GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy
| Autor: | Tanja Alderliesten, Bradley R. Pieters, Anton Bouter, Peter A. N. Bosman, Arjan Bel, Yury Niatsetski |
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| Přispěvatelé: | Radiotherapy, CCA - Imaging and biomarkers, Radiation Oncology, Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Rok vydání: | 2019 |
| Předmět: |
Male
HDR brachytherapy treatment planning Mathematical optimization Computer science bi-objective optimization medicine.medical_treatment Brachytherapy GPU Graphics processing unit Evolutionary algorithm Radiation Dosage 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Prostate Computer Graphics medicine Bi objective Humans evolutionary algorithms Radiation treatment planning Protocol (science) Radiotherapy Planning Computer-Assisted Prostatic Neoplasms Radiotherapy Dosage General Medicine High-Dose Rate Brachytherapy medicine.anatomical_structure 030220 oncology & carcinogenesis Algorithms |
| Zdroj: | Medical physics, 46(9), 3776-3787. AAPM-American Association of Physicists in Medicine Medical Physics, 46(9), 3776-3787. AAPM-American Association of Physicists in Medicine Medical Physics, 46(9), 3776-3787 Bouter, A, Alderliesten, T, Pieters, B R, Bel, A, Niatsetski, Y & Bosman, P A N 2019, ' GPU-accelerated bi-objective treatment planning for prostate high-dose-rate brachytherapy ', Medical Physics, vol. 46, no. 9, pp. 3776-3787 . https://doi.org/10.1002/mp.13681 |
| ISSN: | 2473-4209 0094-2405 |
| DOI: | 10.1002/mp.13681 |
| Popis: | Purpose: The purpose of this study is to improve upon a recently introduced bi-objective treatment planning method for prostate high-dose-rate (HDR) brachytherapy (BT), both in terms of resulting plan quality and runtime requirements, to the extent that its execution time is clinically acceptable. Methods: Bi-objective treatment planning is done using a state-of-the-art multiobjective evolutionary algorithm, which produces a large number of potential treatment plans with different trade-offs between coverage of the target volumes and sparing organs at risk. A graphics processing unit (GPU) is used for large-scale parallelization of dose calculations and the calculation of the dose-volume (DV) indices of potential treatment plans. Moreover, the objectives of the previously used bi-objective optimization model are modified to produce better results. Results: We applied the GPU-accelerated bi-objective treatment planning method to a set of 18 patients, resulting in a set containing a few hundred potential treatment plans with different trade-offs for each of these patients. Due to accelerations introduced in this article, results previously achieved after 1 hour are now achieved within 30 seconds of optimization. We found plans satisfying the clinical protocol for 15 of 18 patients, whereas this was the case for only 4 of 18 clinical plans. Higher quality treatment plans are obtained when the accuracy of DV index calculation is increased using more dose calculation points, requiring still no more than 3 minutes of optimization for 100 000 points. Conclusions: Large sets of high-quality treatment plans that trade-off coverage and sparing are now achievable within 30 seconds, due to the GPU-acceleration of a previously introduced bi-objective treatment planning method for prostate HDR brachytherapy. Higher quality plans can be achieved when optimizing for 3 minutes, which we still consider to be clinically acceptable. This allows for more insightful treatment plan selection in a clinical setting. |
| Databáze: | OpenAIRE |
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