Evidence map›Paper›PMID 35697017›Full record

ArticlePhysics in medicine and biology2022

A personalized deep learning denoising strategy for low-count PET images.

Qiong Liu, Hui Liu, Niloufar Mirian, Sijin Ren, Varsha Viswanath, Joel Karp, Suleman Surti, Chi Liu

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026
    Article
  3. Article
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  6. Review
  7. Article
  8. Article
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  10. Article
  11. Article
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  13. Article
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  15. Article
  16. Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.IEEE transactions on radiation and plasma medical sciences · 2024
    Article
  17. Article
  18. Article
  19. Article
  20. Inter-pass motion correction for whole-body dynamic PET and parametric imaging.IEEE transactions on radiation and plasma medical sciences · 2023
    Article
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

8 authors.

Qiong LiuDepartment of Biomedical Engineering, Yale University, United States of America.
Hui LiuDepartment of Radiology and Biomedical Imaging, Yale University, United States of America.
Niloufar MirianDepartment of Radiology and Biomedical Imaging, Yale University, United States of America.
Sijin RenDepartment of Radiology and Biomedical Imaging, Yale University, United States of America.
Varsha ViswanathDepartment of Radiology, University of Pennsylvania, United States of America.
Joel KarpDepartment of Radiology, University of Pennsylvania, United States of America.
Suleman SurtiDepartment of Radiology, University of Pennsylvania, United States of America.
Chi LiuDepartment of Biomedical Engineering, Yale University, United States of America.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Quantitative Low-Dose PET ImagingR01EB025468 · NIBIB · YALE UNIVERSITY · PI CARSON, RICHARD E., LIU, CHI · 2018 to 2022
$3.2M
Optimization of Clinical and Research PET ImagingR01EB028764 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI SURTI, SULEMAN · 2020 to 2023
$2.2M
NCATS NIH HHS UL1 TR001863NIBIB NIH HHS R01 EB025468NIBIB NIH HHS R01 EB028764
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Deep LearningImage Processing, Computer-AssistedPositron-Emission TomographySignal-To-Noise Ratiodeep learninglow count PETnoise level disparitypersonalized denoising

Identifiers

PMID35697017
PMCPMC9321225

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.