Transitioning from pencil beam to Monte Carlo for electron dose calculations

Transcription

1 Transitioning from pencil beam to Monte Carlo for electron dose calculations Jessie Huang-Vredevoogd University of Wisconsin NCC AAPM October 12,

2 Topics to cover Background RayStation electron Monte Carlo algorithm Electron beam modeling in RayStation Required beam data Modeling steps Model validation MPPG 5.a Complex phantom validation Clinical considerations: Statistical noise and Rx Secondary MU calc vs. MC results for clinical cases 2

3 Raystation electron Monte Carlo algorithm Uses the VMC++ code as the Monte Carlo dose engine Developed at the National Research Council of Canada In-patient energy transport and energy scoring Dose to water Incorporated into several planning systems: Oncentra Masterplan and CMS XiO 3

4 Raystation electron Monte Carlo algorithm Source phase space Defined at a plane at the secondary scattering foil Analytical model with parameters that specify the energy spectrum and spatial distribution of the electron beam Exit phase space Defined at a plane downstream of the last collimating structure (electron applicator) An exit phase space is defined for each electron applicator Direct vs. indirect electrons + contamination photons The exit phase space is fed into the VMC++ code for dose calculation 4

5 Exit phase space generation 5 Components modeled in the generation of exit phase space data Double scattering foil Position of jaws for each applicator and energy Jaw thickness and position MLC thickness and position Vendor-specific cones / applicators Cutout thickness

6 Electron beam modeling 6

7 Required measured data Data was collected for Varian TrueBeam 6, 9, 12, and 15MeV In-air data at 70 and 90 cm SSD (no applicator) Output factors for several field sizes Profiles In-water measurements PDDs w/o applicator for each energy PDDs w/ applicator Profiles at two depths for each energy and applicator combination Absolute calibration measurements (dose per MU) 8x8 20x8 30x8 30x30 Cutout material transmission factor 7 in-air "output factors" cm SSD 6MeV EFD 6MeV cc04 9MeV EFD 9MeV cc04 12 MeV EFD 12MeV cc04 15MeV EFD 15MeV cc04

8 Step 1: Optimize energy spectrum 8

9 Step 1: Optimize energy spectrum Use the open (no applicator) PDD to adjust the electron energy spectrum of the source phase space MLCs and jaws fully retracted only direct electrons and Brem. photons 9

10 Step 2: Adjust source phase space parameters Use in-air profiles to adjust other source phase space parameters: Angular spread Distance to virtual source Shape of the fluence distribution as a function of radius 10

11 Step 2: Adjust source phase space parameters Adjustment of different source phase space parameters changes the Shape of the calculated inair profiles (horns vs. rounded shoulders) Width of the profiles Penumbra steepness 11

12 Step 3: Photon contamination parameters 12 Gaussian width Photon dose normalization Applicator transmission

13 Step 4: Fine-tune source phase space 13 parameters Make fine adjustments to energy spectrum based on in-water PDDs Make fine adjustments to source phase space parameters based on inwater profiles

14 Calculation parameters As the model was fine-tuned, used: Finer dose grid (smaller calculation voxels) Larger number of histories Final dose calculation is at a resolution of 1mm with 2 million histories per cm 2 Dose grid [mm] Histories per cm 2 Final 1 2,000,000 Normalization 2 1,000,000 Medium 3 500,000 Coarse 4 250,000 14

15 Final models

16 Model validation

17 TG 105 MC-based treatment planning The TG strongly encourages verification testing with experiments emphasizing electronic disequilibrium effects in addition to standard tests in heterogeneous phantoms Electron beams: Comparison to measured output factors over the full clinical range of field sizes and SSDs Heterogeneous phantom measurements are recommended

18 MPPG 5.a validation Output as a function of field size and SSD Small and large custom cutout at 100 and 105 cm SSD Point dose measurements and profiles Beam obliquity (10x10) Point dose measurements and profiles Heterogeneity using a lung slab phantom Point dose measurements for relative output and range (R 50 )

19 MPPG 5.a results

20 Output factors for square cutouts RayStation-calculated output factors compared to measurements with cc04 for various square cutouts Of the 312 output factors evaluated: 20 (6.4%) were outside of 3% (yellow) but <5% 5 (1.6%) were outside of 5% (red) No obvious trend but larger errors generally were for the smallest cutout sizes within a given applicator.

