Low-Rank Modeling of Local $k$-Space Neighborhoods (LORAKS) for Constrained MRI
Top Cited Papers
- 5 December 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 33 (3), 668-681
- https://doi.org/10.1109/tmi.2013.2293974
Abstract
Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.Keywords
This publication has 46 references indexed in Scilit:
- Parallel reconstruction using null operationsMagnetic Resonance in Medicine, 2011
- SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐spaceMagnetic Resonance in Medicine, 2010
- Patient‐adaptive reconstruction and acquisition in dynamic imaging with sensitivity encoding (PARADISE)Magnetic Resonance in Medicine, 2010
- Optimal phased-array combination for spectroscopyMagnetic Resonance Imaging, 2008
- Generalized autocalibrating partially parallel acquisitions (GRAPPA)Magnetic Resonance in Medicine, 2002
- Feature‐recognizing MRIMagnetic Resonance in Medicine, 1993
- Homodyne detection in magnetic resonance imagingIEEE Transactions on Medical Imaging, 1991
- Imaging sampling below the Nyquist density without aliasingJournal of the Optical Society of America A, 1990
- Modified linear prediction modeling in magnetic resonance imagingJournal of Magnetic Resonance (1969), 1989
- Signal enhancement-a composite property mapping algorithmIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988