A Diffeomorphic MLR Framework for Surrogate-based Motion Estimation and Situation-adapted Dose Accumulation

Respiratory motion a is major source of uncertainty in radiotherapy. Current approaches to cope with it – like gating or tracking techniques – usually make use of external breathing signals, interpreted as surrogates of internal motion patterns. Due to the complex nature of internal motion, a trend exists toward the application of multi-dimensional surrogates. This requires the development and evaluation of appropriate correspondence models between the surrogate data and internal motion patterns. We suggest using a multi-linear regression (MLR) and exploit the Log-Euclidean Framework to embed the MLR within a correspondence model yielding diffeomorphic estimates of motion fields of internal structures. The framework is evaluated using 4D CT data of lung tumor patients and different surrogates (spirometry, diaphragm tracking, monitoring chest wall motion). Further, the application of the framework for incorporating surrogate-based information about breathing variations into the process of dose accumulation is illustrated.