Correlation imaging for multiscan MRI with parallel data acquisition

A new approach to high‐speed magnetic resonance imaging (MRI) that uses all the data acquired in a multiscan imaging session is presented. This approach accelerates MRI data acquisition by statistically estimating correlation functions from images with different contrast and/or resolution. In multiscan MRI with parallel data acquisition, the estimation of correlation functions is dynamically improved as imaging proceeds. This allows imaging acceleration factors to be increased in subsequent scans, thereby reducing the total time of a multiscan MRI protocol. Furthermore, the correlation function estimates bring information about both coil sensitivity and anatomical structure into image reconstruction, thereby offering the ability to speed up MRI beyond the parallel imaging acceleration limit posed by a coil array alone. In this study, the feasibility of correlation imaging is demonstrated experimentally using brain and spine imaging protocols. The ability of correlation imaging to achieve an aggregate acceleration factor in excess of the number of coil elements in the phase encoding direction is also demonstrated. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.

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

[2]  Feng Huang,et al.  k‐t GRAPPA: A k‐space implementation for dynamic MRI with high reduction factor , 2005, Magnetic resonance in medicine.

[3]  X Zhang,et al.  New strategy for reconstructing partial‐Fourier imaging data in functional MRI , 2001, Magnetic resonance in medicine.

[4]  J. Hennig,et al.  Fast functional brain imaging using constrained reconstruction based on regularization using arbitrary projections , 2009, Magnetic resonance in medicine.

[5]  Suyash P. Awate,et al.  Temporally constrained reconstruction of dynamic cardiac perfusion MRI , 2007, Magnetic resonance in medicine.

[6]  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.

[7]  Zhi-Pei Liang,et al.  Parallel MRI Using Phased Array Coils: Multichannel Sampling Theory Meeting Spin Physics , 2010 .

[8]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[9]  Norbert Schuff,et al.  Improved Model-Based Magnetic Resonance Spectroscopic Imaging , 2007, IEEE Transactions on Medical Imaging.

[10]  Jeff W. M. Bulte,et al.  Magnetic resonance neuroimaging : methods and protocols , 2011 .

[11]  Yoram Bresler,et al.  Distortion-optimal self-calibrating parallel MRI by blind interpolation in subsampled filter banks , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[13]  Zhi-Pei Liang,et al.  Parallel MRI Using Phased Array Coils , 2010, IEEE Signal Processing Magazine.

[14]  A G Webb,et al.  Applications of reduced‐encoding MR imaging with generalized‐series reconstruction (RIGR) , 1993, Journal of magnetic resonance imaging : JMRI.

[15]  F-H. Lin Prior-regularized GRAPPA Reconstruction , 2005 .

[16]  L. Ying,et al.  Parallel Mri Reconstruction: A Filter-Bank Approach , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

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

[18]  Christina Triantafyllou,et al.  A 128‐channel receive‐only cardiac coil for highly accelerated cardiac MRI at 3 Tesla , 2008, Magnetic resonance in medicine.

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

[20]  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.

[21]  Kenneth A Buckwalter,et al.  Optimizing imaging techniques in the postoperative patient. , 2007, Seminars in musculoskeletal radiology.

[22]  Jeffrey Tsao,et al.  Ultrafast imaging: Principles, pitfalls, solutions, and applications , 2010, Journal of magnetic resonance imaging : JMRI.

[23]  Kay Nehrke,et al.  k‐t PCA: Temporally constrained k‐t BLAST reconstruction using principal component analysis , 2009, Magnetic resonance in medicine.

[24]  Kyung K Peck,et al.  Functional MRI in the Brain Tumor Patient , 2004, Topics in magnetic resonance imaging : TMRI.

[25]  L. Wald,et al.  32‐channel 3 Tesla receive‐only phased‐array head coil with soccer‐ball element geometry , 2006, Magnetic resonance in medicine.

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

[27]  X Hu,et al.  Reduction of field of view for dynamic imaging , 1994, Magnetic resonance in medicine.

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

[29]  P. Roemer,et al.  The NMR phased array , 1990, Magnetic resonance in medicine.

[30]  S Saini,et al.  Fast MR imaging: technical strategies. , 1995, AJR. American journal of roentgenology.

[31]  S. T. Nichols,et al.  Quantitative evaluation of several partial fourier reconstruction algorithms used in mri , 1993, Magnetic resonance in medicine.

[32]  J. Selvanayagam,et al.  High field cardiac magnetic resonance imaging--current and future perspectives. , 2010, Heart, lung & circulation.

[33]  D. Noll,et al.  Homodyne detection in magnetic resonance imaging. , 1991, IEEE transactions on medical imaging.

[34]  Z P Liang,et al.  A generalized series approach to MR spectroscopic imaging. , 1991, IEEE transactions on medical imaging.

[35]  Yoram Bresler,et al.  AUTO-CALIBRATED PARALLEL IMAGING USING A DISTORTION-OPTIMAL FILTER-BANK , 2010 .

[36]  J Velikina,et al.  Highly constrained backprojection for time‐resolved MRI , 2006, Magnetic resonance in medicine.

[37]  Koichi Oshio,et al.  Phase errors in multi‐shot echo planar imaging , 1994, Magnetic resonance in medicine.

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

[39]  Zhi-Pei Liang,et al.  Superresolution reconstruction through object modeling and parameter estimation , 1989, IEEE Trans. Acoust. Speech Signal Process..

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

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

[42]  Hong Jiang,et al.  Dynamic imaging by model estimation , 1997 .

[43]  X Hu,et al.  On the “keyhole” technique , 1994, Journal of magnetic resonance imaging : JMRI.

[44]  Matti S Hämäläinen,et al.  Dynamic magnetic resonance inverse imaging of human brain function , 2006, Magnetic resonance in medicine.

[45]  G. Glover Overview of functional magnetic resonance imaging. , 2011, Neurosurgery clinics of North America.

[46]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[47]  Zhi-Pei Liang,et al.  Maximum cross-entropy generalized series reconstruction , 1999, Int. J. Imaging Syst. Technol..

[48]  José Millet-Roig,et al.  Noquist: Reduced field‐of‐view imaging by direct Fourier inversion , 2004, Magnetic resonance in medicine.

[49]  Jong Chul Ye,et al.  Improved k–t BLAST and k–t SENSE using FOCUSS , 2007, Physics in medicine and biology.

[50]  Jeff W. M. Bulte,et al.  Magnetic Resonance Neuroimaging , 2011, Methods in Molecular Biology.

[51]  Diego R Martin,et al.  Abdominal Magnetic Resonance Imaging at 3.0 T: Problem or a Promise for the Future? , 2005, Topics in magnetic resonance imaging : TMRI.