Patryk Szwargulski, M.Sc.
Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
2ter Stock, Raum 203
Technische Universität Hamburg (TUHH)
Institut für Biomedizinische Bildgebung
Gebäude E, Raum 4.044
Am Schwarzenberg-Campus 3
- Magnetic Particle Imaging
- Image Reconstruction
- Signal and Image Processing
In 2015 Patryk Szwargulski graduated with a master's degree thesis on Fast Reconstruction of Magnetic Particle Imaging Data using the Focusfields. Currently he is a PhD student in the group of Tobias Knopp for experimental Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology.
|Title: Discriminating nanoparticle core size using multi-contrast MPI|
|Written by: C. Shasha, E. Teeman, K. M. Krishnan, P. Szwargulski, T. Knopp, and M. Möddel|
|in: Physics in Medicine and Biology 2019|
Note: article, multi-contrast
Abstract: Magnetic particle imaging (MPI) is an imaging modality that detects the response of a distribution of magnetic nanoparticle tracers to static and alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that could enable temperature or viscosity quantification. In this work, we apply a multi-contrast reconstruction technique to discriminate between nanoparticle tracers of different core sizes. Three nanoparticle types with core diameters of 21.9nm, 25.3nm, and 27.7nm were each imaged at 21 different locations within the scanner field of view. Multi-channel images were reconstructed for each sample and location, with each channel corresponding to one of the three core sizes. For each image, signal weight vectors were calculated, which were then used to classify each image by core size. With a block averaging length of 10000, the median signal-to-noise ratio was 40 or higher for all three sample types, and a correct prediction rate of 96.7% was achieved, indicating that core size can effectively be predicted using signal weight vector classification with close to 100% accuracy while retaining high MPI image quality.