Advanced kinetic modelling strategies: towards adoption in clinical PET imaging

Abstract

Positron emission tomography (PET) is a highly quantitative imaging modality that can probe a number of functional and biological processes, depending on the radiolabelled tracer used. Static imaging, followed by analysis using semi-quantitative indices, such as the standardised uptake value, is used in the majority of clinical assessments in which PET has a role. However, considerably more information can be extracted from dynamic image acquisition protocols, followed by application of appropriate image reconstruction and tracer kinetic modelling techniques, but the latter approaches have mainly been restricted to drug development and clinical research applications due to their complexity in terms of both protocol design and parameter estimation methodology. To make dynamic imaging more feasible and valuable in routine clinical imaging, novel research outcomes are needed. Research areas include noninvasive input function extraction, protocol design for wholebody imaging application, and kinetic parameter estimation methods using spatiotemporal (4D) image reconstruction algorithms. Furthermore, with the advent of sequential and simultaneous PET/magnetic resonance (MR) data acquisition, strategies for obtaining synergistic benefits in kinetic modelling are emerging and potentially enhancing the role and clinical importance of PET/MR imaging. In this article, we review and discuss various advances in kinetic modelling both from a protocol design and a methodological development perspective. Moreover, we discuss future trends and potential outcomes, which could facilitate more routine use of tracer kinetic modelling techniques in clinical practice.

Hybrid Imaging: Instrumentation and Data Processing

“…For absolute quantification, i.e., the determination of physiological parameters (such as metabolic rate of glucose consumption in mmol/g/min), pharmaco-kinetic modeling is required [68]; this entails the knowledge of the time course of tracer concentration in arterial blood as well as in the tissue. From that, the pharmaco-kinetic parameters can be derived from a temporal relationship between these functions [70]. However, both the requirement of invasive arterial blood sampling and the complexity of kinetic modeling are significant limitations for the routine adoption of absolute quantification in clinical routine so far [71].…”

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation–maximization reconstruction

“…On the contrary, extension of current dynamic PET protocols to multi-bed FOVs is more challenging, as it involves multiple WB passes within the same time, resulting in very short scan time frames per bed. Nevertheless, dynamic PET has been steadily garnering clinical interest in oncology for the quantitative assessment of the progress and response to treatment of an increasing range of tumor types (Gupta et al 1998, Prytz et al 2006, Castell and Cook 2008, Kotasidis et al 2014). With the advent of commercial PET scanners with larger axial FOVs, improved electronics, time-of-flight (TOF) and resolution modeling capabilities, studies of higher statistical quality may now be possible in shorter time sessions, paving the way for clinical WB parametric PET imaging (Panin et al 2006, Karp et al 2008, Rahmim et al 2013, Karakatsanis et al 2014b).…”

Towards enhanced PET quantification in clinical oncology

“…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 . 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.…”