Author: Maria Jose Almada, Oscar Apaza, Carlos Bohorquez, Ezequiel Cabrera, Rogelio Diaz Moreno, Maximiliano Musso, Rosa Petit, Carlos Daniel Venencia, Lucy D. Wolfsberger 👨🔬
Affiliation: LAP, FaMAF, UNC, Instituto Zunino - Fundacion Marie Curie 🌍
Purpose:
PSQA employs gamma analysis to verify the planned versus delivered dose. Evaluating the effectiveness of PSQA techniques in identifying errors is crucial. This study aims to incorporate MLC and collimator errors into a breast VMAT plan and assess the detection capabilities of various PSQA methods.
Methods:
A VMAT right breast plan with two semi-arcs (6 MV) was created in Eclipse v15.6. A Python script (LogDicomv.1) modified the patient's original DICOM RT Plan, introducing MLC and collimator errors. The errors included (1) shifts of all MLC leaves away from the central axis by 0.5, 1.0, and 2.0 mm and (2) collimator rotation errors of 1°, 2°, and 3°. The plans with errors were analyzed using gamma index evaluation criteria of 3%/2 mm, 3%/1 mm, 1%/2 mm, and 1%/1 mm, using multiple PSQA techniques: Portal Dosimetry (2D), RadCalc v.7.3 IDC (3D) with RT Plan and LogFiles, Logfile delivery reconstruction (3D, custom software), and RadCalc v.7.3 EPID Dosimetry (3D) using IPS Images and Log Files.
Results:
The PSQA evaluation indicates that the gamma passing rate decreases as MLC and collimator errors increase. Portal Dosimetry remained close to 100%, with a slight decrease associated with 2.0 mm errors. RadCalc IDC (3D), Logfile Delivery Reconstruction (3D), and RadCalc EPID Dosimetry (3D) methods displayed a more significant decline in accuracy as errors rose, suggesting higher sensitivity to MLC leaf displacements. Regarding collimator errors using the 3%/1 mm gamma criterion, all methods exhibited increased sensitivity starting from 2° errors, with Portal Dosimetry being particularly notable. Under a stricter 1%/1 mm criterion, all techniques reported a gamma index below 90%.
Conclusion:
We evaluated the capabilities of PSQA techniques and their effectiveness in identifying errors. Stricter gamma index criteria can detect errors, indicating their impact on the plan's quality.