[www] [BibTex]

Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.

[www]

Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.

[www] [BibTex]

Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.

[www]

Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.

[www]

Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.

[www] [BibTex]

Note: article, openaccess

Abstract: In Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, which can be a limiting factor in practical applications. Calibration time can be reduced using compressed sensing, which exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. This work seeks to further enhance sparse system matrix recovery by optimizing the sampling points to the signal class at hand. For this purpose we introduce an experiment design method based on the Bayesian Fisher information matrix. Our technique uses a previously measured system matrix to tailor the sampling pattern to the signal class at hand. Our tests show that the optimized sampling patterns lead to a more accurate system matrix recovery than popular random sampling approaches. Moreover, our tests demonstrate that the optimized sampling patterns are sufficiently robust to enhance the recovery of system matrices for other types of particles or other experimental conditions.