# OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging

arXiv: https://arxiv.org/abs/2008.03410 · HTML: https://mlsanity.com/paper/2008.03410/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 30 of 36)

- [Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction](https://mlsanity.com/paper/2301.00127/) (2022)
- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction](https://mlsanity.com/paper/2307.13220/) (2023)
- [Deep Learning for Accelerated and Robust MRI Reconstruction: a Review](https://mlsanity.com/paper/2404.15692/) (2024)
- [Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation](https://mlsanity.com/paper/2302.02504/) (2023)
- [Memory-efficient model-based deep learning with convergence and robustness guarantees](https://mlsanity.com/paper/2206.04797/) (2022)
- [Learned Half-Quadratic Splitting Network for MR Image Reconstruction](https://mlsanity.com/paper/2112.09760/) (2021)
- [T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction](https://mlsanity.com/paper/2209.03832/) (2022)
- [Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer](https://mlsanity.com/paper/2309.10227/) (2023)
- [Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI](https://mlsanity.com/paper/2402.15939/) (2024)
- [Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems](https://mlsanity.com/paper/2411.14141/) (2024)
- [Multi-dynamic deep image prior for cardiac MRI](https://mlsanity.com/paper/2412.04639/) (2024)
- [Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction](https://mlsanity.com/paper/2603.05681/) (2026)
- [Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers](https://mlsanity.com/paper/2204.01706/) (2022)
- [Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT2LR-Net)](https://mlsanity.com/paper/2206.00850/) (2022)
- [Deep unrolling Shrinkage Network for Dynamic MR imaging](https://mlsanity.com/paper/2307.09818/) (2023)
- [TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories](https://mlsanity.com/paper/2409.12777/) (2024)
- [An unsupervised method for MRI recovery: Deep image prior with structured sparsity](https://mlsanity.com/paper/2501.01482/) (2025)
- [Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database](https://mlsanity.com/paper/2512.21652/) (2025)
- [Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data](https://mlsanity.com/paper/2206.00775/) (2022)
- [JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI](https://mlsanity.com/paper/2502.11749/) (2025)
- [Multi PILOT: Learned Feasible Multiple Acquisition Trajectories for Dynamic MRI](https://mlsanity.com/paper/2303.07150/) (2023)
- [Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences](https://mlsanity.com/paper/2308.14946/) (2023)
- [Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstruction](https://mlsanity.com/paper/2403.15770/) (2024)
- [Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model](https://mlsanity.com/paper/2411.03723/) (2024)
- [Training-Free Adversarial Robustness in Computational MRI](https://mlsanity.com/paper/2501.01908/) (2025)
- [T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration](https://mlsanity.com/paper/2502.20333/) (2025)
- [DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction](https://mlsanity.com/paper/2506.10309/) (2025)
- [Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding](https://mlsanity.com/paper/2507.07707/) (2025)
- [No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space](https://mlsanity.com/paper/2603.09945/) (2026)

## Abstract (arXiv)

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 53 fully sampled and 212 prospectively undersampled cardiac cine series.