An in vivo validation dataset for dynamic volumetric MRI
Abstract
This dataset contains the data described in the article "An in vivo validation dataset for dynamic volumetric MRI". For each subject, the following files are provided: anatomy.nii.gz: High-resolution fully sampled anatomical reference image outphase.nii.gz: Out-of-phase image from the Dixon scan segmentation.nii.gz: Muscle segmentation masks as determined from the out-of-phase image; each voxel is assigned an integer - corresponding to a certain segmentation label segmentation.txt: Label descriptions of the segmentation masks validation.mrd: Fully sampled multichannel k-space data for each of the nine pressure levels; the pressure levels are indexed by the 'repetition' counter in the MRD AcquisitionHeader hybrid.mrd: Multichannel k-space data for the hybrid dynamic and validation scan; the static part has a value of 1 in the first user-defined counter in the MRD AcquisitionHeader dynamic1.mrd, ..., dynamic4.mrd: Undersampled multichannel k-space data for the four dynamic scans: dynamic1.mrd: Six consecutive repetitions of the pressure cycle at 35-second intervals; the six repetitions are indexed using the first user-defined counter in the MRD AcquisitionHeader dynamic2.mrd: A single pressure cycle with an isometric knee flexion dynamic3.mrd: A single pressure cycle with rotated pressure cuff dynamic4.mrd: A single pressure cycle with rotated pressure cuff and an isometric knee flexion Each .mrd file is a HDF5 file that contains the raw k-space and noise data in MRD format in /dataset/data, the MRD header in /dataset/xml, and the coil sensitivity maps in /csm.The encoding counters stored in the MRD AcquisitionHeader contain the ky and kz coordinates of each readout line, specifying the exact sampling pattern for each scan. The Matlab script example_recon.m shows how the k-space data can be loaded and reconstructed into an image.In addition, Matlab scripts are provided to reproduce the results in Figures 4 and 5 of the paper.To run the scripts, install the ISMRMRD code (https://github.com/ismrmrd/ismrmrd/tree/master) and BART (https://mrirecon.codeberg.page/) and add the installation paths to setup.m.
Fields of Study & Subjects
Advanced MRI Techniques and Applications
Medical Image Segmentation Techniques
MRI in cancer diagnosis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 6 July 2026
- Document type
- Dataset