[www] [BibTex]

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.

[www]

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.

[www] [BibTex]

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.

[www]

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.

[www]

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.

[www] [BibTex]

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.