ML-EM based dual tracer PET image reconstruction with inclusion of prompt gamma attenuation.

Conventionally, if two metabolic processes are of interest for image analysis, two separate, sequential positron emission tomography (PET) scans are performed. However, sequential PET scans cannot simultaneously display the metabolic targets. The concurrent study of two simultaneous PET scans could provide new insights into the causes of diseases.In this work, we propose a reconstruction algorithm for the simultaneous injection of aβ+-emitter emitting only annihilation photons and aβ+--emitter emitting annihilation photons and an additional prompt-photon. As in previous works, the-photon is used to identify events originating from theβ+--emitter. However, due to e.g. attenuation and down-scatter, the-photon is often not detected and not all events can correctly be associated with theβ+--emitter as they are detected as double coincidences. In contrast to previous works, we estimate this number of double coincidences with origin in theβ+-, emitter including the attenuation of the prompt, and incorporate this estimation in the forward-projection of the maximum likelihood expectation maximization algorithm. For evaluation, we simulate different scenarios with varying objects and attenuation maps. The nuclideF serves asβ+-emitter, whileSc functions asβ+-emitter. The performance of the algorithm is assessed by calculating the residual error of theβ+--emitter in the reconstructedβ+-emitter image. Additionally, the intensity values in the simulated cylinders of the ground truth (GT) and the reconstructed images are compared.The remaining activity in theβ+-emitter image varied from 0.4% to 3.7%. The absolute percentage difference between GT and reconstructed intensity for the pureβ+emitter images was found to be between 3.0% and 7.4% for all cases. The absolute percentage difference between the GT and the reconstructed intensity for theβ+-emitter images ranged from 8.7% to 10.4% for all simulated cases.These results demonstrate that our approach can reconstruct two separate images with a good quantitation accuracy.