Former Projects

  • 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. 


  • 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).  [2]

  • Deblurring microscopy

     In this project we developed new tools for reconstructing images at a higher resolution. [3] [4]

  • High-throughput cell analysis

     Tools for an automatic workflow of large data analysis were developed [5]

  • CT Angiography

     A diagnostic tool has been developed that allows to separate bone from vessel and calicifications in CT angiography. [6]

  • Training Simulators

     We developed several tools for interventional simulation such as a simulator for coronary arteries #paper#, ultrasound [7] [8], physiology [9], and ECG [10]


  1. Shmarlouski A, Shulhevich Y, Geraci S, et al. Automatic artifact removal from GFR measurements. Biomedical Signal Processing and Control. 2014;14:30-41. Available at:
  2. Schock-Kusch D, Geraci S, Ermeling E, et al. Reliability of transcutaneous measurement of renal function in various strains of conscious mice. Remuzzi G. PloS one. 2013;8:e71519. doi:10.1371/journal.pone.0071519.
  3. Remmele S, Hesser J. Constrained RLTV Deblurring for Confocal Microscopy. In: World Congress on Medical Physics and Biomedical Engineering - 2009. World Congress on Medical Physics and Biomedical Engineering - 2009. IUPESM; 2009.
  4. Remmele S, Oehm B, Staier F, Eipel H, Cremer C, Hesser J. Reconstruction of high-resolution fluorescence microscopy images based on axial tomography. Proc. SPIE Medical Imaging. 2011;7962.
  5. Remmele S, Ritzerfeld J, Nickel W, Hesser J. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning. Proc. SPIE Medical Imaging. 2011;7962.
  6. Maksimov D, Hesser J, Dietz T, et al. Graph-matching based CTA. IEEE Transactions on Medical Imaging. 2009;28:1940-1954.
  7. Abkai C, Becherer N, Hesser J, Maenner R. Real-Time Simulator for Intravascular Ultrasound (IVUS). Proc. SPIE Medical Imaging. 2007;8. Available at: images/Publication/2007/2007_abkai_ivus.pdf.
  8. Bürger B, Bettinghausen S, Rädle M, Hesser J. Real-time GPU-based ultrasound simulation using deformable mesh models. IEEE transactions on medical imaging. 2013;32:609–18. doi:10.1109/TMI.2012.2234474.
  9. Abkai C, Hesser J. Physiological Modeling and Real-Time Simulations Based on Dynamic Bayesian Networks. MICCAI2009. 2009.
  10. Abkai C, Hesser J. Real-Time ECG Emulation: A Multiple Dipole Model for Electrocardiography Simulation. Studies in Health Technology and Informatics. 2009;142:7–9.