Abstract
Radiotherapy treatments apply high doses of radiation to tumorous cells to break the structure of cancer DNA, trying at the same time to minimize radiation doses absorbed by healthy cells. The personalized design of radiotherapy plans has been a relevant challenge since the beginning of these therapies. A wide set of models have been defined to translate complex clinical prescriptions into optimization problems. The model based on the generalized equivalent uniform dose, gEUD, is very relevant for IMRT radiotherapy planning in clinical practice. This way, the expert physicists can tune plans near the prescriptions, solving the optimization problem based on gEUD in a trial-and-error process. The gradient descent methods can be applied for solving these models personalized for every patient. However, their computational requirements are huge. So, to facilitate their use in clinical practice it is necessary to apply HPC techniques to implement such models. In this work, we have developed two parallel implementations of an gEUD model for IMRT planning on multi-core and GPU architectures, as they are increasingly available in clinical settings. Both implementations are evaluated with two Head &Neck clinical tumor cases on modern GPU and multi-core CPU platforms. Our implementations are very useful since they help expert physicists obtain fast plans that can satisfy all the prescriptions.
This work has been supported by the projects: RTI2018-095993-B-I00 and PID2021-123278OB-I00 (funded by MCIN/AEI/10.13039/501 100011033/FEDER “A way to make Europe”); UAL18-TIC-A020-B (funded by Junta de Andalucía and the European Regional Development Fund, ERDF).
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Acknowledgements
The authors wish to express their deep gratitude to following persons: Paweł Kukołowicz and Anna Zawadzka form Department of Medicine Physics, Memorial Skłodowska-Curie Cancer Center and Institute of Oncology, Warsaw, Poland, for data acquisition and methodological guidance; Jacek Starzyński, Robert Szmurło, Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland, for access to their stand-alone dose deposition calculation software.
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Moreno, J.J., Miroforidis, J., Kaliszewski, I., Martín Garzón, G.E. (2023). Parallel gEUD Models for Accelerated IMRT Planning on Modern HPC Platforms. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2022. Lecture Notes in Computer Science, vol 13827. Springer, Cham. https://doi.org/10.1007/978-3-031-30445-3_12
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