Hybrid regularization for MRI reconstruction with static field inhomogeneity correction

Ryan Compton; Stanley Osher; Louis-S. Bouchard

doi:10.3934/ipi.2013.7.1215

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November 2013, 7(4): 1215-1233. doi: 10.3934/ipi.2013.7.1215

Hybrid regularization for MRI reconstruction with static field inhomogeneity correction

Ryan Compton ^1, , Stanley Osher ^1, and Louis-S. Bouchard ^2,

1.	Department of Mathematics, University of California, Los Angeles, CA 90095, United States
2.	Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, 90095, United States

Received June 2012 Revised January 2013 Published November 2013

Abstract
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Rapid acquisition of magnetic resonance (MR) images via reconstruction from undersampled $k$-space data has the potential to greatly decrease MRI scan time on existing medical hardware. To this end, iterative image reconstruction based on the technique of compressed sensing has become the method choice for many researchers [1]. However, while conventional compressed sensing relies on random measurements from a discrete Fourier transform, actual MR scans often suffer from off-resonance effects and thus generate data by way of a non-Fourier operator [2]. Correcting for these effects requires that one employs more sophisticated image reconstruction methods and introduces computational bottlenecks that are not encountered in standard compressed sensing.
In this work, we demonstrate how one may accelerate the convergence of algorithms for solving the image reconstruction problem, \begin{equation}\label{eq:caneq} (1) \underset{\rho}{argmin} J(\rho) subject to A \rho = s \end{equation} by opting for a regularization of the form: \begin{equation}\label{eq:hybrid} (2) J(\rho) = | \nabla \rho| + \nu | F \rho | \end{equation} when $F$ is a tight frame and $A$ is only approximately a Fourier transform. In our experiments, reconstructing field-corrected MR images with the hybrid regularization of 2 provides a speedup of roughly one order of magnitude when compared with an approach based solely on total-variation and may produce higher quality images than an approach based solely on tight frames.

Keywords: Optimization and variational techniques, Image processing, biomedical imaging and signal processing..

Mathematics Subject Classification: 65K10, 94A08, 92C5.

Citation: Ryan Compton, Stanley Osher, Louis-S. Bouchard. Hybrid regularization for MRI reconstruction with static field inhomogeneity correction. Inverse Problems & Imaging, 2013, 7 (4) : 1215-1233. doi: 10.3934/ipi.2013.7.1215

References:

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[2]	J. Fessler, S. Lee, V. T. Olafsson, H. R. Shi and C. D. Noll, Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity, IEEE Transactions on Signal Processing, 53 (2005), 3393-3402. doi: 10.1109/TSP.2005.853152. Google Scholar
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show all references

References:

