(Tuesday, 7/16/2019) 3:45 PM - 4:15 PM
Room: Exhibit Hall | Forum 7
Purpose: To explore considerations and requirements for consistent, scalable, site-non-specific automated field-in-field planning aimed at exceeding the capabilities of manual planning.
Methods: A threshold-driven beamlet-based auto-planning algorithm determines fluence maps to be used as the “ideal� case for a field-in-field plan. The fluence patterns are stratified and then iteratively sequenced using a combination of graph-based optimization algorithms and heuristics to maintain deliverability and form clinically “reasonable� apertures. Intensities are optimized to match the original fluence’s dose distribution and adhere to minimum MU limits. Leaf closing positions and jaw shifts are optimized for delivery speed and minimal leakage.
Results: Significant gains were made in both planning time and plan quality. Automatically selecting the “best� of multiple sequencing runs yielded field-in-field plans dosimetrically similar to the input fluence-based plans. Plans can be generated with our methodology in minutes as implemented in EZFluence, an Eclipse plugin application.
Conclusion: This work highlights the benefits of modeling the field-in-field problem to be scalable beyond what is currently being done clinically. Extending this framework, IMRT plans can be automatically converted to field-in-field, boost volumes can be integrated, and non-isocentric field-in-field planning can be easily achieved.
Funding Support, Disclosures, and Conflict of Interest: Work supported by and done at Radformation by its employees for commercial product, EZFluence.
Keywords
Treatment Planning, 3D, Optimization
Taxonomy
TH- External beam- photons: Development (new technology and techniques)
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