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IEEE Radar conference

作者： Michael Leigsnering Fauzia Ahmad Moeness G. Amin Abdelhak M. Zoubir Signal Processing Group Institute of Telecommunications Technische Universitat Darmstadt Radar Imaging Laboratory Center for Advanced Communications Villanova University

ISBN: (纸本)9781467357920

Wall ringing and reverberation pose a challenging problem for through-the-wall radar image formation algorithms. We propose a novel method based on compressive sensing (CS) that enables high reconstruction quality combined with efficient data collection. A joint wall reverberation and target model is developed and the reconstructed scene is obtained based on a group sparse CS approach. Thus, the wall returns can be separated from the target returns resulting in a clean image. We demonstrate the performance using simulation results.

关键词： image reconstruction Reverberation Radar imaging Arrays Compressed sensing Imaging Joints

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Prototype testing and algorithm development for the Cosmic Ray Inspection and Passive Tomography (CRIPT) project

Prototype testing and algorithm development for the Cosmic R...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： v. Anghel J. Armitage J. Botte K. Boudjemline J. Bueno D. Bryman E. Charles T. Cousins P.-L. Drouin A. Erlandson G. Gallant C. Jewett G. Jonkmans Z. Liu S. Noel G. Oakham T.J. Stocki M. Thompson D. Waller Atomic Energy of Canada Limited Chalk River Ontario Canada Department of Physics Carleton University Ottawa Ontario Canada Advanced Annlied Physics Solutions Vancouver British Columbia Canada Canada Border Services Agency Ottawa Ontario Canada Intemational Safety Research Ottawa Ontario Canada Defence Research and Development Canada Ottawa Ontario Canada Health Canada Ottawa ONT Canada Health Canada Ottawa Ontario Canada

The Cosmic Ray Inspection and Passive Tomography (CRIPT) collaboration has completed the testing of small muon detector prototypes and has commenced construction of a 12 layer, 4m 2 prototype muon scattering tomography system. Three areas of CRIPT's progress are reported: (1) results from the testing of one of drift chamber muon detector prototypes; (2) algorithms for muon momentum estimation and tomographic image reconstruction; and (3) the status of the large prototype construction. The intrinsic resolution of the 2.4 m long, 1.2 m wide drift chamber muon detector prototype has been measured to be 1.73 mm perpendicular to the anode wire, and 2.9 mm parallel to the anode. A Bayesian estimator algorithm has been developed for muon momentum estimation. from simulations, the momentum resolution is expected to be highly asymmetric, varying from -18% to +92% integrated across the cosmic ray muon spectrum. A novel Point-of-Closest-Approach (PoCA) algorithm has also been developed for tomographic imaging. Multiple possible muon trajectories are assumed for each muon. The expected completion date for the construction is summer 2012, with first tomographic data following soon afterward.

关键词： Containers Size measurement Chemicals Tomography Biology

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Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising

