A simple application of compressed sensing to further accelerate partially parallel imaging.

Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, "CS+GRAPPA," to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.

[1]  Steve B. Jiang,et al.  GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation. , 2010, Medical physics.

[2]  Robin M Heidemann,et al.  SMASH, SENSE, PILS, GRAPPA: How to Choose the Optimal Method , 2004, Topics in magnetic resonance imaging : TMRI.

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

[4]  Donglai Huo,et al.  Quantitative image quality evaluation of MR images using perceptual difference models. , 2008, Medical physics.

[5]  Zhi-Pei Liang,et al.  Spatiotemporal Imaging with Partially Separable Functions , 2007, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging.

[6]  Chen Zhao,et al.  Compressed sensing parallel Magnetic Resonance Imaging , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[8]  Armando Manduca,et al.  Practical nonconvex Compressive Sensing reconstruction of highly-accelerated 3D parallel MR angiograms , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  L. Ying,et al.  Accelerating SENSE using compressed sensing , 2009, Magnetic resonance in medicine.

[10]  K. F. King,et al.  Adaptive regularization in compressed sensing using the discrepancy principle , 2009 .

[11]  Wei Lin,et al.  A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: Self‐feeding sparse SENSE , 2010, Magnetic resonance in medicine.

[12]  M. Lustig,et al.  SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space , 2010, Magnetic resonance in medicine.

[13]  Andriyan Bayu Suksmono.  Compressive Sampling with Known Spectral Energy Density , 2009, ArXiv.

[14]  Weihong Guo,et al.  A Local Mutual Information Guided Denoising Technique and Its Application to Self-calibrated Partially Parallel Imaging , 2008, MICCAI.

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

[16]  Graeme P. Penney,et al.  2007 4TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING : MACRO TO NANO, VOLS 1-3 , 2007 .

[17]  Junfeng Yang,et al.  A Fast TVL1-L2 Minimization Algorithm for Signal Reconstruction from Partial Fourier Data , 2008 .

[18]  Steve B. Jiang,et al.  GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation , 2010 .

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

[20]  Rick Chartrand,et al.  Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[22]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[23]  Zhi-Pei Liang,et al.  SPATIOTEMPORAL IMAGINGWITH PARTIALLY SEPARABLE FUNCTIONS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[24]  Wotao Yin,et al.  EdgeCS: edge guided compressive sensing reconstruction , 2010, Visual Communications and Image Processing.

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

[26]  Xiaoping P. Hu,et al.  Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction , 2008, Magnetic resonance in medicine.

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