检索结果-内蒙古大学图书馆

Integrated thermoelectric energy generator and organic storage device

Energy Harvesting and Systems 2018年第3-4期5卷 73-79页

作者： Al-Haik, Mohammad Y. Alothman, Abdulmohsen A. Hajj, Muhammad R. Department of Electromechanical Engineering Technology Abu Dhabi Polytechnic Abu Dhabi111499 United Arab Emirates King Abdulaziz City for Science and Technology National Satellite Technology Center Riyadh Saudi Arabia Department of Biomedical Engineering and Mechanics Virginia Tech BlacksburgVA United States

We evaluate the storage performance of an integrated energy harvesting and storage system using a thermoelectric generator (TEG) and an organic energy storage device. The power generated by the TEG is obtained by varying the temperature of the heat source on the bottom surface of the TEG while keeping the opposite surface at a constant cooling temperature. The difference in temperature gradient (BT) increases the power generated by the TEG. Capacitance-voltage measurements were performed on the organic storage device to evaluate the storage capabilities of the embedded storage elements, CdS nanoparticles. The objective is to assess the possibility of the integrated system and evaluate the effectiveness of the storage device. Voltage, current and power density values are determined and compared for different load resistance. The results revealed that for the 100 K load resistor, the voltage across the capacitor was the maximum at a lower temperature gradient with a charge density of 5.14 × 10–3mC2 © 2018 Walter de Gruyter GmbH. All rights reserved.

关键词： Thermoelectric equipment

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence applications in psychoradiology

引用

Psychoradiology 2021年第2期1卷 94-107页

作者： Fei Li Huaiqiang Sun Bharat B.Biswal John A.Sweeney Qiyong Gong Huaxi MR Research Center(HMRRC) Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041SichuanP.R.China Research Unit of Psychoradiology Chinese Academy of Medical SciencesChengdu 610041SichuanP.R.China Functional and Molecular Imaging Key Laboratory of Sichuan Provience Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041SichuanP.R.China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 610054SichuanP.R.China Department of Psychiatry and Behavioral Neuroscience University of CincinnatiCincinnatiOH 45219USA

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness,pre-diction of prognosis before treatment,and guidance for selection of effective treatments that target patient-relevant pathophysiological *** is the primary aim of the field of *** databases collected from large samples at multiple centers,sophisticated artificial intelligence(AI)algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose,predict,and make treatment *** this review,we selectively summarize psychoradiological research using magnetic reso-nance imaging of the brain to explore the neural mechanism of psychiatric disorders,and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders,as well as limitations in the application of AI that should be considered in future translational research.

关键词： Psychoradiology magnetice resonance imaging brain artificial intelligence machine learning deep learning graph neural network

来源：评论

学校读者我要写书评

暂无评论

Correction to: MR beyond diagnostics at the ESMRMB annual meeting: MR theranostics and intervention

引用

Magma (New York, N.Y.) 2024年第5期37卷 947-948页

作者： Milan Hájek Ulrich Flögel Adriana A S Tavares Lucia Nichelli Aneurin Kennerley Thomas Kahn Jurgen J Futterer Aikaterini Fitsiori Holger Grüll Nandita Saha Felipe Couñago Dogu Baran Aydogan Maria Eugenia Caligiuri Cornelius Faber Laura C Bell Patrícia Figueiredo Joan C Vilanova Francesco Santini Ralf Mekle Sonia Waiczies Department of Diagnostic and Interventional Radiology Institute for Clinical and Experimental Medicine Prague Czech Republic. Experimental Cardiovascular Imaging Institute for Molecular Cardiology Heinrich Heine University Düsseldorf Düsseldorf Germany. Centre for Cardiovascular Sciences and Edinburgh Imaging University of Edinburgh Edinburgh UK. Sorbonne Université Inserm CNRS UMR S 1127 Paris Brain Institute ICM Paris France. Department of Neuroradiology AP-HP Pitié-Salpêtrière Hospital Paris France. Department of Sports and Exercise Science Institute of Sport Manchester Metropolitan University Manchester UK. Department of Biology University of York York UK. Department of Diagnostic and Interventional Radiology University of Leipzig Leipzig Germany. Minimally Invasive Image-Guided Intervention Center (MAGIC) Department of Medical Imaging Radboudumc Nijmegen The Netherlands. Unit of Diagnostic and Interventional Neuroradiology Diagnostic Department University Hospitals of Geneva Geneva Switzerland. Institute of Diagnostic and Interventional Radiology Faculty of Medicine University Hospital of Cologne University of Cologne Cologne Germany. Department of Chemistry Faculty of Mathematics and Natural Sciences University of Cologne Cologne Germany. Max-Delbrück-Centrum Für Molekulare Medizin (MDC) Berlin Ultrahigh Field Facility Berlin Germany. Charité-Universitätsmedizin Berlin Berlin Germany. Department of Radiation Oncology Hospital Universitario San Francisco de Asís Hospital Universitario Vithas La Milagrosa GenesisCare 28010 Madrid Spain. A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland Kuopio Finland. Neuroscience Research Center Department of Medical and Surgical Sciences Università Degli Studi "Magna Graecia" Catanzaro Italy. Translational Research Imaging Center (TRIC) Clinic of Radiology University of Münster Münster Germany. Early Clinical Development Genentech Inc. South San Francisco USA. Institute for S

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Fully Automated Scan Range Detection in Chest CT Imaging

Deep Learning-based Fully Automated Scan Range Detection in ...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Y. Salimi A. Akhavanallaf I. Shiri Z. Mansouri A. Saberimanesh A. Sanaat M. Pakbin D. Askari S. Sandoughdaran E. Sharifipour H. Arabi H. Zaidi Department of Medical Imaging Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Switzerland Department of Biomedical Engineering and Medical Physics Shahid Beheshti University of Medical Sciences Tehran Iran Imaging Department Qom University of Medical Sciences Qom Iran Department of Radiology Technology Shahid Beheshti University of Medical Tehran Iran Department of Radiation Oncology Shahid Beheshti University of Medical Sciences Tehran Iran Neuroscience Research Center Qom University of Medical Sciences Qom Iran Geneva University Neurocenter Geneva University Geneva Switzerland Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen RB Groningen Netherlands Department of Nuclear Medicine University of Southern Denmark Odense Denmark

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

This study aimed to develop an automated scan range selection for minimal patient irradiation in CT examinations within the chest region. A total number of 20,820 chest CT images acquired for various indications were collected, the 3D lung masks were generated using a Deep Neural Network (DNN) developed by our group. Consequently, 2D projected localizer images and masks were computed in lateral (lat) and anterior-posterior (AP) directions. We developed a deep learning algorithm to predict the lung mask from 2D localizer images. Thereby, the scan range is automatically determined without the technologist’s intervention. Lastly, the impact of over-ranging on patients’ effective dose was investigated through personalized dosimetry of the given cohort. A significant over-scanning range (31±24 mm) was observed in the clinical setting for more than 95% of cases. The average Dice coefficient for 2D lung segmentation was 0.96 and 0.97 for AP and lateral projections, respectively. The proposed approach resulted in errors of 0.08±1.46 and -1.5±4.1 mm in the superior and inferior directions, respectively. The effective dose (ED) was reduced by 21% in the unseen external dataset when using the proposed automated scan range selection.

关键词： Deep learning Radiation effects Three-dimensional displays Computed tomography Neural networks Lung Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "Secondary Bjerknes Forces Deform Targeted Microbubbles" (Ultrasound Med Biol 2013;39:490-506)

引用

Ultrasound in medicine & biology 2021年第6期47卷 1639页

作者： Tom J A Kokhuis Valeria Garbin Klazina Kooiman Benno A Naaijkens Lynda J M Juffermans Otto Kamp Antonius F W van der Steen Michel Versluis Nico de Jong Biomedical Engineering Thorax Center Erasmus MC Rotterdam The Netherlands Interuniversity Cardiology Institute of the Netherlands Utrecht The Netherlands. Department of Chemical Engineering Imperial College London London UK. Biomedical Engineering Thorax Center Erasmus MC Rotterdam The Netherlands. Interuniversity Cardiology Institute of the Netherlands Utrecht The Netherlands Department of Pathology VU University Medical Center Amsterdam The Netherlands. Department of Physiology VU University Medical Center Amsterdam The Netherlands. Department of Cardiology VU University Medical Center Amsterdam The Netherlands. Physics of Fluids Group and MIRA Institute of Biomedical Technology and Technical Medicine University of Twente Enschede The Netherlands.

