Failure mode and effect analysis-based quality assurance for dynamic MLC tracking systems

Abstract

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 > or = 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 approximately 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 approximately 35 min, while that taken for comprehensive testing was approximately 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.

Figure 1

Process flow of real-time DMLC tracking-based radiotherapy. The steps highlighted by the shaded region are specific to tracking. All other steps are common to current motion-managed IGRT.

Figure 2

Measurement setup to perform (a) comprehensive (commissioning∕annual QA) and (b) frequent (monthly QA) tests for an electromagnetic position monitoring based DMLC tracking system

Figure 3

Three-dimensional motion trajectories recorded from lung cancer patients using the Synchrony system used as patient representative motion in this study (Suh et al., Ref. 27).

Figure 4

(a) Snapshot of picket fence pattern proposed for monthly QA. The thin horizontal and vertical lines drawn across the frame depict the positions of the X and Y jaws, respectively. (b) Simulated, integrated image of entire delivery

Figure 5

Isodose maps for the lung SBRT field delivery for a target undergoing typical motion (Fig. 3). Dose maps are shown for three cases: (a) Moving target, no tracking, (b) static target, and (c) moving target with MLC tracking. The solid squares indicate points on the PTW array that failed a 3%–3 mm gamma test with respect to the dose map shown in (b), corresponding to the static delivery.

Figure 6

Percentage points failing a 3%–3 mm gamma index criterion in the absence and presence of DMLC tracking-based delivery for (a) a lung SBRT field (commissioning∕annual QA) and (b) ten measurements using a picket fence pattern (monthly QA)

Figure 7

(a) EPID image of target (small circle) and aperture (crosshairs) and (b) the corresponding motion trajectories (red, target; blue, aperture), used to calculate the total temporal latency of the integrated system.

Figure 8

Schematic illustration (not to scale) of the Calypso QA fixture showing horizontal and vertical scribe lines which can be used to align the phantom with respect to the room lasers.