Automation and uncertainty analysis of a method for in-vivo range verification in particle therapy

Abstract

We introduce the automation of the range difference calculation deduced from particle-irradiation induced β+-activity distributions with the so-called most-likely-shift approach, and evaluate its reliability via the monitoring of algorithm- and patient-specific uncertainty factors. The calculation of the range deviation is based on the minimization of the absolute profile differences in the distal part of two activity depth profiles shifted against each other. Depending on the workflow of positron emission tomography (PET)-based range verification, the two profiles under evaluation can correspond to measured and simulated distributions, or only measured data from different treatment sessions. In comparison to previous work, the proposed approach includes an automated identification of the distal region of interest for each pair of PET depth profiles and under consideration of the planned dose distribution, resulting in the optimal shift distance. Moreover, it introduces an estimate of uncertainty associated to the identified shift, which is then used as weighting factor to 'red flag' problematic large range differences. Furthermore, additional patient-specific uncertainty factors are calculated using available computed tomography (CT) data to support the range analysis. The performance of the new method for in-vivo treatment verification in the clinical routine is investigated with in-room PET images for proton therapy as well as with offline PET images for proton and carbon ion therapy. The comparison between measured PET activity distributions and predictions obtained by Monte Carlo simulations or measurements from previous treatment fractions is performed. For this purpose, a total of 15 patient datasets were analyzed, which were acquired at Massachusetts General Hospital and Heidelberg Ion-Beam Therapy Center with in-room PET and offline PET/CT scanners, respectively. Calculated range differences between the compared activity distributions are reported in a 2D map in beam-eye-view. In comparison to previously proposed approaches, the new most-likely-shift method shows more robust results for assessing in-vivo the range from strongly varying PET distributions caused by differing patient geometry, ion beam species, beam delivery techniques, PET imaging concepts and counting statistics. The additional visualization of the uncertainties and the dedicated weighting strategy contribute to the understanding of the reliability of observed range differences and the complexity in the prediction of activity distributions. The proposed method promises to offer a feasible technique for clinical routine of PET-based range verification.