来源：评论

学校读者我要写书评

暂无评论

Possible Contexts of Use for in Silico Trials methodologies: A consensus-based review

arXiv

引用

arXiv 2021年

作者： Viceconti, Marco Emili, Luca Afshari, Payman Courcelles, Eulalie Curreli, Cristina Famaey, Nele Geris, Liesbet Horner, Marc Jori, Maria Cristina Kulesza, Alexander Loewe, Axel Neidlin, Michael Reiterer, Markus Rousseau, Cecile F. Russo, Giulia Sonntag, Simon J. Voisin, Emmanuelle M. Pappalardo, Francesco The Department of Industrial Engineering Alma Mater Studiorum University of Bologna Bologna40136 Italy The Medical Technology Lab IRCCS Istituto Ortopedico Rizzoli Bologna40136 Italy The In Silico Trials Technologies Via Parini 9 Milano20121 Italy DePuy Synthes Spine Johnson and Johnson RaynhamMA United States Novadiscovery SAS Lyon France The Biomechanics Section Department of Mechanical Engineering KU Leuven Leuven Belgium The Division of Skeletal Biology & Engineering Research Center KU Leuven Belgium The Biomechanics Research Unit University of Liège Liège Belgium ANSYS Inc. EvanstonIL United States Mediolanum Cardio Research Milano Italy The Institute of Biomedical Engineering Karlsruhe Institute of Technology Germany The Department of Cardiovascular Engineering Institute of Applied Medical Engineering RWTH Aachen University Germany Medtronic PLC United States France The Department of Drug and Health Sciences University of Catania Italy Virtonomy GmbH Munich Germany France

The term "In Silico Trial" indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature;the remaining 15 should, for now, be considered speculative. © 2021, CC BY-NC-ND.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Correction to: Temperature self-regulating flat electric heaters based on MWCNTs-modified polymers

引用

Polymer Bulletin 2021年第11期78卷 6705-6706页

作者： Ali, Imran AlGarni, Tahani Saad Shchegolkov, Alexandr Shchegolkov, Aleksei Jang, Sung-Hwan Galunin, Evgeny Komarov, Fadey Borovskikh, Pavel Imanova, Gunel T. Chemistry Department College of Science King Saud University Riyadh Saudi Arabia Department of Chemistry Jamia Millia Islamia (Central University) Delhi India Research Department Technology and Methods of Nanoproducts Manufacturing Tambov State Technical University Tambov Russia Department of Civil and Environmental Engineering Hanyang University ERICA Ansan South Korea Research School of Chemistry and Applied Biomedical Sciences Tomsk Polytechnic University Tomsk Russia A.N. Sevchenko Scientific-Research Institute for Applied Physical Problems Belarusian State University Minsk Republic of Belarus Center for Economics of Materials Halle (Saale) Germany Department of Physical Mathematical and Technical Sciences Institute of Radiation Problems National Academy of Sciences Baku Azerbaijan

There was a typo error in the second author name.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MRI-based radiomics to determine the role of thigh muscle quality in the development of knee osteoarthritis: A discovery and validation study

引用

Journal of Advanced Research 2025年

作者： Dang, Qin Hong, Zixuan Zeng, Dong Cao, Peihua Li, Shengfa Cen, Han Wang, Xiaoshuai Zhu, Zhaohua Han, Weiyu Wang, Jian Zhao, Liang Mobasheri, Ali Hunter, David J Ma, Jianhua Li, Jia Ding, Changhai Clinical Research Centre Zhujiang Hospital Southern Medical University Guangdong Guangzhou China Department of Radiation Oncology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Orthopaedic Zhujiang Hospital Southern Medical University Guangdong Guangzhou China Division of Orthopaedic Surgery Department of Orthopedics Nanfang Hospital Southern Medical University Guangdong Guangzhou China Research Unit of Health Sciences and Technology Faculty of Medicine University of Oulu Oulu Finland Department of Regenerative Medicine State Research Institute Centre for Innovative Medicine Vilnius Lithuania Department of Joint Surgery First Affiliated Hospital of Sun Yat-sen University Guangzhou Guangdong China World Health Organization Collaborating Center for Public Health Aspects of Musculoskeletal Health and Aging Université de Liège Liège Belgium Department of Rheumatology Royal North Shore Hospital and Sydney Musculoskeletal Health Kolling Institute University of Sydney Australia Menzies Institute for Medical Research University of Tasmania Hobart TAS Australia Joint Surgery Department the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong China Guangzhou Orthopedic Institute Guangzhou Medical University Guangdong Guangzhou China

