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Poster communications

Accelerated Prompt Gamma estimation for clinical Proton Therapy simulations

B. Huisman 1, 2 Jean Michel Létang 1 E. Testa 2 D. Sarrut 1
1 Imagerie Tomographique et Radiothérapie
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : Purpose: 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 converges slowly. Recently, different approaches to accelerating the estimation of PG yield have been presented. Sterpin et al. (2015) described an analytic method that is about to be implemented in a commercial product, but has as drawback sensitivity to heterogeneities. Kanawati et al. (2015) described a variance reduction method (pgTLE) that accelerates the PG estimation by precomputing PG production probabilities as a function of energy and target materials, but has as drawback that it only works for analytical phantoms.Materials/Methods: We present a two-stage method, voxellized pgTLE (vpgTLE) that extends pgTLE to voxellized volumes. PG production probabilities are precomputed once, stored, and reused. In stage one, we simulate the interactions between the treatment plan and the patient CT with low statistic MC to obtain the spatial and spectral distribution of the PGs. As primary particles are propagated throughout the patient CT, the PG yields are computed in each voxel from the initial database, as function of the current energy of the primary, the material in the voxel and the step length. The result is a voxellized PG yield image, normalized to a single primary. The second stage uses the intermediate PG image as a source to generate andpropagate PGs throughout the rest of the scene geometry, e.g. into a detection device, proportional to the number of primaries desired.Results: We have achieved a global speed-up of around 103 for a heterogeneous phantom, for a 2% relative uncertainty on the PG yield. The method agrees with a reference Monte Carlo simulation to within 1% at the level of 2% relative uncertainty on the PG. Preliminary work on a full simulation of a clinical spot-scanning treatment plan and a patient CT image indicates a similar gain factor. Gains per voxel range from 10E2 to 10E5.Conclusion: We presented a generic PG yield estimator, dropin usable with any geometry and beam configuration. We showed a gain of around three orders of magnitude compared to analog MC. With a large number of voxels and materials, memory consumption may be a concern and we will discuss the consequence and possible trade-offs. The method will be available in the next release of Gate.
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Submitted on : Tuesday, February 14, 2017 - 11:35:16 AM
Last modification on : Wednesday, November 3, 2021 - 10:01:42 AM
Long-term archiving on: : Monday, May 15, 2017 - 12:24:25 PM

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  • HAL Id : hal-01276369, version 1

Citation

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⟩

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