新しいGPU対応VMAT多基準最適化計画生成アルゴリズムの評価

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Med Dosim.2020 Jul;S0958-3947(20)30096-0. doi: 10.1016/j.meddos.2020.05.007.Epub 2020-07-12.

新しいGPU対応VMAT多基準最適化計画生成アルゴリズムの評価

Evaluation of a new GPU-enabled VMAT multi-criteria optimisation plan generation algorithm.

Myles Spalding
Anthony Walsh
Trent Aland

PMID: 32669220 DOI: 10.1016/j.meddos.2020.05.007.

抄録

To evaluate the new Varian, graphical processing unit (GPU)-enabled, volumetric-modulated arc therapy (VMAT) multi-criteria optimisation (MCO) tool for both its dosimetric accuracy and calculation time. This is a new capability within V16.0 and greater of the Varian Eclipse treatment planning system that allows VMAT optimisation and dose calculation using the GPU (termed GPU-VMAT). In versions prior to V16.0 VMAT multi-criteria optimisation calculations were only possible using central processing unit (CPU) (termed CPU-VMAT) and Hybrid-VMAT (H-VMAT). The H-VMAT method breaks down the VMAT plan into IMRT fields which utilised GPU calculations. The study consisted of a cohort of 50 patients representing a range of anatomical treatment sites; bladder (5), brain (5), gynae (5), head & neck (5), lung (7), mediastinum (7) prostate (4), oesophagus (7) and rectum (5). Each case was planned to that of a clinical standard (Base) which was compared to a CPU-VMAT, GPU-VMAT and H-VMAT approaches. The study analysed dose to organ at risk (OAR) and target coverage, plan calculation time data and plan complexity through monitor unit (MU) for each approach. Negligible dosimetric differences were found between the CPU-VMAT, GPU-VMAT and H-VMAT approaches for the cohort of patients evaluated. The largest dosimetric change were observed in the lacrimal gland for a head and neck case, where the GPU-VMAT and H-VMAT achieved a max dose of +2.8 ± 0.0 Gy and -4.6 ± 0.0 Gy, respectively, when compared to CPU-VMAT. The majority of organ at risk's (OAR) provided indistinguishable dosimetric outcomes, namely: heart, kidneys, femur, lens, oral cavity and oesophagus. Large time savings were found using the GPU-VMAT technique compared to CPU-VMAT, a mean decrease in calculation time across all sites of 60.2% ± 15.6%. Negligible dosimetric change between the 2 techniques and large time saving were observed with the GPU-VMAT and H-VMAT approaches when compared to the CPU-VMAT. We have shown that the GPU-VMAT technique has been safely implemented with minimal differences from CPU-VMAT, but with significant optimisation and calculation times savings.