mlsanityData to 13 Oct 2026

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

arXiv 2008.03410 ↗First posted 8 Aug 2020eess.IVcited by 36

Not on the map. Why?

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.

Key facts. Cited by 36 papers in the map (data through 13 Oct 2026). First posted on arXiv 8 Aug 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 7; 2024: 7; 2025: 7; 2026: 8.

36 papers.

Sort:

By area:

  1. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  2. 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
  3. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 15
  4. 2024Deep Learning for Accelerated and Robust MRI Reconstruction: a Reviewcited by 9
  5. 2023Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimationcited by 8
  6. 2022Memory-efficient model-based deep learning with convergence and robustness guaranteescited by 7
  7. 2021Learned Half-Quadratic Splitting Network for MR Image Reconstructioncited by 5
  8. 2022T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstructioncited by 4
  9. 2023Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformercited by 4
  10. 2024Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRIcited by 4
  11. 2024Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problemscited by 3
  12. 2024Multi-dynamic deep image prior for cardiac MRIcited by 3
24 more
  1. 2026Gabor Primitives for Accelerated Cardiac Cine MRI Reconstructioncited by 3
  2. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  3. 2022Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT2LR-Net)cited by 2
  4. 2023Deep unrolling Shrinkage Network for Dynamic MR imagingcited by 2
  5. 2024TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectoriescited by 2
  6. 2025An unsupervised method for MRI recovery: Deep image prior with structured sparsitycited by 2
  7. 2025Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Databasecited by 2
  8. 2022Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Datacited by 1
  9. 2025JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRIcited by 1
  10. 2023Multi PILOT: Learned Feasible Multiple Acquisition Trajectories for Dynamic MRIcited by 0
  11. 2023Reinforcement Learning for Sampling on Temporal Medical Imaging Sequencescited by 0
  12. 2024Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstructioncited by 0
  13. 2024Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Modelcited by 0
  14. 2025Training-Free Adversarial Robustness in Computational MRIcited by 0
  15. 2025T1-PILOT: Optimized Trajectories for T1 Mapping Accelerationcited by 0
  16. 2025DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstructioncited by 0
  17. 2025Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encodingcited by 0
  18. 2026No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Spacecited by 0
  19. 2026MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRIcited by 0
  20. 2026Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansionscited by 0
  21. 2026Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 Tcited by 0
  22. 2026MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstructioncited by 0
  23. 2026Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRIcited by 0
  24. 2026Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priorscited by 0