(a) L+S (b) Mod-CS (c) Priori-L+S
Figure 5: Comparison of the reconstructed images (1/7 of samples taken), together with the difference images that are ampli-
fied by a factor of 4.
ACKNOWLEDGEMENTS
This work is supported by a grant (TDSI/11-014/1A)
from the Temasek Defence Systems Institute (TDSI),
Singapore.
REFERENCES
Beck, A. and Teboulle, M. (2009). A fast iterative
shrinkage-thresholding algorithm for linear inverse
problems. SIAM Journal on Imaging Sciences,
2(1):183–202.
Cai, J.-F., Cand`es, E. J., and Shen, Z. (2010). A singular
value thresholding algorithm for matrix completion.
SIAM Journal on Optimization, 20(4):1956–1982.
Candes, E., Li, X., Ma, Y., and Wright., J. (2009). Robust
principal component analysis? Journals of the ACM,
58(3):1–37.
Ensafi, S., Lu, S., Kassim, A. A., and Tan, C. L. (2014). 3d
reconstruction of neurons in electron microscopy im-
ages. In Engineering in Medicine and Biology Society
(EMBC), 2014 36th Annual International Conference
of the IEEE, pages 6732–6735.
Feng, L., Srichai, M. B., Lim, R. P., Harrison, A., King, W.,
Adluru, G., Dibella, E. V., Sodickson, D. K., Otazo,
R., and Kim, D. (2012). Highly accelerated real-time
cardiac cine MRI using k–t SPARSE-SENSE. Mag-
netic Resonance in Medicine.
Gamper, U., Boesiger, P., and Kozerkey, S. (2008). Com-
pressed sensing in dynamic MRI. Magnetic Reso-
nance in Medicine, 59(2):365–373.
Gao, H., Rapacchi, S., Wang, D., Moriarty, J., Meehan, C.,
Sayre, J., Laub, G., Finn, P., and Hu, P. (2012). Com-
pressed 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.
Goud, S., Hu, Y., and Jacob, M. (2010). Real-time car-
diac MRI using low-rank and sparsity penalties. In
Biomedical Imaging: From Nano to Macro, 2010
IEEE International Symposium on, pages 988–991.
IEEE.
Haldar, J. P. and Liang, Z.-P. (2011). Low-rank approxima-
tions for dynamic imaging. In Biomedical Imaging:
From Nano to Macro, 2011 IEEE International Sym-
posium on, pages 1052–1055.
Hu, Y., Lingala, S. G., and Jacob, M. (2012). A
fast majorize–minimize algorithm for the recovery
of sparse and low-rank matrices. Image Processing,
IEEE Transactions on, 21(2):742–753.
Jung, H., Sung, K., Nayak, K. S., Kim, E. Y., and Ye, J. C.
(2009). k-t FOCUSS: A general compressed sensing
framework for high resolution dynamic MRI. Mag-
netic Resonance in Medicine, 61(1):103–116.
Kassim, A. A., Yan, N., and Zonoobi, D. (2008). Wavelet
packet transform basis selection method for set par-
titioning in hierarchical trees. Journal of Electronic
Imaging, 17(3):033007.
Low-rankandSparseMatrixDecompositionwitha-prioriKnowledgeforDynamic3DMRIReconstruction
87