     Hybrid Journal   (Followers: 10)
Annual Journal of Technical University of Varna     Open Access   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 2)
Applications in Energy and Combustion Science     Open Access   (Followers: 3)
Applications in Engineering Science     Open Access   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 8)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 22)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Engineering Letters     Open Access   (Followers: 5)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 11)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 6)
Applied Physics Research     Open Access   (Followers: 7)
Applied Sciences     Open Access   (Followers: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Basic and Applied Sciences     Open Access  
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Archives of Thermodynamics     Open Access   (Followers: 13)
Arctic     Open Access   (Followers: 7)
Arid Zone Journal of Engineering, Technology and Environment     Open Access   (Followers: 2)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ArtefaCToS : Revista de estudios sobre la ciencia y la tecnología     Open Access   (Followers: 1)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 2)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 9)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 7)
Assembly Automation     Hybrid Journal   (Followers: 2)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
AURUM : Mühendislik Sistemleri ve Mimarlık Dergisi = Aurum Journal of Engineering Systems and Architecture     Open Access   (Followers: 1)
Australasian Journal of Engineering Education     Hybrid Journal   (Followers: 3)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Hybrid Journal   (Followers: 2)
Autocracy : Jurnal Otomasi, Kendali, dan Aplikasi Industri     Open Access  
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal   (Followers: 1)
Avances en Ciencias e Ingenierías     Open Access  
Avances: Investigación en Ingeniería     Open Access   (Followers: 6)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 2)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 6)
Batteries     Open Access   (Followers: 11)
Batteries & Supercaps     Hybrid Journal   (Followers: 7)
Bautechnik     Hybrid Journal   (Followers: 3)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 29)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
Beyond : Undergraduate Research Journal     Open Access  
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Bilge International Journal of Science and Technology Research     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 14)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 6)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 6)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 8)
Biomicrofluidics     Open Access   (Followers: 7)
Biotechnology Progress     Hybrid Journal   (Followers: 44)
Black Sea Journal of Engineering and Science     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 13)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers Droit, Sciences & Technologies     Open Access   (Followers: 1)
Calphad     Hybrid Journal   (Followers: 2)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 30)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 6)
Case Studies in Thermal Engineering     Open Access   (Followers: 8)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Catalysis Letters     Hybrid Journal   (Followers: 3)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 9)
Catalysis Science and Technology     Hybrid Journal   (Followers: 13)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal   (Followers: 6)
Cell Reports Physical Science     Open Access  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
Central European Journal of Engineering     Hybrid Journal  
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 3)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Journal of Population, Resources and Environment     Open Access  
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencia y Tecnología     Open Access  
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Hybrid Journal   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 27)
Clay Minerals     Hybrid Journal   (Followers: 9)
Coal Science and Technology     Full-text available via subscription   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 14)
Coastal Engineering Journal     Hybrid Journal   (Followers: 9)
Coastal Engineering Proceedings : Proceedings of the International Conference on Coastal Engineering     Open Access   (Followers: 2)
Coastal Management     Hybrid Journal   (Followers: 30)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 3)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Color Research & Application     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 17)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 20)
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering     Open Access  
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 28)
Composite Interfaces     Hybrid Journal   (Followers: 10)
Composite Structures     Hybrid Journal   (Followers: 335)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 279)
Composites Part B : Engineering     Hybrid Journal   (Followers: 312)
Composites Part C : Open Access     Open Access   (Followers: 3)
Composites Science and Technology     Hybrid Journal   (Followers: 247)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Computation     Open Access   (Followers: 1)
Computational Geosciences     Hybrid Journal   (Followers: 20)
Computational Optimization and Applications     Hybrid Journal   (Followers: 11)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 21)
Computers & Geosciences     Hybrid Journal   (Followers: 30)

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Australasian Physical & Engineering Sciences in Medicine
Journal Prestige (SJR): 0.336
Citation Impact (citeScore): 1
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0158-9938 - ISSN (Online) 1879-5447
Published by Springer-Verlag Homepage  [2658 journals]
  • Estimating subjective evaluation of low-contrast resolution using
           convolutional neural networks

