Trajectory Log File Sensitivity: A Critical Analysis Using DVH and EPID

Overview

Journal Rep Pract Oncol Radiother

Specialty Oncology

Date 2018 Aug 22

PMID 30127675

Citations 4

Authors

Wui Ann Woon

Paul B Ravindran

Piyasiri Ekayanake

Subramani Vikraman

Siti Amirah

Yivonne Y F Lim

Christopher H S Vun

Jamsari Khalid

Affiliations

Soon will be listed here.

Abstract

Aim: The aim of this study was to investigate the sensitivity of the trajectory log file based quality assurance to detect potential errors such as MLC positioning and gantry positioning by comparing it with EPID measurement using the most commonly used criteria of 3%/3 mm.

Materials And Methods: An in-house program was used to modified plans using information from log files, which can then be used to recalculate a new dose distribution. The recalculated dose volume histograms (DVH) were compared with the originals to assess differences in target and critical organ dose. The dose according to the differences in DVH was also compared with dosimetry from an electronic portal imaging device.

Results: In all organs at risk (OARs) and planning target volumes (PTVs), there was a strong positive linear relationship between MLC positioning and dose error, in both IMRT and VMAT plans. However, gantry positioning errors exhibited little impact in VMAT delivery. For the ten clinical cases, no significant correlations were found between gamma passing rates under the criteria of 3%/3 mm for the composite dose and the mean dose error in DVH ( < 0.3, > 0.05); however, a significant positive correlation was found between the gamma passing rate of 3%/3 mm (%) averaged over all fields and the mean dose error in the DVH of the VMAT plans ( = 0.59, < 0.001).

Conclusions: This study has successfully shown the sensitivity of the trajectory log file to detect the impact of systematic MLC errors and random errors in dose delivery and analyzed the correlation of gamma passing rates with DVH.

Citing Articles

Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods.

Quintero P, Benoit D, Cheng Y, Moore C, Beavis A Br J Radiol. 2023; 96(1147):20220302.

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A method for patient-specific DVH verification using a high-sampling-rate log file in an Elekta linac.

Nishiyama S, Takemura A J Appl Clin Med Phys. 2022; 24(3):e13849.

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Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation.

Barnes M, Pomare D, Doebrich M, Standen T, Wolf J, Greer P J Appl Clin Med Phys. 2022; 23(9):e13660.

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A novel quality assurance procedure for trajectory log validation using phantom-less real-time latency corrected EPID images.

Lim S, Zwan B, Lee D, Greer P, Lovelock D J Appl Clin Med Phys. 2021; 22(3):176-185.

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