[145082] |
Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
Volume: <strong>6</strong>. Number: (2), |
on pages: 1-3 |
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DOI: 10.18416/IJMPI.2020.2009035 |
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
ARXIVID: |
PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
[145082] |
Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
Volume: <strong>6</strong>. Number: (2), |
on pages: 1-3 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.18416/IJMPI.2020.2009035 |
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
ARXIVID: |
PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
[145082] |
Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
Volume: <strong>6</strong>. Number: (2), |
on pages: 1-3 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.18416/IJMPI.2020.2009035 |
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
ARXIVID: |
PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
[145082] |
Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
Volume: <strong>6</strong>. Number: (2), |
on pages: 1-3 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.18416/IJMPI.2020.2009035 |
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
ARXIVID: |
PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
[145082] |
Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
Volume: <strong>6</strong>. Number: (2), |
on pages: 1-3 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.18416/IJMPI.2020.2009035 |
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
ARXIVID: |
PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.
[145082] |
Title: Enhanced compressed sensing recovery of multi-patch system matrices in MPI. |
Written by: M. Grosser, M. Boberg, M. Bahe, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020). |
Volume: <strong>6</strong>. Number: (2), |
on pages: 1-3 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.18416/IJMPI.2020.2009035 |
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/287 |
ARXIVID: |
PMID: |
Note: inproceedings
Abstract: In magnetic particle imaging, the time consuming measurement of a system matrix is required before image reconstruction.Reduction of measurement time can be achieved with the help of compressed sensing, which is based on the sparsity of the system matrix in a suitable transform domain. In this work, we propose regularization functions to exploit the additional correlations in multi-patch system matrices. Experiments show that the resulting recovery method allows successful matrix recovery at higher undersampling factors than a standard compressed sensing recovery.