Program Information
A Novel Method for EPID-Based In-Vivo Exit Dose Verification for Intensity Modulated Radiotherapy
Z Wu*, J Wang , J Peng , J Wang , W Hu , Fudan University Shanghai Cancer Center, Shanghai, Shanghai
Presentations
SU-F-T-229 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:Electronic portal imaging device (EPID) can be used to acquire a two-dimensional exit dose distribution during treatment delivery, thus allowing the in-vivo verification of the dose delivery through a comparison of measured portal images to predicted portal dose images (PDI). The aim of this study was to present a novel method to easily and accurately predict PDI, and to establish an EPID-based in-vivo dose verification method during IMRT treatments.
Methods:We developed a model to determine the predicted portal dose at the same plane of the EPID detector location. The Varian EPID (aS1000) positions at 150cm source-to-detector-distance (SDD), and can be used to acquire in-vivo exit dose using Portal Dosimetry (PD) function. Our model was generated to make an equivalent water thickness represent the buildup plate of EPID. The exit dose at extend SDD plane with patient CT data in the beam can be calculated as the predicted PDI in the treatment planning system (TPS). After that, the PDI was converted to the fluence at SDD of 150cm using the inverse square law coded in MATLAB. Five head-and-neck and prostate IMRT patient plans contain 32 fields were investigated to evaluate the feasibility of this new method. The measured EPID image was compared with PDI using the gamma analysis.
Results:The average results for cumulative dose comparison were 81.9% and 91.6% for 3%, 3mm and 4%, 4mm gamma criteria, respectively. Results indicate that the patient transit dosimetry predicted algorithm compares well with EPID measured PD doses for test situations.
Conclusion:Our new method can be used as an easy and feasible tool for online EPID-based in-vivo dose delivery verification for IMRT treatments. It can be implemented for fast detecting those obvious treatment delivery errors for individual field and patient quality assurance.
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