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Beyond SPC: a nonlinear state-space model for explainable prediction of linac output drift
Dimitri Reynard1, Clément Pereira2, Christophe Mazzara1
1GHM-Institut Daniel Hollard, Grenoble, France.
Summary
This study introduces a new model to predict linear accelerator monitor chamber calibration needs weeks in advance. This proactive approach enhances quality assurance (QA) and data-driven maintenance planning.
Area of Science:
- Medical Physics
- Radiation Oncology
- Accelerator Technology
Background:
- Linear accelerator output drift is mainly caused by monitor chamber aging.
- Current quality assurance (QA) relies on fixed schedules or reactive measures.
- Predictive insights from daily QA data are underutilized.
Purpose of the Study:
- To develop and validate a nonlinear, physically interpretable state-space model.
- To enable proactive forecasting of calibration-threshold exceedance for monitor chambers.
- To transition QA towards predictive and data-driven maintenance.
Main Methods:
- Modeled monitor-chamber sensitivity as a first-order relaxation process.
- Employed an Extended Kalman Filter (EKF) for parameter estimation.
- Evaluated forecasting accuracy for various prediction horizons and deviation thresholds.
Main Results:
- The EKF accurately reconstructed sensitivity decay and provided stable parameter estimates.
- Forecast accuracy improved with shorter prediction horizons.
- Mean Absolute Error (MAE) was <2 days for horizons <15 days.
Conclusions:
- The framework offers an explainable, uncertainty-aware method for anticipating calibration needs.
- Enables proactive QA and supports data-driven maintenance planning.
- Potential to significantly improve the reliability and efficiency of linear accelerator operations.
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