(Wednesday, 7/17/2019) 4:30 PM - 6:00 PM
Room: 301
Purpose: The recently emerged commercial “stacked and staggered� dual-layer Multi-Leaf Collimator (DLMLC) achieves a good balance among modulation resolution, low leakage, and high fabricability. However, existing progressive sampling Volumetric Modulated Arc Therapy (VMAT) methods for DLMLC typically require more arcs to achieve comparable dosimetry to VMAT plans using higher resolution single-layer MLC (SLMLC). In this study, we develop a novel optimization framework for single-arc VMAT to take advantage of the unique DLMLC characteristics fully.
Methods: The single-arc DLMLC VMAT was achieved by optimizing a least square dose fidelity objective, along with an anisotropic total variation term to encourage the fluence smoothness and a single segment term for imposing simple apertures. The MLC leaf speed limit and deliverability constraint were included as optimization constraints. An alternating optimization strategy was implemented to solve the optimization problem, which runs through three modules and optimizes concerning the fluence map, aperture, and fluence intensity respectively. The graph optimization algorithm was utilized to solve the non-convex optimization constraints. The proposed single-arc DLMLC-10mm (leaf width) VMAT plans were compared with single-arc SLMLC-5mm and SLMLC-10mm VMAT plans, evaluated on a brain, a lung, and a prostate cancer patient, as well as a simultaneous integrated boost (SIB) case.
Results: Compared with the SLMLC-10mm plan, with comparable PTV statistics, the DLMLC-10mm plan reduced R50 by 30.7%, showing a remarkable improvement in dose compactness. The average max OAR dose and mean OAR dose were reduced by 5.79% and 4.18% of the prescription dose, respectively. The plan quality of DLMLC-10mm is comparable to that of the SLMLC-5mm plan.
Conclusion: The single-arc global-sampling DLMLC VMAT optimization framework fully takes advantage of the DLMLC for sophisticated modulation, leading to dosimetry superior to SLMLC with the same leaf width and comparable with SLMLC VMAT using half the leaf width.
Funding Support, Disclosures, and Conflict of Interest: NIH R01CA230278 NIH R44CA183390 NIH R01CA188300
Keywords
Optimization, Inverse Planning
Taxonomy
TH- External beam- photons: VMAT dose optimization algorithms
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