Accelerating SENSE using compressed sensing

Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories. The proposed method, named CS‐SENSE, sequentially reconstructs a set of aliased reduced‐field‐of‐view images in each channel using SparseMRI and then reconstructs the final image from the aliased images using Cartesian SENSE. The results from simulations and phantom and in vivo experiments demonstrate that CS‐SENSE can achieve a reduction factor higher than those achieved by SparseMRI and SENSE individually and outperform the existing method that combines parallel MRI and CS. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.

[1]  A. Papoulis,et al.  Generalized sampling expansion , 1977 .

[2]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[3]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[4]  O. Haraldseth,et al.  K‐space substitution: A novel dynamic imaging technique , 1993, Magnetic resonance in medicine.

[5]  J. J. van Vaals,et al.  “Keyhole” method for accelerating imaging of contrast agent uptake , 1993, Journal of magnetic resonance imaging : JMRI.

[6]  Zhi-Pei Liang,et al.  An efficient method for dynamic magnetic resonance imaging , 1994, IEEE Trans. Medical Imaging.

[7]  W. Manning,et al.  Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays , 1997, Magnetic resonance in medicine.

[8]  Hong Jiang,et al.  Dynamic imaging by model estimation , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[9]  Fawwaz T. Ulaby Fundamentals of Applied Electromagnetics, 1999 Edition , 1998 .

[10]  F. Ulaby Fundamentals of applied electromagnetics , 1998 .

[11]  N J Pelc,et al.  Unaliasing by Fourier‐encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI , 1999, Magnetic resonance in medicine.

[12]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[13]  P. Boesiger,et al.  Advances in sensitivity encoding with arbitrary k‐space trajectories , 2001, Magnetic resonance in medicine.

[14]  N. Pelc,et al.  Making Better SENSE : Wavelet Denoising , Tikhonov Regularization , and Total Least Squares , 2001 .

[15]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[16]  Jeffrey A. Fessler,et al.  Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities , 2003, IEEE Transactions on Medical Imaging.

[17]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[18]  K. Kwong,et al.  Parallel imaging reconstruction using automatic regularization , 2004, Magnetic resonance in medicine.

[19]  Wilson Fong Handbook of MRI Pulse Sequences , 2005 .

[20]  L. Ying,et al.  On Tikhonov regularization for image reconstruction in parallel MRI , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Bruno Madore,et al.  UNFOLD‐SENSE: A parallel MRI method with self‐calibration and artifact suppression , 2004, Magnetic resonance in medicine.

[22]  Bhaskar D. Rao,et al.  Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.

[23]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[24]  Dana H. Brooks,et al.  A tour of accelerated parallel MR imaging from a linear systems perspective , 2005 .

[25]  Robert D. Nowak,et al.  Signal Reconstruction From Noisy Random Projections , 2006, IEEE Transactions on Information Theory.

[26]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[27]  J. Tropp Algorithms for simultaneous sparse approximation. Part II: Convex relaxation , 2006, Signal Process..

[28]  R.G. Baraniuk,et al.  Universal distributed sensing via random projections , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[29]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[30]  Leslie Ying,et al.  Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) , 2007, Magnetic resonance in medicine.

[31]  K. T. Block,et al.  Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint , 2007, Magnetic resonance in medicine.

[32]  J. C. Ye,et al.  Projection reconstruction MR imaging using FOCUSS , 2007, Magnetic resonance in medicine.

[33]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[34]  Rick Chartrand,et al.  Exact Reconstruction of Sparse Signals via Nonconvex Minimization , 2007, IEEE Signal Processing Letters.

[35]  F. Sebert,et al.  SparseSENSE: Randomly-Sampled Parallel Imaging using Compressed Sensing , 2007 .

[36]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[37]  Bo Liu,et al.  A statistical approach to SENSE regularization with arbitrary k‐space trajectories , 2008, Magnetic resonance in medicine.

[38]  Dong Liang,et al.  Accelerating sensitivity encoding using Compressed Sensing , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[40]  David Atkinson,et al.  Cartesian SENSE and k‐t SENSE reconstruction using commodity graphics hardware , 2008, Magnetic resonance in medicine.

[41]  Homotopic L0-Minimization for Highly-Undersampled MRI Reconstruction , 2008 .

[42]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[43]  Peter M. Jakob,et al.  Introduction of a Nonconvex Compressed Sensing Algorithm for MR Imaging , 2008 .

[44]  Peter Boesiger,et al.  Compressed sensing in dynamic MRI , 2008, Magnetic resonance in medicine.

[45]  Armando Manduca,et al.  Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization , 2009, IEEE Transactions on Medical Imaging.

[46]  L. Ying,et al.  Regularized sensitivity encoding (SENSE) reconstruction using bregman iterations , 2009, Magnetic resonance in medicine.

[47]  Dong Liang,et al.  Parallel MRI Acceleration Using M-FOCUSS , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[48]  Philip J. Bones,et al.  An improved approach in applying compressed sensing in parallel MR imaging , 2009 .

[49]  Leslie Ying,et al.  Accelerating SENSE using distributed compressed sensing , 2009 .