Moreno, J.J. orcid.org/0000-0002-2194-2318, Puertas‐Martín, S., Redondo, J.L. et al. (2 more authors) (2025) Where high‐performance computing meets radiotherapy for enhanced intensity‐modulated radiation therapy planning. Concurrency and Computation: Practice and Experience, 37 (12-14). e70133. ISSN 1532-0626
Abstract
Intensity Modulated Radiotherapy (IMRT) employs radiation beams with varying angles and intensities to precisely target cancerous tissues while sparing healthy organs. Planning methods based on the generalized Equivalent Uniform Dose (gEUD) metric achieve excellent Planning Target Volume coverage. However, computing these plans requires extensive parameter adjustments and multiple model evaluations, making the process resource-intensive and time-consuming. This study aims to enhance the computational efficiency of radiotherapy plans by automating the adjustment of gEUD parameters, reducing solution times, and facilitating clinical integration. We introduced a novel approach that combines Gradient Descent algorithms with evolutionary optimization to explore the gEUD parameter space. This hybrid methodology generates radiation plans that meet clinical constraints. To address the high computational costs, we implemented parallelization and batching strategies, leveraging multicore servers to accelerate the optimization process and enable real-time clinical applications. Benchmarking was conducted on three multicore platforms with distinct micro-architectures, testing various batch sizes and thread configurations. Using a dataset of three Head and Neck IMRT patients treated with nine beams, our approach demonstrated substantial computational speed-ups. Results confirmed the ability of the method to consistently produce high-quality radiation therapy plans that meet clinical constraints. By effectively exploiting multicore servers, this approach overcomes the computational challenges of gEUD parameter tuning, enabling its integration into clinical practice. This advancement reduces planning times, supports medical physicists, and ultimately enhances patient care in radiotherapy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
Keywords: | evolutionary optimization; high performance computing; IMRT; radiotherapy |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jun 2025 11:17 |
Last Modified: | 03 Jun 2025 22:24 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/cpe.70133 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227303 |