21 Complex phantom validation Phantom geometries: Nose phantom Bone phantom Half of the beam intersects 1cm cortical bone Breast phantom Lung phantom Measurements with EBT3 film in edge-on orientation 10x10 field size, 100cm SSD

22 Complex phantom validation Gamma analysis (3% global / 3mm, 10% dose threshold) Phantom Energy 6MeV 9MeV 12MeV 15MeV Nose Bone Lung Breast Average across all phantoms and energies = 89.3 % pixels passing Thick line = RayStation, thin line = film

23 Clinical considerations

24 Number of histories and dose grid size affect statistical uncertainty RayStation reports mean relative statistical uncertainty for all voxels with dose > 50% of max dose from the beam Clinic settings: Electron beam set cannot be approved with uncertainty > 1% Planning recommendations: Compute final dose with 500K histories per cm 2 ~0.7% uncertainty for 2mm dose grid Statistical uncertainty

25 Statistical uncertainty Affects point/voxel doses (Dmax and Dmin) most of all, while volume metrics such as DVH values are less sensitive to noise (TG-105). TG-105 encourages a shift in paradigm from point-based to volume-based prescriptions Rx to isodose surface, sphere near dmax, or DVH value Our clinical planning procedures state that volume-based prescriptions should be used whenever possible. Plan Energy (MeV) Cutout dimensions (cm x cm) RayStation prescription method Pinnacle prescription method Pinnacle MU RayStation MU Breast boost x % of PTV receives 95% Rx 96% IDL Breast boost x % of PTV receives 95% Rx 96% IDL Breast boost x % of PTV receives 100% Rx 90% IDL Breast boost x % of PTV receives 95% Rx 93% IDL Breast boost x % of PTV receives 95% Rx 90% IDL Ear x % of PTV receives 100% Rx 90% IDL

26 Secondary MU calculations MC-generated MU can differ from water phantom MU calcs by as much as 10%, depending on surface irregularities and inhomogeneities present (TG-105) Found differences > 5% for RayStation vs. Mobius3D due to patient modeling and sophistication of dose calculation algorithm Treatment Plan Energy (MeV) Cutout dimensions (cm x cm) % difference between Mobius MU and RayStation MU Clinical plan Breast boost x % Breast boost x % Breast boost x % Breast boost x % Breast boost x % Chestwall scar boost x % Forehead x % Hand x % Hand x % Ear x % 26

27 Secondary MU calcs Solution: Compute a water phantom QA plan (i.e., compare a water phantom calc to a water phantom calc) 27

28 Secondary MU calculations Treatment Plan Energy (MeV) Cutout dimensions (cm x cm) % difference between Mobius MU and RayStation MU Clinical plan QA water phantom plan Breast boost x % -0.2% Breast boost x % 0.2% Breast boost x % -0.3% Breast boost x % -1.1% Breast boost x % 0.7% Chestwall scar boost x % 3.4% Forehead x % 0.5% Hand x % 0.4% Hand x % -0.8% Ear x % -1.4% 28

29 Summary Electron MC dose calculation algorithm Analytical model for the source phase space MC transport through linac head used to generate exit phase space Modeling process uses in-air and in-water measurements to adjust the parameters of the the source phase space Validation should include testing with heterogeneities and irregular surface contours Clinical implications of MC dose calculation algorithm Statistical noise need a minimum number of histories Rx to volumes instead of points More accurate handling of heterogeneities and irregular surface contours differences in MU as high as 10% in comparison to water phantom MU calcs 29

30 Acknowledgements Jeni Smilowitz Adam Bayliss Sean Frigo Patrick Hill Mark Geurts David Dunkerley Zac Labby Dustin Jacqmin 30