[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization 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, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization 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, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization 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, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization 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, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization 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, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization 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, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.