Speaker
Description
Introduction:
Positron emission tomography (PET) and dynamic contrast-enhanced MRI (DCE-MRI) probe complementary transport processes; $^{18}$F-FDG can undergo carrier-mediated uptake, while gadolinium chelates distribute via passive diffusion. Imaging both modalities in a shared permeability phantom isolates passive transport, allowing the establishment of a baseline permeability ratio (P$_{FDG}$/P$_{Gd}$) free of transporter effects. Measuring deviations from this passive baseline in vivo may allow transporter activity to be mapped in regions of altered vascular integrity that would otherwise confound PET analysis.
Methods:
A DCE-MRI permeability phantom (Sarwar MS et al. Magn Reson Med. 2025) underwent sequential imaging with dynamic $^{18}$F-FDG PET and gadobutrol-enhanced MRI using the Siemens Biograph Vision Quadra PET and 3T Magnetom Skyra scanners.
PET acquisition was gated at 5s intervals during the early dynamic phase to enable high temporal resolution measurement of tracer arrival. This allowed for estimation of flow (F) through an adiabatic approximation (St Lawrence KS, Lee TY. J Cereb Blood Flow Metab. 1998). During the adiabatic window, a sigmoid-gated plateau function was fitted to the flow curve, with plateau boundaries defined at 95% and 5% of the on and off sigmoid functions respectively. This allowed for quantification of both flow (F) and channel transit time (T$_{c}$), from which channel volume (V$_{chan}$) could be estimated using V$_{chan}$ = F*T$_{c}$.
K$_{1}$ (PET) and K$_{Trans}$ (DCE-MRI) were derived using kinetic modelling. Under the Renkin–Crone model, these parameters were combined with the measured flow to estimate the permeability–surface area product (PS). Flow was assumed to remain constant across modalities owing to the fixed experimental pump speed. A ratio of PS values was then taken to estimate the permeability ratio, P$_{FDG}$/P$_{Gd}$. A delay parameter was included to account for differences between radiotracer arrival at the measured arterial input; this also reflected differences in dispersion behaviour evident between modalities, which are thought to be due to differences in tracer viscosity. Both channel (V$_{chan}$) and pore (V$_{pore}$) volume parameters were compared against modalities and material characteristics (V$_{chan} = 0.11$; V$_{pore} = 0.75$) to validate model performance.
Results:
Volume parameter Vchan was overestimated by the flow analysis, which is consistent with the uncertain bounds of the adiabatic window. A two tissue irreversible compartment model best described the $^{18}$F-FDG kinetics, consistent with slow tracer accumulation around the phantom material over the scan timescale. Estimates of V$_{chan}$ and V$_{pore}$ were consistent across modalities and showed agreement with the phantom’s material specifications, supporting the validity of the fitted transport parameters and the derived permeability measures. The permeability ratio satisfied P$_{FDG}$/P$_{Gd}>1$, as expected given the relative molecular sizes of FDG and gadobutrol.
Conclusion:
This study establishes a multimodal, multiparametric framework for quantifying PET tracer transport using a baseline permeability ratio under passive conditions. Agreement in volume parameters between modalities supports the validity of these measurements despite differences in tracer kinetics. Additional scans are planned across phantoms with varying channel and porosity dimensions to further characterise this permeability ratio and explore the concurrent dynamics between modalities. Future work will also refine the definition of the adiabatic window boundaries.
| Track | PSMR |
|---|---|
| Presentation type | Oral |