Fully automated segmentally boosted VMAT

Author:

Huang Charles1,Nomura Yusuke2,Yang Yong2,Xing Lei2

Affiliation:

1. Department of Bioengineering Stanford University Stanford USA

2. Department of Radiation Oncology Stanford University Stanford USA

Abstract

AbstractPurposeTreatment planning for volumetric modulated arc therapy (VMAT) typically involves the use of multiple arcs to achieve sufficient intensity modulation. Alternatively, we can perform segment boosting to achieve similar intensity modulation while also reducing the number of control points used. Here, we propose the MetaPlanner Boosted VMAT (MPBV) approach, which generates boosted VMAT plans through a fully automated framework.MethodsThe proposed MPBV approach is an open‐source framework that consists of three main stages: meta‐optimization of treatment plan hyperparameters, fast beam angle optimization on a coarse dose grid to select desirable segments for boosting, and final plan generation (i.e., constructing the boosted VMAT arc and performing optimization).ResultsPerformance for the MPBV approach is evaluated on 21 prostate cases and 6 head and neck cases using clinically relevant plan quality metrics (i.e., target coverage, dose conformity, dose homogeneity, and OAR sparing). As compared to two baseline methods with multiple arcs, MPBV maintains or improves dosimetric performance for the evaluated metrics while substantially reducing average estimated delivery times (from 2.6 to 2.1 min).ConclusionOur proposed MPBV approach provides an automated framework for producing high‐quality VMAT plans that uses fewer control points and reduces delivery time as compared to traditional approaches with multiple arcs. MPBV applies automated treatment planning to segmentally boosted VMAT to address the beam utilization inefficiencies of traditional VMAT approaches that use multiple full arcs.

Publisher

Wiley

Subject

General Medicine

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