ArticlePhysics in medicine and biology2015
Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.
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.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review.Abdominal radiology (New York) · 2022Pooled it
- Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Whole-Body Dynamic Positron Emission and Computed Tomography (WBD-PET/CT): Latest Developments, Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Parametric analysis of dynamic long axial field-of-viewEJNMMI physics · 2026Article
- Evaluation of a clinically efficient [EJNMMI research · 2026Article
- Patlak-PBNT: a simple population-based Patlak model to generate [EJNMMI physics · 2026Article
- Differences in the quantitative parameter of lung lesions in dynamic chest FDG PET/CT imaging: framing protocol, reconstruction algorithm, software processing.Frontiers in oncology · 2026Article
- The value of metabolic parameters on dynamicFrontiers in oncology · 2026Article
- Regional impact of time-to-equilibrium on indirect Patlak whole-body parametric imaging: a multi-tissue class analysis at the entire body level.EJNMMI physics · 2025Article
- Long Axial Field-of-view PET: A New Era of Quantitative PET Imaging for Theranostic Applications in Clinic.PET clinics · 2025Review
- Long Axial Field of View PET: A New Era of Quantitative PET Imaging for Prospective Applications Beyond Classical Theranostics.PET clinics · 2025Review
- Fast 4D-PET parametric imaging computation at the whole field of view level: Reliability under simulated conditions of PET KinetiX, a dedicated software solution.European journal of nuclear medicine and molecular imaging · 2025Article
- Article
- Self-supervised neural network for Patlak-based parametric imaging in dynamic [European journal of nuclear medicine and molecular imaging · 2025Article
- Permeability Benchmarking: Guidelines for ComparingJournal of chemical information and modeling · 2025Review
- Localized FDG loss in lung cancer lesions.EJNMMI research · 2024Article
- Performance and application of the total-body PET/CT scanner: a literature review.EJNMMI research · 2024Review
- Validation of image-derived input function using a long axial field of view PET/CT scanner for two different tracers.EJNMMI physics · 2024Article
- Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [European journal of nuclear medicine and molecular imaging · 2024Article
- 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 · 2024Article
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7 authors.
Funding
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.
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