An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system

Abstract

Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for prompt gamma range verification compares the detected prompt gamma profile with a predicted one. Recently, a novel analytical prompt gamma prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. In this work, the said algorithm is validated against experimental data and benchmarked with another well-established prompt gamma prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation. The mean shifts between the experimental data and the simulated PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be 1.5~2.1 mm and -0.6~2.2 mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach.