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Submitted on 14 Feb 2017
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Accelerated Prompt Gamma estimation for clinical Proton Therapy simulations
B. Huisman, Jean Michel Létang, E. Testa, D. Sarrut
To cite this version:
B. Huisman, Jean Michel Létang, E. Testa, D. Sarrut. Accelerated Prompt Gamma estimation for clinical Proton Therapy simulations. International Conference on Translational Research in Radio- Oncology | Physics for Health in Europe (ICTR-PHE 2016), Feb 2016, Genève, Switzerland. . �hal- 01276369�
Accelerated Prompt Gamma estimation for clinical Proton Therapy simulations
B.F.B. Huisman
1,2, J.M. Létang
1, É. Testa
2, D. Sarrut
11 CREATIS, Université de Lyon; CNRS UMR5220; INSERM U1044; INSA-Lyon; Université Lyon 1; Centre Léon Bérard, Lyon, France
2 IPNL, Université de Lyon; CNRS/IN2P3 UMR5822; Université Lyon 1 Lyon, France
brent.huisman@creatis.insa-lyon.fr
1. P URPOSE
There is interest in the particle therapy community to use prompt gammas (PG), a natural byproduct of particle treatment, for range verification and eventually dose control (Knopf et al. 2015). However, PG production is a rare process and therefore estimating PGs exiting a patient during a proton treatment plan executed by a Monte Carlo simulation (MC) converges slowly.
Primaries
PGs Exiting patient
Solid angle detector
Post-collimator Detector
Efficiency
?
Reconstruction
Eff. ? 103
104 105 106 107 108 109
Counts
Protons
Prompt Gammas
We present a generic PG yield estimator, drop-in usable with any geometry and beam configuration. We show a gain of three orders of magnitude compared to analog MC. We analyze the depth profile and the PG energy spectrum of a simple phantom and a clinical head and neck CT image.
2. C ONCEPT
1 2 3
1. Regular Monte Carlo tracking
A regular MC simulation propagates particles throughout geometry. The propagation is broken up into steps, at which point the engine compiles a list of all possible futures, weights them, and using a random number selects the actual future.
2. At each step: Prompt Gamma production probability
Parallel to executing this conventional tracking, we may request and store the PG production probabilities. At each step, as function of PG energy, a production probability spectrum is stored at the current voxel.
3. Limited MC to touch all relevant voxels
By propagating a number of primary protons in this way, we obtain probabilities in all the voxels that a beam may touch. We need a minimum number of primaries, since we can only request PG probabilities in the voxels the primary passes through. However, we require fewer primary propagations with respect to a fully analog MC.
A CKNOWLEDGMENTS
This work was partly supported by Labex PRIMES ANR-11-LABX-0063, t-Gate ANR-14-CE23-0008, France Hadron ANR-11-INBS-0007 and LYric INCa-DGOS-4664.
3. M ETHOD
Stage 0:
Generate PGdb Stage 1:
Compute PGyd
Stage 2:
Propagate PG through geometry
A voxelized Prompt-Gamma Track Length Estimator (Kanawati et al. 2015) simulation is broken up into two stages. A PGdb (Stage 0) is presupposed, computed once and reused. It contains an estimate of the effective linear PG production coefficient ΓΓΓZ modulo the density ρZ , per element (k). At the start of Stage 1, the coefficients are computed for the materials found in the phantom (eq. 1).
ΓΓΓm(E) = ρmv
kmv
X
k=1
ωk
ΓΓΓZk (E ) ρZk
(1)
SSSbbbi (v) = ΓΓΓmv (Eg )Lg (Eg , v) (2) Per step, per voxel v in the PGyd, alongside executing the analog MC processes, we compute and add the product of the step length Lg and ΓΓΓmv , with mv the material at voxel v and g the proton energy bin (eq. 2). Put into words, we compute the PG yield probability energy spectrum at every step, and add it to any pre-existing spectrum in the current voxel v. The PGyd computed in stage 1 is used as a PG production source in Stage 2. If the user is interested in the PG signal of 1011 protons, the PGyd can be requested to give the expected output for that number of protons. Each PG is then propagated through the geometry and into the detector with regular analog MC processes.
