Tumor tracking with non-linear internal/external correlation models in the presence of respiratory motion baseline drifts and phase shifts

Abstract

Purpose: In CyberKnife® respiratory tracking, tumor positions are predicted from external marker positions using correlation models. With available models, prediction accuracy may deteriorate when respiratory motion baseline drifts occur. Previous investigations have demonstrated that for linear models this can be mitigated by adding a time-dependent term. In this study, we have focused on added value of time-dependent terms for the available non-linear correlation models, and on phase shifts between internal and external motion tracks.

Methods: Treatment simulations for tracking with and without time-dependent terms were performed using computer generated respiratory motion tracks for 60.000 patients with variable baseline drifts and phase shifts. The protocol for acquisition of X-ray images was always the same. Tumor position prediction accuracies in simulated treatments were largely based on cumulative error-time histograms and quantified with R95: in 95% of time the prediction error is < R95 mm.

Results: For all available correlation models, prediction accuracy improved by adding a time-dependent term in case of occurring baseline drifts, with and without phase shifts present. For the most accurate model and 150 s between model updates, adding time dependency reduced R95 from 3.9 to 3.1 mm and from 5.4 to 3.3 mm for 0.25 and 0.50 mm/min drift, respectively. Tumor position prediction accuracy improvements with time-dependent models were obtained without increases in X-ray imaging.

Conclusions: Using available correlation models with an added time-dependent term could largely mitigate negative impact of respiratory motion baseline drifts on tumor position prediction accuracy, also in case of large phase shifts.