Objective: Prostate cancer remains one of the most prevalent forms of cancer in men aged over 65 years, with yearly around 11,200 new cases and 2,600 deaths in the Netherlands. Diagnosis of prostate cancer still relies on biopsies, which are unpleasant and invasive procedures for the patient. The new PET tracer, [68Ga]Sarabesin 3, aims to offer an accurate and minimally invasive way to locate and diagnose PC by taking benefit of the overexpression of the gastrin releasing peptide receptor on prostate cancer cells. In this research, the pharmacokinetics of the tracer in cancerous and healthy tissues from dynamic PET imaging was studied to identify receptor-specific uptake in prostate cancer tissue. Methods: An image derived input function was obtained from a volume of interest drawn over the femoral artery. The image derived input function correction methods proposed by Chen[1], Mourik[2] and Hackett[3] were evaulated using Monte Carlo simulation of the fermoral artery. The simulation was done in GATE using a model of the Siemens Biograph mCT. Iteratively reweighted least squares and step-wise model fitting were used to increase the accuracy in the small spots suspicious of cancerous tissue. In order to validate the applied arterial input function correction methods, a model of the Siemens Biograph mCT was built in the GATE Monte Carlo software. The absorbed dose was calculated using the MIRD male dosimetry model, OLINDA/EXM and the time activity curves in the various organs. A simple one-way perfusion model of the kidney was applied to estimate the bladder filling over time. Results: The sensitivity and spatial resolution of the GATE model at the field-of-view center were respectively 11,1 ± 0,027 kcps/MBq and 4,00 ± 0,56 mm full width half maximum in the radial direction, both closely matching the values of reported by Jakoby[4]. Applying the arterial input function correction methods to the simulated arterial and venous concentration curves showed that Hackett’s method[3] most accurately estimates the true arterial concentration with less than 1% error after the first pass peak. Step-wise fitting of the 1-, FDG- and 2-compartment models showed to be less vulnerable to local minima. An increase up to 280% receptor density was observed in the spots suspicious of cancerous tissue compared to the surrounding tissue healthy prostate tissue, thereby providing proof for the specific uptake of [68Ga]Sarabesin 3 by gastrin releasing peptide receptor in prostate cancer tissue. The pharmacokinetics of [68Ga]Sarabesin 3 were best described by a FDG model or a 2 compartment model with a small retention coefficient. It was not possible to determine the retention coefficient of the latter due the relatively large noise and small number of measurement points 50 minutes post-injection. Conclusion: Hackett’s method estimated the arterial concentration well in Monte Carlo simulations. [68Ga]Sarabesin 3 showed a good uptake in spots suspicious of cancerous tissue and had a high retention despite being an antagonist. The absorbed dose per administered activity to the prostate, red bone marrow and pancreas were estimated at 16,5, 13,5 and 196 ?Gy/MBq and the effective dose was 22,40 ?Sv/MBq, which is similar to the average [18F]DG effective dose.