Visual Analysis of the Daily QA Results of Photon and Electron Beams of a Trilogy Linac over a Five-Year Period

Abstract

Data visualization technique was applied to analyze the daily QA results of photon and electron beams. Special attention was paid to any trend the beams might display. A Varian Trilogy Linac equipped with dual photon energies and five electron energies was commissioned in early 2010. Daily Linac QA tests including the output constancy, beam flatness and symmetry (radial and transverse directions) were performed with an ionization chamber array device (QA BeamChecker Plus, Standard Imaging). The data of five years were collected and analyzed. For each energy, the measured data were exported and processed for visual trending using an in-house Matlab program. These daily data were cross-correlated with the monthly QA and annual QA results, as well as the preventive maintenance records. Majority of the output were within 1% of variation, with a consistent positive/upward drift for all seven energies (~+0.25% per month). The baseline of daily device is reset annually right after the TG-51 calibration. This results in a sudden drop of the output. On the other hand, the large amount of data using the same baseline exhibits a sinusoidal behavior (cycle = 12 months; amplitude = 0.8%, 0.5% for photons, electrons, respectively) on symmetry and flatness when normalization of baselines is accounted for. The well known phenomenon of new Linac output drift was clearly displayed. This output drift was a result of the air leakage of the over-pressurized sealed monitor chambers for the specific vendor. Data visualization is a new trend in the era of big data in radiation oncology research. It allows the data to be displayed visually and therefore more intuitive. Based on the visual display from the past, the physicist might predict the trend of the Linac and take actions proactively. It also makes comparisons, alerts failures, and potentially identifies causalities.

IMRT QA using machine learning: A multi‐institutional validation

“…Machine learning is a subfield of data science that focuses on designing algorithms that can learn from and make predictions on data. Machine learning applications in radiotherapy have emerged increasingly in recent years, with applications including predictive modeling of treatment outcome in radiation oncology,1, 2, 3, 4, 5, 6, 7 treatment optimization,8, 9, 10, 11 error detection and prevention,12, 13, 14, 15 and treatment machine quality assurance (QA) 16, 17, 18, 19. These machine learning techniques have provided physicians and physicists information for more effective and accurate treatment delivery as well as the ability to achieve personalized treatment.…”

Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

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Artificial intelligence will reduce the need for clinical medical physicists

“…Machine QA is the third area that will need less physicist efforts. There has been research on predicting machine output trends using AI‐based algorithms . The proposed data visualization can predict the Linac performance over time and prompt physicists to perform output calibration before the output is drifted away from the tolerance.…”