Evidence map›Paper›PMID 40365117›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

Simulating scanner-and algorithm-specific 3D CT noise texture using physics-informed 2D and 2.5D generative neural network models.

Hao Gong, Thomas M Huber, Timothy Winfree, Scott S Hsieh, Lifeng Yu, Shuai Leng, Cynthia H McCollough

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hao GongDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.
Thomas M HuberDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.
Timothy WinfreeDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.
Scott S HsiehDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.
Lifeng YuDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.
Shuai LengDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.
Cynthia H McColloughDepartment of Radiology, Mayo Clinic, Rochester, MN, 55901.

Funding

Trade-offs in human observer performance, image quality metrics, and patient doseR01EB017095 · NIBIB · MAYO CLINIC ROCHESTER · PI Hao Gong, Cynthia H McCollough · 2012 to 2026
$7.0M
NIBIB NIH HHS R01 EB017095
6 · The paper itself

Abstract

Low-dose CT simulation is needed to assess reconstruction/denoising techniques and optimize dose. Projection-domain noise-insertion methods require manufacturers' proprietary tools. Image-domain noise-insertion methods face various challenges that affect generalizability, and few have been systematically validated for 3D noise synthesis. To improve generalizability, we presented a

Indexed as

Deep learningdiagnostic image quality assessmentgenerative modellow dose CTsimulation

Identifiers

PMID40365117
PMCPMC12070530

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.