Paper Citation Record · LEDGER

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2411.12181.

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

pith.paper-citation-record.v1
2411.12181 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:55:04.353112Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy13
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac7f2913-d8cd-4ecb-8681-332eafb30735 · outbound

This paper cites Diffusion models in vision: A survey.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Diffusion models in vision: A survey

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.844302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 558b5bc5-7700-45ce-b2fe-7ddb82abd0e7 · outbound

This paper cites Diffusion models beat gans on image synthesis, 2021.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Diffusion models beat gans on image synthesis, 2021

Reference 2

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Source-reported events for the cited work

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Observation b5bb88ea-007a-4a16-9bf0-6c5478c309ef · outbound

This paper cites Block matching 3d random noise filtering for absorption optical projection tomography.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Block matching 3d random noise filtering for absorption optical projection tomography

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.800175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c053f61-23bc-4e09-b7f1-6c61e3e9dd16 · outbound

This paper cites Poisson flow consistency models for low-dose ct image denoising, 2024.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Poisson flow consistency models for low-dose ct image denoising, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.775393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 285fb3ef-05a1-4ab3-bf86-b15bcd4f93fc · outbound

This paper cites Denoising diffusion probabilistic models, 2020.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Denoising diffusion probabilistic models, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.246319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 388e8821-b7fc-4e79-84ab-3990e0a50431 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Elucidating the design space of diffusion-based generative models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.739772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0df055df-fb3e-4796-8b2f-99d0165e7602 · outbound

This paper cites Elucidating the design space of diffusion-based generative models, 2022.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Elucidating the design space of diffusion-based generative models, 2022

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.262237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c11bde86-ff79-441d-846a-f92506963320 · outbound

This paper cites Learning multiple layers of features from tiny images.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Learning multiple layers of features from tiny images

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.271506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28466c3b-2a1c-4969-a3b7-5d05d2f431f1 · outbound

This paper cites Trzasko, David S.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Trzasko, David S

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.678537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 388cebf0-46e1-42b2-86dd-74ac606178e0 · outbound

This paper cites Diffusion probabilistic priors for zero-shot low-dose ct image denoising, 2023.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Diffusion probabilistic priors for zero-shot low-dose ct image denoising, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.659849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ee682ed-ffcb-44ed-be1e-a4a6e4476bc1 · outbound

This paper cites Deep learning face attributes in the wild.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Deep learning face attributes in the wild

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.289275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a70fe86-f2f7-4bc9-a8f1-84d519df333d · outbound

This paper cites McCollough, Adam C.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques McCollough, Adam C

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.626490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88e99b85-a085-4c2f-8885-d5f3017cdac9 · outbound

This paper cites Attention u-net: Learning where to look for the pancreas, 2018.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Attention u-net: Learning where to look for the pancreas, 2018

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.603818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 693684d7-be9c-4b80-8df6-bd12c7caead8 · outbound

This paper cites Denoising diffusion implicit models, 2020.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Denoising diffusion implicit models, 2020

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.306868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a925c668-33fb-40c1-a475-1bf14d5b7cb2 · outbound

This paper cites Improved techniques for training consistency models.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Improved techniques for training consistency models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.565981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e58cacb6-0481-4bb2-b787-d80a37ba411a · outbound

This paper cites Consistency models.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Consistency models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.316990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 984d105e-0ff8-4be3-ae61-1da78d325c10 · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Score-based generative modeling through stochastic differential equations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.322530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d71f7329-c0fe-4242-849a-e0d3aa08c6d3 · outbound

This paper cites Learning models for object recognition from natural language descriptions.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Learning models for object recognition from natural language descriptions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.507389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f258f7b-c2f8-48ac-8c77-c03b7d17b54e · outbound

This paper cites Low-dose ct using denoising diffusion probabilistic model for 20× speedup, 2022.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Low-dose ct using denoising diffusion probabilistic model for 20× speedup, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.481919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d44e2a2-3f54-4833-a93b-86fd404d4380 · outbound

This paper cites Pfgm++: Unlocking the potential of physics-inspired generative models, 2023.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Pfgm++: Unlocking the potential of physics-inspired generative models, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.459690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 874cf2f6-30c7-4bcc-9171-1c61e82fb164 · outbound

This paper cites Generative adversarial network in medical imaging: A review.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Generative adversarial network in medical imaging: A review

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:04.344229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f55a9e-cadd-48ba-9470-d8af40216db8 · outbound

This paper cites Beta diffusion, 2023.

Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques Beta diffusion, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:55:04.416473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.