Lina Nawwas, M.Sc.

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 212
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 56309
E-Mail: l.nawwas(at)uke.de
E-Mail: lina.nawwas(at)tuhh.de

Research Interests

  • Magnetic Particle Imaging
  • Image Reconstruction

Curriculum Vitae

Lina Nawwas 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. In 2016 she earned a Bachelor's degree in Mathematics from An Najah National University in Palestine. From 2016 to 2018 she pursued her Master's degree in Applied Mathematics, majoring Mathematical Modeling for Engineering with MathMods Erasmus Mundus Program in three European universities: University of L’Aquila in Italy, University of Hamburg in Germany, and Gdańsk University of Technology in Poland.

Journal Publications

[168322]
Title: Accelerated Kaczmarz for Convergence Speed-up in Multi-Contrast Magnetic Particle Imaging.
Written by: L. Nawwas, M. Grosser, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. (2022).
Volume: <strong>8</strong>. Number: (1),
on pages: 1-4
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DOI: https://doi.org/10.18416/IJMPI.2022.2203022
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/431
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, multi-contrast

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatio-temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by the use of regularization methods. The corresponding optimization problem is most commonly solved using the Kaczmarz algorithm. Reconstruction using the Kaczmarz method for single-contrast MPI is very efficient as it produces the desired images fast with a small number of iterations. For multi-contrast MPI, however, the regular Kaczmarz algorithm fails to obtain good quality images without channel leakage when using a small number of iterations. In this work, we propose to use an accelerated Kaczmarz method in order to reduce the reconstruction time needed to achieve a good separation of the channels and a good image quality in multi-contrast MPI.

Conference Proceedings

[168322]
Title: Accelerated Kaczmarz for Convergence Speed-up in Multi-Contrast Magnetic Particle Imaging.
Written by: L. Nawwas, M. Grosser, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. (2022).
Volume: <strong>8</strong>. Number: (1),
on pages: 1-4
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: https://doi.org/10.18416/IJMPI.2022.2203022
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/431
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, multi-contrast

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatio-temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by the use of regularization methods. The corresponding optimization problem is most commonly solved using the Kaczmarz algorithm. Reconstruction using the Kaczmarz method for single-contrast MPI is very efficient as it produces the desired images fast with a small number of iterations. For multi-contrast MPI, however, the regular Kaczmarz algorithm fails to obtain good quality images without channel leakage when using a small number of iterations. In this work, we propose to use an accelerated Kaczmarz method in order to reduce the reconstruction time needed to achieve a good separation of the channels and a good image quality in multi-contrast MPI.