k-t SLR: MATLAB package

k-t SLR: Accelerated dynamic MRI using low rank and sparse penalties [1,2]

  1. 1.CPU version: MATLAB codes

  1. 2.GPU version: MATLAB-JACKET codes (under construction).

3. The above codes considers the minimization of the cost:

  1. 4.Please refer to Readme.pdf for the use of the codes.

  1. 5.The algorithm is demonstrated based on retrospective under-sampling of a numerical PINCAT phantom and an in-vivo fully sampled data set, both in the context of cardiac perfusion MRI. Below are some screen shots of the reconstructions along with the image time series:

  1. 6.For details, please refer the below papers:    

    [1] S.G.Lingala, Y.Hu, E.DiBella and M.Jacob, “Accelerated dynamic MRI exploiting sparsity and low rank structure: k-t SLR”, IEEE Transactions on Medical Imaging (IEEE-TMI), pp:1042-1054, vol.30, May 2011.

    [2] S.G.Lingala, Y.Hu, E.DiBella, and M.Jacob, “Accelerated myocardial perfusion imaging using improved k-t SLR”, IEEE International Symposia on Biomedical Imaging (IEEE-ISBI), 2011.

  1. 7.Please credit the above papers if this code is used in an abstract or a paper.

  1. 8.For any questions or any bug reports, please contact sajangoud-lingala@uiowa.edu or mathews-jacob@uiowa.edu.

  1. 9.This work is supported by NSF Awards CCF-0844812, CCF-1116067, and                  

    NIH 1R211HL109710-01A1.

non-convex Schatten p-norm; p < 1

convex nuclear norm; p = 1

Spatio-temporal total variation norm


signal matrix

Fourier sampling


Measured k-t data




are the singular values of

,    are the gradients and step sizes respectively