Regularization methods in dynamic MRI

DOI10.1016/S0096-3003(01)00196-5zbMath1024.65137MaRDI QIDQ1855840

A. Formiconi, Gaetano Zanghirati, Elena Loli Piccolomini, Fabiana Zama

Publication date: 28 January 2003

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

zbMATH Keywords

numerical examplestruncated singular value decompositionill-conditioned linear systemsconjugate gradientsregularization methodsregularization parameterinverse ill-posed problemHermitian Toeplitz matrixdynamic magnetic resonance imaging

Mathematics Subject Classification ID

Numerical methods for integral equations (65R20) Biomedical imaging and signal processing (92C55) Numerical methods for ill-posed problems for integral equations (65R30) Fredholm integral equations (45B05) Numerical methods for inverse problems for integral equations (65R32)

Related Items

Representation of high resolution images from low sampled Fourier data: applications to dynamic MRI, Regularization in model updating, A B-spline parametric model for high resolution dynamic magnetic resonance imaging, Numerical methods for the reconstruction of dynamic magnetic resonance images, A variational approach to Gibbs artifacts removal in MRI

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The discrete Picard condition for discrete ill-posed problems
• The truncated SVD as a method for regularization
• Regularization methods for large-scale problems
• Minimum Principles for Ill-Posed Problems
• Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
• The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
• Rank-Deficient and Discrete Ill-Posed Problems