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GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy
Author(s) -
Bouter Anton,
Alderliesten Tanja,
Pieters Bradley R.,
Bel Arjan,
Niatsetski Yury,
Bosman Peter A. N.
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13681
Subject(s) - radiation treatment planning , brachytherapy , computer science , graphics processing unit , set (abstract data type) , monitor unit , graphics , prostate brachytherapy , medical physics , medicine , nuclear medicine , radiology , radiation therapy , parallel computing , computer graphics (images) , programming language
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.