The biv-me rescans dataset consists of cine cardiovascular magnetic resonance imaging (CMR) from ten healthy subjects at two time points, and corresponding biventricular surface meshes reconstructed from each CMR scan using the open-source biv-me framework. For each subject, the first and second CMR scan was performed within 3 months of each other. The CMR images are provided in DICOM format, and the surface mesh objects are provided in .vtk format. Researchers are encouraged to use this dataset to test the reproducibility of their CMR analysis.

For more information on mesh generation from biv-me and subsequent benchmark performance on the biv-me rescans dataset, please refer to the following publication:

J. Dillon, C. Mauger, D. Zhao, S.E. Petersen, A.D. McCulloch, A.A. Young, M.P. Nash, biv-me: Open-source software for generating time-varying biventricular meshes from cine cardiovascular magnetic resonance imaging with multi-cohort validation. Medical Image Analysis, 104252, ISSN 1361-8415, 2026.

Data

Please complete and submit this form to request access to the MITEA dataset. Only applications submitted using official institutional email addresses will be accepted.

Intellectual Property Notice

The biv-me rescans dataset, including the content, software and methodologies supported and detailed on the website, and all intellectual property rights subsisting in them, are and remain the property of the University of Auckland. Access to the biv-me rescans dataset is provided strictly to the person(s) to whom access is provided by the University. No part of the biv-me rescans dataset may be copied, or distributed or disclosed to any other person(s) without consent. The biv-me rescans dataset can be provided under a non-commercial license CC BY-NC-SA 4.0 for research purposes. For commercial or other use, please contact the Data Contributors.

Contributors

  1. Joshua Dillon - University of Auckland, NZ
  2. Debbie Zhao - University of Auckland, New Zealand
  3. Martyn Nash - University of Auckland, New Zealand
  4. Alistair Young – University of Auckland, New Zealand; King's College London, UK

Contact

Please email joshua.dillon@auckland.ac.nz for further information about this dataset.