Evidence map›Paper›PMID 38558200›Full record

ArticleEJNMMI research2024

Automated extraction of the arterial input function from brain images for parametric PET studies.

Hamed Moradi, Rajat Vashistha, Soumen Ghosh, Kieran O'Brien, Amanda Hammond, Axel Rominger, Hasan Sari, Kuangyu Shi, Viktor Vegh, David Reutens

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. 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

10 authors.

Hamed MoradiCentre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
Rajat VashisthaCentre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
Soumen GhoshCentre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
Kieran O'BrienCentre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
Amanda HammondARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
Axel RomingerDepartment of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
Hasan SariDepartment of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
Kuangyu ShiDepartment of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
Viktor VeghCentre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia. v.vegh@uq.edu.au.ORCID http://orcid.org/0000-0002-4310-1421
David ReutensCentre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.

Funding

Australian Research Council IC170100035
6 · The paper itself

Abstract

backgroundAccurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images.

resultsTotal body

conclusionsOur automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Indexed as

Automatic AIF estimationDynamic PETNon-invasive arterial input functionParametric mapping

Identifiers

PMID38558200
PMCPMC11372015

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.