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

TitleGeneralized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.
Publication TypeJournal Article
Year of Publication2015
AuthorsKarakatsanis NA, Zhou Y, Lodge MA, Casey ME, Wahl RL, Zaidi H, Rahmim A
JournalPhys Med Biol
Volume60
Issue22
Pagination8643-73
Date Published2015 Nov 21
ISSN1361-6560
KeywordsComputer Graphics, Computer Simulation, Four-Dimensional Computed Tomography, Humans, Image Processing, Computer-Assisted, Kinetics, Neoplasms, Phantoms, Imaging, Positron-Emission Tomography, Whole Body Imaging
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.

DOI10.1088/0031-9155/60/22/8643
Alternate JournalPhys Med Biol
PubMed ID26509251
PubMed Central IDPMC4710061
Grant ListS10 RR023623 / RR / NCRR NIH HHS / United States
1S10RR023623 / RR / NCRR NIH HHS / United States

Weill Cornell Medicine
Department of Radiology
525 East 68th Street New York, NY 10065