Clinical implementation of an exit detector‐based dose reconstruction tool for helical tomotherapy delivery quality assurance

Deshpande, Shrikant; Xing, Aitang; Metcalfe, Peter E; Holloway, Lois; Vial, P; Geurts, Mark

doi:10.1002/mp.12484

Cited by 10 publications

(17 citation statements)

References 32 publications

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“…The discrepancies we found between planned and measured LOTs are consistent with the findings of others . We could not otherwise compare the method to an external standard on clinical procedures, because there is none.…”

Section: Discussionsupporting

confidence: 88%

“…A bijective leaf‐to‐channel map was determined by matching a leaf to the center of the beamlet it produced. To determine the leaves state, it is sufficient to observe the signal at these specific channels …”

Section: Methodsmentioning

confidence: 99%

“…Some authors have used the detector data to measure the LOTs . Others used the exit fluence to verify MLC operation or to reconstruct the delivered dose without measuring the LOTs .…”

Section: Introductionmentioning

confidence: 99%

“…The manufacturer has implemented mechanisms to prevent severe MLC failures, yet, it tolerates smaller errors. These can lead to observable dose discrepancies, [10][11][12][13][14] but phantom-based DQA procedures do not evaluate the actual dose distribution inside the patient.…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16][17] Others used the exit fluence to verify MLC operation [18][19][20] or to reconstruct the delivered dose without measuring the LOTs. 11,[21][22][23] Handsfield et al 12 and Deshpande et al 14 calculated the delivered dose from the measured LOTs. Handsfield et al 12 used the method described by Chen et al 15 to measure the LOTs.…”

Section: Introductionmentioning

confidence: 99%

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In air and in vivo measurement of the leaf open time in tomotherapy using the on‐board detector pulse‐by‐pulse data

et al. 2019

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Purpose We developed an algorithm to measure the leaf open times (LOT) from the on‐board detector (OBD) pulse‐by‐pulse data in tomotherapy. We assessed the feasibility of measuring the LOTs in dynamic jaw mode and validated the algorithm on machine QA and clinical data. Knowledge of the actual LOTs is a basis toward calculating the delivered dose and performing efficient phantom‐less delivery quality assurance (DQA) controls of the multileaf collimator (MLC). In tomotherapy, the quality of the delivered dose depends on the correct performance of the MLC, hence on the accuracy of the LOTs. Materials and methods In the detector signal, the period of time during which a leaf is open corresponds to a high intensity region. The algorithm described here locally normalizes the detector signal and measures the FWHM of the high intensity regions. The Daily QA module of the TomoTherapy Quality Assurance (TQA) tool measures LOT errors. The Daily QA detector data were collected during 9 days on two tomotherapy units. The errors yielded by the method were compared to these reported by the Daily QA module. In addition, clinical data were acquired on the two units (25 plans in total), in air without attenuation material in the beam path and in vivo during a treatment fraction. The study included plans with fields of all existing sizes (1.05, 2.51, 5.05 cm). The collimator jaws were in dynamic mode (TomoEDGETM). The feasibility of measuring the LOTs was assessed with respect to the jaw aperture. Results The mean discrepancy between LOTs measured by the algorithm and those measured by TQA was of 0 ms, with a standard deviation of 0.3 ms. The LOT measured by the method had thus an uncertainty of 1 ms with a confidence level of 99%. In 5.05 cm dynamic jaw procedures, the detector is in the beam umbra at the beginning and at the end of the delivery. In such procedures, the algorithm could not measure the LOTs at jaw apertures between 7 and maximum 12.4 mm. Otherwise, no measurement error due to the jaw movement was observed. No LOT measurement difference between air and in vivo data was observed either. Conclusion The method we propose is reliable. It can equivalently measure the LOTs from data acquired in air or in vivo. It handles fully the static procedures and the 2.51 cm dynamic procedures. It handles partially the 5.05 cm dynamic procedures. The limitation was evaluated with respect to the jaw aperture.

