A parallel subgradient projections method for the convex feasibility problem
Journal of Computational and Applied Mathematics
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
The mathematics of computerized tomography
The mathematics of computerized tomography
Principles of computerized tomographic imaging
Principles of computerized tomographic imaging
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
SIAM Journal on Matrix Analysis and Applications
On Diagonally Relaxed Orthogonal Projection Methods
SIAM Journal on Scientific Computing
Necessary and sufficient convergence conditions for algebraic image reconstruction algorithms
IEEE Transactions on Image Processing
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Fundamentals of Computerized Tomography: Image Reconstruction from Projections
Discrete Inverse Problems: Insight and Algorithms
Discrete Inverse Problems: Insight and Algorithms
Convergence of the simultaneous algebraic reconstruction technique (SART)
IEEE Transactions on Image Processing
SNARK09 - A software package for reconstruction of 2D images from 1D projections
Computer Methods and Programs in Biomedicine
| Hi-index | 7.29 |
We present a MATLAB package with implementations of several algebraic iterative reconstruction methods for discretizations of inverse problems. These so-called row action methods rely on semi-convergence for achieving the necessary regularization of the problem. Two classes of methods are implemented: Algebraic Reconstruction Techniques (ART) and Simultaneous Iterative Reconstruction Techniques (SIRT). In addition we provide a few simplified test problems from medical and seismic tomography. For each iterative method, a number of strategies are available for choosing the relaxation parameter and the stopping rule. The relaxation parameter can be fixed, or chosen adaptively in each iteration; in the former case we provide a new ''training'' algorithm that finds the optimal parameter for a given test problem. The stopping rules provided are the discrepancy principle, the monotone error rule, and the NCP criterion; for the first two methods ''training'' can be used to find the optimal discrepancy parameter.