A diffeomorphic framework for surrogate-based motion estimation in radiation therapy: Concept and first evaluation

Abstract

Respiratory motion is a major obstacle in radiation therapy of thoracic and abdominal tumors. Techniques to cope with it such as gating and tracking techniques are based on the use of breathing signals that can be acquired easily and in real-time. These signals represent only surrogates of the motion of the inner organs and tumors. Consequently, methods are needed to estimate respiratory motion patterns of the internal structures based on surrogate measurements. In this contribution, a diffeomorphic framework based on a multi-linear regression and the Log-Euclidean framework recently introduced in the context of diffeomorphic registration is proposed to establish such a correspondence model. The feasibility of the approach is demonstrated by means of a leave-out evaluation using 4D CT image sequences of ten lung tumor patients and simulating three different types of breathing signals: spirometry records, tracking motion of points on the diaphragm, and assessing the raising/lifting of chest wall points.