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Paper Citation Record · LEDGER

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.03901.

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

pith.paper-citation-record.v1
2305.03901 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:23:28.537889Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7e28aeb8-e972-410c-919c-1fd090a41043 · inbound

Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET cites this paper.

Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Reference 85

Resolution
verified exact
arxiv_id, observed 2026-05-24T00:13:38.696466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-24T00:12:26.618333Z digest=sha256:5c33c55f23ba3e32a1b1e5d372221a8543eb5ebffd275fcddeedba745ccd672d

Observation 3ddd84d9-0073-4c1f-90ac-b34e75fd015b · inbound

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models cites this paper.

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-15T22:23:28.537889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:23:28.537889Z digest=sha256:fcfb24f7ac76f96eb30a00e1d8ede494738ef8ab7485383f912a4f7ebb059f7a