CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
This addresses a data gap for researchers developing accelerated cardiac MRI reconstruction methods, though it is incremental as it provides a resource rather than a new algorithmic breakthrough.
The authors tackled the lack of public datasets for deep learning-based cardiac MRI reconstruction by releasing CMRxRecon, a dataset from 300 subjects with multi-contrast, multi-view, multi-slice, and multi-coil data, including manual segmentations and reference algorithms, to standardize evaluation and advance the field.
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.