Tomography and Instrumentation

Group leader: D. Stsepankou


  •          Computer Tomography

          We are developing a framework for computer tomography. This framework consists of several elements: 
  • Efficient GPU-implementation of all modern iterative and non-iterative reconstruction techniques.
  • New, non-linear sparsity regularizers like Anisotropic Total Variation (ATV) [1], combined first order ATV and second order TV [2]  and Generalized Anisotropic Total Variation (GATV) [3] for accurate , low-dose iterative CT reconstruction from few projections.
  • Accurate forward and backward modeling using the footprint method.
  • Tools for estimating projection matrices from image data.
  • Calibration tools and software for cone-beam CT systems.
  • Dosimetric tools for cone-beam CT systems.
  • GPU-based MC simulators.
  • New and fast optimizers for computed tomography.
  • Multi-energy reconstruction using kV and MV energies.
  • Publications: [1] [2] [3] [4] [5] [6] [7]


  • Organ Function Measurement Instrumentation

        In this project we have developed a small sensor device for kidney function measurement. The fluorescent substance FITC-sinistrin has been used as the marker. Device’s principle of operation is based on the ability of FITC-Sinistrin to emit green fluorescent light (520nm) absorbing blue light (480nm).  [8]


  • Artifact Detection and Elimination

      In this project, we study the motion artefacts obtained due to motion of the subject, with above sensor attached. During the measurement, relative motion between plaster and skin leads to a variation of the illumination conditions, which emerge as artifacts in the data. The artefacts correction method combines cluster analysis and nonlinear regression with a priori knowledge about signal morphology to correct data. 








  1. M. Debatin, Zygmanski, P., Dzimitry, S., and Hesser, J., CT Reconstruction from Few-Views by Anisotropic Total Variation Minimization, IEEE MIC. IEEE Nuclear Science Symposium and Medical Imaging Conference, 2012.
  2. M. Debatin, Dzimitry, S., and Hesser, J., CT Reconstruction from Few-Views by Higher Order Adaptive Weighted Total Variation Minimization, Proc. Intl. Mtg. on Fully 3D Image Reconstruction. Proc. Intl. Mtg. on Fully 3D Image Reconstruction, 2013.
  3. M. Debatin and Hesser, J., Accurate low-dose iterative CT reconstruction from few projections by Generalized Anisotropic Total Variation minimization for industrial CT, Journal of X-ray Science and Technology, vol. 23, pp. 701–726, 2015.
  4. J. Muders and Hesser, J., Stable and robust geometric self-calibration for cone-beam CT using mutual information, IEEE Transaction on Nuclear Science, vol. 61, p. 202,217, 2014.
  5. M. Blessing, Arns, A., Wertz, H., Stsepankou, D., Boda-Heggemann, J., Lohr, F., Hesser, J., and Wenz, F., Image Guided Radiation Therapy Using Ultrafast kV-MV CBCT: End-to-End Test Results of the Finalized Implementation, International Journal of Radiation Oncology Biology Physics, vol. 90, no. 1, pp. S828–S829 , 2014.
  6. A. Arns, Blessing, M., Stsepankou, D., Boda-Heggemann, J., Hesser, J., Wenz, F., Lohr, F., and Wertz, H., Matching Accuracy of Ultrafast Kilovoltage-Megavoltage (kV-MV) Cone Beam CT for Image Guided Radiation Therapy, International Journal of Radiation Oncology Biology Physics, vol. 90, no. 1, pp. S829-S830, 2014.
  7. D. Stsepankou, Arns, A., Ng, S. K., Zygmanski, P., and Hesser, J., Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization., Physics in medicine and biology, vol. 57, pp. 5955–5970, 2012.
  8. D. Schock-Kusch, Geraci, S., Ermeling, E., Shulhevich, Y., Sticht, C., Hesser, J., Stsepankou, D., Neudecker, S., Pill, J., Schmitt, R., and Melk, A., Reliability of transcutaneous measurement of renal function in various strains of conscious mice., PloS one, vol. 8, p. e71519, 2013.
  9. A. Shmarlouski, Shulhevich, Y., Geraci, S., Friedemann, J., Gretz, N., Neudecker, S., Hesser, J., and Stsepankou, D., Automatic artifact removal from GFR measurements, Biomedical Signal Processing and Control, vol. 14, pp. 30-41, 2014.