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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/D9575238-644F-4211-9771-5BAE1F9417A4 | - |
| Αναγνωριστικό | https://doi.org/10.23919/DATE48585.2020.9116391 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/9116391 | - |
| Γλώσσα | en | - |
| Μέγεθος | 6 pages | en |
| Τίτλος | A novel FPGA-based system for tumor growth prediction | en |
| Δημιουργός | Malavazos Konstantinos | en |
| Δημιουργός | Μαλαβαζος Κωνσταντινος | el |
| Δημιουργός | Papadogiorgaki Maria | en |
| Δημιουργός | Παπαδογιωργακη Μαρια | el |
| Δημιουργός | Malakonakis Pavlos | en |
| Δημιουργός | Μαλακωνακης Παυλος | el |
| Δημιουργός | Papaefstathiou Ioannis | en |
| Δημιουργός | Παπαευσταθιου Ιωαννης | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | An emerging trend in the biomedical community is to create models that take advantage of the increasing available computational power, in order to manage and analyze new biological data as well as to model complex biological processes. Such biomedical software applications require significant computational resources since they process and analyze large amounts of data, such as medical image sequences. This paper presents a novel FPGA-based system that implements a novel model for the prediction of the spatio-temporal evolution of glioma. Glioma is a rapidly evolving type of brain cancer, well known for its aggressive and diffusive behavior. The developed system simulates the glioma tumor growth in the brain tissue, which consists of different anatomic structures, by utilizing individual MRI slices. The presented innovative hardware system is more than 60% faster than a high-end server consisting of 20 physical cores (and 40 virtual ones) and more than 28x more energy efficient. | en |
| Τύπος | Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Publication | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2022-05-05 | - |
| Ημερομηνία Δημοσίευσης | 2020 | - |
| Θεματική Κατηγορία | High performance computing | en |
| Θεματική Κατηγορία | Field programmable gate arrays | en |
| Θεματική Κατηγορία | High level synthesis | en |
| Θεματική Κατηγορία | Magnetic resonance imaging | en |
| Βιβλιογραφική Αναφορά | K. Malavazos, M. Papadogiorgaki, P. Malakonakis and I. Papaefstathiou, "A novel FPGA-based system for tumor growth prediction," in Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, (DATE 2020), Grenoble, France, 2020, pp. 252-257, doi: 10.23919/DATE48585.2020.9116391. | en |
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