Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis

BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated with the PET modality. Artificial intelligence (AI)-based techniques offer a promising approach to overcome these limitations.ObjectiveThis study evaluates the AI-based image enhancement methods integrated into the United Imaging PET/MR system, focusing on improvements in image quality, reduced injection dose, and shortened acquisition duration.MethodSixty-three patients underwent ¹⁸F-FDG PET/MR scans using uPMR790 (0.09 ± 0.01 mCi/kg, 5 min/bed, n = 29) and uPMR890 (0.05 ± 0.01 mCi/kg, 2.5 min/bed for PET and accelerated MR protocols, n = 34) with advanced AI-enhanced method. Shortened MR protocols included T1 W and T2 W sequences. Image quality was evaluated subjectively by two physicians and objectively using SNR and artifact ratios.ResultsThe AI-enhanced system achieved high-quality PET and MR images despite reduced PET doses and scan durations for both PET and MR components. AI-based reconstruction images showed higher SNR, fewer artifacts, and reduced noise compared to the conventional system.ConclusionsAI-enhanced PET/MR significantly improves imaging efficiency by reducing PET/MR acquisition durations, lowering radiation dose, and enhancing overall image quality, making it a valuable tool for clinical hybrid imaging.