Abstract
Magnetic Resonance Imaging (MRI) system in recent times demands a high rate of acceleration in data acquisition to reduce the scanning time. The data acquisition rate can be accelerated to a significant order through Parallel MRI (pMRI) approach. An additional improvement in low sensing time for data acquisition can be achieved using Compressed Sensing (CS) or Compressive Sampling that enables reconstruction of a sparse signal from sub-sample (incomplete) measurements. This paper proposes an efficient pMRI scheme by combining CS with Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) to produce an MR image at high data acquisition rate. A kernel of reduced size is used within GRAPPA for estimating the unobserved encoded samples. Instead of all the unobserved samples, a certain number of the same are estimated randomly. Now, an \(l_{1}\)-minimization based CS reconstruction technique is used in which the observed and the estimated unobserved samples are taken as measurements to reconstruct the final MR images. Extensive simulation results show that a significant reduction in artifacts and thereby consequent visual improvement in the reconstructed MRIs are achieved even when a high rate of acceleration factor is used. Simulation results also demonstrate that the proposed method outperforms some state-of-art pMRI methods, both in terms of subjective and objective quality assessment for the reconstructed images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Semelka, R.C., Armao, D.M., Elias, J., Huda, W.: Imaging strategies to reduce the risk of radiation in CT studies, including selective substitution with MRI. J. Magn. Reson. Imaging 25(5), 900–909 (2007)
Wright, G.: Magnetic resonance imaging. IEEE Sig. Process. Mag. 14(1), 56–66 (1997)
Jolesz, F.A.: Future perspectives for intraoperative MRI. Neurosurg. Clin. N. Am. 16(1), 201–213 (2005)
Sasaki, M., Ehara, S., Nakasato, T., Tamakawa, Y., Kuboya, Y., Sugisawa, M., Sato, T.: MR of the shoulder with a 0.2-t permanent-magnet unit. Am. J. Roentgenol. 154(4), 777–778 (1990)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Candes, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. J. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)
Baraniuk, R.G.: Compressive sensing. IEEE Sig. Process. Mag. 24(4), 118–120 (2007)
Edelman, R.R., Warach, S.: Magnetic resonance imaging. N. Engl. J. Med. 328(10), 708–716 (1993)
Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.: Compressed sensing MRI. IEEE Sig. Process. Mag. 25(2), 72–82 (2008)
Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P., et al.: Sense: sensitivity encoding for fast MRI. Magn. Reson. Med. 42(5), 952–962 (1999)
Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A.: Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202–1210 (2002)
Weller, D., Polimeni, J., Grady, L., Wald, L., Adalsteinsson, E., Goyal, V.: Combining nonconvex compressed sensing and GRAPPA using the nullspace method. In: 18th Annual Meeting of ISMRM, p. 4880 (2010)
Weller, D.S., Polimeni, J.R., Grady, L., Wald, L.L., Adalsteinsson, E., Goyal, V.K.: Sparsity-promoting calibration for GRAPPA accelerated parallel MRI reconstruction. IEEE Trans. Med. Imaging 32(7), 1325–1335 (2013)
Xie, G., Song, Y., Shi, C., Feng, X., Zheng, H., Weng, D., Qiu, B., Liu, X.: Accelerated magnetic resonance imaging using the sparsity of multi-channel coil images. Magn. Reson. Imaging 32(2), 175–183 (2014)
Schmidt, R., Baishya, B., Ben-Eliezer, N., Seginer, A., Frydman, L.: Super-resolved parallel MRI by spatiotemporal encoding. Magn. Reson. Imaging 32(1), 60–70 (2014)
Zhou, J., Li, J., Gombaniro, J.C.: Combining sense and compressed sensing MRI with a fast iterative contourlet thresholding algorithm. In: 12th IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1123–1127 (2015)
Chun, I.Y., Adcock, B., Talavage, T.M.: Efficient compressed sensing sense pMRI reconstruction with joint sparsity promotion. IEEE Trans. Med. Imaging 35(1), 354–368 (2016)
Fischer, A., Seiberlich, N., Blaimer, M., Jakob, P., Breuer, F., Griswold, M.: A combination of nonconvex compressed sensing and GRAPPA (CS-GRAPPA). In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 17, p. 2813 (2009)
Miao, J., Guo, W., Narayan, S., Wilson, D.L.: A simple application of compressed sensing to further accelerate partially parallel imaging. Magn. Reson. Imaging 31(1), 75–85 (2013)
Chang, Y., King, K.F., Liang, D., Ying, L.: Combining compressed sensing and nonlinear GRAPPA for highly accelerated parallel MRI. In: Proceedings of the International Society for Magnetic Resonance in Medicine, p. 2219 (2012)
Yang, Z., Zhang, C., Deng, J., Lu, W.: Orthonormal expansion l1-minimization algorithms for compressed sensing. IEEE Trans. Sig. Process. 59(12), 6285–6290 (2011)
Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)
Bredies, K., Lorenz, D.A.: Linear convergence of iterative soft-thresholding. J. Fourier Anal. Appl. 14(5–6), 813–837 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Islam, S.R., Maity, S., Maity, S.P., Ray, A.K. (2017). MR Imaging via Reduced Generalized Autocalibrating Partially Parallel Acquisition Compressed Sensing. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-68124-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68123-8
Online ISBN: 978-3-319-68124-5
eBook Packages: Computer ScienceComputer Science (R0)