The impact of MLC transmitted radiation on EPID dosimetry for dynamic MLC beams

Vial, P; Greer, Peter B.; Hunt, Peter; Oliver, Lawrence K.; Baldock, Clive

doi:10.1118/1.2885368

Cited by 52 publications

(62 citation statements)

References 50 publications

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“…The remaining measurements that used 6 MV (19) had large areas of low CU, where portal dosimetry exhibits poorer agreement with the calculated dose. This is due to the difference in response of an a‐Si electronic portal imaging device to open and MLC transmitted radiation, and further modeling using an algorithm such as that presented by Vial et al can improve the under‐response in low‐dose regions 23, 24. These 19 IMRT beams with mean CU value of 0.127 ± 0.071 (range 0.022–0.254) have been excluded from the study pool.…”

Section: Resultsmentioning

confidence: 99%

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IMRT QA using machine learning: A multi‐institutional validation

Valdés

Chan

Lim

et al. 2017

J Applied Clin Med Phys

120

138

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PurposeTo validate a machine learning approach to Virtual intensity‐modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

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Section: Resultsmentioning

confidence: 99%

“…Note that the inherent Varian's Portal Dose Image Prediction algorithm assumes a radially symmetric response which is certainly different than the reality in 2D profiles of portal dosimetry 23. This may add the additional uncertainty of this measurement.…”

Section: Resultsmentioning

confidence: 99%

IMRT QA using machine learning: A multi‐institutional validation

Valdés

Chan

Lim

et al. 2017

J Applied Clin Med Phys

120

138

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“…The author validated that these variation are associated with acquisition readout scheme (i.e., missing frames). We have not addressed the additional scatter or spectral corrections that can be applied to account for patient transit or MLC transmission effects 5 , 11 . Dynamic MLC and VMAT were not available for this study, and further work is required to determine how these results apply to that setting.…”

Section: Discussionmentioning

confidence: 99%

“…There are issues related to the non‐water equivalence of the EPID 2 , 3 , 4 , 5 and the detector's image acquisition process 6 , 7 , 8 . Previous studies of the dose response characteristics of a‐Si EPIDs have reported an underresponse at small monitor unit (MU) exposures relative to longer exposures 9 , 10 , 11 , 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

Dose calibration of EPIDs for segmented IMRT dosimetry

Deshpande

Xing

Holloway

et al. 2014

J Applied Clin Med Phys

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The purpose of this study was to investigate the dose response of amorphous silicon (a‐Si) electronic portal imaging devices (EPIDs) under different acquisition settings for both open jaw defined fields and segmented intensity‐modulated radiation therapy (IMRT) fields. Four different EPIDs were used. Two Siemens and one Elekta plus a standalone Perkin Elmer research EPID. Each was operated with different acquisition systems and settings. Dose response linearity was measured for open static jaw defined fields and ‘simple’ segmented IMRT fields for a range of equipment and system settings. Six ‘simple’ segmented IMRT fields were used. The segments of each IMRT field were fixed at 10×10.15emcm2 field size with equal MU per segment, each field having a total of 20 MU. Simultaneous measurements with an ionization chamber array (ICA) and EPID were performed to separate beam and detector response characteristics. Three different pixel calibration methods were demonstrated and compared for an example ‘clinical IMRT field’. The dose response with the Elekta EPID for ‘simple’ segmented IMRT fields versus static fields agreed to within 2.5% for monitor unit (MU)≥2. The dose response for the Siemens systems was difficult to interpret due to the poor reproducibility for segmented delivery, at MU≤5, which was not observed with the standalone research EPID nor ICA on the same machine. The dose response measured under different acquisition settings and different linac/EPID combinations matched closely (≤1%), except for the Siemens EPID. Clinical IMRT EPID dosimetry implemented with the different pixel‐to‐dose calibration methods indicated that calibration at 20 MU provides equivalent results to implementing a ghosting correction model. The nonlinear dose response was consistent across both clinical EPIDs and the standalone research EPID, with the exception of the poor reproducibility seen with Siemens EPID images of IMRT fields. The nonlinear dose response was relatively insensitive to acquisition settings and appears to be primarily due to gain ghosting effects. No additional ghosting correction factor is necessary when the pixel‐to‐dose calibration factor at small MU calibration method is used.PACS numbers: 87.53.Bn, 87.55.Qr, 87.56.Fc, 87.57.uq

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“…The fields of VMAT treatment plans are divided in control points with multileaf collimator (MLC) openings of various size and shape. Irregularly shaped MLC openings with small sub-opening components are challenging from a dosimetric point of view in consideration of both calculation, delivery and measurement [1,2]. For example, small MLC openings increase the sensitivity of small MLC positioning errors during delivery [3] and increase the challenge of measuring as well as calculating dose because of volumes lacking charged particle equilibrium (CPE) [4].…”

Section: Introductionmentioning

confidence: 99%

Complexity metric based on fraction of penumbra dose - initial study

Bäck

Nordström

Gustafsson

et al. 2017

J. Phys.: Conf. Ser.

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Abstract. Volumetric modulated arc therapy improve radiotherapy outcome for many patients compared to conventional three dimensional conformal radiotherapy but require a more extensive, most often measurement based, quality assurance. Multi leaf collimator (MLC) aperture-based complexity metrics have been suggested to be used to distinguish complex treatment plans unsuitable for treatment without time consuming measurements. This study introduce a spatially resolved complexity score that correlate to the fraction of penumbra dose and will give information on the spatial distribution and the clinical relevance of the calculated complexity. The complexity metric is described and an initial study on the correlation between the complexity score and the difference between measured and calculated dose for 30 MLC openings is presented. The result of an analysis of the complexity scores were found to correlate to differences between measurements and calculations with a Pearson's r-value of 0.97.

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The impact of MLC transmitted radiation on EPID dosimetry for dynamic MLC beams

Cited by 52 publications

References 50 publications

IMRT QA using machine learning: A multi‐institutional validation

IMRT QA using machine learning: A multi‐institutional validation

Dose calibration of EPIDs for segmented IMRT dosimetry

Complexity metric based on fraction of penumbra dose - initial study

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