Dosimetric Performance of A-Si Electronic Portal Imaging Devices

Alshanqity, Mukhtar; Nisbet, Andrew

doi:10.4236/ijmpcero.2016.52018

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…For the aS1000, the amorphous‐silicon pixel array consists of 128 pixel columns connected to independent channel charge amplifiers 23 . A gate driver is connected to the rows of a pixel array which stages the image readout through the pixels thin film transistors (TFT) 24 . Thus, the aS1000 EPID consists of eight vertical signal reading groups which may feature step‐wise linear‐signal patterns due to independent circuitry.…”

Section: Methodsmentioning

confidence: 99%

A regression‐based approach to compute the pixels sensitivity map of linear accelerator portal imaging devices

2021

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Purpose To determine the pixel sensitivity map (PSM) for amorphous silicon electronic portal imaging devices (EPIDs) using a single flood field signal. Method and materials A raw EPID signal results from the incident particle energy fluence, the inherent pixels response, and the background signal. In large open fields, particle energy fluence is a slow‐varying signal that is locally considered spatially constant. Pixels response is a fast and abrupt varying behavior. The background signal is due to the EPID panel electronics, which is determined during radiation absence. To determine the PSM, after correcting for the background signal, we apply a model that captures the underlying smooth particle energy fluence‐induced signal. This fluence signal‐fitted model is then used to determine the PSM. Here, we use a polynomial‐based regression surface model in both x and y dimensions. To validate the generated PSM, we measure beams and compute PSMs for multiple beam energies with and without flattening filters and for multiple source‐to‐imager distances. Since the PSM is a detector characteristic, it should be independent of those variables. We also intercompare measurements of fixed slit fields with the EPID being shifted between measurements. Results The fluence signal of the flattening filter‐free (FFF) beams was optimally modeled as a 12th degree polynomial surfaces, which had ≤0.1% residuals near the central axis. The 6 and 10 MV FFF PSMs were within ˜0.1%, and independent of the EPID SID, suggesting that the PSM is energy independent. The 6, 10, and 15 MV flattened‐beam PSMs were well modeled as 12th degree polynomial surfaces, which were equivalent within ˜0.24% but differed from the FFF PSM by up to 0.5% near the beam central axis. Applying the FFF PSMs to the flattened‐beam measurements reduced the central‐axis deviation between the raw and corrected signal to <0.1%, confirming the PSM energy independence hypothesis. When the FFF PSM is utilized, output verification with shifted slit deliveries agreed within ˜0.5% for all beam energies, which is within the radiation delivery uncertainty of ˜0.57%. Conclusion PSM for MV EPIDs can be determined by separating out the slowly varying, well‐behaved fluence signal from the pixel‐to‐pixel sensitivity variations. The quality of the PSM is found to be dependent on the quality of the surface fit, which is best for the 6 MV FFF beam measured at SID equal to 180 cm. Within fitting errors, the PSM is independent of beam energy for 6, 10, and 15 MV beams with and without flattening filters. The PSM generation does not require shifting the EPID panel nor multiple EPID panel irradiations and should be usable for linacs with fixed geometry EPIDs.

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Section: Methodsmentioning

confidence: 99%

A regression‐based approach to compute the pixels sensitivity map of linear accelerator portal imaging devices

2021

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“…A linear fitting was used to convert the pixel value to dose of each image frame. Thanks to the energy preserving feature of the Elekta's EPID (Podesta et al 2012, Alshanqity andNisbet 2016), the dose response linearity is also valid for low MUs and has insignificant dependency of dose rate and integration time. Consequently, the dose per image frame was acquired, facilitating the time-resolved dosimetry required for VMAT.…”

Section: Pixel Value To Dose Conversionmentioning

confidence: 99%

A novel approach to EPID-based 3D volumetric dosimetry for IMRT and VMAT QA

et al. 2018

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Intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) are relatively complex treatment delivery techniques and require quality assurance (QA) procedures. Pre-treatment dosimetric verification represents a fundamental QA procedure in daily clinical routine in radiation therapy. The purpose of this study is to develop an EPID-based approach to reconstruct a 3D dose distribution as imparted to a virtual cylindrical water phantom to be used for plan-specific pre-treatment dosimetric verification for IMRT and VMAT plans. For each depth, the planar 2D dose distributions acquired in air were back-projected and convolved by depth-specific scatter and attenuation kernels. The kernels were obtained by making use of scatter and attenuation models to iteratively estimate the parameters from a set of reference measurements. The derived parameters served as a look-up table for reconstruction of arbitrary measurements. The summation of the reconstructed 3D dose distributions resulted in the integrated 3D dose distribution of the treatment delivery. The accuracy of the proposed approach was validated in clinical IMRT and VMAT plans by means of gamma evaluation, comparing the reconstructed 3D dose distributions with Octavius measurement. The comparison was carried out using (3%, 3 mm) criteria scoring 99% and 96% passing rates for IMRT and VMAT, respectively. An accuracy comparable to the one of the commercial device for 3D volumetric dosimetry was demonstrated. In addition, five IMRT and five VMAT were validated against the 3D dose calculation performed by the TPS in a water phantom using the same passing rate criteria. The median passing rates within the ten treatment plans was 97.3%, whereas the lowest was 95%. Besides, the reconstructed 3D distribution is obtained without predictions relying on forward dose calculation and without external phantom or dosimetric devices. Thus, the approach provides a fully automated, fast and easy QA procedure for plan-specific pre-treatment dosimetric verification.

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“…In 2004, Mc Dermott et al studied this behavior as a so‐called “ghosting/lag” effect and proposed a beam‐time correction to solve this problem. More recently, Alshanqity et al demonstrated that this nonlinearity could be explained by the lack of definition in the acquisition and readout characteristics of EPID system. They indicated that if the total integrated EPID signal is a function of the total delivered dose, EPID signal in each frame depends on the dose delivered during the integration time, and so depends on both dose rate and integration time.…”

Section: Introductionmentioning

confidence: 99%