SU‐C‐204‐01: A Fast Analytical Approach for Prompt Gamma and PET Predictions in a TPS for Proton Range Verification

Abstract

Purpose: We describe and demonstrate a fast analytical tool for prompt‐gamma emission prediction based on filter functions applied on the depth dose profile. We present the implementation in a treatment planning system (TPS) of the same algorithm for positron emitter distributions. Methods: The prediction of the desired observable is based on the convolution of filter functions with the depth dose profile. For both prompt‐gammas and positron emitters, the results of Monte Carlo simulations (MC) are compared with those of the analytical tool. For prompt‐gamma emission from inelastic proton‐induced reactions, homogeneous and inhomogeneous phantoms alongside with patient data are used as irradiation targets of mono‐energetic proton pencil beams. The accuracy of the tool is assessed in terms of the shape of the analytically calculated depth profiles and their absolute yields, compared to MC. For the positron emitters, the method is implemented in a research RayStation TPS and compared to MC predictions. Digital phantoms and patient data are used and positron emitter spatial density distributions are analyzed. Results: Calculated prompt‐gamma profiles agree with MC within 3 % in terms of absolute yield and reproduce the correct shape. Based on an arbitrary reference material and by means of 6 filter functions (one per chemical element), profiles in any other material composed of those elements can be predicted. The TPS implemented algorithm is accurate enough to enable, via the analytically calculated positron emitters profiles, detection of range differences between the TPS and MC with errors of the order of 1–2 mm. Conclusion: The proposed analytical method predicts prompt‐gamma and positron emitter profiles which generally agree with the distributions obtained by a full MC. The implementation of the tool in a TPS shows that reliable profiles can be obtained directly from the dose calculated by the TPS, without the need of full MC simulation.

Prompt-gamma monitoring in hadrontherapy: A review

“…Filtering approaches. Instead of precomputing reference prompt-gamma emission profiles, filtration of the dose distribution maps (already available from the TPS) makes it possible to directly get the expected prompt-gamma distribution [50], or 𝛽+ emitters [51][52][53]. This eludes the modeling of the beam model in the Monte Carlo source setup [54].…”

From prompt gamma distribution to dose: a novel approach combining an evolutionary algorithm and filtering based on Gaussian-powerlaw convolutions

“…These reference distributions are obtained either from previous treatment fractions (Nishio et al 2010) or from modeling of an expected distribution. This modeling can be performed by a full Monte Carlo simulation (Parodi et al 2007, Seravalli et al 2012, Robert et al 2013, Rohling et al 2013, Dedes et al 2014, Schumann et al 2015 as well as analytically by means of a filtering approach (Parodi and Bortfeld 2006, Attanasi et al 2011, Frey et al 2014, Kroniger et al 2015, with pre-calculated look-up databases (Pönisch et al 2004, Kanawati et al 2015, Sterpin et al 2015 or based on experimental data (Miyatake et al 2011, Priegnitz et al 2012, Helmbrecht et al 2016.…”

“…While all the mentioned publications concentrate on dose estimation from PET images, within our work, we present an approach for dose estimation from prompt γ-ray distributions. Similar to Kroniger et al (2015), we are using the convolution formalism based on the Q ˜ functions introduced by Parodi and Bortfeld (2006) in order to deduce prompt γ-ray distributions from dose distributions. But, unlike Kroniger et al (2015), we take yet another step and tackle the inverse problem.…”

“…Similar to Kroniger et al (2015), we are using the convolution formalism based on the Q ˜ functions introduced by Parodi and Bortfeld (2006) in order to deduce prompt γ-ray distributions from dose distributions. But, unlike Kroniger et al (2015), we take yet another step and tackle the inverse problem. In order to perform this dose deconvolution from prompt γ-ray distributions, we use an optimization technique based on an evolutionary algorithm (EA).…”