Statistical analysis of surrogate signals to incorporate respiratory motion variability into radiotherapy treatment planning

Abstract

Respiratory motion and its variability lead to location uncertainties in radiation therapy (RT) of thoracic and abdominal tumors. Current approaches for motion compensation in RT are usually driven by respiratory surrogate signals, e.g., spirometry. In this contribution, we present an approach for statistical analysis, modeling and subsequent simulation of surrogate signals on a cycle-by-cycle basis. The simulated signals represent typical patient-specific variations of, e.g., breathing amplitude and cycle period. For the underlying statistical analysis, all breathing cycles of an observed signal are consistently parameterized using approximating B-spline curves. Statistics on breathing cycles are then performed by using the parameters of the B-spline approximations. Assuming that these parameters follow a multivariate Gaussian distribution, realistic time-continuous surrogate signals of arbitrary length can be generated and used to simulate the internal motion of tumors and organs based on a patient-specific diffeomorphic correspondence model. As an example, we show how this approach can be employed in RT treatment planning to calculate tumor appearance probabilities and to statistically assess the impact of respiratory motion and its variability on planned dose distributions.