Evidence map›Paper›PMID 34056150›Full record

ArticleIEEE transactions on radiation and plasma medical sciences2020

Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation-Maximization.

Abolfazl Mehranian, Andrew J Reader

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on radiation and plasma medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed, 1 pooled it
9.4field-weighted citation impact, top 1% of its field
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

74 citing papers in PubMed, 1 synthesis or guideline pooled it, 136 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  5. PET Image Reconstruction Using Deep Diffusion Image Prior.IEEE transactions on medical imaging · 2026
    Article
  6. Article
  7. Review
  8. Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction.IEEE transactions on radiation and plasma medical sciences · 2026
    Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. PET rapid image reconstruction challenge (PETRIC).Frontiers in nuclear medicine · 2026
    Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

14 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 1 country.

Abolfazl MehranianSchool of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, London SE1 7EH, U.K.ORCID https://orcid.org/0000-0003-4584-4453
Andrew J ReaderSchool of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, London SE1 7EH, U.K.ORCID https://orcid.org/0000-0002-2726-3383
King's College London · GB

Funding

Wellcome Trust 203148
6 · The paper itself

Abstract

We propose a forward-backward splitting algorithm to integrate deep learning into maximum-

Indexed as

Deep learning (DL)image reconstructionMRIpositron emission tomography (PET)

Identifiers

PMID34056150
PMCPMC7610859
OpenAlexW3037333647

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.