[1]	M. Lustig, D. Donoho and J. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic Resonance in Medicine, 58 (2007), 1182-1195. doi: 10.1002/mrm.21391. Google Scholar
[2]	J. Fessler, S. Lee, V. T. Olafsson, H. R. Shi and C. D. Noll, Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity, IEEE Transactions on Signal Processing, 53 (2005), 3393-3402. doi: 10.1109/TSP.2005.853152. Google Scholar
[3]	P. Mansfield, NMR Imaging in Biomedicine (Advances in Magnetic Resonance), Academic Press, 1982. Google Scholar
[4]	P. J. Prado, Single sided imaging sensor, Magnetic Resonance Imaging, 21 (2003), 397-400. Google Scholar
[5]	J. Perlo, F. Casanova and B. Blümich, 3D imaging with a single-sided sensor: An open tomograph, Journal of Magnetic Resonance, 166 (2004), 228-235. doi: 10.1016/j.jmr.2003.10.018. Google Scholar
[6]	J. Paulsen, J. Franck, V. Demas and L.-S. Bouchard, Least squares magnetic-field optimization for portable nuclear magnetic resonance magnet design, IEEE Transactions On, 44 (2008), 4582-4590. doi: 10.1109/TMAG.2008.2001697. Google Scholar
[7]	B. Blumich, P. Blumer, G. Eidman, A. Guthausen, R. Haken, U. Schmitz, K. Saito and G. Zimmer, The NMR-mouse: Construction, excitation, and applications, Magnetic Resonance Imaging, 16 (1998), 479-484. doi: 10.1016/S0730-725X(98)00069-1. Google Scholar
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[9]	J. M. Franck, V. Demas, R. W. Martin, L.-S. Bouchard and A. Pines, Shimmed matching pulses: Simultaneous control of rf and static gradients for inhomogeneity correction, The Journal of Chemical Physics, 131 (2009), 234506. doi: 10.1063/1.3243850. Google Scholar
[10]	D. Topgaard, R. W. Martin, D. Sakellariou, C. A. Meriles and A. Pines, "Shim pulses" for NMR spectroscopy and imaging, Proceedings of the National Academy of Sciences of the United States of America, 101 (2004), 17576-17581. doi: 10.1073/pnas.0408296102. Google Scholar
[11]	C. A. Meriles, D. Sakellariou, H. Heise and A. Pines, Approach to High-Resolution ex Situ NMR spectroscopy, Science, 293 (2001), 82-85. doi: 10.1126/science.1061498. Google Scholar
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[13]	N. Kelso, S.-K. Lee, L.-S. Bouchard, V. Demas, M. Mück, A. Pines and J. Clarke, Distortion-free magnetic resonance imaging in the zero-field limit, Journal of magnetic resonance (San Diego, Calif. : 1997), 200 (2009), 285-290. doi: 10.1016/j.jmr.2009.07.016. Google Scholar
[14]	L.-S. Bouchard, Unidirectional magnetic-field gradients and geometric-phase errors during Fourier encoding using orthogonal ac fields, Physical Review B, 74 (2006), 1-11. doi: 10.1103/PhysRevB.74.054103. Google Scholar
[15]	L.-S. Bouchard and M. Anwar, Synthesis of matched magnetic fields for controlled spin precession, Physical Review B, 76 (2007), 1-10. doi: 10.1103/PhysRevB.76.014430. Google Scholar
[16]	E. M. Haacke and R. Brown, Magnetic Resonance Imaging Physical Principles and Sequence Design,, 1999., (). Google Scholar
[17]	J. Romberg, Imaging via compressive sampling, IEEE Signal Processing Magazine, 25 (2008), 14-20. doi: 10.1109/MSP.2007.914729. Google Scholar
[18]	M. Guerquin-Kern, M. Häberlin, K. P. Pruessmann and M. Unser, A fast wavelet-based reconstruction method for magnetic resonance imaging, IEEE transactions on medical imaging, 30 (2011), 1649-1460. doi: 10.1109/TMI.2011.2140121. Google Scholar
[19]	J. Aelterman, H. Q. Luong, B. Goossens, A. Pižurica and W. Philips, Augmented Lagrangian based reconstruction of non-uniformly sub-Nyquist sampled MRI data, Signal Processing, 91 (2011), 2731-2742. doi: 10.1016/j.sigpro.2011.04.033. Google Scholar
[20]	L. Chaâri, J.-C. Pesquet, A. Benazza-Benyahia and P. Ciuciu, A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging, Medical Image Analysis, 15 (2011), 185-201. Google Scholar
[21]	Q. Yang, M. Smith and J. Wang, Magnetic susceptibility effects in high field MRI, Biological Magnetic Resonance, 26 (2006), 249-284. doi: 10.1007/978-0-387-49648-1_9. Google Scholar
[22]	T.-k. Truong, D. W. Chakeres and P. Schmalbrock, Effects of B 0 and B 1 Inhomogeneity in Ultra-High Field MRI, Proc. Intl. Soc. Mag. Reson. Med., 11 (2004), 2170. Google Scholar
[23]	J. Reichenbach, R. Venkatesan, D. Yablonskiy, M. R. Thompson and E. M. Haacke, Theory and application of static field inhomogeneity effects in gradient- echo imaging, Journal of Magnetic Resonance Imaging, 7 (1997), 266-279. doi: 10.1002/jmri.1880070203. Google Scholar
[24]	M. A. Moerland, R. Beersma, R. Bhagwandien, H. K. Wijrdeman and C. J. Bakker, Analysis and correction of geometric distortions in 1.5 T magnetic resonance images for use in radiotherapy treatment planning, Physics in Medicine and Biology, 40 (1995), 1651-1654. Google Scholar
