4D Medical Image Computing for Model-based Analysis of Respiratory Tumor and Organ Motion

Breathing motion is a significant source of error in radiation therapy planning of the thorax and upper abdomen. The development of 4D (= 3D+t) imaging methods opened up the possibility to capture the spatio-temporal behaviour of tumors and inner organs. This project aims at developing methods for modelling, analysis, and visualization of respiratory motion of tumors and inner organs. The project is based on artefact reduced 4D CT patient data with high spatial and temporal resolution. The methods will complement possibilities offered by 4D imaging techniques to improve radiation therapy of thoracic and abdominal tumors.

The main focus of the project is to develop and evaluate improved non-linear registration methods in order to enable a precise estimation of 3D motion fields in the 4D CT image data. These dense vector fields are used for subsequent analysis and modelling of respiratory motion of structures of interest in radiation therapy such as tumors and organs at risk (fig. 1 and 2). Based on the patient collective we study the interpatient variability of tumor and lung motion whereas different lung regions are considered to analyze regional lung motion. Results are used to compare internal target volumes (ITV, i.e. the volume covered by the moving target) for different patients and, e.g., to examine whether it is possible to identify different but typical patterns of regional lung motion.

The project is funded by Deutsche Forschungsgemeinschaft (DFG) (HA 2355/9-1).

Fig. 1: Visualization of the 3D motion field between the phase of end-expiration and end-inspiration. The motion field estimation is based on optical flow based registration. Absolute values of the displacement fields are visualized color-coded. Red arrows indicate displacements of more than 20 mm. Figure taken from Handels et al., IJMI 76S, 433-9, 2007.
Fig. 2: Color-coded visualization of estimated appearance probabilities of lung tumors of two patients, displayed in a 2D slice.

Selected Publications

  1. Alexander Schmidt-Richberg, Jan Ehrhardt, René Werner, Heinz Handels
    Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-dependent Regularization
    In: G.-Z. Yang Hawkes D., Reuckert D., Noble A., Taylor C. (eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, Part I, LNCS 5761, Springer Verlag, Berlin, 755-762, 2009.

  2. H. Handels, R. Werner, T. Frenzel, D. Säring, W. Lu, D. Low, and J. Ehrhardt:
    4D Medical Image Computing and Visualization of Lung Tumor Mobility in Spatio-temporal CT Image Data, International Journal of Medical Informatics, 76S, S433-S439, 2007.

  3. J. Ehrhardt, R. Werner, T. Frenzel, W. Lu, D. Low,  H. Handels:
    Analysis of Free Breathing Motion Using Artifact Reduced 4D CT Image Data, In: P.W. Pluim, J.M. Reinhardt (eds.), SPIE Medical Imaging 2007: Image Processing, San Diego, Proc. SPIE, Vol. 6512, 1N1-1N11, 2007.

  4. R. Werner, J. Ehrhardt, T. Frenzel, W. Lu, D. Low, H. Handels:
    Analysis of Tumor-influenced Respiratory Dynamics using Motion Artifact Reduced Thoracic 4D CT Images. In: T. Buzug et al. (eds.), Advances in Medical Engineering, Springer Verlag, Berlin, 181-186, 2007.

Project Team

Dipl.-Inf. Dipl.-Phys. René Werner
Dr. Jan Ehrhardt
Dipl.-Inf. Alexander Schmidt-Richberg
Prof. Dr. Heinz Handels

Cooperation Partners

Dr. rer. nat. Florian Cremers
Klinik und Poliklinik für Strahlentherapie und Radioonkologie
Universitätsklinikum Hamburg-Eppendorf (UKE)

Dr. med. Dr. rer. nat. Thorsten Frenzel
Ambulanzzentrum des UKE GmbH
Bereich für Strahlentherapie

Prof. Daniel Low and Dr. Wei Lu
Washington University in St. Louis, School of Medicine
St. Louis, MO, USA