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Software Based Pre-Failure Characterization of Faulty MLC Drivetrains Using Trajectory Logs

J Kowalski*1, E Wolf1, N Kumar1,D Moyer2, B McGill1, M Lamba 1, D Ionascu1, (1) University of Cincinnati College of Medicine, Cincinnati, OH, (2) Lehigh Valley Health Network, Allentown, PA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Radiotherapy techniques rely on precise Multi-leaf collimator (MLC) leaf movements for accurate dose delivery. Unexpected leaf drive-train failure is a common source of clinical workflow disruption, frequently requiring machine servicing to correct. Clinical QA practices have demonstrated limited capacity to detect impending failures, forcing clinics to adopt a purely reactionary posture. A trajectory log analysis method is proposed to facilitate prospective investigation of aberrant leaf behaviors and minimize machine downtime.


Methods: A custom, 2-arc plan with dynamic MLC motion was developed to test collimator leaf drive-trains under near-maximal clinical operating conditions. A wrapper was coded in Python around Pylinac to ingest and manipulate trajectory log data, and a graphical user interface (GUI) was developed to improve ease of analysis. This program parsed MLC and gantry speed/position data, identifying error maxima and characterizing system behavior over time using a set of straightforward graphical displays. Method validation was achieved by carrying out a series of tests in which random, functioning leaf drive nuts ("t-nuts") were replaced with degraded versions and "discovered" using our software.


Results: Two worn t-nuts were sequentially placed on three different MLC leaves. Trajectory log files from 14 arcs were collected and analyzed using our software. The degraded t-nut was distinguishable in 14 of 14 log files, despite yielding maximal position deviations up to 95% below the Varian TrueBeam interlock threshold of 2.5 mm. Abnormal behavior was also apparent in MLC velocity metrics, with faulty t-nut deviations consistently 10-25% above background.


Conclusion: This analysis technique demonstrates superior sensitivity to abnormal MLC leaf drive-train behavior compared with standard QA practices at levels substantially below interlock thresholds, allowing for pre-failure intervention and minimization of associated machine downtime. Equally significant is that this diagnostic capability has been paired with a user-friendly GUI permitting easy integration into routine clinical operations.

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Keywords

Not Applicable / None Entered.

Taxonomy

TH- External Beam- Photons: Quality Assurance - Linear accelerator

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