Journal Publications
| [146886] |
| Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging. |
| Written by: L. Nawwas, M. Möddel, T. Knopp, and C. Brandt |
| in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
| Volume: <strong>6</strong>. 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: |
Note: inproceedings, artifact
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, and C. Brandt |
| in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
| Volume: <strong>6</strong>. 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: |
Note: inproceedings, artifact
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.
