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Titlebook: Hybrid PET/MR Neuroimaging; A Comprehensive Appr Ana M. Franceschi,Dinko Franceschi Book 2022 The Editor(s) (if applicable) and The Author(

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發(fā)表于 2025-3-21 18:33:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Hybrid PET/MR Neuroimaging
副標(biāo)題A Comprehensive Appr
編輯Ana M. Franceschi,Dinko Franceschi
視頻videohttp://file.papertrans.cn/431/430154/430154.mp4
概述Offers a thorough and concise introduction to PET/MR neuroimaging.Chapters include different disease processes, systems, as well as future research directions.Written by experts in the fields of neuro
圖書封面Titlebook: Hybrid PET/MR Neuroimaging; A Comprehensive Appr Ana M. Franceschi,Dinko Franceschi Book 2022 The Editor(s) (if applicable) and The Author(
描述.This book serves as a reference and comprehensive guide for PET/MR neuroimaging. The field of PET/MR is rapidly evolving, however, there is no standard resource summarizing the vast information and its potential applications. This book will guide neurological molecular imaging applications in both clinical practice and the research setting.?..Experts from multiple disciplines, including radiologists, researchers, and physicists, have collaborated to bring their knowledge and expertise together. Sections begin by covering general considerations, including public health and economic implications, the physics of PET/MR systems, an overview of hot lab and cyclotron, and radiotracers used in neurologic PET/MRI. There is then coverage of each major disease/systemic category, including dementia and neurodegenerative disease, epilepsy localization, brain tumors, inflammatory and infectious CNS disorders, head and neck imaging, as well as vascular hybrid imaging. Together, we have created a thorough, concise and up-to-date textbook in a unique, user-friendly format.?This is an ideal guide for neuroradiologists, nuclear medicine specialists, medical physicists, clinical trainees and researc
出版日期Book 2022
關(guān)鍵詞PET/MRI; PET/MR; Neuroimaging; hybrid imaging; neuroradiology; radiotracers; dementia; epilepsy
版次1
doihttps://doi.org/10.1007/978-3-030-82367-2
isbn_softcover978-3-030-82369-6
isbn_ebook978-3-030-82367-2
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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rk, we assess domain transfer of mitotic figure recognition using domain adversarial training on four data sets, two from dogs and two from humans. We were able to show that domain adversarial training considerably improves accuracy when applying mitotic _gure classification learned from the canine
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Paul Vaska,Lemise Salehr study with 19 volunteers each performing 40 trials of five different visualizations. Our results indicate that two of the five visualizations (. and .) are able to reduce the occurrent error, making training in Virtual Reality more suitable for minimally invasive surgery.
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Mario Serrano-Sosa,Ana M. Franceschi,Chuan Huang. It extends the As-Rigid- As-Possible (ARAP) deformation algorithm by a smart initialization of the required 3D readout mesh which is fitted to the CoW. Depending on the resulting degree of distortion, it is also possible to merge neighboring arteries directly into the same view. In cases of high d
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Mario Serrano-Sosa,Chuan Huangtages and drawbacks of the two identified model categories in the context of AVI’s inherent requirements. Last, we outline open research questions, such as the need for an improved detection performance of semantic anomalies, and propose potential ways to address them.
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Maria Rosana Ponisio,Pooya Iranpour,Tammie L. S. Benzingercher Gesellschaften: Deutsche Gesellschaft für angewandte Optik (DGaO), Deutsche Gesellschaft für Ortung und Navigation (DGON), Deutsche Gesellschaft für Medizinische Dokumentation, Information und Statistik (GMDS), Deutsche Gesellschaft für Angewandte Datenverarbeitung und Automation in der Medizin
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Maria Rosana Ponisio,Pooya Iranpour,Tammie L. S. Benzingerft nicht ganz korrekte Formulierungen verwendet. So ist die Aussage: ?Lineare Filter werden durch eine Faltung beschrieben“ falsch. Die Faltung ist eine lineare Operation, aber zus?tzlich noch verschiebungsinvariant. Daher ist richtig: die Faltung beschreibt ein lineares Filter, aber nicht jdes line
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Maria Rosana Ponisio,Pooya Iranpour,Tammie L. S. Benzingerr, aber dennoch visuell homogen. Hauptziele in der Bildverabeitung von Texturen sind:Umgangssprachlich ordnet man Texturen Eigenschaften zu wie: k?rnig, glatt, l?nglich, fein usw. Mit diesen Eigenschaften k?nnen wir aber algorithmisch nichts anfangen. Zur Texturbeschreibung gibt es im Wesentlichen d
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