A library of proton lineal energy spectra spanning the full range of clinically relevant energies.

Purpose: In locations where the proton energy spectrum is broad, lineal energy spectrum-based proton biological effects models may be more accurate than dose-averaged linear energy transfer (LET) based models. However, the development of microdosimetric spectrum-based biological effects models is hampered by the extreme computational difficulty of calculating microdosimetric spectra. Given a precomputed library of lineal energy spectra for monoenergetic protons, a weighted summation can be performed which yields the lineal energy spectrum of an arbitrary polyenergetic beam.

A Practical Primer on Particle Therapy.

Purpose: Particle therapy is a promising treatment technique that is becoming more commonly used. Although proton beam therapy remains the most commonly used particle therapy, multiple other heavier ions have been used in the preclinical and clinical settings, each with its own unique properties. This practical review aims to summarize the differences between the studied particles, discussing their radiobiological and physical properties with additional review of the available clinical data.

GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size.

Shortcomings of dose-averaged linear energy transfer (LET), the quantity which is most commonly used to quantify proton relative biological effectiveness, have long been recognized. Microdosimetric spectra may overcome the limitations of LETbut are extremely computationally demanding to calculate. A systematic library of lineal energy spectra for monoenergetic protons could enable rapid determination of microdosimetric spectra in a clinical environment.

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning.

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.

Comparing 2 Monte Carlo Systems in Use for Proton Therapy Research.

Purpose: Several Monte Carlo transport codes are available for medical physics users. To ensure confidence in the accuracy of the codes, they must be continually cross-validated. This study provides comparisons between MC and Tool for Particle Simulation (TOPAS) simulations, that is, between medical physics applications for Monte Carlo N-Particle Transport Code (MCNPX) and Geant4.