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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： image enhancement

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SU-D-206-07: CBCT Scatter Correction Based On Rotating Collimator

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Medical Physics 2016年第6PART4期43卷 3346-3346页

作者： G Yu Y Yin L Qiang B Li Z Feng P Huang D Li Shandong Normal University Jinan Shandong Shandong Cancer Hospital and Institute China Jinan Shandong Zhang Jiagang STFK Medical Device Co Zhangjiangkang Suzhou Shandong Academy of Medical Sciences Jinan Shandong provice Shandong Province Key Laboratory of Medical Physics and Image Processing Te Ji'nan Shandong province School of Physics and Electronics Shandong Normal University Jinan Shandong

Purpose: Scatter correction in cone-beam computed tomography (CBCT) has obvious effect on the removal of image noise, the cup artifact and the increase of image contrast. Several methods using a beam blocker for the estimation and subtraction of scatter have been proposed. However, the inconvenience of mechanics and propensity to residual artifacts limited the further evolution of basic and clinical research. Here, we propose a rotating collimator-based approach, in conjunction with reconstruction based on a discrete Radon transform and Tchebichef moments algorithm, to correct scatter-induced artifacts. Methods: A rotating-collimator, comprising round tungsten alloy strips, was mounted on a linear actuator. The rotating-collimator is divided into 6 portions equally. The round strips space is evenly spaced on each portion but staggered between different portions. A step motor connected to the rotating collimator drove the blocker to around x-ray source during the CBCT acquisition. The CBCT reconstruction based on a discrete Radon transform and Tchebichef moments algorithm is performed. Experimental studies using water phantom and Catphan504 were carried out to evaluate the performance of the proposed scheme. Results: The proposed algorithm was tested on both the Monte Carlo simulation and actual experiments with the Catphan504 phantom. From the simulation result, the mean square error of the reconstruction error decreases from 16% to 1.18%, the cupping (τcup) from 14.005% to 0.66%, and the peak signal-to-noise ratio increase from 16.9594 to 31.45. From the actual experiments, the induced visual artifacts are significantly reduced. Conclusion: We conducted an experiment on CBCT imaging system with a rotating collimator to develop and optimize x-ray scatter control and reduction technique. The proposed method is attractive in applications where a high CBCT image quality is critical, for example, dose calculation in adaptive radiation therapy. We want to thank Dr. Lei X

关键词： Cone beam computed tomography X-ray scattering Collimators Medical image artifacts Medical X-ray imaging Medical image noise Medical image contrast Monte Carlo methods Tungsten

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High-Performance Ultraviolet Organic Light-Emitting Diode Enabled by High-Lying Reverse Intersystem Crossing

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Angewandte Chemie 2021年第41期133卷

作者： Han Zhang Ganggang Li Xiaomin Guo Kai Zhang Bing Zhang Xuecheng Guo Yuxuan Li Jianzhong Fan Prof. Zhiming Wang Prof. Dongge Ma Prof. Ben Zhong Tang State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Luminescence from Molecular Aggregates Center for Aggregation-Induced Emission AIE Institute Guangzhou International Campus South China University of Technology Guangzhou 510640 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan 250014 China Shenzhen Institute of Aggregate Science and Technology School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen 518172 China

Ultraviolet (UV) organic emitters that can open up applications for future organic light-emitting diodes (OLEDs) are of great value but rarely developed. Here, we report a high-quality UV emitter with hybridized local and charge-transfer (HLCT) excited state and its application in UV OLEDs. The UV emitter, 2BuCz-CNCz, shows the features of low-lying locally excited (LE) emissive state and high-lying reverse intersystem crossing (hRISC) process, which helps to balance the color purity and exciton utilization of UV OLED. Consequently, the OLED based on 2BuCz-CNCz exhibits not only a desired narrowband UV electroluminescent (EL) at 396 nm with satisfactory color purity (CIE x , y =0.161, 0.031), but also a record-high maximum external quantum efficiency (EQE) of 10.79 % with small efficiency roll-off. The state-of-the-art device performance can inspire the design of UV emitters, and pave a way for the further development of high-performance UV OLEDs.

