Dr.-Ing. Matthias Gräser

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 212
22529 Hamburg

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

Tel.: 040 / 7410 25812
E-Mail: matthias.graeser(at)tuhh.de
E-Mail: ma.graeser(at)uke.de

Research Interests

  • Magnetic Particle Imaging
  • Low Noise Electronics
  • Inductive Sensors
  • Passive Electrical Devices

Curriculum Vitae

Matthias Gräser submitted his Dr.-Ing. thesis in january 2016 at the institute of medical engineering (IMT) at the university of Lübeck and is now working as a Research Scientist at the institute for biomedical imaging (IBI) at the technical university in Hamburg, Germany.  Here he develops concepts for Magnetic-Particle-Imaging (MPI) devices. His main aim is to improve the sensitivity of the imageing devices and improve resolution and application possibilities of MPI technology.

In 2011 Matthias Gräser started to work at the IMT as a Research Associate in the Magnetic Particle Imaging Technology (MAPIT) project. In this project he devolped the analog signal chains for a rabbit sized field free line imager. Additionally he developed a two-dimensional Magnetic-Particle-Spectrometer. This device can apply various field sequences and measure the particle response with a very high signal-to-noise ratio (SNR).

The dynamic behaviour of magnetic nanoparticles is still not fully understood. Matthias Gräser investigated the particle behaviour by modeling the particle behaviour with stochastic differential equations. With this model it is possible to simulate the impact of several particle parameters and field sequences on the particle response .

In 2010 Matthias Gräser finished his diploma at the Karlsruhe Institue of Technology (KIT). His diploma thesis investigated the nerve stimulation of magnetic fields in the range from 4 kHz to 25 kHz.

Journal Publications

[76874]
Title: Analog receive signal processing for magnetic particle imaging.
Written by: M. Graeser, T. Knopp, M. Grüttner, T. F. Sattel, and T. M. Buzug
in: <em>Medical Physics</em>. (2013).
Volume: <strong>40</strong>. Number: (4),
on pages: 042303
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DOI: 10.1118/1.4794482
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ARXIVID:
PMID: 23556916

[BibTex] [pmid]

Note: article

Abstract: {PURPOSE}: Magnetic particle imaging ({MPI}) applies oscillating magnetic fields to determine the distribution of magnetic nanoparticles in vivo. Using a receive coil, the change of the particle magnetization can be detected. However, the signal induced by the nanoparticles is superimposed by the direct feedthrough interference of the sinusoidal excitation field, which couples into the receive coils. As the latter is several magnitudes higher, the extraction of the particle signal from the excitation signal is a challenging task. {METHODS}: One way to remove the interfering signal is to suppress the excitation signal by means of a band-stop filter. However, this technique removes parts of the desired particle signal, which are essential for direct reconstruction of the particle concentration. A way to recover the entire particle signal is to cancel out the excitation signal by coupling a matching cancellation signal into the receive chain. However, the suppression rates that can be achieved by signal cancellation are not as high as with the filtering method, which limits the sensitivity of this method. In order to unite the advantages of both methods, in this work the authors propose to combine the filtering and the cancellation technique. All methods were compared by measuring 10 ?l Resovist, in the same field generator only switching the signal processing parts. {RESULTS}: The reconstructed time signals of the three methods, show the advantage of the proposed combination of filtering and cancellation. The method preserves the fundamental frequency and is able to detect the tracer signal at its full bandwidth even for low concentrations. {CONCLUSIONS}: By recovering the full particle signal the {SNR} can be improved and errors in the x-space reconstruction are prevented. The authors show that the combined method provides this full particle signal and makes it possible to improve image quality.

Conference Proceedings

[76874]
Title: Analog receive signal processing for magnetic particle imaging.
Written by: M. Graeser, T. Knopp, M. Grüttner, T. F. Sattel, and T. M. Buzug
in: <em>Medical Physics</em>. (2013).
Volume: <strong>40</strong>. Number: (4),
on pages: 042303
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.1118/1.4794482
URL:
ARXIVID:
PMID: 23556916

[BibTex] [pmid]

Note: article

Abstract: {PURPOSE}: Magnetic particle imaging ({MPI}) applies oscillating magnetic fields to determine the distribution of magnetic nanoparticles in vivo. Using a receive coil, the change of the particle magnetization can be detected. However, the signal induced by the nanoparticles is superimposed by the direct feedthrough interference of the sinusoidal excitation field, which couples into the receive coils. As the latter is several magnitudes higher, the extraction of the particle signal from the excitation signal is a challenging task. {METHODS}: One way to remove the interfering signal is to suppress the excitation signal by means of a band-stop filter. However, this technique removes parts of the desired particle signal, which are essential for direct reconstruction of the particle concentration. A way to recover the entire particle signal is to cancel out the excitation signal by coupling a matching cancellation signal into the receive chain. However, the suppression rates that can be achieved by signal cancellation are not as high as with the filtering method, which limits the sensitivity of this method. In order to unite the advantages of both methods, in this work the authors propose to combine the filtering and the cancellation technique. All methods were compared by measuring 10 ?l Resovist, in the same field generator only switching the signal processing parts. {RESULTS}: The reconstructed time signals of the three methods, show the advantage of the proposed combination of filtering and cancellation. The method preserves the fundamental frequency and is able to detect the tracer signal at its full bandwidth even for low concentrations. {CONCLUSIONS}: By recovering the full particle signal the {SNR} can be improved and errors in the x-space reconstruction are prevented. The authors show that the combined method provides this full particle signal and makes it possible to improve image quality.