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Development of the open‐source dose calculation and optimization toolkit matRad
Author(s) -
Wieser HansPeter,
Cisternas Eduardo,
Wahl Niklas,
Ulrich Silke,
Stadler Alexander,
Mescher Henning,
Müller LucasRaphael,
Klinge Thomas,
Gabrys Hubert,
Burigo Lucas,
Mairani Andrea,
Ecker Swantje,
Ackermann Benjamin,
Ellerbrock Malte,
Parodi Katia,
Jäkel Oliver,
Bangert Mark
Publication year - 2017
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.12251
Subject(s) - radiation treatment planning , dosimetry , computer science , matlab , tomotherapy , visualization , computational science , workflow , computation , graphical user interface , software , dicom , modular design , medical imaging , nuclear medicine , medical physics , radiation therapy , physics , algorithm , artificial intelligence , medicine , database , programming language , operating system
Purpose We report on the development of the open‐source cross‐platform radiation treatment planning toolkit matRad and its comparison against validated treatment planning systems. The toolkit enables three‐dimensional intensity‐modulated radiation therapy treatment planning for photons, scanned protons and scanned carbon ions. Methods matRad is entirely written in Matlab and is freely available online. It re‐implements well‐established algorithms employing a modular and sequential software design to model the entire treatment planning workflow. It comprises core functionalities to import DICOM data, to calculate and optimize dose as well as a graphical user interface for visualization. matRad dose calculation algorithms (for carbon ions this also includes the computation of the relative biological effect) are compared against dose calculation results originating from clinically approved treatment planning systems. Results We observe three‐dimensional γ ‐analysis pass rates ≥ 99.67% for all three radiation modalities utilizing a distance to agreement of 2 mm and a dose difference criterion of 2%. The computational efficiency of matRad is evaluated in a treatment planning study considering three different treatment scenarios for every radiation modality. For photons, we measure total run times of 145 s–1260 s for dose calculation and fluence optimization combined considering 4–72 beam orientations and 2608–13597 beamlets. For charged particles, we measure total run times of 63 s–993 s for dose calculation and fluence optimization combined considering 9963–45574 pencil beams. Using a CT and dose grid resolution of 0.3 cm 3 requires a memory consumption of 1.59 GB –9.07 GB and 0.29 GB –17.94 GB for photons and charged particles, respectively. Conclusion The dosimetric accuracy, computational performance and open‐source character of matRad encourages a future application of matRad for both educational and research purposes.