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Updated: Jun 19, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
Published on: May 20, 2016
Development of a new VMAT QA framework for Mobius3D using control-point specific EPID images
JaeHyun Seok1,2,3, Hojin Kim3, Min Cheol Han3
1Department of Integrative Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Purpose:
This study presents novel quality assurance (QA) approach for volumetric modulated arc therapy (VMAT) that leverages frame-by-frame electronic portal imaging device (EPID) images integrated into Mobius3D for accurate three-dimensional dose calculations.
Methods:
Sequential EPID images for VMAT plans were acquired every 0.4-second by iView system and processed through iterative deconvolution to mitigate blurring from photon scattering. Deconvolved images were binarized to define multi-leaf collimator (MLC) positions. Pre-acquired box fluences determined optimal threshold for binarization and adjusted for detector shift depending on gantry and collimator angles. Sequential EPID images were re-scaled using pixel scaling factor (PSF) and converted to monitor unit (MU) proportional values. Generated EPID-based log file, including control-point specific MLC and monitor units (MU) information, were analyzed in Mobius3D for Gamma passing rate (GPR) of VMAT plans from 18 patients. Plan complexity indices were calculated and correlated with GPR.
Results:
Clinically appropriate threshold was defined to be 20000 that can extract accurate MLC data from the deconvolved binarized EPID images. Positional deviations due to gantry and collimator rotations were observed to be up to 4.5 pixels. Recalibrated EPID pixel values showed linearity with MU regardless of changes in dose rate. Consequently, average GPR for 18 patients evaluated using Mobius3D reached 95.2% ± 3.7%%, based on 3% dose difference and 3mm distance-to-agreement criterion. It was found that two plan complexity indices showed statistically significant correlation with GPR.
Conclusion:
This study successfully implemented novel measurement-based VMAT QA framework based on control-point specific EPID, based upon accurate MLC and MU data at each frame.
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