Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2309.10836.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2309.10836 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:43:36.457961Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T20:26:11.372342Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 13bef1e7-6085-402c-a79e-4bc75665128b · inbound

On the Foundation Model for Cardiac MRI Reconstruction cites this paper.

On the Foundation Model for Cardiac MRI Reconstruction CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T19:43:36.457961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e406710e-0543-4b88-aa3e-c034875f5df3 · inbound

Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI cites this paper.

Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T11:26:54.354693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8004c214-2ee7-4c6f-bf90-93da55a60d0d · inbound

Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration cites this paper.

Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Reference 204

Resolution: unresolved; no resolver link, observed 2026-08-10T15:25:27.924962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9707be9-8cb2-439d-8f36-ac71ee41293e · inbound

Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction cites this paper.

Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:50:43.853662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f91ab49-2b65-449f-a2eb-931302d3571a · inbound

Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction? cites this paper.

Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction? CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:26:11.378279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 353a9197-f1d3-4b7d-9ac7-6c47f3196ef2 · inbound

Measuring and Evaluating the Performance of Generative AI Models for Scam Detection cites this paper.

Measuring and Evaluating the Performance of Generative AI Models for Scam Detection CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T18:19:26.716063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.