High-resolution dynamic cardiac MRI on small animals using reconstruction based on Split Bregman methodology

IEEE Nuclear Science Symposium Conference Record

Volumn , Issue , 2011, Pages 3462-3464

  Montesinos, P ^a   Perez Juste Abascal J F ^a   Chamorro, J ^a   Chavarrias C ^b   Benito, M ^b   Vaquero, J J ^a   Desco, M ^a,b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b HOSPITAL GENERAL UNIVERSITARIO GREGORIO MARAÑÓN   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMALS; COMPRESSED SENSING; IMAGE RECONSTRUCTION; MAGNETIC RESONANCE IMAGING; SIGNAL TO NOISE RATIO;

ACCELERATION TECHNIQUE; CARDIAC MAGNETIC RESONANCE IMAGING; CARDIO-VASCULAR DISEASE; CARDIOVASCULAR APPLICATIONS; COMPUTATIONALLY EFFICIENT; DYNAMIC MAGNETIC RESONANCE IMAGING; NON LINEAR RECONSTRUCTION; SPATIO-TEMPORAL RESOLUTION;

MEDICAL IMAGING;

EID: 84858668847     PISSN: 10957863     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NSSMIC.2011.6152633     Document Type: Conference Paper

References (17)

1. B. Madore et al., "Unaliasing by Fourier-encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI," Magn Reson Med, vol. 42, pp. 813-828, 1999.
2. J. Tsao et al., "k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations," Magn Reson Med, vol. 50, pp. 1031-1042, 2003.
3. M. Lustig et al., "k-t SPARSE: High Frame Rate Dynamic MRI Exploiting Spatio-Temporal Sparsity" Proc. Int. Soc. Mag. Reson. Med., Seattle, WA, 2006, p. 2420
4. N. Aggarwal et al, "Patient-adapted reconstruction and acquisition dynamic imaging method (PARADIGM) for MRI," Inverse Probl, vol. 24, 2008
5. H. Jung et al., "k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI," Magn Reson Med, vol. 61, pp. 103-116, 2009.
6. H. Pedersen et al., "k-t PCA: temporally constrained k-t BLAST reconstruction using principal component anaJisis". Magn Reson Med. 2009 Sep;62(3):706-16.
7. D. Liang et al., "K-T ISD: Compressed sensing with iterative support detection for dynamic MRI", to appear Proc. of IEEE International Symposium on Biomedical Imaging (ISBI), 2011. accepted, 10/2010.
8. E. J. Candes et al., "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information". IEEE Trans. Inform. Theory, 52(2) 489-509. 2006.
9. M. Lustig et aI., "Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging", Magnetic Resonance in Medicine, 2007; 58(6):1182-1195
10. A.M. Bruckstein et al., "From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images", SIAM Review, Vol. 51, No. 1, Pages 34-81, February 2009.
11. E. Garduno et al., "Reconstruction from a few projections by 11- minimization of the Haar transform", Inverse Problems 27, 055006, 2011
12. S. Osher et al., "An iterative Regularization Method for Total Variation-Based Image Restoration". Multiscale Model. Simul. 4, pp. 460-489, 2005.
13. T. Goldstein et aI., "The Split Bregman Method for L1 Regularized Problems." SIAM Journal on Imaging Sciences. Volume 2, Issue 2, pp. 323-343 (2009).
14. L. He et al. "MR image reconstruction from undersampled data by using the iterative refinement procedure". PAMM, 7: 1011207- 1011208.
15. S. Lingala et al., "Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR", IEEE Transactions on Medical Imaging, 30, Issue 5, 1042 - 1054, 2011.
16. S.M Bovens et al., "Evaluation of infarcted murine heart function: comparison of prospectively triggered with self-gated MRI" NMR Biomed. 2011 Apr;24(3):307-15.
17. Y. Wang et al., "A new alternating minimization algorithm for total variation image reconstruction", SIAM J. Imaging Sci, 248-272, 2008