IMRT QA result prediction via MLC transmission decomposition

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Imaging

Background:

• Quality assurance (QA) for Intensity-Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) treatment plans is time-consuming.
• Developing more efficient methods for QA is crucial for radiation oncology workflows.

Purpose of the Study:

• To develop an external tool for analyzing treatment planning system (TPS) data.
• To predict which IMRT/VMAT plans may fail standard QA measurements.
• To provide insights into TPS dose modeling and beam parameter sensitivities.

Main Methods:

• A Python-based tool was developed to read DICOM plan files.
• The tool calculates beam fluence fractions in seven zones based on the RayStation MLC model, termed grid point fractions.
• Grid point fractions were correlated with gamma analysis pass rates and median dose differences for 46 treatment plans.

Main Results:

• Significant correlations were observed between grid point fraction metrics and median dose differences.
• No significant correlation was found with gamma analysis pass percentages.
• The findings suggest the tool can offer insights into TPS dose calculation accuracy and model sensitivities.

Conclusions:

• A novel metric derived from MLC control points can predict plan performance in QA from a dose calculation accuracy perspective.
• The developed tool and metrics can aid in comparing clinical beam models and identifying TPS weaknesses.
• Integration into TPS could offer advanced plan optimization capabilities.