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
- 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)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.

Publications

Google Scholar Profile

[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: <em>European Congress of Radiology 2020</em>. 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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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).

Prof. Dr.-Ing. Tobias Knopp

Roles

Consulting Hours

Research Interests

Curriculum Vitae

Publications

TU Hamburg

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