P
Panagiotis Papadimitroulas
Researcher at University of Patras
Publications - 46
Citations - 1046
Panagiotis Papadimitroulas is an academic researcher from University of Patras. The author has contributed to research in topics: Dosimetry & Monte Carlo method. The author has an hindex of 11, co-authored 39 publications receiving 650 citations. Previous affiliations of Panagiotis Papadimitroulas include Technological Educational Institute of Athens.
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A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications
David Sarrut,Manuel Bardiès,Nicolas Boussion,Nicolas Freud,Sébastien Jan,Jean Michel Létang,George Loudos,Lydia Maigne,Sara Marcatili,Thibault Mauxion,Panagiotis Papadimitroulas,Yann Perrot,Uwe Pietrzyk,Charlotte Robert,Dennis R. Schaart,Dimitris Visvikis,Irène Buvat +16 more
TL;DR: The authors' review the applicability of the open-source GATE Monte Carlo simulation platform based on the GEANT4 toolkit for radiation therapy and dosimetry applications and the computational times associated with several applications are provided to illustrate the practical feasibility of the simulations using current computing facilities.
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Cloud computing in medical imaging.
George C. Kagadis,Christos Kloukinas,Kevin L. Moore,Jim Philbin,Panagiotis Papadimitroulas,Christos Alexakos,Paul Nagy,Dimitris Visvikis,William R. Hendee +8 more
TL;DR: This Vision 20/20 paper addresses major questions related to the applicability of advanced cloud computing in medical imaging and considers security and ethical issues that accompany cloud computing.
Journal ArticleDOI
A dose point kernel database using GATE Monte Carlo simulation toolkit for nuclear medicine applications: comparison with other Monte Carlo codes.
TL;DR: In this study, the authors have checked GATE's reliability for absorbed dose calculation when transporting different kind of particles, which indicates its robustness for dosimetry applications.
Journal ArticleDOI
Conjugated Photosensitizers for Imaging and PDT in Cancer Research.
TL;DR: The purpose of this review is to present the current state of conjugated photosensitizers for imaging and therapy.
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Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.
Panagiotis Papadimitroulas,Lennart Brocki,Neo Christopher Chung,Wistan Marchadour,Franck Vermet,Laurent Gaubert,Vasilis Eleftheriadis,Dimitris Plachouris,Dimitris Visvikis,George C. Kagadis,Mathieu Hatt +10 more
TL;DR: In this article, the authors reviewed the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI for radiomics.