Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent soft-tissue contrast without the use of ionizing radiation. Physiological motions and limited speed of MRI data acquisition necessitate development of accelerated methods, which typically rely on undersampling. Recovering diagnostic quality CMR images from highly undersampled data has been an active area of research. Recently, several data acquisition and processing methods have been proposed to accelerate CMR. The availability of data to objectively evaluate and compare different reconstruction methods could expedite innovation and promote clinical translation of these methods. In this work, we introduce an open-access dataset, called OCMR, that provides multi-coil k-space data from 74 fully sampled and 212 prospectively undersampled cardiac cine series, comprising of 183 and 842 slices, respectively.
October 08, 2020 (added more fully sampled data)
| sampling | number of files | number of slices |
|---|---|---|
| fully sampled | 74 | 183 |
| prospectively undersampled | 212 | 842 |
August 10, 2020 (original launch)
| sampling | number of files | number of slices |
|---|---|---|
| fully sampled | 53 | 81 |
| prospectively undersampled | 212 | 842 |