Couch-Indexed 3d-Printed Mounts for Routine Linear Accelerator Qa
Abstract
Purpose
Setup variability in routine linear accelerator quality assurance (QA), such as daily and monthly output checks, can introduce undesirable measurement uncertainty and reduce the accuracy of QA testing. This work aims to design and evaluate 3D-printed, couch-indexed mounts for daily and monthly QA devices to reduce user-dependent setup error while maintaining dosimetric accuracy and improving setup efficiency.
Methods
Two custom 3D-printed mounts were designed: one for a monthly cube phantom used for output constancy measurements and, one for a daily QA Standard Imaging BeamChecker device used to assess output, flatness, and symmetry constancy. Both mounts mechanically index to the treatment couch and constrain device positioning to enforce reproducible placement. Mounts were printed using low-density infill patterns selected based on published dosimetric equivalence to air. Repeated QA measurements were performed for a 6 MV photon beam with multiple users setting up each device both manually and using the mount. For each configuration, output constancy and BeamChecker flatness and symmetry metrics were recorded across repeated setup cycles. Measurement variability was quantified using standard deviation and coefficient of variation. Setup time was recorded for manual and mounted configurations. Additional measurements were performed with and without the mounts while maintaining identical device positioning to assess dosimetric perturbation.
Results
The indexed mounts are hypothesized to reduce inter- and intra-operator variability for both monthly and daily QA measurements while preserving mean QA metrics. Use of the mounts is anticipated to decrease setup time relative to manual positioning. Dosimetric perturbation due to the mounts is expected to be negligible.
Conclusion
Indexed 3D-printed mounts for routine linac QA devices are expected to improve measurement reproducibility and setup efficiency without compromising dosimetric accuracy. This low-cost, easily implementable approach may enhance the reliability of daily and monthly QA workflows.