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Abstract #3597

Calgary-Campinas raw k-space dataset: a benchmark for brain magnetic resonance image reconstruction

Roberto Souza1, M Louis Lauzon1, Marina Salluzzi1, Letícia Rittner2, and Richard Frayne1
1University of Calgary, Calgary, AB, Canada, 2University of Campinas, Campinas, Brazil

Machine learning is a new frontier for magnetic resonance (MR) image reconstruction, but progress is hampered by a lack of benchmark datasets. Our datasets provides ~200 GB of brain MR data (both raw and reconstructed data) acquired with different acquisition parameters on different scanners from different vendors and different magnetic field intensities. The fastMRI initiative (https://fastmri.org/), also provides raw data but otherwise is complementary. For instance, fastMRI provides raw k-space data corresponding to 2D acquisitions, while our dataset is composed of 3D acquisitions (i.e., with our data, you can under-sample in two directions).

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