Prediction Framework for Statistical Respiratory Motion Modeling

• DOI: 10.1007/978-3-642-15711-0_41
• Publication Date: 2010-09-20T06:11:02Z
• Authors: Tobias Klinder; Cristian Lorenz; Jörn Ostermann
• Keywords: Respiratory-Gated Imaging Techniques; Models, Statistical; Movement; Reproducibility of Results; Models, Biological; Sensitivity and Specificity; Imaging, Three-Dimensional; Data Interpretation, Statistical; Respiratory Mechanics; Humans; Radiographic Image Interpretation, Computer-Assisted; Computer Simulation; Radiography, Thoracic; Artifacts; Tomography, X-Ray Computed

Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.