Variable density band-based undersampling scheme for Compressed Sensing MRI reconstruction

Nicolás F. Sánchez, Juan José Rojas, Juan Carlos Cabral, Christian E. Schaerer


Magnetic resonance of brain images have an implicit sparsity in an appropriate transformdomain [1]. Based on compressed sensing theory (CS), images with a sparse representation can berecovered from undersampledk-space data. However, to have an efficient reconstruction [1], theundersampling scheme should be incoherent with respect to the sparsifying transform [2]. [...]

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Lustig, M., Donoho, D. L., Santos, J. M. and Pauly,J. M. Compressed sensing MRI. IEEEsignal processing magazine, 25(2), 72-82, March 2008.

Lustig, M., Donoho, D. and Pauly J. M.Sparse MRI: The application of compressed sensing forrapid MR imaging. Magnetic Resonance in Medicine: An Official Journal of the InternationalSociety for Magnetic Resonance in Medicine 58.6: pg 1182-1195, 2007.

Donoho, David L.Compressed sensing. IEEE Transactions on information theory 52.4: 1289-1306, 2006.

Paul, J.S. and Mathew, R.S.Regularized Image Reconstruction in Parallel MRI with MAT-LAB. CRC Press, 2019.

Adcock, B., Hansen, A. C., Poon, C., and Roman, B.Breaking the coherence barrier: A newtheory for compressed sensing. Forum of Mathematics, Sigma. Vol. 5. Cambridge UniversityPress, 2017.

Krahmer, Felix, and Rachel Ward.Stable and robust sampling strategies for compressive imag-ing. IEEE transactions on image processing 23.2 (2013): 612-622.


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