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A Correlation Study On 3D Fluence-Based QA and 2D Dose Measurement-Based QA


S Liu

S Liu*, T Mazur , H Li , O Green , B Sun , S Mutic , D Yang , Washington University School of Medicine, Saint Louis, MO

Presentations

SU-F-T-275 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall


Purpose: The aim of this paper was to demonstrate the feasibility and creditability of computing and verifying 3D fluencies to assure IMRT and VMAT treatment deliveries, by correlating the passing rates of the 3D fluence-based QA (P(Φ)) to the passing rates of 2D dose measurement-based QA (P(Dm)).

Methods: 3D volumetric primary fluencies are calculated by forward-projecting the beam apertures and modulated by beam MU values at all gantry angles. We first introduce simulated machine parameter errors (MU, MLC positions, jaw, gantry and collimator) to the plan. Using passing rates of voxel intensity differences (P(Ir)) and 3D gamma analysis (P(γ)), calculated 3D fluencies, calculated 3D delivered dose, and measured 2D planar dose in phantom from the original plan are then compared with those from corresponding plans with errors, respectively. The correlations of these three groups of resultant passing rates, i.e. 3D fluence-based QA (P(Φ,Ir) and P(Φ,γ)), calculated 3D dose (P(Dc,Ir) and P(Dc,γ)), and 2D dose measurement-based QA (P(Dm,Ir) and P(Dm,γ)), will be investigated.

Results: 20 treatment plans with 5 different types of errors were tested. Spearman's correlations were found between P(Φ,Ir) and P(Dc,Ir), and also between P(Φ,γ) and P(Dc,γ), with averaged p-value 0.037, 0.065, and averaged correlation coefficient ρ-value 0.942, 0.871 respectively. Using Matrixx QA for IMRT plans, Spearman's correlations were also obtained between P(Φ,Ir) and P(Dm,Ir) and also between P(Φ,γ) and P(Dm,γ), with p-value being 0.048, 0.071 and ρ-value being 0.897, 0.779 respectively.

Conclusion: The demonstrated correlations improve the creditability of using 3D fluence-based QA for assuring treatment deliveries for IMRT/VMAT plans. Together with advantages of high detection sensitivity and better visualization of machine parameter errors, this study further demonstrates the accuracy and feasibility of 3D fluence based-QA in pre-treatment QA and daily QA.

Funding Support, Disclosures, and Conflict of Interest: Research reported in this study is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System


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