Literature DB >> 25088064

Time dependent pre-treatment EPID dosimetry for standard and FFF VMAT.

Mark Podesta1, Sebastiaan M J J G Nijsten, Lucas C G G Persoon, Stefan G Scheib, Christof Baltes, Frank Verhaegen.   

Abstract

Methods to calibrate Megavoltage electronic portal imaging devices (EPIDs) for dosimetry have been previously documented for dynamic treatments such as intensity modulated radiotherapy (IMRT) using flattened beams and typically using integrated fields. While these methods verify the accumulated field shape and dose, the dose rate and differential fields remain unverified. The aim of this work is to provide an accurate calibration model for time dependent pre-treatment dose verification using amorphous silicon (a-Si) EPIDs in volumetric modulated arc therapy (VMAT) for both flattened and flattening filter free (FFF) beams. A general calibration model was created using a Varian TrueBeam accelerator, equipped with an aS1000 EPID, for each photon spectrum 6 MV, 10 MV, 6 MV-FFF, 10 MV-FFF. As planned VMAT treatments use control points (CPs) for optimization, measured images are separated into corresponding time intervals for direct comparison with predictions. The accuracy of the calibration model was determined for a range of treatment conditions. Measured and predicted CP dose images were compared using a time dependent gamma evaluation using criteria (3%, 3 mm, 0.5 sec). Time dependent pre-treatment dose verification is possible without an additional measurement device or phantom, using the on-board EPID. Sufficient data is present in trajectory log files and EPID frame headers to reliably synchronize and resample portal images. For the VMAT plans tested, significantly more deviation is observed when analysed in a time dependent manner for FFF and non-FFF plans than when analysed using only the integrated field. We show EPID-based pre-treatment dose verification can be performed on a CP basis for VMAT plans. This model can measure pre-treatment doses for both flattened and unflattened beams in a time dependent manner which highlights deviations that are missed in integrated field verifications.

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Year:  2014        PMID: 25088064     DOI: 10.1088/0031-9155/59/16/4749

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  8 in total

1.  Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields.

Authors:  Seied Rabie Mahdavi; Asieh Tavakol; Mastaneh Sanei; Seyed Hadi Molana; Farshid Arbabi; Aram Rostami; Sohrab Barimani
Journal:  Br J Radiol       Date:  2019-07-24       Impact factor: 3.039

2.  Intensity-modulated radiation therapy dose verification using fluence and portal imaging device.

Authors:  Iori Sumida; Hajime Yamaguchi; Indra J Das; Hisao Kizaki; Keiko Aboshi; Mari Tsujii; Yuji Yamada; Osamu Suzuki; Yuji Seo; Fumiaki Isohashi; Kazuhiko Ogawa
Journal:  J Appl Clin Med Phys       Date:  2016-01-08       Impact factor: 2.102

3.  EPID-based dosimetry to verify IMRT planar dose distribution for the aS1200 EPID and FFF beams.

Authors:  Narges Miri; Peter Keller; Benjamin J Zwan; Peter Greer
Journal:  J Appl Clin Med Phys       Date:  2016-11-08       Impact factor: 2.102

Review 4.  In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice.

Authors:  Igor Olaciregui-Ruiz; Sam Beddar; Peter Greer; Nuria Jornet; Boyd McCurdy; Gabriel Paiva-Fonseca; Ben Mijnheer; Frank Verhaegen
Journal:  Phys Imaging Radiat Oncol       Date:  2020-08-29

5.  Determination of the electronic portal imaging device pixel-sensitivity-map for quality assurance applications. Part 2: Photon beam dependence.

Authors:  Michael Paul Barnes; Baozhou Sun; Brad Michael Oborn; Bishnu Lamichhane; Stuart Szwec; Matthew Schmidt; Bin Cai; Frederick Menk; Peter Greer
Journal:  J Appl Clin Med Phys       Date:  2022-04-15       Impact factor: 2.243

6.  A facile synthesis of high entropy alloy nanoparticle-activated carbon nanocomposites for synergetic degradation of methylene blue.

Authors:  Yuyu Liu; Zheng Chen; Xiaoqin Yang; Jinyong Zhang; Zhonggang Sun; Yuzeng Chen; Feng Liu
Journal:  RSC Adv       Date:  2021-07-14       Impact factor: 4.036

7.  What is the optimal input information for deep learning-based pre-treatment error identification in radiotherapy?

Authors:  Cecile J A Wolfs; Frank Verhaegen
Journal:  Phys Imaging Radiat Oncol       Date:  2022-08-27

8.  Evaluating the sensitivity of Halcyon's automatic transit image acquisition for treatment error detection: A phantom study using static IMRT.

Authors:  Xenia Ray; Casey Bojechko; Kevin L Moore
Journal:  J Appl Clin Med Phys       Date:  2019-10-06       Impact factor: 2.102

  8 in total

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