Kinetic Modelling of [⁶⁸Ga]Ga-DOTA-Siglec-9 in Porcine Osteomyelitis and Soft Tissue Infections

Background: [68Ga]Ga-DOTA-Siglec-9 is a positron emission tomography (PET) radioligand for vascular adhesion protein 1 (VAP-1), a protein involved in leukocyte trafficking. The tracer facilitates the imaging of inflammation and infection. Here, we studied the pharmacokinetic modelling of [68Ga]Ga-DOTA-Siglec-9 in osteomyelitis and soft tissue infections in pigs. Methods: Eight pigs with osteomyelitis and soft tissue infections in the right hind limb were dynamically PET scanned for 60 min along with arterial blood sampling. The fraction of radioactivity in the blood accounted for by the parent tracer was evaluated with radio-high-performance liquid chromatography. One- and two-tissue compartment models were used for pharmacokinetic evaluation. Post-mortem soft tissue samples from one pig were analysed with anti-VAP-1 immunofluorescence. In each analysis, the animal’s non-infected left hind limb was used as a control. Results: Tracer uptake was elevated in soft tissue infections but remained low in osteomyelitis. The kinetics of [68Ga]Ga-DOTA-Siglec-9 followed a reversible 2-tissue compartment model. The tracer metabolized quickly; however, taking this into account, produced more ambiguous results. Infected soft tissue samples showed endothelial cell surface expression of the Siglec-9 receptor VAP-1. Conclusion: The kinetics of [68Ga]Ga-DOTA-Siglec-9 uptake in porcine soft tissue infections are best described by the 2-tissue compartment model

Kinetic Modelling of [Ga-68]Ga-DOTA-Siglec-9 in Porcine Osteomyelitis and Soft Tissue Infections

Background: [Ga-68]Ga-DOTA-Siglec-9 is a positron emission tomography (PET) radioligand for vascular adhesion protein 1 (VAP-1), a protein involved in leukocyte trafficking. The tracer facilitates the imaging of inflammation and infection. Here, we studied the pharmacokinetic modelling of [Ga-68]Ga-DOTA-Siglec-9 in osteomyelitis and soft tissue infections in pigs. Methods: Eight pigs with osteomyelitis and soft tissue infections in the right hind limb were dynamically PET scanned for 60 min along with arterial blood sampling. The fraction of radioactivity in the blood accounted for by the parent tracer was evaluated with radio-high-performance liquid chromatography. One- and two-tissue compartment models were used for pharmacokinetic evaluation. Post-mortem soft tissue samples from one pig were analysed with anti-VAP-1 immunofluorescence. In each analysis, the animal's non-infected left hind limb was used as a control. Results: Tracer uptake was elevated in soft tissue infections but remained low in osteomyelitis. The kinetics of [Ga-68]Ga-DOTA-Siglec-9 followed a reversible 2-tissue compartment model. The tracer metabolized quickly; however, taking this into account, produced more ambiguous results. Infected soft tissue samples showed endothelial cell surface expression of the Siglec-9 receptor VAP-1. Conclusion: The kinetics of [Ga-68]Ga-DOTA-Siglec-9 uptake in porcine soft tissue infections are best described by the 2-tissue compartment model.</div

Kinetic Modelling of Infection Tracers [ 18

Introduction. Positron emission tomography (PET) is increasingly applied for infection imaging using [18F]FDG as tracer, but uptake is unspecific. The present study compares the kinetics of [18F]FDG and three other PET tracers with relevance for infection imaging. Methods. A juvenile porcine osteomyelitis model was used. Eleven pigs underwent PET/CT with 60-minute dynamic PET imaging of [18F]FDG, [68Ga]Ga-citrate, [11C]methionine, and/or [11C]donepezil, along with blood sampling. For infectious lesions, kinetic modelling with one- and two-tissue-compartment models was conducted for each tracer. Results. Irreversible uptake was found for [18F]FDG and [68Ga]Ga-citrate; reversible uptake was found for [11C]methionine (two-tissue model) and [11C]donepezil (one-tissue model). The uptake rate for [68Ga]Ga-citrate was slow and diffusion-limited. For the other tracers, the uptake rate was primarily determined by perfusion (flow-limited uptake). Net uptake rate for [18F]FDG and distribution volume for [11C]methionine were significantly higher for infectious lesions than for correspondingly noninfected tissue. For [11C]donepezil in pigs, labelled metabolite products appeared to be important for the analysis. Conclusions. The kinetics of the four studied tracers in infection was characterized. For clinical applications, [18F]FDG remains the first-choice PET tracer. [11C]methionine may have a potential for detecting soft tissue infections. [68Ga]Ga-citrate and [11C]donepezil were not found useful for imaging of osteomyelitis

