[BibTex]

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.

[BibTex]

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.

[BibTex]

Note: inproceedings

Abstract: In magnetic particle imaging, the time consuming measurement of a system function is required before image reconstruction. Reduction of measurement time has been achieved with the help of compressed sensing, which is based on the sparsity of the system function in some transform domain. In this work we demonstrate that the rows of a system function can be approximated by low-rank tensors. We develop a recovery method exploiting both the low rank of system function rows and the sparsity of their DCT coefficients. Experiments show that the proposed method yields system functions with increased accuracy and reduced noise.