Dose calibration of EPIDs for segmented IMRT dosimetry

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

doi:10.1120/jacmp.v15i6.4895

Cited by 10 publications

(9 citation statements)

References 23 publications

(35 reference statements)

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“…One limitation of this study was the requirement for MU in each segment to exceed 10 MU because the OPTIVUE 1000ST exhibits pixel variations and reduced reproducibility below MU (18,20) . Therefore, MU scaling was incorporated to solve this issue.…”

Section: Discussionmentioning

confidence: 99%

“…A fluence map of the

30 \times 30 {mm}^{2}

open field was calculated in the TPS at an irradiation dose of 10 MU to the EPID. This was selected because the OPTIVUE 1000ST experiences pixel variations and reduced reproducibility at doses

< 10 MU

(18,20) . Regarding the hardware correction the EPID image at 10 MU was used to correct for the combination of pixel sensitivity variations (7) and the horn effect (4,8) because the pixel profile created by an automatic flood‐field correction in the image acquisition software was almost flat (data not shown).…”

Section: Methodsmentioning

confidence: 99%

“…Despite the flat pixel profile, the 2D pixel sensitivity map (PSM) was calculated as the ratio of the off‐axis pixel value to the central axis, as discussed by Greer (7) . After creating the PSM, a raw EPID image was calculated by multiplying the acquired image by the number of frames, (20) subtracting the dark current image, and dividing by the PSM. Regarding the TPS correction to correct the horn effect on the fluence map, both the fluence map and raw

30 \times 30 {mm}^{2}

EPID image were normalized to the central axis value, after which a horn correction map (HCM) was calculated by dividing the normalized raw EPID data by the normalized fluence map.…”

Section: Methodsmentioning

confidence: 99%

“…Ten head and neck cancer patients who underwent IMRT via fixed gantry step‐and‐shoot delivery were selected for this study. First, each treatment plan (2 Gy/fraction) was developed; for cases in which the MU of a specified segment received less than 10 MU, the prescribed dose of 2 Gy was rescaled to each segment MU exceeding 10 MU because of the unstable linearity and repeatability of EPID (18,20) . For fluence comparison, the fluence map for each beam was exported from the TPS, and the EPID image for each beam was acquired using the same MU and gantry angle as the treatment plan.…”

Section: Methodsmentioning

confidence: 99%

“…To accurately deliver steep dose distributions to the patient, image‐guided devices such as electronic portal imaging devices (EPIDs), computed tomography (CT) on‐rail, infrared markers, and ultrasound systems have been used before or during irradiation. Although an EPID is commonly used during imaging to detect anatomical and field locations while conducting patient setup, these devices have also been used for dose verification with suitable algorithms and corrections (1–20) . Various groups have used EPID image conversion via deconvolution of the incident fluence distribution and optical glare kernel to dose distribution via the convolution kernel (4,6,8,9) .…”

Section: Introductionmentioning

confidence: 99%

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Intensity‐modulated radiation therapy dose verification using fluence and portal imaging device

Sumida

Yamaguchi

Das

et al. 2016

J Applied Clin Med Phys

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Patient‐specific quality assurance for intensity‐modulated radiation therapy (IMRT) dose verification is essential. The aim of this study is to provide a new method based on the relative error distribution by comparing the fluence map from the treatment planning system (TPS) and the incident fluence deconvolved from the electronic portal imaging device (EPID) images. This method is validated for 10 head and neck IMRT cases. The fluence map of each beam was exported from the TPS and EPID images of the treatment beams were acquired. Measured EPID images were deconvolved to the incident fluence with proper corrections. The relative error distribution between the TPS fluence map and the incident fluence from the EPID was created. This was also created for a 2D diode array detector. The absolute point dose was measured with an ionization chamber, and the dose distribution was measured by a radiochromic film. In three cases, MLC leaf positions were intentionally changed to create the dose error as much as 5% against the planned dose and our fluence‐based method was tested using gamma index. Absolute errors between the predicted dose of 2D diode detector and of our method and measurements were 1.26%±0.65% and 0.78%±0.81% respectively. The gamma passing rate (3% global / 3 mm) of the TPS was higher than that of the 2D diode detector (p<0.02), and lower than that of the EPID (p<0.04). The gamma passing rate (2% global / 2 mm) of the TPS was higher than that of the 2D diode detector, while the gamma passing rate of the TPS was lower than that of EPID (p<0.02). For three modified plans, the predicted dose errors against the measured dose were 1.10%, 2.14%, and −0.87%. The predicted dose distributions from the EPID were well matched to the measurements. Our fluence‐based method provides very accurate dosimetry for IMRT patients. The method is simple and can be adapted to any clinic for complex cases.PACS numbers: 87.55.D‐, 87.55.km, 87.55.Qr, 87.57.uq

