Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET. (28th October 2015)

• Record Type: Journal Article
• Authors: Karakatsanis, Nicolas A; Zhou, Yun; Lodge, Martin A; Casey, Michael E; Wahl, Richard L; Zaidi, Habib; Rahmim, Arman
• Is Part Of: Physics in medicine & biology. Volume 60:Number 22(2015:Nov.)
• Page Start: 8643
• Page End: 8673
• Publication Date: 2015-10-28
• Subjects: Patlak -- whole-body -- generalized -- PET -- quantification -- detectability -- parametric; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/60/22/8643
• Languages: English
• ISSNs: 0031-9155

Abstract

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 k loss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate K i 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 K i images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true k loss relative to K i, 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 k loss 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.