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      Abstract: Abstract To develop a convolutional neural network-based method for the subjective evaluation of computed tomography (CT) images having low-contrast resolution due to imaging conditions and nonlinear image processing. Four radiological technologists visually evaluated CT images that were reconstructed using three nonlinear noise reduction processes (AIDR 3D, AIDR 3D Enhanced, AiCE) on a CT system manufactured by CANON. The visual evaluation consisted of two items: low contrast detectability (score: 0–9) and texture pattern (score: 1–5). Four AI models with different convolutional and max pooling layers were constructed and trained on pairs of CANON CT images and average visual assessment scores of four radiological technologists. CANON CT images not used for training were used to evaluate prediction performance. In addition, CT images scanned with a SIEMENS CT system were input to each AI model for external validation. The mean absolute error and correlation coefficients were used as evaluation metrics. Our proposed AI model can evaluate low-contrast detectability and texture patterns with high accuracy, which varies with the dose administered and the nonlinear noise reduction process. The proposed AI model is also expected to be suitable for upcoming reconstruction algorithms that will be released in the future.
      PubDate: 2021-10-11
       
  • Comparison of non-coplanar optimization of static beams and arc
           trajectories for intensity-modulated treatments of meningioma cases

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      Abstract: Abstract Two methods for non-coplanar beam direction optimization, one for static beams and another for arc trajectories, were proposed for intracranial tumours. The results of the beam angle optimizations were compared with the beam directions used in the clinical plans. Ten meningioma cases already treated were selected for this retrospective planning study. Algorithms for non-coplanar beam angle optimization (BAO) and arc trajectory optimization (ATO) were used to generate the corresponding plans. A plan quality score, calculated by a graphical method for plan assessment and comparison, was used to guide the beam angle optimization process. For each patient, the clinical plans (CLIN), created with the static beam orientations used for treatment, and coplanar VMAT approximated plans (VMAT) were also generated. To make fair plan comparisons, all plan optimizations were performed in an automated multicriteria calculation engine and the dosimetric plan quality was assessed. BAO and ATO plans presented, on average, moderate global plan score improvements over VMAT and CLIN plans. Nevertheless, while BAO and CLIN plans assured a more efficient OARs sparing, the ATO and VMAT plans presented a higher coverage and conformity of the PTV. Globally, all plans presented high-quality dose distributions. No statistically significant quality differences were found, on average, between BAO, ATO and CLIN plans. However, automated plan solution optimizations (BAO or ATO) may improve plan generation efficiency and standardization. In some individual patients, plan quality improvements were achieved with ATO plans, demonstrating the possible benefits of this automated optimized delivery technique.
      PubDate: 2021-10-07
       
  • AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic
           ensemble of CNNs

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      Abstract: Abstract According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.
      PubDate: 2021-10-05
       
  • Effect of foam insertion in aneurysm sac on flow structures in parent
           lumen: relating vortex structures with disturbed shear

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      Abstract: Abstract Numerous studies suggest that disturbed shear, causing endothelium dysfunction, can be related to neighboring vortex structures. With this motivation, this study presents a methodology to characterize the vortex structures. Precisely, we use mapping and characterization of vortex structures' changes to relate it with the hemodynamic indicators of disturbed shear. Topological features of vortex core lines (VCLs) are used to quantify the changes in vortex structures. We use the Sujudi-Haimes algorithm to extract the VCLs from the flow simulation results. The idea of relating vortex structures with disturbed shear is demonstrated for cerebral arteries with aneurysms virtually treated by inserting foam in the sac. To get physiologically realistic flow fields, we simulate blood flow in two patient-specific geometries before and after foam insertion, with realistic velocity waveform imposed at the inlet, using the Carreau-Yasuda model to mimic the shear-thinning behavior. With homogenous porous medium assumption, flow through the foam is modeled using the Forchheimer-Brinkman extended Darcy model. Results show that foam insertion increases the number of VCLs in the parent lumen. The average length of VCL increases by 168.9% and 55.6% in both geometries. For both geometries under consideration, results demonstrate that the region with increased disturbed shear lies in the same arterial segment exhibiting an increase in the number of oblique VCLs. Based on the findings, we conjecture that an increase in oblique VCLs is related to increased disturbed shear at the neighboring portion of the arterial wall.
      PubDate: 2021-09-28
       