Patlak-Guided Self-Supervised Learning for Dynamic PET Denoi...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Q. Liu X. Guo Y.-J. Tsai J.-D. Gallezot M.-K. Chen L. Guo H. Xie D. Pucar C. Young v. Panin R. E. Carson C. Liu Biomedical Engineering Yale University New Haven Connecticut United States of America Canon Medical Research USA Vernon Hill Illinois United States of America Radiology & Biomedical Imaging Yale University New Haven Connecticut United States of America Siemens Medical Solutions USA Knoxville Tennessee United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Dynamic PET imaging delivers crucial FDG metabolism data for diagnosis and treatment response assessment. Count limitations pose challenges to existing methods for estimating Ki images. We proposed a deep-learning framework that integrates the Patlak model into UNet-LSTM without imposing image size constraints. The study used a dynamic PET dataset with 28 patients, employing a three-fold training scheme. Training from scratch was challenging due to noise and weak supervision, soa pre-trained 3D UNet model on prompts-matched static images from 100 subjects was utilized. The pre-trained model was then used to initialize the downsampling and upsampling blocks of the UNet-LSTM network, facilitating convergence. After weight initialization, two approaches were used: Model A utilized raw dynamic frames as the input, while Model B utilized denoised dynamic frames by the pre-trained model as the input. Evaluation of 28 patients with 54 lesions reveals that the proposed approach achieves superior noise reduction and lesion detectability compared to both the prompts-matched model and direct reconstruction methods. Overall, Model B exhibits the most promising outcomes, as indicated by the enhancement in lesion Signal-to-Noise Ratio from 20.6 ± 13.6 (Indirect Recon), 33.7 ± 25.9 (Prompts-Matched), 34.5 ± 22.2 (Model A) to 47.6 ± 40.2 for 14 -frame prediction and from 20.9 ± 13.5 (Direct Recon), 14.2 ± 9.3 (Indirect Recon), 24.8 ± 18.1 (Prompts-Matched), and 26.9 ± 17.9 (Model A) to 32.9 ± 26.6 for 6 -frame prediction. In conclusion, we propose the Patlak-Guided Self-Supervised learning method, which integrates spatial and temporal data for dynamic PET denoising, enhances noise reduction and preserves lesions. The superior lesion SNR highlights its potential clinical utility and imaging quality improvement.

关键词： Training Solid modeling Three-dimensional displays Semiconductor detectors Noise reduction Self-supervised learning Predictive models

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A Parameter-Free MAP image reconstruction Algorithm for Impulse-Based UWB Ground Penetrating Radar

A Parameter-Free MAP Image Reconstruction Algorithm for Impu...

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IEEE Global conference on Signal and Information Processing

作者： Henry C. Ogworonjo John M.M. Anderson Lam Nguyen Department of Electrical and Computer Engineering Howard University Washington DC 20059 U.S Army Research Laboratory Adelphi Maryland

ISBN: (纸本)9781479975921

In this paper, we propose a parameter-free image reconstruction algorithm that is based on the maximum a posteriori (MAP) estimation method and so called "integrate-out" approach for determining the prior distribution. The resulting objective function is minimized using the majorize-minimize optimization technique. The proposed algorithm avoids the computational burden associated with using cross-validation methods for choosing the penalty parameter in the popular least absolute shrinkage and selector operator (LASSO) method. We tested the algorithm on synthetic data obtained from the U.S Army Research Laboratory (ARL) in Adelphi, MD. As compared to the image reconstructed using the delay-and-sum algorithm, the reconstructed images is more sparse, with suppressed side lobes and background noise.

关键词： LASSO ARL MD

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Assessing the Hemodynamic Influence between Multiple Lesions in a Realistic Right Coronary Artery Segment: a Computational Study

Assessing the Hemodynamic Influence between Multiple Lesions...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society

作者： Panagiotis K. Siogkas Antonis I. Sakellarios Michail I. Papafaklis Kostas A. Stefanou Lambros M. Athanasiou Themis P. Exarchos Katerina K. Naka Lampros K. Michalis Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina Cardiovascular Division Brigham and Women's Hospital Harvard Medical School Biomedical Research Institute - FORTH Michaelideion Cardiac Center Dept. of Cardiology in Medical School University of Ioannina

ISBN: (纸本)9781424479276

Coronary artery disease is the primary cause of morbidity and mortality worldwide. Therefore, detailed assessment of lesions in the coronary vasculature is critical in current clinical practice. Fractional flow reserve (FFR) has been proven as an efficient method for assessing the hemodynamic severity of a coronary stenosis. However, functional assessment of a coronary segment with multiple stenoses (≥ 2) remains complex for guiding the strategy of percutaneous coronary intervention due to the hemodynamic interplay between adjacent stenoses. In this work, we created four 3-dimensional (3D) arterial models that derive from a healthy patient-specific right coronary artery segment. The initial healthy model was reconstructed using fusion of intravascular ultrasound (IvUS) and biplane angiographic patient data. The healthy 3D model presented a measured FFR value of 0.96 (pressure-wire) and a simulated FFR value of 0.98. We then created diseased models with two artificial sequential stenoses of 90% lumen area reduction or with the proximal and distal stenosis separately. We calculated the FFR value for each case: 0.65 for the case with the two stenoses, 0.73 for the case with the distal stenosis and 0.90 for the case with the proximal stenosis. This leads to the conclusion that although both stenoses had the same degree of lumen area stenosis, there was a large difference in hemodynamic severity, thereby indicating that angiographic lumen assessment by itself is often not adequate for accurate assessment of coronary lesions.

