Year: 2025
Type: article
Abstract: This paper presents a novel image reconstruction pipeline for three-gamma (3-γ) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nuclear medicine. The proposed Direct3c pipeline addresses the inherent challenges in 3-c PET systems, such as detector imperfections and uncertainty in photon interaction points. A key feature of the pipeline is its ability to determine the order of interactions through a model trained on Monte Carlo (MC) simulations using the Geant4 Application for Tomography Emission (GATE) toolkit, thus providing the necessary information to construct Compton cones which intersects with the line of response (LOR) to provide an estimate of the emission point. The pipeline processes 3-c PET raw data, reconstructs histoimages by propagating energy and spatial uncertainties along the LOR, and applies a 3-D convolutional neural network (CNN) to refine these intermediate images into high-quality reconstructions. To further enhance image quality, the pipeline leverages both supervised learning and adversarial losses, the latter preserving fine structural details. Experimental results show that Direct3c consistently outperforms conventional 200-ps time-of-flight (TOF) PET in terms of SSIM and PSNR.
Institutions: Inserm, Université de Bretagne Occidentale
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
Awards: G2014946915, G3343749973, G7240651115
OpenAlex ID: W4411143532