4. R ESULT S IMPLE PHANTOM
0 50 100 150 200
0.0 0.5 1.0 1.5 2.0 2.5
IntegratedYield [PG/proton/voxel]
×10−3
1 2 3 4 5 6 7 8
0.0 0.5 1.0 1.5 2.0 2.5
×10−3
103 primaries 104 primaries 105 primaries 106 primaries Reference
0 50 100 150 200
−3
−2
−1 0 1 2 3
Integrated Rel.Diff.[%]
1 2 3 4 5 6 7 8
−3
−2
−1 0 1 2 3
0 50 100 150 200
Depth [mm]
−6
−4
−2 0 2 4 6
Voxelsbeampath Rel.Diff.[%]
1 2 3 4 5 6 7 8
PG energy [MeV]
−6
−4
−2 0 2 4 6
102 103 104
Gain factor w.r.t. Reference
0.0 0.2 0.4 0.6 0.8 1.0
Numberofvoxels(scaled)
vpgTLE gain distribution Median gain: 1.40 × 103
103 primaries Min: 6.30×101 Max: 4.64×104 104 primaries Min: 6.19×101 Max: 3.73×104 105 primaries Min: 9.03×101 Max: 5.21×104 106 primaries Min: 8.63×101 Max: 3.21×104
101 102 103 104 105 106
Runtime t [s]
0 2 4 6 8 10 12
RelativeUncertainty[%]
Median relative uncertainty Gain: 1.55 × 103
vpgTLE, Fit:
2.3×10−1
√t
Analog, Fit:
8.9×100√ t
5. R ESULT C LINICAL PHANTOM
0 20 40 60 80 100 120 140 160 0.0
0.5 1.0 1.5 2.0
IntegratedYield [PG/proton/bin]
×10−3
1 2 3 4 5 6 7 8
0.0 0.5 1.0 1.5 2.0
×10−3 103 primaries
104 primaries 105 primaries 106 primaries Reference
0 20 40 60 80 100 120 140 160
Depth [mm]
−3
−2
−1 0 1 2 3
Integrated Rel.Diff.[%]
1 2 3 4 5 6 7 8
PG energy [MeV]
−3
−2
−1 0 1 2 3
102 103 104
Gain factor w.r.t. Reference
0.0 0.2 0.4 0.6 0.8 1.0
Numberofvoxels(scaled)
vpgTLE gain distribution Median gain: 9.98 × 102
103 primaries Min: 0
Max: 2.76×105 104 primaries Min: 3.85×101 Max: 3.29×104 105 primaries Min: 4.70×101 Max: 4.88×104 106 primaries Min: 2.70×101 Max: 8.96×104
102 103 104 105 106 107
Runtime t [s]
0 10 20 30 40 50 60 70
RelativeUncertainty[%]
Median relative uncertainty Gain: 1.03 × 103
vpgTLE, Fit:
2.5√×100 t
Analog, Fit:
7.9√×101 t
6. C ONCLUSION
vpgTLE is a generic drop-in alternative for computing the expected PG output in voxelized geometries. The method reaches a global gain factor of 101010333 for a clinical CT image and treatment plan with respect to analog MC. A median convergence of 2% for the most distal energy layer is reached in approximately four hours on a single core, with the output stabilized to within 10−4 of an analog reference simulation, when the PG yield along proton range and PG spectrum are considered. Those interested in developing and simulating PG detection devices, as well as clinicians studying complex clinical cases, may benefit from the precision and accuracy of vpgTLE simulations not offered by analytic algorithms.
The vpgTLE method is open source, fully integrated and available in the next Gate release.
This study has been submitted to Physics in Medicine and Biology.
R EFERENCES
Knopf et al. (2015) Phys. Med. Biol.
Kanawati et al. (2015) Phys. Med. Biol.
Sterpin et al. (2015) Phys. Med. Biol.