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Section: Discussionsupporting

confidence: 88%

Section: Methodsmentioning

confidence: 99%

“…Some authors have used the detector data to measure the LOTs . Others used the exit fluence to verify MLC operation or to reconstruct the delivered dose without measuring the LOTs .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In air and in vivo measurement of the leaf open time in tomotherapy using the on‐board detector pulse‐by‐pulse data

et al. 2019

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Performance of binary MLC using real‐time optical sensor feedback system

Corradini,

Vite,

Urso

2024

J Applied Clin Med Phys

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The Radixact system (Accuray Inc., Sunnyvale, CA) is the latest platform release based on the TomoTherapy technology. The most recent system does not apply a leaf latency model correction after plan optimization to ensure the correct MLC leaf‐open time (LOT) agreement between the TPS and machine delivery. The MLC uses optical sensors to measure the delivered LOTs in real‐time and individual leaf‐specific latency corrections are made to ensure agreement. The aim of this study was to assess the performance of the Radixact MLC with leaf‐specific latency correction using the optical sensor's real‐time feedback. Specifically, the study statistically evaluated the MLC LOT errors observed from 290 plan‐specific quality assurance (PSQA) measurements. Repeatability testing was performed to quantify the uncertainty in the MLC feedback system delivery by analyzing > 1300 delivered treatment fractions throughout the course of radiotherapy. The clinical impact was evaluated by estimating the resulting dose difference in the patient targets due to the measured plan latencies. Our study measured an average plan latency equal to 2.0 ± 0.4 ms (0.6% ± 0.2%) for 290 PSQAs. Repeatability tests showed a mean standard deviation in plan latencies measuring 0.05 ms (0.02%). The deviation from the TPS in the mean target dose due to the plan latencies was estimated to be 0.0% ± 0.2% (range: ‐0.7%–1.1%). The current MLC system with real‐time optical sensor feedback is capable of accurately delivering the TPS‐generated sinograms. Repeatability test results showed that the system allows for high reliability in daily sinogram delivery. The MLC latency deviations were shown to have minimal clinical impact on the overall target dosimetry.

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A delivery quality assurance tool based on the actual leaf open times in tomotherapy

et al. 2020

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Purpose: To validate a delivery quality assurance (DQA) protocol for tomotherapy based on the measurement of the leaf open times (LOTs). In addition, to show the correlation between the mean relative LOT discrepancy and the dose deviation in the planning target volume (PTV). Materials and methods: We used a LOT measurement algorithm presented in a previous work on our two tomotherapy treatment units (TOMO1 and TOMO2). We generated TomoPhant plans with intentional random LOT discrepancies following Gaussian distributions of À6%, À4%, À2%, 2%, 4%, and 6%. We irradiated them on the Cheese Phantom with two ion chambers and collected the raw data on both our treatment units. Using the raw data, we measured the actual LOTs and verified that the induced discrepancies were highlightable. Then, we calculated the actual dose using Accuray's standalone dose calculator and verified that the calculated dose agreed with the ion chamber measurement. We randomly chose 60 clinical treatment plans, delivered them in air, and collected the raw detector data. We measured the actual LOTs from the raw data and calculated the corresponding dose distributions using Accuray's standalone dose calculator. We assessed the Pearson coefficient correlation of the deviation between expected and actual dose in the PTV (a) with the mean relative LOT discrepancy and (b) with the c-index pass rate for different tolerances. Results: The mean relative discrepancy between actual (measured by the algorithm) and expected LOTs on the modified TomoPhant plans was 1.10 AE 0.05% on TOMO1 and 0.02 AE 0.03% on TOMO2, respectively. The agreement between measured and calculated dose was 0.2 AE 0.3% on TOMO1 and 0.1 AE 0.3% on TOMO2, respectively. On clinical plans, the means of the relative LOT discrepancies ranged from À3.0 % to 1.4%. The dose deviation in the PTVs ranged from À1.6% to 2.4%. The Pearson coefficient correlation between the mean relative LOT discrepancy and the dose deviation in the PTV was 0.76 (P % 10 À15) on TOMO1 and 0.65 (P % 10 À10) on TOMO2, respectively. There was no correlation between the c-index pass rate and the dose deviation in the PTV. Conclusion: The method made it possible to measure and to correctly highlight the LOT discrepancies on the TomoPhant plans. The dose subsequently calculated was accurate. On clinical plans, the mean LOT discrepancy correlated with the dose deviation in the PTV. This makes the mean LOT discrepancy a handy indicator of the plan quality.

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Clinical implementation of an exit detector‐based dose reconstruction tool for helical tomotherapy delivery quality assurance

Cited by 10 publications

References 32 publications

In air and in vivo measurement of the leaf open time in tomotherapy using the on‐board detector pulse‐by‐pulse data

In air and in vivo measurement of the leaf open time in tomotherapy using the on‐board detector pulse‐by‐pulse data

Performance of binary MLC using real‐time optical sensor feedback system

A delivery quality assurance tool based on the actual leaf open times in tomotherapy

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