Introduction: Although the reduced muscle quality is common in elderly people, its role in the development of knee osteoarthritis (OA) remains unclear. Magnetic Resonance Imaging (MRI) is considered the gold standard imaging modality to assess muscle quality, which might holistically capture the underlying pathology. Objectives: This study aimed to apply MRI-based radiomics to assess the quality of thigh muscles comprehensively, and examine its prediction performance for incident knee OA. Methods: The participants at risk of knee OA during the 48-month visit were selected from a multicenter cohort and analyzed using a nested case-control design. Cases were matched 1:1 to controls according to age, sex, and contralateral knee status. A multitask framework was developed to comprehensively evaluate thigh muscle quality by calculating conventional quantitative markers and radiomic features. Baseline collected thigh and knee MRI scans were respectively used for discovery and validation procedures. In each procedure, three (cross-sectional area [CSA], intramuscular adipose tissue [intra-MAT] CSA, and radiomic signature score [Rad-score]) imaging models of each muscle group (extensors, flexors, and auxiliaries) were trained and tested. The prediction performance of each muscle Rad-score was compared with conventional markers respectively. Furthermore, the incremental predictive value of clinical characteristics for Rad-score model was investigated. Results: 448 participants (mean age 61 years, 63 % female) were identified and split into training (314 [70.1 %]) and testing (134 [29.9 %]) cohorts. In the discovery procedure, the extensors Rad-score provided superior prediction performance than extensors CSA and intra-MAT CSA (area under the curve [AUC] 0.832 [95 % CI 0.765–0.899] vs 0.595 [0.498–0.692] and 0.600 [0.503–0.697] in testing cohort;all p < 0.001). Contrarily, the performances of the Rad-score and conventional markers of flexors were relatively poor, as were those

关键词： Knee osteoarthritis Magnetic resonance imaging Muscle quality Radiomics Thigh muscle

来源：评论

学校读者我要写书评

暂无评论

Thoracic bone density measured by automatic phantom-less quantitative computed tomography was a valuable opportunistic osteoporosis diagnosis tool during chest scan

引用

Journal of Medical Imaging and Radiation Sciences 2024年第3期55卷 101527-101527页

作者： Zhou, Miss Wenqin Weng, Mr Yuanzhi Wei, Mrs Miao Zheng, Mr Chen Li, Mrs Wen Pu, Miss Jiayi Lu, Prof Weijia William Lv, Prof Fajin Department of Radiology The First Affiliated Hospital of Chongqing Medical University Chongqing China Department of Orthopaedics and Traumatology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong China Department of Orthopaedics and Traumatology The University of Hong Kong-Shenzhen Hospital Shenzhen China Research Center for Human Tissue and Organs Degeneration Institute of Biomedicine and Biotechnology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Department of Biomedical Engineering City University of Hong Kong Hong Kong China Medical imaging department Yubei District Traditional Chinese Medicine Hospital Chongqing China

Background/PurposeRoutine physical exams often use a chest computed tomography (CT) scan to screen pulmonary nodules, generally it only includes thoracic vertebrae. Phantom-less quantitative computed tomography (PL-QCT) has advantage on retrospective bone mineral density (BMD) assessment, especially for chest CT scan. This study aimed to evaluate the performance of different single vertebrae and different thoracic vertebrae combinations in predicting osteoporosis or osteopenia. Methods246 patients (mean age of 55.97±14.91; 48% female) who underwent the whole abdominal computed tomography (CT) were involved in this retrospective study. The BMD of lower thoracic and lumbar vertebrae were measured by PL-QCT. Diagnostic performance measures and the receiver operating characteristic (ROC) curves for diagnosing osteoporosis and osteopenia were calculated. Average of L1 and L2 BMD was used as diagnosis standard value of osteoporosis and osteopenia. ResultsA total of 1230 vertebrae were measured for BMD via this PL-QCT system. The BMD of lower thoracic vertebrae decreased successively from T10 to L2. And the BMD of lower thoracic and lumbar vertebrae are highly correlated. To distinguish osteoporosis from non-osteoporosis (osteopenia + normal), the maximal area under the receiver operating characteristic curve (AUC) was 0.951(BMD average of T10, T11 and T12). At the optimal threshold of 107.85 mg/cc, the specificity was 94.7% at the combination of the entire 3 lower thoracic vertebrae (BMD average of T10, T11 and T12). ConclusionsDuring chest CT scan, BMD of lower thoracic measured by automatic PL-QCT could be a valuable opportunistic osteoporosis diagnosis tool as it has similar performance in comparison with its lumbar counterpart.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

引用

Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：