Simulations in Radiation Therapy

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1 Simulations in Radiation Therapy D. Sarrut Directeur de recherche CNRS Université de Lyon, France CREATIS-CNRS ; IPNL-CNRS ; Centre Léon Bérard

2 Numerical simulations and random processes treat cancer 2

3 Cancer Cancer 11 millions new/year worldwide 1st mortality cause in France Principal treatment modalities Surgery, chemotherapy, radiation therapy 3

4 Radiation Therapy cancer treatment About 2/3 of all patients Treatment in fractions, i.e. 64 Gy in 32 fractions of 2Gy Tradeoff: dose to tumor / dose to healthy tissue Security margins 4

5 Radiation therapy Advanced technology Hadrontherapy Linac Tomotherapy CyberKnife 5

6 IGRT Image guided Radiation Therapy On board CBCT Portal imaging6

7 Imaging devices CT Optical system CBCT PET-CT Portal 7

8 Imaging devices CT Optical system Portal CBCT PET-CT 8

9 Medical Physics Imaging & Therapy Beam Target - Detector Interaction between particles and matter = physical processes Absorbed energy = dose (Also biological for the effect ) 9

10 Plan Introduction Example: dose computation in protontherapy Prompt-gamma imaging device Conclusion 10

11 < 500M ev /u Numerical simulations in RT e Predict absorbed dose Optimise treatment plan (100% of treatment) me Z β ZT NCalcul de dose 2 e4zef de 2meβ2 f 2 = 4 N Z [ ln ( ) β ] T dx mec β I (1 β ) 11

Interaction with matter Track particles by particles

12 How? Several methods: Analytical : fast Monte-Carlo : gold-standard Monte-Carlo particles tracking Interaction with matter Track particles by particles Probability of interaction (cross-section) : stochastic resolution 12

13 Example: proton dose 13

14 Stoichiometric calibration 14

15 Tracking Proton Properties Position (x,y,z) Direction (dx, dy, dz) Energy changed Energy absorbed Absorbed energy Dose : Energy/mass 15

16 Tacking secondary particles Electron 16

17 Primary particles 17

18 Result : 3D dose distribution 18

19 Particles tracking Millions (billions!) of primaries proton Tracking: Path composed of several steps Interaction at each steps (physical / geometrical) Change in properties (energy, position, direction) Absorbed dose Other particles emissions (electron etc), also tracked Electro-magnetic and nuclear interactions Until convergence Notion of statistical uncertainties 19

20 Numerical simulations Complex situations Anatomical variability (toward personalized medicine) Numerous beams condition (photon, hadron) Imaging / treatment Numerical modelling (in-silico) Scene (geometrical components): machine, patient Physics : beam, particles, interactions Observable 20

21 Timing considerations Particles interactions with matter, one by one (one history ) Long computation time (more or less) easily parallelisable Broad range : few minutes to several days/months. In practice Workstation ~10 jobs (threads) Cluster labo ~50 jobs Computing center IN2P3 ~150 jobs EGI grid ~300 jobs 21

22 o our knowledge this is the ﬁrst time such a comparative analysis between d and the same computer, is provided. It illustrates the computation time Examples e I I. simulation features and durations for the several examples shown in the pa dose uncertainties below few percent in MRT dosimetry it is typically n 22 imary particles needed to obtain similar uncertainties in EBRT. T he rea

23 o our knowledge this is the ﬁrst time such a comparative analysis between d and the same computer, is provided. It illustrates the computation time Examples 9 e I I. simulation features and durations for the several examples shown in the pa dose uncertainties below few percent in MRT dosimetry it is typically n 23 imary particles needed to obtain similar uncertainties in EBRT. T he rea

