Evidence map›Paper›PMID 34795014›Full record

ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2022

Total-Body PET Multiparametric Imaging of Cancer Using a Voxelwise Strategy of Compartmental Modeling.

Guobao Wang, Lorenzo Nardo, Mamta Parikh, Yasser G Abdelhafez, Elizabeth Li, Benjamin A Spencer, Jinyi Qi, Terry Jones, Simon R Cherry, Ramsey D Badawi

Abstract read
In one paragraph

Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing 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

62 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Total Body PET.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026
    Review
  6. Review
  7. Quantification ofJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  8. Review
  9. Article
  10. A scoping review on potential of novel developments in fever of unknown origin and inflammation of unknown origin: long-axial-field-of-view positron emission tomography/computed tomography and novel radiotracers.The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of... · 2025
    Article
  11. Article
  12. Article
  13. New compartment model for hepatic blood flow quantification in humans from 15O-water PET images.European journal of nuclear medicine and molecular imaging · 2025
    Article
  14. Quantitative Total-Body Imaging of Blood Flow with High-Temporal-Resolution Early DynamicJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  15. Article
  16. Kinetic modeling ofEJNMMI research · 2025
    Article
  17. Article
  18. Total-Body Parametric Imaging Using Relative Patlak Plot.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  19. Article
  20. Article

2 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

10 authors.

Guobao WangDepartment of Radiology, University of California Davis Medical Center, Sacramento, California; gbwang@ucdavis.edu.
Lorenzo NardoDepartment of Radiology, University of California Davis Medical Center, Sacramento, California.
Mamta ParikhUC Davis Comprehensive Cancer Center, Sacramento, California; and.
Yasser G AbdelhafezDepartment of Radiology, University of California Davis Medical Center, Sacramento, California.
Elizabeth LiDepartment of Biomedical Engineering, University of California at Davis, Davis, California.
Benjamin A SpencerDepartment of Radiology, University of California Davis Medical Center, Sacramento, California.
Jinyi QiDepartment of Biomedical Engineering, University of California at Davis, Davis, California.
Terry JonesDepartment of Radiology, University of California Davis Medical Center, Sacramento, California.
Simon R CherryDepartment of Radiology, University of California Davis Medical Center, Sacramento, California.
Ramsey D BadawiDepartment of Radiology, University of California Davis Medical Center, Sacramento, California.

Funding

Staff InvestigatorsP30CA093373 · NCI · UNIVERSITY OF CALIFORNIA DAVIS · PI Gina M Dayton · 2002 to 2026
$84.9M
Single-tracer Multiparametric PET ImagingR01EB033435 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI WANG, GUOBAO · 2022 to 2025
$2.4M
NCI NIH HHS P30 CA093373NIBIB NIH HHS R01 EB033435
6 · The paper itself

Abstract

Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification than static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiologic models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on a scanner with a long axial field of view. In this paper, we evaluate the necessity of voxelwise compartmental modeling strategies, including time delay correction (TDC) and model selection, for total-body multiparametric imaging.

Indexed as

Fluorodeoxyglucose F18NeoplasmsAlgorithmsHumansPositron-Emission TomographyPositron Emission Tomography Computed TomographyFluorodeoxyglucose F18compartmental modelingimage processingparametric imagingPETradiotracer tissue kineticstotal-body dynamic PET

Identifiers

PMID34795014
PMCPMC9364337

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.