LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Principles of computerized tomographic imaging
Principles of computerized tomographic imaging
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
3D Reconstruction of Volume Defects from Few X-Ray Images
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Fast and accurate three-dimensional reconstruction from cone-beam projection data using algebraic methods
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This paper presents a three-dimensional GPU-accelerated algebraic reconstruction method in a few-projection cone-beam setting with arbitrary acquisition geometry. To achieve artifact-reduced reconstructions in the challenging case of unconstrained geometry and extremely limited input data, we use linear methods and an artifact-avoiding projection algorithm to provide high reconstruction quality. We apply the conjugate gradient method in the linear case of Tikhonov regularization and the two-point-step-size gradient method in the nonlinear case of total variation regularization to solve the system of equations. By taking advantage of modern graphics hardware we achieve acceleration of up to two orders of magnitude over classical CPU implementations.