Automating linear accelerator quality assurance

Eckhause, T. A.; Al‐Hallaq, Hania; Ritter, Timothy; DeMarco, John J.; Farrey, Karl; Pawlicki, Todd; Kim, Gwe Ya; Popple, Richard A.; Sharma, Vijeshwar; Pérez, Manuel Asensi; Park, Sung-Yong; Booth, Jeremy; Thorwarth, Ryan; Moran, Jean M.

doi:10.1118/1.4931415

Cited by 37 publications

(55 citation statements)

References 25 publications

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“…The position which showed the largest error in the MLC width between slits coincided with the offset position. This is comparable to studies that showed that MLC position errors between slits were less than 0.5 mm at the offset position 18, 23. Hence, these results also indicate the effectiveness of the calibration method for the misaligned EPID geometry.…”

Section: Discussionsupporting

confidence: 86%

“…Eckhause et al. examined whether or not a small MLC displacement (0.1–0.5 mm) could be detected by using EPID images (0.39 mm/pixel), and they were able to distinguish MLC deviations exceeding 0.3 mm, which is smaller than the EPID pixel size 18. Thus, subpixel estimation can be implemented using interpolated pixel values.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies reported that an electronic portal imaging device (EPID) and log data analysis had sufficiently verified the accuracy of the mechanical uncertainty, that is, MLC and gantry position as well as delivery error, during VMAT 17, 18, 19. Therefore, we investigated the application of the QA method based on the EPID and log data analysis to DWA irradiation.…”

Section: Introductionmentioning

confidence: 99%

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Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

Hirashima

Miyabe

Nakamura

et al. 2018

J Applied Clin Med Phys

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The purpose of this study was to develop a simple verification method for the routine quality assurance (QA) of Dynamic WaveArc (DWA) irradiation using electronic portal imaging device (EPID) images and log data analysis. First, an automatic calibration method utilizing the outermost multileaf collimator (MLC) slits was developed to correct the misalignment between the center of the EPID and the beam axis. Moreover, to verify the detection accuracy of the MLC position according to the EPID images, various positions of the MLC with intentional errors in the range 0.1–1 mm were assessed. Second, to validate the geometric accuracy during DWA irradiation, tests were designed in consideration of three indices. Test 1 evaluated the accuracy of the MLC position. Test 2 assessed dose output consistency with variable dose rate (160–400 MU/min), gantry speed (2.2–6°/s), and ring speed (0.5–2.7°/s). Test 3 validated dose output consistency with variable values of the above parameters plus MLC speed (1.6–4.2 cm/s). All tests were delivered to the EPID and compared with those obtained using a stationary radiation beam with a 0° gantry angle. Irradiation log data were recorded simultaneously. The 0.1‐mm intentional error on the MLC position could be detected by the EPID, which is smaller than the EPID pixel size. In Test 1, the MLC slit widths agreed within 0.20 mm of their exposed values. The averaged root‐mean‐square error (RMSE) of the dose outputs was less than 0.8% in Test 2 and Test 3. Using log data analysis in Test 3, the RMSE between the planned and recorded data was 0.1 mm, 0.12°, and 0.07° for the MLC position, gantry angle, and ring angle, respectively. The proposed method is useful for routine QA of the accuracy of DWA.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

Hirashima

Miyabe

Nakamura

et al. 2018

J Applied Clin Med Phys

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“…Presently, four sets of leaf positions are available for quantifying MLC performance [1][2][3][4][5][6][7][8][9][10][11] : (a) DICOM RT-plan's positions generated from treatment planning system; (b) EPID-acquired positions; (c) on-fly generated 4D Integrated Treatment Console (4DITC)-ordered positions; (d) actual positions read from the motor's encoder. Positions (c) and (d) are one-on-one recorded in LINAC-generated logs.…”