24 o our knowledge this is the ﬁrst time such a comparative analysis between d We provide here an example of a proton treatment plan of a head and neck cancer (Figure 3). It is compo and the same computer, is provided. It illustrates the computation time wo ﬁelds with about 30 energy layers per ﬁeld, with an energy range of MeV, 1547 spots in total, Examples pot sampling of 8 mm. T he patient CT image was resampled to 2 2 2mm3. T he physic list was the on by [53] and included electromagnetic and hadronic processes, chosen for high accuracy in imaging application as hadron-pet or prompt-gamma dose monitoring (see section III F 3). T he electron production cut was set to and the maximum step size to 1 mm. T he number of materials was given by the density tolerance parameter 0.01 (290 materials). With a reduced tolerance of 0.05 (75 materials), the mean dose difference in the planning volume (PT V) between the two tolerance values is lower than 0.1%. We also checked that the number of materia almost no inﬂuence on the computation time. T he computation speed was about 268 PPS. To get a mean stat uncertainty around 2% in the PT V with a dose map of 2 2 2mm3, particles by ﬁeld were needed, le o about 3 hours of computation time. e I I. simulation features and durations for the several examples shown in the pa dose uncertainties below few percent in MRT dosimetry it is typically n 24 imary particles needed to obtain similar uncertainties in EBRT. T he rea

simulation features and durations for the several examples shown in the pa dose uncertainties

25 o our knowledge this is the ﬁrst time such a comparative analysis between d and the same computer, is provided. It illustrates the computation time Examples e I I. simulation features and durations for the several examples shown in the pa dose uncertainties below few percent in MRT dosimetry it is typically n 25 imary particles needed to obtain similar uncertainties in EBRT. T he rea

26 Code Open source Geant4 toolkit Developed by an international collaboration Managed at CERN Nuclear physics Open source GATE platform Developed by an international collaboration Focus on Medical Physics (dose & imaging) Others toolkit : MCNPX, EGSNRC, Fluka etc C++ classes Development cycle : design, run, analyze 26

27 Plan Introduction Example: dose computation in protontherapy Prompt-gamma imaging device Conclusion 27

28 Dose monitoring via prompt-gamma In protontherapy, online dose monitoring is currently being investigated with devices exploiting prompt-gamma [Stichelbaut 2003] [Min 2006] [Testa 2008] [Polf 2009] [Moteabbed 2011] and others Prompt-Gamma: Emitted during inelastic interactions between incident proton and target nuclei. Emitted quasi-instantaneously, decay time < 1 ps Broad energy spectrum : 105 ev ; 107 ev Most of them (80%) have enough energy to escape the patient Correlated with the deposited dose but correlation not well known in clinical conditions 28

(prostate case) PG camera prototype [Testa et al 2008] two-head cylindrical

29 GATE simulations Spot-scanning protontherapy [Grevillot et al, PMB 2012] Realistic beam description (E, emittance etc) Real treatment plan on a CT (prostate case) PG camera prototype [Testa et al 2008] two-head cylindrical collimated multi-slit detector BGO scintillators TOF filtering with beam tagging device (hodoscope) 29

30 Dose monitoring via prompt-gamma Prompt-Gamma Deposited Energy 30

31 Results Simulations : to help design of the imaging device Simulations : to assess new imaging device Simulations : to better describe the limits (The main concern seems to be more the counting rate than the spatial resolution). [Gueth et al, PMB 2013] 31

32 Plan Introduction Example: dose computation in protontherapy Prompt-gamma imaging device Conclusion 32

33 Numerical simulations and random processes treat cancer 33

34 Conclusion Numerical simulations applied in medical physics For cancer treatment Imaging : X-ray, PET, SPECT, nuclear imaging etc Treatment: RT, hadrontherapy, brachytherapy, radioimmunotherapy etc Monte-Carlo simulations for particles tracking Open-source software (Geant4, GATE) Multi-disciplinary : medicine physic computer science 34

Prompt gamma measurements for the verification of dose deposition in proton therapy. Contents. Two Proton Beam Facilities for Therapy and Research

Prompt gamma measurements for the verification of dose deposition in proton therapy Two Proton Beam Facilities for Therapy and Research Ion Beam Facilities in Korea 1. Proton therapy facility at National