Respiratory liver motion estimation and its effect on scanned proton beam therapy

Proton therapy with active scanning beam delivery has significant advantages compared to conventional radiotherapy. However, so far only static targets have been treated in this way, since moving targets potentially lead to interplay effects. For 4D treatment planning, information on the target motion is needed to calculate time-resolved dose distributions. In this study, respiratory liver motion has been extracted from 4D CT data using two deformable image registration algorithms. In moderately moving patient cases (mean motion range around 6 mm), the registration error was no more than 3 mm, while it reached 7 mm for larger motions (range around 13 mm). The obtained deformation fields have then been used to calculate different time-resolved 4D treatment plans. Averaged over both motion estimations, interplay effects can increase the D-5-D-95 value for the clinical target volume (CTV) from 8.8% in a static plan to 23.4% when motion is considered. It has also been found that the different deformable registration algorithms can provide different motion estimations despite performing similarly for the selected landmarks, which in turn can lead to differing 4D dose distributions. Especially for single-field treatments where no motion mitigation is used, a maximum (mean) dose difference (averaged over three cases) of 32.8% (2.9%) can be observed. However, this registration ambiguity-induced uncertainty can be reduced if rescanning is applied or if the treatment plan consists of multiple fields, where the maximum (mean) difference can decrease to 15.2% (0.57%). Our results indicate the necessity to interpret 4D dose distributions for scanned proton therapy with some caution or with error bars to reflect the uncertainties resulting from the motion estimation. On the other hand, rescanning has been found to be an appropriate motion mitigation technique and, furthermore, has been shown to be a robust approach to also deal with these motion estimation uncertainties.