Prof. Dr.-Ing. Tobias Knopp

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 209
22529 Hamburg
Tel.: 040 / 7410 56794
Fax: 040 / 7410 45811
E-Mail: t.knopp(at)uke.de

Technische Universität Hamburg (TUHH)
Institut für Biomedizinische Bildgebung
Gebäude E, Raum 4.044
Am Schwarzenberg-Campus 3
21073 Hamburg
E-Mail: tobias.knopp(at)tuhh.de

 

 

Roles

  • Head of the Institute for Biomedical Imaging
  • Editor-in-chief of the International Journal on Magnetic Particle Imaging (IJMPI)

Consulting Hours

  • On appointment

Research Interests

  • Tomographic Imaging
  • Image Reconstruction
  • Signal- and Image Processing
  • Magnetic Particle Imaging

Curriculum Vitae

Tobias Knopp received his Diplom degree in computer science in 2007 and his PhD in 2010, both from the University of Lübeck with highest distinction. For his PHD on the tomographic imaging method Magnetic Particle Imaging (MPI) he was awarded with the Klee award from the DGBMT (VDE) in 2011. From 2010 until 2011 he led the MAPIT project at the University of Lübeck and published the first scientific book on MPI. In 2011 he joined Bruker Biospin to work on the first commercially available MPI system. From 2012 until 2014 he worked at Thorlabs in the field of Optical Coherence Tomography (OCT) as a software developer. In 2014 he has been appointed as Professor for experimental Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology.

Publications

[132516]
Title: Low Rank Approach to Sparse System Matrix Recovery for MPI
Written by: M. Grosser and T. Knopp
in: 9th International Workshop on Magnetic Particle Imaging (IWMPI 2019) 2019
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on pages: 31-32
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[BibTex]

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.