  • A deep neural network approach for P300 detection-based BCI using
           single-channel EEG scalogram images

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      Abstract: Abstract Brain–computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.
      PubDate: 2021-09-22
       
  • Dosimetric characteristics of a thin bolus made of variable shape
           tungsten rubber for photon radiotherapy

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      Abstract: Abstract In this study, we aim to clarify the dosimetric characteristics of a real time variable shape rubber containing tungsten (STR) as a thin bolus in 6-MV photon radiotherapy. The percentage depth doses (PDDs) and lateral dose profiles (irradiation field = 10 × 10  cm2) in the water-equivalent phantom were measured and compared between no bolus, a commercial 5-mm gel bolus, and 0.5-, 1-, 2-, and 3-mm STR boluses. The characteristics of the PDDs were evaluated according to relative doses at 1 mm depth (D1mm) and depth of maximum dose (dmax). To determine the distance of the shift caused by the STR bolus, the PDD value at a depth of 100 mm without a bolus was obtained. For each STR thickness, the difference between the depth corresponding to this PDD value and 100 mm was calculated. The penumbra size and width of the 50% dose were evaluated using lateral dose profiles. The D1mm with no bolus, 5-mm gel bolus, and 0.5-, 1-, 2-, and 3-mm STR boluses were 47.6%, 91.5%, 78.2%, 86.6%, 89.3%, and 89.4%, respectively, and the respective dmax values were 15, 10, 13, 12, 11, and 10 mm. The shifting distance of the 0.5-, 1-, 2-, and 3-mm STR boluses were 2.7, 4.4, 4.8, and 4.9 mm, respectively. There were no differences for those in lateral dose profiles. The 1-mm-thick STR thin bolus shifted the depth dose profile by 4.4 mm and could be used as a customized bolus for photon radiotherapy.
      PubDate: 2021-09-20
       
  • Determining tolerance levels for quality assurance of 3D printed bolus for
           modulated arc radiotherapy of the nose

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      Abstract: Abstract Given the existing literature on the subject, there is obviously a need for specific advice on quality assurance (QA) tolerances for departments using or implementing 3D printed bolus for radiotherapy treatments. With a view to providing initial suggested QA tolerances for 3D printed bolus, this study evaluated the dosimetric effects of changes in bolus geometry and density, for a particularly common and challenging clinical situation: specifically, volumetric modulated arc therapy (VMAT) treatment of the nose. Film-based dose verification measurements demonstrated that both the AAA and the AXB algorithms used by the Varian Eclipse treatment planning system (Varian Medical Systems, Palo Alto, USA) were capable of providing sufficiently accurate dose calculations to allow this planning system to be used to evaluate the effects of bolus errors on dose distributions from VMAT treatments of the nose. Thereafter, the AAA and AXB algorithms were used to calculate the dosimetric effects of applying a range of simulated errors to the design of a virtual bolus, to identify QA tolerances that could be used to avoid clinically significant effects from common printing errors. Results were generally consistent, whether the treatment target was superficial and treated with counter-rotating coplanar arcs or more-penetrating and treated with noncoplanar arcs, and whether the dose was calculated using the AAA algorithm or the AXB algorithm. The results of this study suggest the following QA tolerances are advisable, when 3D printed bolus is fabricated for use in photon VMAT treatments of the nose: bolus relative electron density variation within \(\pm 5\%\) (although an action level at \(\pm 10\%\) may be permissible); bolus thickness variation within \(\pm 1\) mm (or 0.5 mm variation on opposite sides); and air gap between bolus and skin \(\le 5\) mm. These tolerances should be investigated for validity with respect to other treatment modalities and anatomical sites. This study provides a set of baselines for future comparisons and a useful method for identifying additional or alternative 3D printed bolus QA tolerances.
      PubDate: 2021-09-16
       
