Pith. sign in

REVIEW 6 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

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

Chong Chen , Yingmin Liu , Philip Schniter , Matthew Tong , Karolina Zareba , Orlando Simonetti , Lee Potter , Rizwan Ahmad This is my paper
classification eess.IV
keywords datamethodsacquisitionbeencardiovasculardatasetimagingk-space
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 6 Pith papers

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

  1. MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI

    cs.CV 2026-04 accept novelty 8.0

    MosaicMRI provides a diverse raw MSK MRI dataset that enables deep learning models to exploit cross-anatomical correlations, outperforming anatomy-specific training in low-sample regimes for accelerated reconstruction.

  2. Towards a Unified Theoretical Framework for Splitting-based Self-Supervised MRI Reconstruction

    eess.IV 2026-01 unverdicted novelty 7.0

    UNITS framework proves self-supervised splitting risk in MRI reconstruction is a weighted supervised risk, yielding identical Bayes-optimal predictors and relating training residuals to prediction bias.

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

    eess.IV 2026-07 accept novelty 6.0

    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.

  4. MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction

    eess.IV 2026-05 unverdicted novelty 6.0

    MoE-dqINR factorizes INR-based MRI reconstruction into shared spatial experts plus state-conditioned routing to unify dynamic and quantitative reconstruction at roughly 30 seconds per scan.

  5. Dynamic MRI Reconstruction Via Dual Deep Priors and Low-Rank Plus Sparse Modeling

    eess.IV 2026-05 unverdicted novelty 6.0

    Dual deep image priors for low-rank plus sparse decomposition enable training-data-free dynamic MRI reconstruction that outperforms classical and learning-based methods across acceleration factors.

  6. An in vivo validation dataset for dynamic volumetric MRI

    physics.med-ph 2026-02 conditional novelty 6.0

    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.