Evidence map›Paper›PMID 34353745›Full record

ReviewPET clinics2021

Total-Body PET Kinetic Modeling and Potential Opportunities Using Deep Learning.

Yiran Wang, Elizabeth Li, Simon R Cherry, Guobao Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in PET clinics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Cardiovascular imaging with total-body PET.The British journal of radiology · 2026
    Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Kinetic modeling ofEJNMMI research · 2025
    Article
  7. [European journal of nuclear medicine and molecular imaging · 2025
    Article
  8. Article
  9. Distinguishing benign from malignant lesions with high [European journal of nuclear medicine and molecular imaging · 2025
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [European journal of nuclear medicine and molecular imaging · 2024
    Article
  17. Article
  18. Comparison of parametric imaging and SUV imaging with [European journal of nuclear medicine and molecular imaging · 2024
    Article
  19. The feasibility of quantitative assessment of dynamicEuropean journal of nuclear medicine and molecular imaging · 2023
    Article
  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

4 authors.

Yiran WangDepartment of Biomedical Engineering, University of California, 451 E. Health Sciences Drive, Davis, CA 95616, USA; Department of Radiology, University of California Davis Medical Center, Ambulatory Care Center, Building Suite 3100, 4860 Y Street, Sacramento, CA 95817, USA.
Elizabeth LiDepartment of Biomedical Engineering, University of California, 451 E. Health Sciences Drive, Davis, CA 95616, USA.
Simon R CherryDepartment of Biomedical Engineering, University of California, 451 E. Health Sciences Drive, Davis, CA 95616, USA; Department of Radiology, University of California Davis Medical Center, Ambulatory Care Center, Building Suite 3100, 4860 Y Street, Sacramento, CA 95817, USA.
Guobao WangDepartment of Radiology, University of California Davis Medical Center, Ambulatory Care Center, Building Suite 3100, 4860 Y Street, Sacramento, CA 95817, USA. Electronic address: gbwang@ucdavis.edu.

Funding

EXPLORER: Changing the Molecular Imaging Paradigm with Total Body PETR01CA206187 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BADAWI, RAMSEY D., CHERRY, SIMON R · 2015 to 2019
$15.5M
Parametric PET of Neuroinflammation for NAFLD-related ADR01DK124803 · NIDDK · UNIVERSITY OF CALIFORNIA AT DAVIS · PI WANG, GUOBAO · 2020 to 2023
$2.6M
NCI NIH HHS R01 CA206187NIDDK NIH HHS R01 DK124803
6 · The paper itself

Abstract

The uEXPLORER total-body PET/CT system provides a very high level of detection sensitivity and simultaneous coverage of the entire body for dynamic imaging for quantification of tracer kinetics. This article describes the fundamentals and potential benefits of total-body kinetic modeling and parametric imaging focusing on the noninvasive derivation of blood input function, multiparametric imaging, and high-temporal resolution kinetic modeling. Along with its attractive properties, total-body kinetic modeling also brings significant challenges, such as the large scale of total-body dynamic PET data, the need for organ and tissue appropriate input functions and kinetic models, and total-body motion correction. These challenges, and the opportunities using deep learning, are discussed.

Indexed as

Deep LearningPositron Emission Tomography Computed TomographyAlgorithmsHumansKineticsPositron-Emission TomographyDeep learningDynamic imagingKinetic modelingParametric imagingTotal-body PET

Identifiers

PMID34353745
PMCPMC8453049

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.