TH‐C‐213CD‐01: Accuracy of Proton Dose Computation Algorithms and Need for Improvements
Authors: Mohan R , Oelfke U , Paganetti H , Soukup M 
Published: June 2012

There has been a tacit assumption that semi‐empirical (SE) dose calculation algorithms used in the current practice of passively scattered proton therapy (PSPT) and intensity modulated proton therapy (IMPT) are sufficiently accurate. However, the sharp distal fall‐off of protons, scattering from apertures and other beam line components, interactions of secondary particles produced from beam line components and the patient present significant challenges for the current algorithms. The resulting inaccuracies and their potential clinical impact on outcomes have not been fully appreciated, especially for complex geometries such as those encountered in lung and head and neck. Some of the factors affecting accuracy for PSPT and IMPT are similar while others are quite different. For example, PSPT accuracy is affected by the angular and energy spread of the incident beam and its interactions with compensators and apertures; whereas, scattering from beam‐line components and the transport of secondary particles are more important for IMPT. Complex heterogeneities are of relevance for both PSPT and IMPT. Furthermore, distal edge of protons, assumed to have a sharp fall‐off, may be degraded significantly after passage through heterogeneities. While IMPT may be able to compensate for such degradation, semi‐empirical models are unable to accurately predict this effect.The Monte Carlo (MC) is the most accurate method for calculating proton dose distributions. A Monte Carlo system must be appropriately commissioned by fine tuning the characteristics of the beam entering the nozzle and calibrated using measured data. Once appropriately experimentally validated, Monte Carlo may be assumed to be the “gold standard” for situations where measurements where are difficult, impossible or highly time consuming, e.g., dose distributions in patients. While efforts are being made to accelerate Monte Carlo systems through parallelization and algorithmic improvements, they are likely to be still too slow for routine clinical use especially for applications such as IMPT, robustness analyses, robust optimization, etc. Intermediate solutions, that are simplified variations of Monte Carlo, are being developed. Examples include Macro Monte Carlo, Virtual Monte Carlo and Track Repeating Algorithm for Particles. For lack of a better term, we will call the family of such solutions “abridged Monte Carlo” (aMC). They offer considerable improvement in speed with nearly the same accuracy as Monte Carlo. Nevertheless, greater speed offered by SE models would continue to be required for many of the applications mentioned. A hybrid combination of a SE model and a MC or aMC method may then be applied for certain demanding applications. For instance, in IMPT optimization, most of the iterations may be carried out using a fast SE model interspersed with a small number of MC or aMC iterations.A short introduction giving background and explaining the current state of the art and differences between PSPT and IMPT algorithms will be followed by four presentations. The first one will discuss semi‐empirical dose calculation models for PSPT, their limitations and dosimetric and potential clinical consequences, factors underlying the accuracy limitations and possible improvements. The second presentation will address similar issues for semi‐empirical algorithms for scanning beams and IMPT. The third presentation will discuss the use of Monte Carlo techniques to model proton treatment machine heads for passive scattering and patients and to model scanned beams. It will also talk about calibration and validation of MC systems, inherent uncertainties in MC methods and the efforts to make MC systems easy to use. The fourth presentation will focus on abridged‐Monte Carlo techniques. It will give an overview of acceleration of dose calculations using such techniques and describe one or more algorithms in detail. It will include such topics as transport of particles, validation and calibration, implementation of aMC into a treatment planning systems and applications for IMPT optimization.

Learning Objectives:

1. Understanding the physics principles underlying current semi‐empirical proton dose computation models, their assumptions and approximations and their effect on accuracy.

2. Appreciating the potential impact of approximations on clinical outcomes.

3. Understanding the role of Monte Carlo techniques in identifying the causes of differences between results of semi‐empirical models, measurements and Monte Carlo and in the development of more accurate algorithms.

4. Learning about the current status of Monte Carlo and abridged‐Monte Carlo techniques for clinical use.

Conflicts of interest: Mohan ‐ Research grant from Philips. Soukup ‐ Elekta employee.