关键词： Three-dimensional displays Solid modeling Arteries Hemodynamics Computational modeling image reconstruction

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A Nonparametric Bayesian Approach to Joint Multiple Dictionary Learning with Separate image Sources

A Nonparametric Bayesian Approach to Joint Multiple Dictiona...

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IEEE Global conference on Signal and Information Processing

作者： Shaoyang Li Xiaoming Tao Linhao Dong Jianhua Lu State Key Laboratory on Microwave and Digital Communications Tsinghua National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9781479975921

Nonparametric Bayesian approach is considered for learning appropriate dictionaries in sparse image representations. However, for images from multiple separate sources, existing methods have two issues that potentially limit their practical implements: first, learning one unified dictionary is not optimal for representing image samples in different subspaces;second, the required number of dictionaries and their correlations are unknown in advance. In this paper, we address these issues by: 1) modeling multiple dictionaries using a Dirichlet process which can automatically infer the latent dictionary number needed to fit the image data;2) placing a hierarchical Beta process prior to depict the dictionary correlations. To make the Bayesian model inference tractable, we further derive a combination of collapsed Gibbs sampler and auxiliary-variable-based slice sampler. Experimental results demonstrate that our proposed inference approach can achieve an optimized set of dictionaries for multiple source images, while exhibiting performance improvements in the context of image compressive sensing reconstruction.

关键词： Nonparametric Bayes dictionary learning multiple separate image sources Markov chain Monte Carlo image compressive sensing reconstruction

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A Practical Updating Background Method in Moving Target Detection

A Practical Updating Background Method in Moving Target Dete...

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International conference on Consumer Electronics, Communications and Networks

作者： Junfang Song Cao Huang Ru Xue School of Information Engineering Tibet Nationalities Institute

ISBN: (纸本)9781457714146

Background subtraction is a powerful mechanism typically for segmenting moving regions in image sequences taken from a data acquisition system. It compares each new image to a model of the scene background image. In this paper, we present a novel method for the reconstruction of background image in background subtraction. This method firstly calculates the difference of two neighboring images, then deals with this result to determine whether the background image needs to be updated and finally reconstructs the background image by a simple weight formula. The experiment result shows that the proposed method could update background image quickly and timely in the scene of traffic flow monitoring. Using this method we can get a more accurate background, which is conducive to the segmentation of the target.

关键词： Traffic flow monitoring Background subtraction Moving target detection Background image updating

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SU-E-J-177: Improved reconstruction Algorithm for Tomosynthesis Plus Few Discrete Projections

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Medical Physics 2011年第6PART10期38卷 3484-3484页

作者： Y Wei M McKee A Baydush Wake Forest University School of Medicine Winston Salem NC

