Evidence map›Paper›PMID 28687601›Full record

ReviewJournal of nuclear medicine : official publication, Society of Nuclear Medicine2017

Operational and Dosimetric Aspects of Pediatric PET/CT.

Frederic H Fahey, Alison Goodkind, Robert D MacDougall, Leah Oberg, Sonja I Ziniel, Richard Cappock, Michael J Callahan, Neha Kwatra, S Ted Treves, Stephan D Voss

Abstract readReview
In one paragraph

Review in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
–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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Evaluation of pediatric malignancies using total-body PET/CT with half-dose [European journal of nuclear medicine and molecular imaging · 2022
    Article
  12. Article
  13. Article
  14. Validation of Deep Learning-based Augmentation for ReducedRadiology. Artificial intelligence · 2021
    Article
  15. Image quality and lesion detectability in low-dose pediatricEuropean journal of nuclear medicine and molecular imaging · 2021
    Article
  16. Review
  17. Applications of artificial intelligence in nuclear medicine image generation.Quantitative imaging in medicine and surgery · 2021
    Review
  18. Article
  19. Review
  20. Review
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.

Frederic H FaheyDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts frederic.fahey@childrens.harvard.edu.
Alison GoodkindDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.
Robert D MacDougallDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.
Leah ObergDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.
Sonja I ZinielSection of Pediatric Hospital Medicine, Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, Colorado; and.
Richard CappockDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.
Michael J CallahanDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.
Neha KwatraDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.
S Ted TrevesDepartment of Radiology, Harvard Medical School, Boston, Massachusetts.
Stephan D VossDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

No consistent guidelines exist for the acquisition of a CT scan as part of pediatric PET/CT. Given that children may be more vulnerable to the effects of ionizing radiation, it is necessary to develop methods that provide diagnostic-quality imaging when needed, in the shortest time and with the lowest patient radiation exposure. This article describes the basics of CT dosimetry and PET/CT acquisition in children. We describe the variability in pediatric PET/CT techniques, based on a survey of 19 PET/CT pediatric institutions in North America. The results of the survey demonstrated that, although most institutions used automatic tube current modulation, there remained a large variation of practice, on the order of a factor of 2-3, across sites, pointing to the need for guidelines. We introduce the approach developed at our institution for using a multiseries PET/CT acquisition technique that combines diagnostic-quality CT in the essential portion of the field of view and a low-dose technique to image the remainder of the body. This approach leads to a reduction in radiation dose to the patient while combining the PET and the diagnostic CT into a single acquisition. The standardization of pediatric PET/CT provides an opportunity for a reduction in the radiation dose to these patients while maintaining an appropriate level of diagnostic image quality.

Indexed as

ChildHospitalsHumansImage Processing, Computer-AssistedPositron Emission Tomography Computed TomographyRadiometrySurveys and Questionnairescomputed tomographydosimetrypediatricPET/CT

Identifiers

PMID28687601
PMCPMC6944174

What OpenQuestion holds

Textmetadata
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.