DeliCS Preprocessed Data

This data set consists of pre-processed MRI data as presented in Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction [1]. By downloading this dataset you will be able to re-create the figures presented in the paper using the code available on: https://github.com/SetsompopLab/deli-cs .

Each tarball named caseXXX_preprocessed.tar.gz contains data related to that subject:

• deli_2min.npy is the DL genrated initial reconstruction.
• init_adj_2min.npy is the inital gridding reconstructions.
• ref_2min.npy is the reference LLR reconstruction (not initialized with deliCS).
• ref_6min.npy is the reference LLR reconstruction using 6 min of MRF acquisition. This is considered gold standard - NOT AVAILABLE FOR TEST CASES 002-004, which are acquired in the clinic.
• refine_2min_iters_20.npy is the reconstruction from the full proposed deliCS pipeline.
• T1... .npy are T1 maps from various matching reconstructions
• T2... .npy are T2 maps from various matching reconstructions

Additionally, the tarball named bartcompare.tar.gz contains the ref_2min.npy density compensated Sigpy reconstruction along with bartrecon_2min.cfl and bartrecon_2min.hdr, which are the non-density compensated Bart reconstructions shown in figure 3 in [1].

Furthermore, meta-data needed to process the data as presented in [1] are included. Some of the figure generation code requires the subspace basis and dictionary to perform dictionary matching on the fly. The tarball shared.tar.gz contains:

• the k-space trajectory for 2 min data (traj_grp16_inacc2.mat)
• the k-space trajectory for 6 min data (traj_grp48_inacc1.mat)
• the density compensation function for each trajectory (dcf_2min.npy and dcf_6min.npy)
• the subspace basis (phi.mat)
• the dictionary (dictionary.mat)
• a scaling factor for the deli reconstruction (deli_scaling_2min.npy)

[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. BioRxiv: https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1