Purpose: Investigations over the past few years have demonstrated some of the benefits and shortfalls of tomosynthesis in radiation therapy for patient positioning or dose tracking. Tomosynthesis generates excellent image quality in one reconstruction plane; however, there is a loss of edge and frequency information in the additional planes due to the spatial incompleteness of the projection data. In this study, we investigate the results of a new discrete frequency interpolation technique (DFIT), which is used to incorporate tomosynthesis limited arc projection data and three additional “filling” projections. Methods: For the initial investigations, we simulated a Shepp-Logan phantom with parallel beam geometry to generate 93 projections. 90 of these projections were over a tomosynthetic arc at one projection per degree. The remaining three projections were generated to fill in the next 90 degrees evenly (i.e. 22.5, 45, and 67.5 degrees from the end of the arc) to help “fill in” the incomplete data. Simple filtered backprojection (FBP) results show no improvement as FBP is designed for uniform distribution of projections. Per the projection slice theorem, the tomosynthesis data fills in a portion of the frequency plane. The remaining sparsely sampled area of the frequency plane is filled using linear interpolation from the end arc projections and the 3 discrete projections. The DFIT image is the sum of the FBP image and the inverse Fourier transform of the interpolated frequencies Results: The images by traditional FBP and DFIT are compared visually and using line profile plots. Additionally, contrast results are examined. The DFIT images show improved edge delineation in the usual tomosynthesis “blurred” direction and improves reconstructed contrast. Conclusions: The addition of limited “filling” discrete projections and DFIT can be used to improve the reconstruction accuracy and quality of short arc imaging. Research supported by Nucletron, B.v.

关键词： Digital tomosynthesis mammography Medical imaging image reconstruction Interpolation Medical image reconstruction Dosimetry Radiation therapy Fourier transforms Medical image quality

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reconstruction of stereoscopic CTA events using deep learning with CTLearn 37

Reconstruction of stereoscopic CTA events using deep learnin...

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37th International Cosmic Ray conference, ICRC 2021

作者： Miener, T. Nieto, D. Brill, A. Spencer, S. Contreras, J.L. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, v. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, v. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, v. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, v. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, v. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, v. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, v. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Columbia University Physics Department New York United States University of Oxford Department of Physics Oxford United Kingdom Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O'Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d'Informació Científica Edifici D Carrer de l'Albareda Bellaterrra Cerdanyola del Vallès08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS/IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève 41211 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche Università degli Studi dell

The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 Gev to more than 300 Tev. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Cameras

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X-ray simulations with gvXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development 15

X-ray simulations with gVXR as a useful tool for education, ...

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15th SPIE conference on Developments in X-Ray Tomography

作者： vidal, Franck P. Afshari, Shaghayegh Ahmed, Sharif Atkins, Carolyn Béchet, Éric Bellot, Alberto Corbí Bosse, Stefan Chahid, Younes Chou, Cheng-Ying Culver, Robert Dixon, Lewis Friemann, Johan Garbout, Amin Hatton, Clémentine Henry, Audrey Leblanc, Christophe Leonardi, Alberto Létang, Jean Michel Lipscomb, Harry Manchester, Tristan Meere, Bas Middleburgh, Simon Mitchell, Iwan Perera, Liam Puig, Martí Tugwell-Allsup, Jenna Scientific Computing Ada Lovelace Centre Science Technology Facilities Council United Kingdom School of Computer Science & Engineering Bangor University United Kingdom Department of Biomechatronics Engineering National Taiwan University Taiwan DIAD Beamline Diamond Light Source United Kingdom UK Astronomy Technology Centre Royal Observatory Edinburgh United Kingdom Département d’Aérospatiale et Mécanique Université de Liège Belgium Escuela Superior de Ingeniería y Tecnología Universidad Internacional de La Rioja Spain Department of Computer Science University of Koblenz Koblenz Germany Department of Mechanical Engineering University of Siegen Siegen Germany The Manufacturing Technology Centre United Kingdom Department of Industrial and Materials Science Chalmers University of Technology Sweden Henry Royce Institute Henry Moseley X-Ray Imaging Facility Department of Materials The University of Manchester United Kingdom Scalian DS Rennes France INSA-Lyon Université Claude Bernard Lyon 1 CNRS Inserm CREATIS UMR 5220 U1294 Lyon France Department of Mechanical Engineering Eindhoven University of Technology Netherlands Department of Engineering Science University of Oxford United Kingdom Ysbyty Gwynedd United Kingdom

ISBN: (纸本)9781510679641

gvirtualXray (gvXR) is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gvXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gvXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gvXR related to XCT. © 2024 SPIE.

关键词： Computerized tomography

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