Monte Carlo simulations of GeoPET experiments: 3D images of tracer distributions (18F, 124I and 58Co) in Opalinus clay, anhydrite and quartz

• We reproduce the physics of our GeoPET experiments with Monte Carlo simulations.
• Monte-Carlo simulations are suitable for planning PET experiments in geo-material.
• PET-nuclides, geo-material, scanner geometry and energy window effect image quality.
• Reconstructed images of simulations reproduce artefacts as observed in measurements.
• Simulation based scatter correction for 124-I in Opalinus clay achieved exemplarily.

Understanding conservative fluid flow and reactive tracer transport in soils and rock formations requires quantitative transport visualization methods in 3D+t. After a decade of research and development we established the GeoPET as a non-destructive method with unrivalled sensitivity and selectivity, with due spatial and temporal resolution by applying Positron Emission Tomography (PET), a nuclear medicine imaging method, to dense rock material. Requirements for reaching the physical limit of image resolution of nearly 1 mm are (a) a high-resolution PET-camera, like our ClearPET scanner (Raytest), and (b) appropriate correction methods for scatter and attenuation of 511 keV—photons in the dense geological material. The latter are by far more significant in dense geological material than in human and small animal body tissue (water).Here we present data from Monte Carlo simulations (MCS) reflecting selected GeoPET experiments. The MCS consider all involved nuclear physical processes of the measurement with the ClearPET-system and allow us to quantify the sensitivity of the method and the scatter fractions in geological media as function of material (quartz, Opalinus clay and anhydrite compared to water), PET isotope (18F, 58Co and 124I), and geometric system parameters. The synthetic data sets obtained by MCS are the basis for detailed performance assessment studies allowing for image quality improvements. A scatter correction method is applied exemplarily by subtracting projections of simulated scattered coincidences from experimental data sets prior to image reconstruction with an iterative reconstruction process.

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