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Failure mode and effect analysis‐based quality assurance for dynamic MLC tracking systems
Author(s) -
Sawant Amit,
Dieterich Sonja,
Svatos Michelle,
Keall Paul
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3517837
Subject(s) - quality assurance , multileaf collimator , reliability engineering , failure mode and effects analysis , tracking system , computer science , failure rate , acceptance testing , simulation , real time computing , engineering , linear particle accelerator , beam (structure) , operations management , artificial intelligence , external quality assessment , software engineering , kalman filter , civil engineering
Purpose: To develop and implement a failure mode and effect analysis (FMEA)‐based commissioning and quality assurance framework for dynamic multileaf collimator (DMLC) tumor tracking systems. Methods: A systematic failure mode and effect analysis was performed for a prototype real‐time tumor tracking system that uses implanted electromagnetic transponders for tumor position monitoring and a DMLC for real‐time beam adaptation. A detailed process tree of DMLC tracking delivery was created and potential tracking‐specific failure modes were identified. For each failure mode, a risk probability number (RPN) was calculated from the product of the probability of occurrence, the severity of effect, and the detectibility of the failure. Based on the insights obtained from the FMEA, commissioning and QA procedures were developed to check (i) the accuracy of coordinate system transformation, (ii) system latency, (iii) spatial and dosimetric delivery accuracy, (iv) delivery efficiency, and (v) accuracy and consistency of system response to error conditions. The frequency of testing for each failure mode was determined from the RPN value. Results: Failures modes with RPN ≥ 125 were recommended to be tested monthly. Failure modes with RPN < 125 were assigned to be tested during comprehensive evaluations, e.g., during commissioning, annual quality assurance, and after major software/hardware upgrades. System latency was determined to be ∼ 193 ms . The system showed consistent and accurate response to erroneous conditions. Tracking accuracy was within 3%–3 mm gamma (100% pass rate) for sinusoidal as well as a wide variety of patient‐derived respiratory motions. The total time taken for monthly QA was ∼ 35 min , while that taken for comprehensive testing was ∼ 3.5 h . Conclusions: FMEA proved to be a powerful and flexible tool to develop and implement a quality management (QM) framework for DMLC tracking. The authors conclude that the use of FMEA‐based QM ensures efficient allocation of clinical resources because the most critical failure modes receive the most attention. It is expected that the set of guidelines proposed here will serve as a living document that is updated with the accumulation of progressively more intrainstitutional and interinstitutional experience with DMLC tracking.