Resumen de Deep Learning methodologies for direct image reconstruction and integrated attenuation correction in brain PET/MRI

Patricia Díaz Serrano, Juan Enrique Ortuño Fisac, J. López Santiago, Fivos Panetsos Pétrova , G Kontaxakis

• This study proposes the ATTDeepPET model, a novel deep learning architecture crafted specifically for advancing positron emission tomography (PET) image reconstruction in PET/MR scanners. By incorporating magnetic resonance (MR) images into its learning process, ATTDeepPET addresses the persistent challenges associated with attenuation effects in PET/MR scanners, eliminating the need for simulated transmission scans. ATTDeepPET's performance is assessed alongside the deep learning model DeepPET, as well as established methods such as FBP, ML-EM, and ML-EMR for comparison. The findings reveal noteworthy achievements since ATTDeepPET accomplishes competitive image quality compared to FBP, MLEM, and ML-EMR when applied to brain phantoms while also demonstrating a reduction in reconstruction times. Nevertheless, when dealing with real PET images, ATTDeepPET does exhibit some performance variability, underscoring the increased complexity of real-set scenarios and the importance of employing ...