Phantomless patient‐specific TomoTherapy QA via delivery performance monitoring and a secondary Monte Carlo dose calculation

Handsfield, Lydia L.; Jones, Ryan; Wilson, Dan; Siebers, J.; Read, Paul W.; Chen, Quan

doi:10.1118/1.4894721

Cited by 39 publications

(42 citation statements)

References 32 publications

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“…Others used the exit fluence to verify MLC operation or to reconstruct the delivered dose without measuring the LOTs . Handsfield et al . and Deshpande et al .…”

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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“…Others used the exit fluence to verify MLC operation or to reconstruct the delivered dose without measuring the LOTs . Handsfield et al . and Deshpande et al .…”

Section: Introductionmentioning

confidence: 99%

“…calculated the delivered dose from the measured LOTs. Handsfield et al . used the method described by Chen et al .…”

Section: Introductionmentioning

confidence: 99%

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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“…developed a convolution‐based calculation model to correlate the leaf control sinogram from the treatment planning system (TPS) to the data acquired by the OBD during static couch procedure. Handsfield et al . used a fluence measurement acquired in air by OBD to reconstruct the patient dose using Monte Carlo (MC).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…However, interfraction discrepancies can occur during the treatment of the patient and result in significant differences from the initial treatment plan. These variations can be caused by changes in the patient morphology and positioning, as well as by delivery errors that can occur during a specific fraction …”

Section: Introductionmentioning

confidence: 99%

TransitQA — A new method for transit dosimetry of Tomotherapy patients

et al. 2017

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Purpose: TransitQA is an innovative method for Tomotherapy transit dosimetry using the on-board detector (OBD). Our previously published model for Tomotherapy treatment plan verification (AirQA) has been enhanced to take into account patient and couch transmission. AirQA estimates the OBD signal during irradiation with nothing in the beam path from the leaf control sinogram, allowing us to check whether the planned treatment is correctly delivered by the machine. TransitQA allows us to check the treatment delivery with the patient on the couch, potentially showing the effects of changes in the patient anatomy and delivery errors. Methods: Patient and couch transmission have been added to the model using the OBD projections of pretreatment megavoltage computed tomography (MVCT). The difference in the energy spectra between the imaging and treatment beams has been corrected by an exponent from the MVCT projections consisting of the ratio of the mass attenuation coefficients. This exponent has been found to not vary significantly with the atomic number Z, allowing us to apply this procedure to heterogeneous media, such as patients. The attenuated OBD projections acquired during the treatment are compared to the model via a signed global c-index analysis. The dose criterion was 5% of the 95 th percentile of the dose distribution, and the distance to agreement (DTA) was 4 mm. Results: Our method has been applied to a heterogeneous phantom with 98.1% of the points passing the c-evaluation test, showing that the model can predict the attenuated OBD projection. The method has been applied to two representative patients throughout the whole treatment, highlighting variations in the signal transmission and c-index. Conclusion: This paper establishes the proof-of-concept of transit dosimetry for all patients treated by Tomotherapy. Moreover, this method can be used as a surrogate for in vivo dosimetry.

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Phantomless patient‐specific TomoTherapy QA via delivery performance monitoring and a secondary Monte Carlo dose calculation

Cited by 39 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

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

TransitQA — A new method for transit dosimetry of Tomotherapy patients

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