Suppression of MRI Truncation Artifacts Using Total Variation Constrained Data Extrapolation
Open Access
- 7 September 2008
- journal article
- research article
- Published by Hindawi Limited in International Journal of Biomedical Imaging
- Vol. 2008, 1-8
- https://doi.org/10.1155/2008/184123
Abstract
The finite sampling ofk-space in MRI causes spurious image artifacts, known as Gibbs ringing, which result from signal truncation at the border ofk-space. The effect is especially visible for acquisitions at low resolution and commonly reduced by filtering at the expense of image blurring. The present work demonstrates that the simple assumption of a piecewise-constant object can be exploited to extrapolate the data ink-space beyond the measured part. The method allows for a significant reduction of truncation artifacts without compromising resolution. The assumption translates into a total variation minimization problem, which can be solved with a nonlinear optimization algorithm. In the presence of substantial noise, a modified approach offers edge-preserving denoising by allowing for slight deviations from the measured data in addition to supplementing data. The effectiveness of these methods is demonstrated with simulations as well as experimental data for a phantom and human brain in vivo.Keywords
This publication has 13 references indexed in Scilit:
- Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraintMagnetic Resonance in Medicine, 2007
- Padé methods for reconstruction and feature extraction in magnetic resonance imagingMagnetic Resonance in Medicine, 2005
- A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrityIEEE Transactions on Medical Imaging, 2002
- Extension of finite-support extrapolation using the generalized series model for MR spectroscopic imagingIEEE Transactions on Medical Imaging, 2001
- Nonlinear image recovery with half-quadratic regularizationIEEE Transactions on Image Processing, 1995
- Nonlinear total variation based noise removal algorithmsPhysica D: Nonlinear Phenomena, 1992
- Phase‐constrained data extrapolation method for reduction of truncation artifactsJournal of Magnetic Resonance Imaging, 1991
- Modified iterative model based on data extrapolation method to reduce Gibbs ringingJournal of Magnetic Resonance Imaging, 1991
- Data extrapolation for truncation artifact removalMagnetic Resonance in Medicine, 1991
- A comparison of models used as alternative magnetic resonance image reconstruction methodsMagnetic Resonance Imaging, 1990