  • Migraine detection from EEG signals using tunable Q-factor wavelet
           transform and ensemble learning techniques

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      Abstract: Abstract Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values ​​obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.
      PubDate: 2021-09-10
       
  • CT slice alignment to whole-body reference geometry by convolutional
           neural network

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      Abstract: Abstract Volumetric medical imaging lacks a standardised coordinate geometry which links image frame-of-reference to specific anatomical regions. This results in an inability to locate anatomy in medical images without visual assessment and precludes a variety of image analysis tasks which could benefit from a standardised, machine-readable coordinate system. In this work, a proposed geometric system that scales based on patient size is described and applied to a variety of cases in computed tomography imaging. Subsequently, a convolutional neural network is trained to associate axial slice CT image appearance with the standardised coordinate value along the patient superior-inferior axis. The trained neural network showed an accuracy of ± 12 mm in the ability to predict per-slice reference location and was relatively stable across all annotated regions ranging from brain to thighs. A version of the trained model along with scripts to perform network training in other applications are made available. Finally, a selection of potential use applications are illustrated including organ localisation, image registration initialisation, and scan length determination for auditing diagnostic reference levels.
      PubDate: 2021-09-10
       
  • A novel 3D printed curved monopole microstrip antenna design for
           biomedical applications

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      Abstract: Abstract This paper proposes a novel and compact monopole microstrip antenna design with a three-dimensional (3D) printed curved substrate for biomedical applications. A curved substrate was formed by inserting a semi-cylinder structure in the middle of the planar substrate consisting of polylactic acid. The antenna was fed with a microstrip line, and a partial ground plane was formed at the bottom side of the substrate. The copper plane with two triangular slots is arranged on the curved semi-cylinder structure of the substrate. The physical dimensions of the radiating plane and ground plane were optimally determined with the use of the sparrow search algorithm to provide a wide—10 dB bandwidth between 3 and 12 GHz. A total of six microstrip antennas having different parameters related to physical dimensions were designed and simulated to compare the performance of the proposed antenna with the help of full-wave electromagnetic simulation software called CST Microwave Studio. The proposed curved antenna was fabricated, and a PNA network analyzer was used to measure the S11 of the proposed antenna. It was demonstrated that the measured S11 covers the desired frequency range.
      PubDate: 2021-09-04
       
  • AI Notes: A new article type in Physical and Engineering Sciences in
           Medicine

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      PubDate: 2021-09-01
       
  • Winning images from the Photography in Medical Physics (PiMP) competition

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      PubDate: 2021-09-01
       
  • Book review: Science fictions: exposing fraud, bias, negligence and hype
           in science by Stuart J. Ritchie

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      PubDate: 2021-09-01
       
  • Book review: The Modern Technology of Radiation Oncology—a Compendium
           for Medical Physicists and Radiation Oncologists (Volume 4) edited by
           Jacob Van Dyk

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      PubDate: 2021-09-01
       
  • Deep learning based smart health monitoring for automated prediction of
           epileptic seizures using spectral analysis of scalp EEG

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      Abstract: Abstract Being one of the most prevalent neurological disorders, epilepsy affects the lives of patients through the infrequent occurrence of spontaneous seizures. These seizures can result in serious injuries or unexpected deaths in individuals due to accidents. So, there exists a crucial need for an automatic prediction of epileptic seizures to alert the patients well before the onset of seizures, enabling them to have a healthier quality of life. In this era, the Internet of Things (IoT) technologies are being used in a cloud-fog integrated environment to address such healthcare challenges using deep learning approaches. The present paper also proposes a smart health monitoring approach for automated prediction of epileptic seizures using deep learning-based spectral analysis of EEG signals. This approach processes EEG signals using filtering, segmentation into short duration segments and spectral-domain transformation. These signals are then analysed spectrally by separating them into several spectral bands, such as delta, theta, alpha, beta, and sub-bands of gamma. Furthermore, the mean spectral amplitude and spectral power features are retrieved from each spectral band to characterize various seizure states, which are fed to the proposed LSTM and CNN models. The results of the proposed CNN model show a maximum accuracy of 98.3% and 97.4% to obtain a binary classification of preictal and interictal seizure states for two different spectral band combinations respectively. Thus, the proposed CNN architecture accompanied by spectral analysis of EEG signals provides a viable method for reliable and real-time prediction of epileptic seizures.
      PubDate: 2021-09-01
       
