nach oben

Journal of Scientific Computing

Erschienen in:

04.06.2016

A Two-Stage Low Rank Approach for Calibrationless Dynamic Parallel Magnetic Resonance Image Reconstruction

verfasst von: Likun Hou, Hao Gao, Xiaoqun Zhang

Erschienen in: Journal of Scientific Computing | Ausgabe 3/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Parallel magnetic resonance imaging (MRI) is an imaging technique by acquiring a reduced amount of data in Fourier domain with multiple receiver coils. To recover the underlying imaging object, one often needs the explicit knowledge of coil sensitivity maps, or some additional fully acquired data blocks called the auto-calibration signals (ACS). In this paper, we show that by exploiting the between-frame redundancy of dynamic parallel MRI data, it is possible to achieve simultaneous coil sensitivity map estimation and image sequence reconstruction. Specially, we introduce a novel two-stage approach for dynamic parallel MRI reconstruction without pre-calibrating the coil sensitivity maps nor additionally acquiring any fully sampled ACS. Numerical experiments demonstrate that, the performance of the proposed approach is better than other state-of-the-art approaches for calibrationless dynamic parallel MRI reconstruction.

Vorheriger Artikel Minimizing Eigenvalues for Inhomogeneous Rods and Plates

Nächster Artikel Application of WENO-Positivity-Preserving Schemes to Highly Under-Expanded Jets

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Data source - http://challenge.ismrm.org/node/53

One might intuitively think that the the data frame with maximum number of samples in the auto-calibration region should be the best choice, but that is not true. For instance, in the second experiment of Sect. 5, the data frame with maximum number of samples in the auto-calibration region is frame 13, while exhaustive enumeration suggests frame 2 is the best choice, and the later one leads to significantly lower reconstruction error compared with the former one.

Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)MathSciNetCrossRefMATH

Cai, J.-F., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010)MathSciNetCrossRefMATH

Chandrasekaran, V., Sanghavi, S., Parrilo, P., Willsky, A.S., et al.: Sparse and low-rank matrix decompositions. In: 47th Annual Allerton Conference on Communication, Control, and Computing, 2009. Allerton 2009, pp. 962–967. IEEE (2009)

Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward–backward splitting. Multiscale Model.Simul. 4(4), 1168–1200 (2005)MathSciNetCrossRefMATH

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)MathSciNetCrossRefMATH

Ding, Q., Zan, Y., Huang, Q., Zhang, X.: Dynamic SPECT reconstruction from few projections: a sparsity enforced matrix factorization approach. Inverse Probl 31(2), 025004 (2015)MathSciNetCrossRefMATH

Gao, H., Lin, H., Ahn, C.B., Nalcioglu, O.: PRISM: A divide-and-conquer low-rank and sparse decomposition model for dynamic MRI. UCLA CAM Report (2011)

Gao, H., Rapacchi, S., Wang, D., Moriarty, J., Meehan, C., Sayre, J., Laub, G., Finn, P., Hu, P.: Compressed sensing using prior rank, intensity and sparsity model (PRISM): applications in cardiac cine MRI. In: Proceedings of the 20th Annual Meeting of ISMRM, Melbourne, Australia, pp. 2242 (2012)

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)CrossRef

10.

Huang, F., Akao, J., Vijayakumar, S., Duensing, G.R., Limkeman, M.: k–t GRAPPA: a k-space implementation for dynamic MRI with high reduction factor. Magn. Reson. Med. 54(5), 1172–1184 (2005)CrossRef

11.

Kreutz-Delgado, K.: The complex gradient operator and the cr-calculus. University of California, San Diego, version ucsd-ece275cg-s2009v1. 0, 25 june 2009. arXiv:0906.4835

12.

Liang, D., DiBella, E.V., Chen, R.-R., Ying, L.: k-t ISD: Dynamic cardiac MR imaging using compressed sensing with iterative support detection. Magn. Reson. Med. 68(1), 41–53 (2012)CrossRef

13.

Liang, D., Liu, B., Wang, J., Ying, L.: Accelerating SENSE using compressed sensing. Magn. Reson. Med. 62(6), 1574–1584 (2009)CrossRef

14.

Liang, Z.-P.: Spatiotemporal imaging with partially separable functions. In: 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, 2007. NFSI-ICFBI 2007, pp. 181–182. IEEE (2007)

15.

Lustig, M., Donoho, D., Pauly, J.M.: Sparse mri: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med 58(6), 1182–1195 (2007)CrossRef

16.

Lustig, M., Pauly, J.M.: SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn. Reson. Med. 64(2), 457–471 (2010)

17.

Otazo, R., Candès, E., Sodickson, D.K.: Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn. Reson. Med. 73(3), 1125–1136 (2015)CrossRef

18.

Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962 (1999)CrossRef

19.

Shen, Z., Toh, K.-C., Yun, S.: An accelerated proximal gradient algorithm for frame-based image restoration via the balanced approach. SIAM J. Imaging Sci. 4(2), 573–596 (2011)MathSciNetCrossRefMATH

20.

Shin, P.J., Larson, P.E., Ohliger, M.A., Elad, M., Pauly, J.M., Vigneron, D.B., Lustig, M.: Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion. Magn. Reson. Med. 72(4), 959–970 (2014)CrossRef

21.

Sodickson, D.K., Manning, W.J.: Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn. Reson. Med. 38(4), 591–603 (1997)CrossRef

22.

Uecker, M., Lai, P., Murphy, M.J., Virtue, P., Elad, M., Pauly, J.M., Vasanawala, S.S., Lustig, M.: ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn. Reson. Med. 71(3), 990–1001 (2014)CrossRef

23.

Wang, J., Kluge, T., Nittka, M., Jellus, V., Kuhn, B., Kiefer, B.: Using reference lines to improve the SNR of mSENSE. In: Proceedings of the 10th Annual Meeting of ISMRM, Honolulu, p. 2392 (2002)

24.

Zhao, T., Hu, X.: Iterative GRAPPA (iGRAPPA) for improved parallel imaging reconstruction. Magn. Reson. Med. 59(4), 903–907 (2008)CrossRef

Titel: A Two-Stage Low Rank Approach for Calibrationless Dynamic Parallel Magnetic Resonance Image Reconstruction
verfasst von: Likun Hou
Hao Gao
Xiaoqun Zhang
Publikationsdatum: 04.06.2016
Verlag: Springer US
Erschienen in: Journal of Scientific Computing / Ausgabe 3/2016
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI: https://doi.org/10.1007/s10915-016-0225-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3/2016

A Low Complexity Algorithm for Non-Monotonically Evolving Fronts

An h-Adaptive RKDG Method for the Vlasov–Poisson System

Application of WENO-Positivity-Preserving Schemes to Highly Under-Expanded Jets

A Superconvergent Discontinuous Galerkin Method for Kirchhoff Plates: Error Estimates, Hybridization and Postprocessing

On the Numerical Solution of the Stationary Power-Law Stokes Equations: A Penalty Finite Element Approach

A Priori and a Posteriori Error Analyses of an Augmented HDG Method for a Class of Quasi-Newtonian Stokes Flows