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

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arxiv 2008.03410 v2 pith:EAZSMJBX submitted 2020-08-08 eess.IV

Chong Chen , Yingmin Liu , Philip Schniter , Matthew Tong , Karolina Zareba , Orlando Simonetti , Lee Potter , Rizwan Ahmad This is my paper · ORCID
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keywords datamethodsacquisitionbeencardiovasculardatasetimagingk-space
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI

    eess.IV 2026-07 accept novelty 6.0 of 10

    Piecewise variational use of a spatiotemporal diffusion prior reconstructs long free-breathing cardiac cine MRI sequences with higher quality and lower compute than prior methods.

  2. An in vivo validation dataset for dynamic volumetric MRI

    physics.med-ph 2026-02 conditional novelty 6.0 of 10

    A public 3D+t MRI dataset of nine volunteers' thighs under controlled pressure-cuff deformations, with undersampled dynamic k-space data and fully sampled validation images for reconstruction benchmarking.

  3. DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction

    eess.IV 2025-06 conditional novelty 6.0 of 10

    A deep unrolling network with spatiotemporal rotation equivariance outperforms previous methods on cardiac cine MRI at high undersampling factors.

  4. Self-supervised feature learning for cardiac Cine MR image reconstruction

    eess.IV 2025-05 conditional novelty 6.0 of 10

    Using only undersampled cardiac Cine data, SSFL-Recon with contrastive or VICReg feature pretraining reconstructs at up to 16x acceleration with quality comparable to supervised learning.

  5. Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encoding

    eess.IV 2025-07 conditional novelty 5.0 of 10

    GridTD factorizes multi-resolution hash grid encoding into tensor decomposition plus a lightweight MLP, beating existing unsupervised methods in compressive video, spectral, and dynamic MRI reconstruction.

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