Prof. Dr.-Ing. Tobias Knopp

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 209
22529 Hamburg
- Postanschrift -

Technische Universität Hamburg (TUHH)
Institut für Biomedizinische Bildgebung
Gebäude E, Raum 4.044
Am Schwarzenberg-Campus 3
21073 Hamburg

Tel.: 040 / 7410 56794
Fax: 040 / 7410 45811
E-Mail: t.knopp(at)
E-Mail: tobias.knopp(at)



  • 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.


Title: Optimized sampling patterns for the sparse recovery of system matrices in Magnetic Particle Imaging.
Written by: M. Grosser and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. (2021).
Volume: <strong>7</strong>. Number: (2),
on pages: 1-15
how published:
DOI: 10.18416/IJMPI.2021.2112001


Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.