  • EPSM 2020, Engineering and Physical Sciences in Medicine

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      PubDate: 2021-09-01
       
  • Development of a sit-to-stand assistive device with pressure sensor for
           elderly and disabled: a feasibility test

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      Abstract: Abstract Elderly patients face difficulty in performing the sit-to-stand motion; hence, their dependency on assistive devices for activities of daily living is increasing. However, the existing devices do not provide support according to the individual’s characteristics. This study aimed to develop a sit-to-stand motion assistive chair that detects the user’s weight using a load sensor and assists them to stand up by adjusting the speed themselves as per their weight and preference. Additionally, we investigated the feasibility of the developed device. A device for assisting patients in the sit-to-stand motion in rising up from the chair by electrical motorization was developed. This device senses the load on the seat plate using the load sensor and transmits it to the display through which the users can control the speed themselves using the speed control device. To test its feasibility, the electromyographic muscle activation was analyzed for the erector spinae, quadriceps, tibialis anterior, and gastrocnemius muscles in the sit-to-stand motion using this device in five healthy adults. When compared with the non-use of the device, the use of the developed assistive chair device significantly decreased the muscle activation of the erector spinae, quadriceps, tibialis anterior, and gastrocnemius by 37.27%, 20.44%, 14.50%, and 10.56% on the left and by 17.98%, 24.48%, 32.61%, and 6.05% on the right, respectively. The assistive device with a pressure sensor can effectively assist elderly patients with reduced muscle strength and balance in performing the sit-to-stand motion.
      PubDate: 2021-09-01
       
  • A review of dosimetric impact of implementation of model-based dose
           calculation algorithms (MBDCAs) for HDR brachytherapy

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      Abstract: Abstract To obtain dose distributions more physically representative to the patient anatomy in brachytherapy, calculation algorithms that can account for heterogeneity are required. The current standard AAPM Task Group No 43 (TG-43) dose calculation formalism has some clinically relevant dosimetric limitations. Lack of tissue heterogeneity and scattered dose corrections are the major weaknesses of the TG-43 formalism and could lead to systematic dose errors in target volumes and organs at risk. Over the last decade, model-based dose calculation algorithms (MBDCAs) have been clinically offered as complementary algorithms beyond the TG43 formalism for high dose rate (HDR) brachytherapy treatment planning. These algorithms provide enhanced dose calculation accuracy by using the information in the patient’s computed tomography images, which allows modeling the patient’s geometry, material compositions, and the treatment applicator. Several researchers have investigated the implementation of MBDCAs in HDR brachytherapy for dose optimization, but moving toward using them as primary algorithms for dose calculations is still lagging. Therefore, an overview of up-to-date research is needed to familiarize clinicians with the current status of the MBDCAs for different cancers in HDR brachytherapy. In this paper, we review the MBDCAs for HDR brachytherapy from a dosimetric perspective. Treatment sites covered include breast, gynecological, lung, head and neck, esophagus, liver, prostate, and skin cancers. Moreover, we discuss the current status of implementation of MBDCAs and the challenges.
      PubDate: 2021-09-01
       
  • Bi-parametric magnetic resonance imaging based radiomics for the
           identification of benign and malignant prostate lesions: cross-vendor
           validation

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      Abstract: Abstract The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients’ data from Siemens (Vendor 1), and the inner test set was 70 patients’ data from the same vendor. The external test set was 221 patients’ data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.
      PubDate: 2021-09-01
       
  • The perceptions of medical physicists towards relevance and impact of
           artificial intelligence

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      Abstract: Abstract Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists’ clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists’ practices, the possibility of AI threatening/disrupting the medical physicists’ practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists’ practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists’ practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.
      PubDate: 2021-09-01
       
 
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