With the increased commercial availability of intensity modulated arc therapy (IMAT) comes the need for comprehensive QA programs, covering the different aspects of this newly available technology. This manuscript proposes such a program for the RapidArc (RA) (Varian Medical Systems, Palo Alto) IMAT solution.Methods:
The program was developed and tested out for a Millennium120 MLC on iX Clinacs and a HighDefinition MLC on a Novalis TX, using a variety of measurement equipment including Gafchromic film, 2D ion chamber arrays (Seven29 and StarCheck, PTW, Freiburg, Germany) with inclinometer and Octavius phantom, the Delta4 systam (ScandiDos, Uppsala, Sweden) and the portal imager(EPID). First, a number of complementary machine QA tests were developed to monitor the correct interplay between the accelerating/decelerating gantry, the variable dose rate and the MLC position, straining the delivery to the maximum allowed limits. Second, a systematic approach to the validation of the dose calculation for RA was adopted, starting with static gantry and RA specific static MLC shapes and gradually moving to dynamic gantry, dynamic MLC shapes. RA plans were then optimized on a series of artificial structures created within the homogeneous Octavius phantom and within a heterogeneous lung phantom. These served the double purpose of testing the behavior of the optimization algorithm (PRO) as well as the precision of the forward dose calculation. Finally, patient QA on a series of clinical cases was performed with different methods. In addition to the well established in-phantom QA, we evaluated the portal dosimetry solution within the Varian approach.Results:
For routine machine QA, the “Snooker Cue” test on the EPID proved to be the most sensitive to overall problem detection. It is also the most practical one. The “Twinkle” and “Sunrise” tests were useful to obtain well differentiated information on the individual treatment delivery components. The AAA8.9 dose calculations showed excellent agreement with all corresponding measurements, except in areas where the 2.5 mm fixed fluence resolution was insufficient to accurately model the tongue and groove effect or the dose through nearly closed opposing leafs. Such cases benefited from the increased fluence resolution in AAA10.0. In the clinical RA fields, these effects were smeared out spatially and the impact of the fluence resolution was considerably less pronounced. The RA plans on the artificial structure sets demonstrated some interesting characteristics of the PRO8.9 optimizer, such as a sometimes unexpected dependence on the collimator rotation and a suboptimal coverage of targets within lung tissue. Although the portal dosimetry was successfully validated, we are reluctant to use it as a sole means of patient QA as long as no gantry angle information is embedded.Conclusions:
The all-in validation program allows a systematic approach in monitoring the different levels of RA treatments. With the systematic approach comes a better understanding of both the capabilities and the limits of the used solution. The program can be useful for implementation, but also for the validation of major upgrades.
The authors would like to thank PTW (Freiburg, Germany) for the fruitful collaboration and for providing dosimetric equipment. 7Sigma also has a research collaboration with Varian Medical Systems. The authors wish to thank Dr. V. Remouchamps for his enthusiastic support and for allowing the necessary time slots at the Novalis TX treatment unit.
II. METHODS AND MATERIALS
II.A. Machine QA
II.A.1. Static MLC Twinkle: assessing the accuracy of dose rate modulation versus gantry angle (maximum acceleration and deceleration).
II.A.2. Dynamic MLC Twinkle: assessing the accuracy of MLC movement versus gantry angle (maximum MLC speed)
II.A.3. Sunrise: assessing the impact of gantry speed, gravity and inertia on the gantry angle precision
II.A.4. Snooker Cue: combining MU versus gantry angle and MLC movement in one single test
II.B. TPS validation
II.B.1. AAA validation for manually programmed RA-specific fields
II.B.2. Performance assessment of the RA optimization algorithm
II.B.3. AAA validation of RA plans on artificial structures
II.C. Patient QA
II.C.1. Phantom QA
II.C.2. Portal dosimetry
III.A. Machine QA
III.B. TPS validation
III.B.1. AAA validation for manually programmed RA-specific fields
III.B.2. Performance assessment of the RA optimization algorithm
III.B.3. AAA validation of RA plans on artificial structures
III.C. Patient QA
III.C.1. Phantom QA
III.C.2. Portal dosimetry
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