Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
200x Low-dose PET Reconstruction using Deep Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1712.04119.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T15:25:05.921605Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-03T04:27:36.931046Z
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 4383f2c1-141b-4e9f-adb1-d8f44647a7c4 · inbound
Physically-Based Inverse Rendering Framework for PET Image Reconstruction cites this paper.
Physically-Based Inverse Rendering Framework for PET Image Reconstruction 200x Low-dose PET Reconstruction using Deep Learning
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:25:05.921605Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a43727cc-c818-41d6-bab6-2933e6505350 · inbound
Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction cites this paper.
Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction 200x Low-dose PET Reconstruction using Deep Learning
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:43:31.927882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 10cec9a3-b045-464d-85a7-06c1afbef402 · inbound
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors cites this paper.
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors 200x Low-dose PET Reconstruction using Deep Learning
Reference 17
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-03T04:27:36.932508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.