# CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

arXiv: https://arxiv.org/abs/2309.10836 · HTML: https://mlsanity.com/paper/2309.10836/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1712.01751/) (2017)
- [The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)](https://mlsanity.com/paper/2210.06385/) (2022)
- [Simulating single-coil MRI from the responses of multiple coils](https://mlsanity.com/paper/1811.08026/) (2018)
- [Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction](https://mlsanity.com/paper/2012.11974/) (2020)
- [FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging](https://mlsanity.com/paper/2304.09254/) (2023)

## Built on by (most cited first, 21 of 21)

- [Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction](https://mlsanity.com/paper/2309.13839/) (2023)
- [Deep Cardiac MRI Reconstruction with ADMM](https://mlsanity.com/paper/2310.06628/) (2023)
- [Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI](https://mlsanity.com/paper/2402.15939/) (2024)
- [CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation](https://mlsanity.com/paper/2406.05786/) (2024)
- [End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI](https://mlsanity.com/paper/2403.10346/) (2024)
- [CAR: Contrast-Agnostic Deformable Medical Image Registration with Contrast-Invariant Latent Regularization](https://mlsanity.com/paper/2408.05341/) (2024)
- [On the Foundation Model for Cardiac MRI Reconstruction](https://mlsanity.com/paper/2411.10403/) (2024)
- [Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging](https://mlsanity.com/paper/2503.19005/) (2025)
- [NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps](https://mlsanity.com/paper/2309.15608/) (2023)
- [Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2403.00549/) (2024)
- [Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI](https://mlsanity.com/paper/2405.10570/) (2024)
- [JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI](https://mlsanity.com/paper/2502.11749/) (2025)
- [k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction](https://mlsanity.com/paper/2310.11050/) (2023)
- [Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstruction](https://mlsanity.com/paper/2403.15770/) (2024)
- [Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach](https://mlsanity.com/paper/2404.06941/) (2024)
- [Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI](https://mlsanity.com/paper/2411.18249/) (2024)
- [T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration](https://mlsanity.com/paper/2502.20333/) (2025)
- [Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction](https://mlsanity.com/paper/2505.23872/) (2025)
- [Adaptive Conditional Contrast-Agnostic Deformable Image Registration with Uncertainty Estimation](https://mlsanity.com/paper/2601.05981/) (2026)
- [Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?](https://mlsanity.com/paper/2604.22557/) (2026)
- [Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction](https://mlsanity.com/paper/2610.08109/) (2026)

## Abstract (arXiv)

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/.