The mobility of lung tumors during the respiratory cycle is a source of error in radiotherapy treatment planning. Spatiotemporal CT data sets can be used for studying the motion of lung tumors and inner organs during the breathing cycle. We present methods for the analysis of respiratory motion using 4D CT data in high temporal resolution. An optical flow based reconstruction method was used to generate artifact-reduced 4D CT data sets of lung cancer patients. The reconstructed 4D CT data sets were segmented and the respiratory motion of tumors and inner organs was analyzed. A non-linear registration algorithm is used to calculate the velocity field between consecutive time frames of the 4D data. The resulting velocity field is used to analyze trajectories of landmarks and surface points. By this technique, the maximum displacement of any surface point is calculated, and regions with large respiratory motion are marked. To describe the tumor mobility the motion of the lung tumor center in three orthogonal directions is displayed. Estimated 3D appearance probabilities visualize the movement of the tumor during the respiratory cycle in one static image. Furthermore, correlations between trajectories of the skin surface and the trajectory of the tumor center are determined and skin regions are identified which are suitable for prediction of the internal tumor motion. The results of the motion analysis indicate that the described methods are suitable to gain insight into the spatiotemporal behavior of anatomical and pathological structures during the respiratory cycle.
|Title of host publication||Medical Imaging 2007: Image Processing|
|Number of pages||11|
|Publication status||Published - 23.11.2007|
|Event||Medical Imaging 2007: Image Processing - San Diego, United States|
Duration: 17.02.2007 → 22.02.2007
Conference number: 70570