Improved Reconstruction of 4D MSCT Image Data and Motion Analysis of Lung Tumors Using Non-linear Registration Methods

In this paper, a non-linear registration method is used to interpolate and reconstruct (3D+t) CT data sets from multislice CT scans, which are collected simultaneously with digital spirometry. The non-linear registration approach applied is an optical flow based method. It estimates a velocity field between successive scans, which is used to reconstruct a 4D CT data set by interpolating data at user-defined tidal volumes. A qualitative and quantitative evaluation showed that artifacts can be reduced significantly by this technique. The comparison between slice changes inside a data segment and slice changes at segment borders enables a quantitative evaluation. For four patient data sets the artifacts could be reduced by 31.8 %, 29.9 %, 30.7% and 41.6% (mean over all data segments). Furthermore, the reconstructed 4D CT data sets are used for studying the motion of lung tumors and inner organs during the respiratory cycle. The reconstructed 4D data sets of 4 patients with lung tumors were used to quantify the individual tumor and organ movements during a breathing cycle. Based on the determined velocity field, trajectories of landmarks and surface points are analyzed. The motion of the lung tumor center in three orthogonal directions can be displayed and probabilities of lung tumor appearance are computed in 3D. Keywords—optical flow based interpolation, artifact reduction,