[BibTex]

Note: inproceedings, reconstruction

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard methodfor solving such a problem is the regularized least squares approach, which uses, a regularization function toreduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Variousoptimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers(ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, thecreation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptableoptimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, whichprovides a large selection of common optimization algorithms and allows flexible inclusion of regularizationfunctions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[BibTex]

Note: inproceedings, reconstruction

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard methodfor solving such a problem is the regularized least squares approach, which uses, a regularization function toreduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Variousoptimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers(ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, thecreation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptableoptimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, whichprovides a large selection of common optimization algorithms and allows flexible inclusion of regularizationfunctions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[BibTex]

Note: inproceedings, reconstruction

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard methodfor solving such a problem is the regularized least squares approach, which uses, a regularization function toreduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Variousoptimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers(ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, thecreation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptableoptimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, whichprovides a large selection of common optimization algorithms and allows flexible inclusion of regularizationfunctions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[BibTex]

Note: inproceedings, reconstruction

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard methodfor solving such a problem is the regularized least squares approach, which uses, a regularization function toreduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Variousoptimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers(ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, thecreation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptableoptimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, whichprovides a large selection of common optimization algorithms and allows flexible inclusion of regularizationfunctions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.