Lina Nawwas, M.Sc.

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 202
22529 Hamburg

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

Tel.: 040 / 7410 56355
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

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging
Written by: L. Nawwas, M. Möddel, T. Knopp, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 Number: 2
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
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DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[doi] [www] [BibTex]

Note: inproceedings

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.

Conference Proceedings

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging
Written by: L. Nawwas, M. Möddel, T. Knopp, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 Number: 2
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[doi] [www] [BibTex]

Note: inproceedings

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging
Written by: L. Nawwas, M. Möddel, T. Knopp, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 Number: 2
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[doi] [www] [BibTex]

Note: inproceedings

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.