关键词： hybridized local and charge-transfer state OLEDs record-high EQE reverse intersystem crossing UV emission

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3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

arXiv

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arXiv 2022年

作者： Chen, Zhifeng Liao, Congyu Cao, Xiaozhi Poser, Benedikt A. Xu, Zhongbiao Lo, Wei-Ching Wen, Manyi Cho, Jaejin Tian, Qiyuan Wang, Yaohui Feng, Yanqiu Xia, Ling Chen, Wufan Liu, Feng Bilgic, Berkin School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Radiology Stanford University Stanford CA United States Maastricht Brain Imaging Center Faculty of Psychology and Neuroscience University of Maastricht Netherlands Department of Radiotherapy Cancer Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Science Guangzhou China Siemens Medical Solutions BostonMA United States Department of Chemical Pathology The Chinese University of Hong Kong Hong Kong Division of Superconducting Magnet Science and Technology Institute of Electrical Engineering Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Key Laboratory of Mental Health of the Ministry of Education Southern Medical University Guangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1×1.1×1.0 mm3 resolution. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Novel Deep Learning Radiomics Model for Preoperative Evaluation of Hepatocellular Carcinoma Differentiation Based on Computed Tomography Data

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HPB 2022年 24卷 S217-S218页

作者： Ding, Y. Ruan, S. Liang, W. Shao, J. Sun, R. Tian, W. Nan, X. Ge, W. Zhang, X. Su, K. Xia, J. Huang, Q. Liu, W. Sun, Q. Dong, H. F. Mylène, C.Q. Guo, T. Krylov, A.S. Xiao, W. Bai, X. Liang, T. Department of Radiology the First Affiliated Hospital School of Medicine Zhejiang University Hangzhou China College of Information Science & Electronic Engineering Zhejiang University Hangzhou China Key Laboratory of Structural Biology of Zhejiang Province Hangzhou China Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Hangzhou China Westlake Omics (Hangzhou) Biotechnology Co. Ltd. Hangzhou China Department of Pathology the First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious Diseases School of Medicine Zhejiang University Hangzhou China Department of Radiology The People's Hospital of Beilun District Ningbo China Department of Radiology Ningbo Medical Center Lihuili Hospital Ningbo China Department of Electrical Engineering University of Brasilia Brasilia Brazil Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Hangzhou China Laboratory of Mathematical Methods of Image Processing Lomonosov Moscow State Universit Moscow Russian Federation Department of Hepatobiliary and Pancreatic Surgery School of Medicine Zhejiang University Hangzhou China

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Facial emotion detection using thermal and visual images based on deep learning techniques

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The Journal of Photographic Science 2024年第2期72卷

作者： Richa Rashmi U. Snekhalatha Anela L. Salvador Alex Noel Joseph Raj a Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 Tamil Nadu India a Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 Tamil Nadu Indiab College of Engineering Architecture and Fine Arts Batangas State University The National Engineering University Alangilan Campus Batangas City Batangas Philippines 4200 b College of Engineering Architecture and Fine Arts Batangas State University The National Engineering University Alangilan Campus Batangas City Batangas Philippines 4200 c Key laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University People’s Republic of China

ABSTRACTThe main objectives of this study are (i) to determine the different emotions, such as happy, anger, neutral, and sad, based on the visual and thermal images for facial expression recognition of healthy individuals; (ii) to train the modified pre-trained models, such as DenseNet-121, ResNet-50, and VGG-19 based on transfer learning technique, using the visual and thermal image dataset, and (iii) to compare the three different customize model with that of three pre-trained models in terms of accuracy. The customized CNN models were proposed to classify the four classes of emotions with high efficiency. Among thermal and visual image dataset, the thermal images have produced highest classification accuracy of 95.8% using the Customized Net-3 model and have outperformed the three modified pre-trained models while classifying the four facial expressions. Thus, the proposed customized CNN model is proven effective for facial emotion recognition based on thermal imaging using deep learning techniques.

