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Matlab code for reproducing 2019 IEEE Tr. Comp. Im. paper "Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model" by Claire Lin

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reproduce-l-s-dynamic-mri

https://github.com/JeffFessler/reproduce-l-s-dynamic-mri

Matlab code for reproducing the results in the paper: "Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model," IEEE Trans. on Computational Imaging, 5(1):17-26, 2019, by Claire Lin (mailto::yililin@umich.edu) and Jeffrey A. Fessler, EECS Department, University of Michigan

See [http://doi.org/10.1109/TCI.2018.2882089]

To download: git clone https://github.com/JeffFessler/reproduce-l-s-dynamic-mri.git

This code requires the Matlab version of the Michigan Image Reconstruction Toolbox (MIRT) from [http://web.eecs.umich.edu/~fessler/code/index.html]

Please set up MIRT before running the examples.

The authors also thank Ricardo Otazo for sharing the cardiac MRI data.

Before running the code you must populate the data/ directory with five .mat files from [http://web.eecs.umich.edu/~fessler/irt/reproduce/19/lin-19-edp/]

The data is stored separately to keep the git repo light.

The following scripts are in the example folder:

  • example_PINCAT_phantom.m Figs. 4, 5
  • example_abdomen_dce_ga.m Supplement Fig. 6 (non-Cartesian)
  • example_cardiac_cine.m Figs. 1, 3
  • example_cardiac_perf.m Figs. 1, 2

Julia version

There is a Julia language version in the Examples part of JuliaImageRecon.

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Matlab code for reproducing 2019 IEEE Tr. Comp. Im. paper "Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model" by Claire Lin

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