Sparse Principal Axes Statistical Surface Deformation Models for Respiration Analysis and Classification

Abstract

Detection, analysis and compensation of respiratory motion is a key issue for a variety of medical applications, such as tumor tracking in fractionated radiotherapy. One class of approaches aims for predicting the internal target movement by correlating intra-operatively captured body surface deformations to a pre-operatively learned deformable model. Here, range imaging (RI) devices assume a prominent role for dense and real-time surface acquisition due to their non-intrusive and markerless nature. In this work we present an RI based statistical model built upon sparse principal axes for body surface deformations induced by respiratory motion. In contrast to commonly employed global models based on principal component analysis, we exploit orthomax rotations in order to enable the differentiation between distinctive and local respiratory motion patterns such as thoracic and abdominal breathing. In a case study, we demonstrate our model’s capability to capture dense respiration curves and the usage of our model for simulating realistic distinctive respiratory motion patterns.