[25]	A. Neufeld, Y. Assaf, M. Graif, T. Hendler and G. Navon, Susceptibility-matched envelope for the correction of EPI artifacts, Magnetic Resonance Imaging, 23 (2005), 947-951. doi: 10.1016/j.mri.2005.07.011. Google Scholar
[26]	R. C. McKinstry and D. Y. Jarrett, Magnetic susceptibility artifacts on MRI: A hairy situation, American Journal of Roentgenology, 182 (2004), 532-535. doi: 10.2214/ajr.182.2.1820532. Google Scholar
[27]	I. C. Duncan, The "Aura'' sign: An unusual cultural variant affecting MR imaging, American Journal of Roentgenology, 177 (2001), 1485-1489. doi: 10.2214/ajr.177.6.1771487. Google Scholar
[28]	F. Baselice, G. Ferraioli and A. Shabou, Field map reconstruction in magnetic resonance imaging using Bayesian estimation, Sensors, 10 (2010), 266-279. doi: 10.3390/s100100266. Google Scholar
[29]	B. Kressler, T. Liu, P. Spincemaille, Q. Jiang and Y. Wang, Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps, IEEE Transactions on Medical Imaging, 29 (2010), 273-281. Google Scholar
[30]	J. A. Fessler, S. Member and B. P. Sutton, Nonuniform fast Fourier transforms using min-max interpolation, IEEE Trans. Signal Process, 51 (2003), 560-574. doi: 10.1109/TSP.2002.807005. Google Scholar
[31]	J. Fessler, Model-based image reconstruction for MRI, IEEE Signal Processing Magazine, 27 (2010), 81-89. doi: 10.1109/MSP.2010.936726. Google Scholar
[32]	B. P. Sutton, S. Member, D. C. Noll, J. A. Fessler and S. Member, Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities, IEEE Transactions on Medical Imaging, 22 (2003), 178-188. doi: 10.1109/TMI.2002.808360. Google Scholar
[33]	K. T. Block, M. Uecker and J. Frahm, Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint, Magnetic Resonance in Medicine, 57 (2007), 1086-1098. doi: 10.1002/mrm.21236. Google Scholar
[34]	S. Ramani and J. A. Fessler, An accelerated iterative reweighted least squares algorithm for compressed sensing MRI, IEEE ISBI, (2010), 257-260. doi: 10.1109/ISBI.2010.5490364. Google Scholar
[35]	B. J. Wilm, C. Barmet, M. Pavan and K. P. Pruessmann, Higher order reconstruction for MRI in the presence of spatiotemporal field perturbations, Magnetic Resonance in Medicine, 65 (2011), 1690-1701. doi: 10.1002/mrm.22767. Google Scholar
[36]	W. Chen, C. T. Sica and C. H. Meyer, Fast conjugate phase image reconstruction based on a Chebyshev approximation to correct for B0 field inhomogeneity and concomitant gradients, Magnetic Resonance in Medicine, 60 (2008), 1104-1111. doi: 10.1002/mrm.21703. Google Scholar
[37]	H. Schomberg, Off-resonance correction of MR images, IEEE Transactions on Medical Imaging, 18 (1999), 481-495. doi: 10.1109/42.781014. Google Scholar
[38]	D. C. Noll, J. A. Fessler and B. P. Sutton, Conjugate phase MRI reconstruction with spatially variant sample density correction, IEEE Transactions on Medical Imaging, 24 (2005), 325-336. doi: 10.1109/TMI.2004.842452. Google Scholar
[39]	T. Goldstein and S. Osher, The split bregman method for L1-Regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343. doi: 10.1137/080725891. Google Scholar
[40]	J. F. Cai, S. Osher and Z. Shen, Split Bregman methods and frame based image restoration, Multiscale Modeling & Simulation, 8 (2010), 337-369. doi: 10.1137/090753504. Google Scholar
[41]	J. Fessler, S. Lee, V. Olafsson, H. Shi and D. Noll, Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity, IEEE Transactions on Signal Processing, 53 (2005), 3393-3402. doi: 10.1109/TSP.2005.853152. Google Scholar
[42]	L. Greengard and J.-Y. Lee, Accelerating the nonuniform fast fourier transform, SIAM Review, 46 (2004), 443-454. doi: 10.1137/S003614450343200X. Google Scholar
[43]	P. Irarrazabal, C. H. Meyer, D. G. Nishimura and A. Macovski, Inhomogeneity correction using an estimated linear field map, Magnetic Resonance in Medicine, 35 (1996), 278-282. doi: 10.1002/mrm.1910350221. Google Scholar
[44]	L.-C. Man, J. M. Pauly and A. Macovski, Multifrequency interpolation for fast off-resonance correction, Magnetic Resonance in Medicine, 37 (1997), 785-792. doi: 10.1002/mrm.1910370523. Google Scholar
[45]	D. Noll, Reconstruction Techniques for Magnetic Reasonance Imaging, PhD thesis, Stanford University, 1991. Google Scholar
[46]	H. Moriguchi, B. M. Dale, J. S. Lewin and J. L. Duerk, Block regional off-resonance correction (BRORC): A fast and effective deblurring method for spiral imaging, Magnetic Resonance in Medicine, 50 (2003), 643-648. doi: 10.1002/mrm.10570. Google Scholar
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Hybrid regularization for MRI reconstruction with static field inhomogeneity correction

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