IBI: Tobias Knopp (Head of Institute)

IBI > People > Tobias Knopp (Head of Institute)

Prof. Dr.-Ing. Tobias Knopp

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 209
22529 Hamburg
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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)uke.de
E-Mail: tobias.knopp(at)tuhh.de
ORCID: https://orcid.org/0000-0002-1589-8517

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.

Journal Publications

[180975]

Title: A Deep Learning Approach for Automatic Image Reconstruction in MPI.

Written by: T. Knopp, P. Jürß, and M. Grosser

in: <em>International Journal on Magnetic Particle Imaging</em>. (2023).

Volume: <strong>9</strong>. Number: (1),

on pages: 1-4

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DOI: 10.18416/IJMPI.2023.2303008

URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/517

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Note: inproceedings

Abstract: Image reconstruction in magnetic particle imaging is a challenging task because the optimal image quality can only be obtained by tuning the reconstruction parameters for each measurement individually. In particular, it requires a proper selection of the Tikhonov regularization parameter. In this work we propose a deep-learning-based post-processing technique, which removes the need for manual parameter optimization. The proposed neural network takes several images reconstructed with different parameters as input and combines them into a single high-quality image.

TU Hamburg

Institute for Biomedical Imaging

Lottestraße 55, 2nd floor

22529 Hamburg