Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (MLC) leaf transmission, interleaf-leakage, etc. In fluence map optimization (FMO), a simple dose model is used to first generate an intermediate plan based on pencil-beams. In a second step (segmentation phase), this intermediate plan is then converted into a deliverable treatment plan with MLC segments. In this paper, we investigate novel approaches for the use of a clinical dose engine (CDE) for segmentation of FMO plans in robotic radiotherapy. Segments are sequentially added to the plan. Generation of each next segment is based on the total 3D dose distribution, resulting from already selected segments and the desired FMO dose, considering all treatment beams as candidates for delivery of the new segment. Three versions of the segmentation algorithm were investigated with differences in the integration of the CDE. The combined use of pencil-beams and segments in a segmentation method is non-trivial. Therefore, new methods were developed for the use of segment doses calculated with the CDE in combination with pencil-beams, used for the selection of new segments. For 20 patients with prostate cancer and 12 with liver cancer, segmented plans were compared with FMO plans. All three versions of the proposed segmentation algorithm could well mimic FMO dose distributions. Segmentation with a fully integrated CDE provided the best plan quality and lowest numbers of monitor units and segments at the cost of increased calculation time.

Additional Metadata
Keywords step-and-shoot IMRT, treatment plan optimization, inverse IMRT planning, prioritized MLC segmentation, column generation
Persistent URL dx.doi.org/10.1088/1361-6560/ab97e7, hdl.handle.net/1765/130864
Journal Physics in Medicine and Biology
Citation
Schipaanboord, B.W.K., Heijmen, B.J.M, & Breedveld, S. (2020). Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy. Physics in Medicine and Biology, 65(17). doi:10.1088/1361-6560/ab97e7