关键词： Facial expression emotion recognition thermal imaging deep neural network convolution neural network Facial thermal image Visual image Pre-trained models

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第Sup1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

Shaoqing Wang1, Xiancun Yang2, Meixia Su1, Qiang Liu1 1Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2Department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu (2002md@***) Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA.

关键词： Convolutional Neural Network High Resolution Compute Tomography Intensity Inhomogeneity Kernel Support Vector Machine Clonal Selection Algorithm

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Community-developed checklists for publishing images and image analyses

arXiv

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arXiv 2023年

作者： Schmied, Christopher Nelson, Michael S. Avilov, Sergiy Bakker, Gert-Jan Bertocchi, Cristina Bischof, Johanna Boehm, Ulrike Brocher, Jan Carvalho, Mariana Chiritescu, Catalin Christopher, Jana Cimini, Beth A. Conde-Sousa, Eduardo Ebner, Michael Ecker, Rupert Eliceiri, Kevin Fernandez-Rodriguez, Julia Gaudreault, Nathalie Gelman, Laurent Grunwald, David Gu, Tingting Halidi, Nadia Hammer, Mathias Hartley, Matthew Held, Marie Jug, Florian Kapoor, Varun Koksoy, Ayse Aslihan Lacoste, Judith Le Dévédec, Sylvia Le Guyader, Sylvie Liu, Penghuan Martins, Gabriel G. Mathur, Aastha Miura, Kota Llopis, Paula Montero Nitschke, Roland North, Alison Parslow, Adam C. Payne-Dwyer, Alex Plantard, Laure Ali, Rizwan Schroth-Diez, Britta Schütz, Lucas Scott, Ryan T. Seitz, Arne Selchow, Olaf Sharma, Ved P. Spitaler, Martin Srinivasan, Sathya Strambio-De-Castillia, Caterina Taatjes, Douglas Tischer, Christian Jambor, HelenaKlara Fondazione Human Technopole Viale Rita Levi-Montalcini 1 Milano20157 Italy Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Max Planck Institute of Immunobiology and Epigenetics Freiburg79108 Germany Medical BioSciences department Radboud University Medical Centre Nijmegen Netherlands Laboratory for Molecular mechanics of cell adhesions Pontificia Universidad Católica de Chile Santiago Chile Osaka University Graduate School of Engineering Science Japan Euro-BioImaging ERIC Bio-Hub Meyerhofstr. 1 Heidelberg69117 Germany Carl Zeiss AG Carl-Zeiss-Straße 22 Oberkochen73447 Germany BioVoxxel Scientific Image Processing and Analysis Eugen-Roth-Strasse 8 Ludwigshafen67071 Germany Nanophotonics and BioImaging Facility at INL International Iberian Nanotechnology Laboratory 4715-330 Portugal Phi Optics Inc. 1800 S. Oak St Ste 106 ChampaignIL61820 United States Biochemistry Center Heidelberg Heidelberg University Germany Imaging Platform Broad Institute CambridgeMA02142 United States i3S Instituto de Investigação e Inovação Em Saúde and INEB Instituto de Engenharia Biomédica Universidade do Porto Porto Portugal Translational Research Institute Queensland University of Technology 37 Kent Street WoolloongabbaQLD4102 Australia School of Biomedical Sciences Faculty of Health Queensland University of Technology BrisbaneQLD4059 Australia TissueGnostics GmbH Vienna1020 Austria Department of Medical Physics and Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Centre for Cellular Imaging Core Facility Sahlgrenska Academy University of Gothenburg Sweden Allen Institute for Cell Science SeattleWA United States Friedrich Miescher Institute for Biomedical Research Basel Switzerland RNA Therapeutics Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States University of Oklahoma NormanOK United States Advanced Light Microscopy Unit Ce

images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications. © 2023, CC BY-SA.

关键词： image analysis

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