Section: Introductionmentioning

confidence: 99%

Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions

Zhang

Kanchaveli

et al. 2019

Medical Physics

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Purpose This study proposes a proactive maintenance model utilizing historical Multileaf collimator (MLC) performance data to predict potential MLC dysfunctions, promote preemptive maintenance and thereby reduce treatment disruptions. Methods MLC failures were assumed to correlate with MLC performance quantitation from trajectory logs. A cohort of data from service reports and trajectory logs was used to establish a model for predicting MLC dysfunctions. Specifically, the service reports logged by our in‐house engineers recorded failure status, including service date, service reason and actions taken, while trajectory logs recorded the ordered/actual leaf positions in 20‐ms intervals. Leaf performance from trajectory logs was quantified, where an event was defined as detecting a leaf's position deviation ≥a mm. Three a values, 0.05, 0.1, and 0.5 mm, were used as candidates to determine the appropriate threshold for deviation event quantitation. Logged MLC failures from service reports were retrieved and classified into two categories based on the patterns of their deviation events calculated from trajectory logs: (a) failures with continuous deviations: deviation events lasted several days before failure, and (b) failures with a burst of deviations: deviation events only lasted 1 or 2 days and then MLC failed suddenly. The proposed proactive model focused on the failures with continuous deviations since abnormal trends in their deviation events lasted couple of days, allowing preventive maintenance. The model was predefined with three parameters (x, y, z): if a leaf scored ≥x deviation events per day in any y days within up to a z‐day window, the leaf was marked as a “potential failure.” The distributions of the deviation events as functions of time (days or weeks) and leaves using the found a‐value were then associated with logged failures to find model parameters. In a retrospective demonstration, a total of 28 logged failures with continuous deviations and 66 397 trajectory logs from two TBs' 3‐yr records were used to determine the model parameters (x, y, z). The established model was then applied to a third TB for validation. Results Deviation event threshold, a, was determined to be 0.1 mm, and the resulting model parameters were (x = 20, y = 6, z = 10). When validating the third TB's 3‐yr record with 12 logged continuous deviation failures, the model predicted 16 failures: seven were confirmed from the records with a hit rate of 58.3%, while nine were not; further investigation of each unconfirmed failure convinced that some could be actual failures, but somehow not recorded. Conclusion The model offers an addition to preemptive maintenance for reducing treatment disruptions.

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“…The amorphous silicon electronic portal imaging device (EPID) has been used as a detector for linac QA measurements 2, 3, 4, 5, 6, 7, 8, 9, 10. EPID is well suited to a number of linac QA tests as it provides a high spatial and temporal resolution two‐dimensional digital measurement from a device that requires minimal setup time and is integrated into the linac.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the TrueBeam machine performance check (MPC) beam constancy checks for flattened and flattening filter‐free (FFF) photon beams

Barnes

Greer

2016

J Applied Clin Med Phys

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Machine Performance Check (MPC) is an automated and integrated image‐based tool for verification of beam and geometric performance of the TrueBeam linac. The aims of the study were to evaluate the MPC beam performance tests against current daily quality assurance (QA) methods, to compare MPC performance against more accurate monthly QA tests and to test the sensitivity of MPC to changes in beam performance. The MPC beam constancy checks test the beam output, uniformity, and beam center against the user defined baseline. MPC was run daily over a period of 5 months (n = 115) in parallel with the Daily QA3 device. Additionally, IC Profiler, in‐house EPID tests, and ion chamber measurements were performed biweekly and results presented in a form directly comparable to MPC. The sensitivity of MPC was investigated using controlled adjustments of output, beam angle, and beam position steering. Over the period, MPC output agreed with ion chamber to within 0.6%. For an output adjustment of 1.2%, MPC was found to agree with ion chamber to within 0.17%. MPC beam center was found to agree with the in‐house EPID method within 0.1 mm. A focal spot position adjustment of 0.4 mm (at isocenter) was measured with MPC beam center to within 0.01 mm. An average systematic offset of 0.5% was measured in the MPC uniformity and agreement of MPC uniformity with symmetry measurements was found to be within 0.9% for all beams. MPC uniformity detected a change in beam symmetry of 1.5% to within 0.3% and 0.9% of IC Profiler for flattened and FFF beams, respectively.

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Automating linear accelerator quality assurance

Cited by 37 publications

References 25 publications

Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions

Evaluation of the TrueBeam machine performance check (MPC) beam constancy checks for flattened and flattening filter‐free (FFF) photon beams

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