Positron range in PET imaging: an alternative approach for assessing and correcting the blurring

International audiencePositron range impairs resolution in PET imaging, especially for high-energy emitters and for small-animal PET. De-blurring in image reconstruction is possible if the blurring distribution is known. Furthermore, the percentage of annihilation events within a given distance from the point of positron emission is relevant for assessing statistical noise. This paper aims to determine the positron range distribution relevant for blurring for seven medically relevant PET isotopes, $^{18}$F, $^{11}$C, $^{13}$N, $^{15}$O, $^{68}$Ga, $^{62}$Cu and $^{82}$Rb, and derive empirical formulas for the distributions. This paper focuses on allowed-decay isotopes. It is argued that blurring at the detection level should not be described by the positron range $r$, but instead the 2D projected distance $\delta$ (equal to the closest distance between decay and line of response). To determine these 2D distributions, results from a dedicated positron track-structure Monte Carlo code, Electron and POsitron TRANsport (EPOTRAN), were used. Materials other than water were studied with PENELOPE. The radial cumulative probability distribution G$_{2D}$($\delta$) and the radial probability density distribution $g_{2D}$($\delta$) were determined. G$_{2D}$($\delta$) could be approximated by the empirical function 1 - exp(-$A \delta^2$ - $B \delta$), where A = 0.0266 ($E_{mean}$)$^{-1.716}$ and B = 0.1119 ($E_{mean}$)$^{-1.934}$, with $E_{mean}$ being the mean positron energy in MeV and $\delta$ in mm. The radial density distribution $g_{2D}$($\delta$) could be approximated by differentiation of G$_{2D}$($\delta$). Distributions in other media were very similar to water. The positron range is important for improved resolution in PET imaging. Relevant distributions for the positron range have been derived for seven isotopes. Distributions for other allowed-decay isotopes may be estimated with the above formulas

Positron range in PET imaging: non-conventional isotopes

In addition to conventional short-lived radionuclides, longer-lived isotopes are becoming increasingly important to positron emission tomography (PET). The longer half-life both allows for circumvention of the in-house production of radionuclides, and expands the spectrum of physiological processes amenable to PET imaging, including processes with prohibitively slow kinetics for investigation with short-lived radiotracers. However, many of these radionuclides emit 'high-energy' positrons and gamma rays which affect the spatial resolution and quantitative accuracy of PET images. The objective of the present work is to investigate the positron range distribution for some of these long-lived isotopes. Based on existing Monte Carlo simulations of positron interactions in water, the probability distribution of the line of response displacement have been empirically described by means of analytic displacement functions. Relevant distributions have been derived for the isotopes (22)Na, (52)Mn, (89)Zr, (45)Ti, (51)Mn, (94 m)Tc, (52 m)Mn, (38)K, (64)Cu, (86)Y, (124)I, and (120)I. It was found that the distribution functions previously found for a series of conventional isotopes (Jødal et al 2012 Phys. Med. Bio. 57 3931-43), were also applicable to these non-conventional isotopes, except that for (120)I, (124)I, (89)Zr, (52)Mn, and (64)Cu, parameters in the formulae were less well predicted by mean positron energy alone. Both conventional and non-conventional range distributions can be described by relatively simple analytic expressions. The results will be applicable to image-reconstruction software to improve the resolution