Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2209.15136.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:56:47.004150Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T23:16:14.644892Z
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 f133b6a0-66fc-4ecf-aab1-d4441eb9f9e6 · inbound
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review cites this paper.
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup
Reference 173
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:56:47.004150Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa11099c-7e94-4511-bbea-6c14e0c1fb4f · inbound
Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine cites this paper.
Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup
Reference 76
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:34:01.071291Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 76dd32a0-2747-45a9-bd6a-e6c0ce7135c1 · inbound
GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction cites this paper.
GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:16:14.651095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.