Current status of 4D offline PET-based treatment verification at the Heidelberg Ion-Beam Therapy Center

At the Heidelberg Ion-Beam Therapy Center, patient treatment is monitored offline by comparing the irradiation-induced β+-activity, measured by a commercial full-ring PET/CT scanner installed next to the treatment site, with a corresponding Monte-Carlo (MC) simulation on the basis of the planned treatment. While the usefulness of 3D offline PET-based treatment verification has already been shown, the feasibility of 4D offline PET-based treatment monitoring, accounting for the tumour motion during the irradiation and the subsequent PET acquisition, still needs to be demonstrated. In this work, PMMA phantoms of different geometries were irradiated once under stationary and once under moving conditions. In the latter case, a pressure sensor motion surrogate was used to monitor the rigid target movement during the gated ion-beam application and the following PET acquisition. In the same way, respiratory motion was monitored during the irradiation and subsequent PET/CT scans of several patients with respiratory motion affected target volumes in the liver. In all cases, the knowledge or estimation (from 4D CT) of the target trajectory enabled a 4D analysis of the actual ion-beam delivery and the post-irradiation PET. The reconstructed 4D PET data were compared to the stationary reference (phantom study only) and to the results of a dedicated 4D MC simulation framework. In the simplified scenario of high dose irradiation of moving phantoms results comparable to the static reference measurements could be obtained by using the available gated 4D PET image reconstruction. However, time-resolved analysis of the clinical data was found to suffer from the very low counting statistics, hindering a reliable verification of the applied treatment under consideration of the tumour motion. Still, in the case of small respiratory motion amplitudes (below 1cm), therapy application could be verified by comparing the 3D reconstructed PET data to a 3D MC prediction.