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

[140967]
Title: Intelligent Chest X-Ray Worklist Prioritization by Deep Learning
Written by: L. Steinmeister, I. M. Baltruschat, H. Ittrich, A. Saalbach, H. Nickisch, M. Grass, T. Knopp and G. Adam
in: European Congress of Radiology 2020 January 2020
Volume: Number: C-07700
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DOI: 10.26044/ecr2020/C-07700
URL: http://dx.doi.org/10.26044/ecr2020/C-07700
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[doi] [www] [BibTex]

Note: inproceeding

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed reads or even unreported examinations, which bears severe risks for patient’s safety (1,2). The aim of our study was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and could reduce report turnaround times (RTAT) for critical findings in chest radiographs (CXR), instead of reporting according to the First-In-First-Out-Principle (FIFO).