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

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.

Hybrid Imaging: Instrumentation and Data Processing

“…While in step-and shoot acquisitions uniform sensitivity can also be achieved by an appropriate selection of bed overlap, the axial scan range is restricted to a discrete number of bed positions. Another advantage of CTM acquisitions is the possibility of new acquisition protocols that support wholebody parametric imaging [99]. Given the above benefits, CTM can be regarded a particular asset in combined PET/CT imaging.…”

Towards enhanced PET quantification in clinical oncology

“…The three major classes of dynamic PET analysis methods often employed in oncology studies are the fully compartmental kinetic models, which involve the least number of assumptions but also a large number of free microparameters, 4, 5 the graphical analysis methods 14,22,64,65 employing linearized functions of kinetic macro-parameters and the single-scan auto-radiographic techniques that are usually the simplest but often the least accurate. 66 The latter methods require a very small number of dynamic PET frames at later post-injection times without an input function, thus are considered easily adoptable in the clinic.…”

“…22,68 In addition, a useful generalization of the sPatlak model had also been proposed, namely the generalized Patlak (gPatlak) plot, 14 which has recently been adopted for WB 18 F-FDG PET parametric imaging for enhanced quantification of Patlak K i images in the case of 18 F-FDG uptake reversibility. 22,65 In particular, gPatlak begins with the assumption of a small positive k 4 microparameter for the 18 F-FDG 2-tissue compartment model to derive an extended non-linear Patlak plot that is capable of accounting for a potentially non-negligible but mildly positive reversibility in tracer uptake. 69 As a result, gPatlak method may enhance quantitative accuracy of K i parametric images in regions with non-negligible 18 F-FDG uptake reversibility, where sPatlak would have underestimated K i by neglecting k 4 .…”

“…69 As a result, gPatlak method may enhance quantitative accuracy of K i parametric images in regions with non-negligible 18 F-FDG uptake reversibility, where sPatlak would have underestimated K i by neglecting k 4 . 65 Although gPatlak retains most of the robust features of the Patlak family offering high clinical adoptability, it is a non-linear method and thus may not be as robust to noise as the linear sPatlak method, unless applied in the context of direct 4D reconstruction as it has been recently demonstrated with clinical oncology PET data. 70 71 Finally, a combination of the standard-of-care WB PET SUV imaging with the advanced Patlak K i imaging has been recently proposed for oncology studies by introducing a novel and clinically feasible dynamic WB 18 F-FDG PET/CT protocol that utilizes the same exact scan time window previously employed for static SUV protocols.…”

Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation

“…Recently, we proposed a clinically feasible 18 F-FDG PET parametric imaging framework [8], capable of delivering highly quantitative WB parametric images by supporting i) multiple PET acquisition passes over multiple beds and ii) graphical analysis of the acquired 4D WB data using robust standard linear Patlak (sPatlak) [9] or non-linear generalized Patlak (gPatlak) methods [40]. Although the associated total scan duration of ~30-40min may be considered clinically feasible, such protocols are naturally expected to increase the likelihood of bulk motion during the acquisition [41].…”