IMRT QA result prediction via MLC transmission decomposition

• Published in: Journal of applied clinical medical physics Vol. 24; no. 8; pp. e13990 - n/a
• Main Authors: Stasko, John T., Ferris, William S., Adam, David P., Culberson, Wesley S., Frigo, Sean P.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01-08-2023; John Wiley and Sons Inc
• Subjects: Accuracy; Calibration; Classification; Classification schemes; Decomposition; Dimensional analysis; measurement; multi leaf collimator; Planning; quality assurance; Quality control; Radiation Oncology Physics; transmission; Palo Alto California; United States > US; California; transmission; multi leaf collimator; quality assurance; measurement
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.13990

Background
Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable.
Purpose
We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics.
Methods
The tool was created in Python to read in DICOM plan files and determine the beam fluence fraction incident on each of seven different zones, each classified based on the RayStation MLC model. The fractions, termed grid point fractions, were validated by analyzing simple test plans. The average grid point fractions, over all control points for 46 plans were then computed. These values were then compared with gamma analysis pass percentages and median dose differences to determine if any significant correlations existed.
Results
Significant correlation was found between the grid point fraction metrics and median dose differences, but not with gamma analysis pass percentages. Correlations were positive or negative, suggesting differing model parameter value sensitivities, as well as potential insight into the treatment planning system dose model.
Conclusions
By decomposing MLC control points into different transmission zones, it is possible to create a metric that predicts whether the analyzed plan will pass a quality assurance measurement from a dose calculation accuracy standpoint. The tool and metrics developed in this work have potential applications in comparing clinical beam models or identifying their weak points. Implementing the tool within a treatment planning system would also provide more potential plan optimization parameters.