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Section: Discussionmentioning

confidence: 99%

“…A fluence map of the

30 \times 30 {mm}^{2}

open field was calculated in the TPS at an irradiation dose of 10 MU to the EPID. This was selected because the OPTIVUE 1000ST experiences pixel variations and reduced reproducibility at doses

< 10 MU

Section: Methodsmentioning

confidence: 99%

30 \times 30 {mm}^{2}

EPID image were normalized to the central axis value, after which a horn correction map (HCM) was calculated by dividing the normalized raw EPID data by the normalized fluence map.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intensity‐modulated radiation therapy dose verification using fluence and portal imaging device

Sumida

Yamaguchi

Das

et al. 2016

J Applied Clin Med Phys

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Deep learning‐based tools to distinguish plan‐specific from generic deviations in EPID‐based in vivo dosimetry

Olaciregui‐Ruiz,

Simões,

Jan‐Jakob

2023

Medical Physics

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BackgroundDose distributions calculated with electronic portal imaging device (EPID)‐based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan‐specific deviations. Generic deviations are characteristic to a class of plans. Examples include limitations in EIVD dose reconstruction, inaccuracies in treatment planning system (TPS) calculations and systematic machine deviations. Plan‐specific deviations have an unpredictable character. Examples include discrepancies between the patient model used for dose calculation and the patient position or anatomy during delivery, random machine deviations, and data transfer, human or software errors. During the inspection work performed with traditional γ‐evaluation statistical methods: (i) generic deviations raise alerts that need to be inspected but that rarely lead to action as their root cause is usually understood and (ii) the detection of relevant plan‐specific deviations may be hindered by the presence of generic deviations.PurposeTo investigate whether deep learning‐based tools can help in identifying γ‐alerts raised by generic deviations and in improving the detectability of plan‐specific deviations.MethodsA 3D U‐Net was trained as an autoencoder to reconstruct underlying patterns of generic deviations in γ‐distributions. The network was trained for four treatment disease sites differently affected by generic deviations: volumetric modulated arc therapy (VMAT) lung (no known deviations), VMAT prostate (TPS inaccuracies), VMAT head‐and‐neck (EIVD limitations) and intensity modulated radiation therapy (IMRT) breast (large EIVD limitations). The network was trained with virtual non‐transit γ‐distributions: 60 train/10 validation for the VMAT sites and 30 train/10 validation for IMRT breast. It was hypothesized that in vivo γ‐distributions obtained in the presence of plan‐specific deviations would differ from those seen during training. For each disease site, the sensitivity of γ‐analysis and the network to detect (synthetically introduced) patient‐related deviations was compared by receiver operator characteristic analysis. The investigated deviations were patient positioning errors, weight gain or loss, and tumor volume changes. The clinical relevance was illustrated qualitatively with 793 in vivo clinical cases (141 lung, 136 head‐and‐neck, 209 prostate and 307 breast).ResultsError detectability of patient‐related deviations was better with the network than with γ‐analysis. The average area under the curve values over all sites were 0.86 ± 0.12(1SD) and 0.69 ± 0.25(1SD), respectively. Regarding in vivo clinical results, the percentage of cases differently classified by γ‐analysis and the network was 1%, 19%, 18% and 64% for lung, head‐and‐neck, prostate, and breast, respectively. In head‐and‐neck and breast cases, 45 γ‐only alerts were examined, of which 43 were attributed to EPID dose reconstruction limitations. For prostate, all 15 investigated γ‐only alerts were due to known TPS inaccuracies. All 59 investigated network alerts were explained by either patient‐related deviations or EPID acquisition incidents. Some patient‐related deviations detected by the network were not detected by γ‐analysis.ConclusionsDeep learning‐based tools trained to reconstruct underlying patterns of generic deviations in γ‐distributions can be used to (i) automatically identify false positives within the set of γ‐alerts and (ii) improve the detection of plan‐specific deviations, hence minimizing the likelihood of false negatives. The presented method provides clear additional value to the γ‐alert management process for large scale EIVD systems.

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In silico investigation of factors affecting the MV imaging performance of a novel water-equivalent EPID

et al. 2016

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Dose calibration of EPIDs for segmented IMRT dosimetry

Cited by 10 publications

References 23 publications

Intensity‐modulated radiation therapy dose verification using fluence and portal imaging device

Intensity‐modulated radiation therapy dose verification using fluence and portal imaging device

Deep learning‐based tools to distinguish plan‐specific from generic deviations in EPID‐based in vivo dosimetry

In silico investigation of factors affecting the MV imaging performance of a novel water-equivalent EPID

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