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Zernike-coefficient extraction via helical beam reconstruction for optimization(ZEHBRO) in the far field

High Power Laser Science and Engineering 2023年第6期11卷 213-222页

作者： J.B.Ohland d.Posor U.Eisenbarth V.iancu R.Ungureanu d.Ursescu V.Bagnoud GSI Helmholtzzentrum für Schwerionenforschung DarmstadtGermany Institut für angewandte Physik Technische Universität DarmstadtDarmstadtGermany Helmholtz-Institut Jena JenaGermany ELI-NP Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering(IFIN-HH)M agureleIlfovRomania Faculty of Physics University of BucharestM agureleIlfovRomania Center for Advanced Laser Technologies(CETAL) National Institute for LaserPlasma and Radiation Physics(INFLPR)M agureleIlfovRomania

The spatial distribution of beams with orbital angular momentum in the far field is known to be extremely sensitive to angular aberrations,such as astigmatism,coma and *** poses a challenge for conventional beam optimization strategies when a homogeneous ring intensity is required for an *** developed a novel approach for estimating the Zernike coefficients of low-order angular aberrations in the near field based solely on the analysis of the ring deformations in the far field.A fast,iterative reconstruction of the focal ring recreates the deformations and provides insight into the wavefront deformations in the near field without relying on conventional phase retrieval *** output of our algorithm can be used to optimize the focal ring,as demonstrated experimentally at the 100 TW beamline at the Extreme Light Infrastructure-Nuclear Physics facility.

关键词： beam quality far field orbital angular momentum ring intensity phase retrieval wavefront

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designing a High-Performance deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

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Journal of Computer and Communications 2021年第7期9卷 8-20页

作者： Andreea Roxana Luca Tudor Florin Ursuleanu Liliana Gheorghe Roxana Grigorovici Stefan iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania departament Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Iasi Departament of Surgery I Regional Institute of Oncology Iasi Romania department of Radiology “Sf. Spiridon” Hospital Iasi Romania department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

deep learning (dL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (dL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2d/3d) and to validate the dL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined dL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the dL model through a randomized controlled clinical trial. The dL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony densely Connected Network-Medical image Segmentation (HardNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimiz

关键词： Combined Model of U-Net-Based Architectures Medical Image Segmentation 2d/3d/CT/RMN Images

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The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 300-304页

作者： Tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana Grigorovici Stefan iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Department of Surgery I Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accur... 详细信息

dana;">Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAd) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3d images from MRI/CT. We propose a comparative study of current developments in “dICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “dICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAd method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks o

关键词： Artificial Intelligence Cervical Cancer Thyroid Cancer MRI Images CT Images

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卒中介入治疗培训指南：国际多学会共识文件

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国际脑血管病杂志 2017年第5期25卷 396-399页

作者： Lavine Sd Cockroft K Hoh B Bambakidis N Khalessi AA Woo H Riina H Siddiqui A Hirsch JA Chong W Rice H Wenderoth J Mitchell P Coulthard A Signh TJ Phatorous C Khangure M Klurfan P ter Brugge K iancu d Gunnarsson T Pongpech S Rodesch G Soderman M Taylor A Krings T Orbach d Picard L Suh dC Zheng HQ Jansen O Muto M Szikora I Pierot L Brouwer P Gralla J Renowden S Andersson T Fiehler J Turjman F White P Januel AC Spelle L Kulcsar Z Chapot R Biondi A dima S Taschner C Szajner M Krajina A Sakai N Matsumaru Y Yoshknura S Ezura M Fujinaka T Iihara K Ishii A Higashi T Hirohata M Hyodo A Ito Y Kawanishi M Kiyosue H Kobayashi E Kobayashi S Kuwayama N Matsumoto Y Miyachi S Murayama Y Nagata I Nakahara I Nemoto S Niimi Y Oishi H Satomi J Satow T Sugiu K Tanaka M Terada T Yamagami H diaz O Lylyk P Jayaraman MV Patsalides A Gandhi Cd Lee SK Abruzzo T Albani B Ansari SA Arthur AS Baxter BW Bulsara KR Chen M Almandoz JE Fraser JF Heck dV Hetts SW Hussain MS Klucznik RP Leslie-Mawzi TM Mack WJ McTaggart RA Meyers PM Mocco J Prestigiacomo CA Pride GL Rasmussen PA Starke RM Sunenshine PJ Tarr RW Frei dF Pabo M Nogueira RG Zaidat OO Jovin T Linfante I Yavagal d Liebeskind d Novakovic R Pongpech S 许岩孙瑞郭芮兵 From the American Association of Neurological Surgeons/Congress of Neurological Surgeons (AANS/CNS) Rolling Meadows IL USA American Society of Neuroradiology (ASNR) Oak Brook IL USA Asian Australasian Federation of lnterventional and Therapeutic Neuroradiology (AAFITN) Bali Indonesia Australian and New Zealand Society of Neuroradiobgy-Conjomt Committee for Recognition of Training in Interventional Neuroradiology (CCINR) representing the RANZCR (ANZSNR) ANZAN and NSA Sydney Australia Canadian Interventional Neuro Group (CING) Edmonton AB Canada World Federation of Interventional and Therapeutic Neuroradiology (WFITN) Gold Coast Australia European Society of Neuroradiology (ESNR) Milan Italy European Society of Minimally Invasive Neurologic Therapy (ESMINT) Nice France Japanese Society for Neuroendovascular therapy (JSNET) Okayama Japan Sociedad Ibero Latino Americana de Neuroradiologica (SILAN) Cartagena Colombia Society of Neurolnterventional Surgery (SNIS) Washington DC USA Society of Vascular and Interventional Neurology (SVIN) Brooklyn NY USA 南京大学医学院附属金陵医院(南京军区南京总医院)神经内科 210002 解放军第一0一医院(无锡太湖医院)神经内科无锡214004

1背景缺血性卒中是全球人口死亡和残疾的首要原因。很多急性大血管闭塞（emergent large vesselocclusion，ELVO）患者都会遗留长期残疾。事实上，这些颅内大动脉闭塞经常会导致大面积脑损伤，进而造成患者死亡或严重致残。

关键词：缺血性卒中培训指南介入治疗文件学会国际血管闭塞动脉闭塞

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