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100 Knee MRI Cases

Reproducibility data for two seminal machine learning MR image reconstruction studies.

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We are making available training and test data set used in our related publications on machine learning for reconstruction of accelerated MRI data cited below.

Knee MR images; from left: coronal spin density weighted with fat suppression, coronal spin density weighted without fat suppression, axial T2 weighted with fat suppression, sagittal T2 weighted with fat suppression, and sagittal spin density weighted.
Examples of knee images acquired with different sequences: (from left) coronal spin density weighted with fat suppression, coronal spin density weighted without fat suppression, axial T2 weighted with fat suppression, sagittal T2 weighted with fat suppression, and sagittal spin density weighted.

The 100 cases include 20 cases from each of the following five sequences:

  • coronal spin density weighted with fat suppression
  • coronal spin density weighted without fat suppression
  • axial T2 weighted with fat suppression
  • sagittal T2 weighted with fat suppression
  • sagittal spin density weighted

The data are organized slice by slice, and consist of two files:

  • rawdata*.mat:
    The rawdata and some additional metadata variables (the acquisition includes phase oversampling and a rectangular field of view, see example matlab reconstruction script)
  • espirit*.mat:
    ESPIRiT coil sensitivity maps and a reference reconstruction

In addition, in separate folders associated with the data from each sequence, we include the parallel-imaging subsampling used to conduct some of the experiments in the related publications.

Related Publications

Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, Knoll F.
Learning a variational network for reconstruction of accelerated MRI data.
Magn Reson Med. 2017 Aug;78(2):565-576. doi: 10.1002/mrm.26977

Knoll F, Hammernik K, Kobler E, Pock T, Recht MP, Sodickson DK.
Assessment of the generalization of learned image reconstruction and the potential for transfer learning.
Magn Reson Med. 2019 Jan;81(1):116-128. doi: 10.1002/mrm.27355

Please cite these works if you are using the 100 knee MRI cases data set in your research.

References

Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M.
ESPIRiT–an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA.
Magn Reson Med. 2014 Mar;71(3):990-1001. doi: 10.1002/mrm.24751

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Contact

Questions about this resource may be directed to Florian Knoll, PhD.

Learn More about This Research

This is a joint project with Thomas Pock’s vision, learning, and optimization group at the Institute of Computer Graphics and Vision at Graz University of Technology.

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