Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies

Abstract

Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (VT, DVR, or BPND for reversible tracers; Ki or CMRglu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R2 = 0.59–1.00, R2 = 0.71–1.00, R2 = 0.56–0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R2 = 0.71–1.00 and R2 = 0.36–0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Kinetic analysis of cardiac dynamic 18F-Florbetapir PET in healthy volunteers and amyloidosis patients: A pilot study

“…An established approach to enhance the kinetic quantification of 18 F-Florbetapir involves combining dynamic PET imaging with arterial blood sampling, considered the gold standard [ 13 , 22 ]. However, the invasive character of this method restricts its widespread clinical application [ 23 ]. To address this limitation, alternative methods such as the image-derived input function (IDIF) have been developed.…”

A convolutional neural network-based system to estimate the arterial plasma radioactivity curve in 18F-FDG dynamic brain PET study

“…However, a persistent challenge lies in generating a metabolite-corrected plasma curve from a whole blood curve. Consequently, reliance on arterial or venous blood sampling remains necessary [ 2 ]. In the proposed methodology, blood sampling is used to train the CNN.…”

“…Dynamic brain PET imaging using Fluorine-18 fluorodeoxyglucose ( 18 F-FDG) permits the examination of the cerebral metabolic rate of glucose (CMRGlc) [ 1 ]. Nevertheless, the conventional and widely accepted technique for analyzing the CMRGlc involves the extraction of arterial blood samples and the derivation of the time-radioactivity curve from arterial plasma, which necessitates an invasive procedure for patients and exposes medical personnel to radiation [ 2 ]. Consequently, the search for noninvasive alternatives to generate input functions has been underway [ 3 , 4 ].…”

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

“…A problem here presents itself with long axial FOV scanners, with real‐time blood sampling being difficult because of restricted access to the radial artery and antecubital vein, which are inside the scanner bore [78]. Even with a catheter implanted, the extra‐long tube connected to the wrist and sampling site can cause a serious dispersion effect and the blood input function needs to be corrected [79]. Using TB‐PET, the development of data‐driven methods to estimate the metabolite portion in blood is highly desired to avoid the need for invasive blood sampling.…”