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

Abstract

Objective: Conventionally, if two metabolic processes are of interest for image analysis, two separate, sequential 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. Approach: In this work, we propose a reconstruction algorithm for the simultaneous injection of a β+-emitter emitting only annihilation photons and a β+-γ-emitter emitting annihilation photons and an additional prompt γ-photon. As in previous works, the γ-photon is used to identify events originating from the β+-γ-emitter. However, due to e.g. attenuation, the γ-photon is often not detected and not all events can correctly be associated with the β+-γ-emitter as they are detected as double coincidences. In contrast to previous works, we estimate this number of double coincidences with origin in the β+-γ, emitter including the attenuation of the prompt γ, and incorporate this estimation in the forward-projection of the ML-EM algorithm. For evaluation, we simulate different scenarios with varying objects and attenuation maps. The nuclide 18F serves as β+-emitter, while 44Sc functions as β+-γ emitter. The performance of the algorithm is assessed by calculating the residual error of the β+-γ-emitter in the reconstructed β+-emitter image. Additionally, the intensity values in the simulated cylinders of the ground truth (GT) and the reconstructed images are compared. Main Results: The remaining activity in the β+-emitter image varied from 0.4% to 3.7%. The absolute percentage difference between GT and reconstructed intensity for the pure β+ emitter images was found to be between 3.0 and 7.4% for all cases. The absolute percentage difference between GT and reconstructed intensity for the β+-γ emitter images ranged from 8.7 to 10.4% for all simulated cases. Significance: These results demonstrate that our approach can reconstruct two separate images with a good quantitation accurac.