ESTRO 36 Abstract Book

S221 ESTRO 36 _______________________________________________________________________________________________

PV-0420 Learn before you measure: Method of single- isolated errors analysis for ArcCheck. M. Gizynska 1,2 , M. Bukat 2 , J. Cybowska 2 , M. Filipek 2 , M. Garbacz 2 , I. Scisniak 2 , A. Spyra 2 , D. Szalkowski 3 , A. Walewska 1 1 The Maria Sklodowska-Curie Memorial Cancer Center, Medical Physics Department, Warsaw, Poland 2 University of Warsaw, Faculty of Physics, Department of Biomedical Physics 3 Warsaw University of Technology, Faculty of Physics, Warsaw, Poland Purpose or Objective IMRT and VMAT are nowadays techniques used in therapy for many cancer sites. The independent, pre-treatment verification in this type of irradiation is recommended and in some countries – even required. There are different ways of pre-treatment verification and there are many phantoms and softwares design for this. One of them are ArcCheck and SNCPatient (designed by SunNuclear). During gamma evaluation [ Depuydt, 2002 ] it is hard to detect different sources of errors (both phantom and machine). That is the reason to test a method of analysing isolated errors and their influence on gamma results. The method described below can show the dominant sources of errors helping physicists in interpreting gamma evaluation results. Material and Methods We divided sources of errors into two groups: phantom based (e.g. dose rate (DR) dependence, inclinometer tolerance) and machine based (e.g. gantry angle and leaf positions errors). For all types of errors we prepared an ideal dose distribution (calculated in Eclipse TPS and imported into SNCPatient Software) and a measurement file with an error (MWE). The diodes readouts were calculated from TPS dose distribution. Errors were introduced or into TPS itself (inclinometer tolerance, leaf and gantry positions error) or outside the TPS in Python script (DR dependence of each diode). The resolution of readouts in the MWE were identical to those of real measurement. Results The influence of ArcCheck DR dependence on VMAT plans results is shown in Table. In about 14% of patients with the mean DR below 100MU/min gamma did not reach 95% passing rate. It means that in case of low DR reason of plan failure can be caused by ArcCheck DR dependence not by wrong realisation. The examples of leaf influence errors are shown in Figure. It can be seen that gap width error is more important for highly modulated plans (H&N versus brain plans). Even some 1mm errors can be detected with global gamma 3mm/3% method which is a good result. In the same time it would be more complicated to detect 2mm systematic shift in leafs which can be connected with phantom position error or misalignment of radiation field defined by MLC. Inclinometer tolerance is the same as gantry positioning tolerance. ArcCheck rotation error and gantry position error can sum up. Rotation error of 0.5deg cannot be seen in gamma 2mm/2%. In the same case dose difference of more than 3% can be seen in high gradient region resulting in lower DTA passing rate. Rotation error does not seem to be symmetrical which can be connected with diodes placement. Tilt error of 0.5deg cannot be seen in gamma 2mm/2% and is symmetrical.

code of practice (TRS-398) was introduced based on the calibration of the ionization chambers in terms of absorbed dose to water instead of the previous code based on air kerma determination (TRS-277). Not all standard laboratories have beams with the same user beam qualities. One common approach is for the Standard Dosimetry Laboratory (SDL) to perform a calibration of the user’s ionization chamber in the beam quality of the Co-60 source. They may also provide calibration factors for the chamber at other beam qualities whereby correction factors for a particular beam quality are determined by interpolation. When no experimental k QQo values are provided, the user can calculate them by using a set of expressions derived from Bragg-Gray theory or obtain k QQo values by Monte Carlo (MC) simulation. Analytical and MC k QQo values are derived from the nominal geometry of each chamber model since there is no way of knowing the exact dimensions of each user chamber. In contrast, calibration in terms of absorbed dose to water in a SDL, at different beam qualities, is the only method where the response of each individual IC is taken into account. Material and Methods Three waterproof IC models were selected, PTW-30013 (0.6 cm 3 ), PTW-31010 (0.125 cm 3 ) and PTW-31016 (0.016 cm 3 ). A fourth non-waterproof IC from Nuclear Enterprise (NE2571) was used to validate the MC process and the different Particle Space Files (PSF) used in the MC simulations. Three different geometries were defined for each of the PTW IC using information about the geometric tolerances provided by the manufacturer, which were labelled nominal, maximum and minimum. The maximum geometry was defined as the maximum cavity walls and the minimum dimensions for the central electrode together with the minimum geometry with the minimum cavity walls and the maximum central electrode. k QQo values were determined, for the ten geometries defined, at three energies (TPR 20,10 ) 6 MV (0.674), 15 MV (0.757) and 18 MV (0.778) using MC simulation performed with the PENELOPE code system, using PenEasy as the main program. Results Differences in the active collecting volume for the three PTW ionization chambers affects the N D,w,Qo coefficients to the same proportion, and their influence on the k Q,Qo is less than 0.5% ±0.2% (sigma=1) (Table 1) Table 1. k QQo factors determined by simulation for the different geometries defined on each ionization chamber at the different energies. Uncertainties on all values are smaller than 0.2% (sigma =1).

Conclusion The results provide an estimate of the influence that geometrical uncertainties in the manufacturing process have on k QQo, identifying the differences on wall thickness as the main source of influence.