Evidence map›Paper›PMID 26509251›Full record

ArticlePhysics in medicine and biology2015

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.

Nicolas A Karakatsanis, Yun Zhou, Martin A Lodge, Michael E Casey, Richard L Wahl, Habib Zaidi, Arman Rahmim

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 1 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

57 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026
    Article
  3. Review
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  14. Self-supervised neural network for Patlak-based parametric imaging in dynamic [European journal of nuclear medicine and molecular imaging · 2025
    Article
  15. Permeability Benchmarking: Guidelines for ComparingJournal of chemical information and modeling · 2025
    Review
  16. Article
  17. Review
  18. Article
  19. Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [European journal of nuclear medicine and molecular imaging · 2024
    Article
  20. The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    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

7 authors.

Nicolas A KarakatsanisDivision of Nuclear Medicine and Molecular Imaging, School of Medicine, University of Geneva, Geneva, CH-1211, Switzerland.
Yun ZhouDivision of Nuclear Medicine, Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA.
Martin A LodgeDivision of Nuclear Medicine, Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA.
Michael E CaseySiemens Molecular Imaging, Knoxville, TN 37932, USA.
Richard L WahlDivision of Nuclear Medicine, Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA.
Habib ZaidiDivision of Nuclear Medicine and Molecular Imaging, School of Medicine, University of Geneva, Geneva, CH-1211, Switzerland.
Arman RahmimDivision of Nuclear Medicine, Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA.

Funding

High End Computing for PET/SPECTS10RR023623 · NCRR · JOHNS HOPKINS UNIVERSITY · PI WONG, DEAN FOSTER · 2007 to 2007
$205k
NCRR NIH HHS 1S10RR023623NCRR NIH HHS S10 RR023623
6 · The paper itself

Abstract

We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16-18% and 20-40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

Indexed as

Computer SimulationPhantoms, ImagingComputer GraphicsFour-Dimensional Computed TomographyHumansImage Processing, Computer-AssistedKineticsNeoplasmsPositron-Emission TomographyWhole Body Imaging

Identifiers

PMID26509251
PMCPMC4710061

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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.