Magnetic Resonance Spectroscopy (MRS) is a powerful non-invasive tool for metabolic tissue analysis but is often degraded by patient motion, limiting clinical utility. The RECENTRE project (REal-time motion CorrEctioN in magneTic Resonance) presents an AI-driven, real-time motion correction pipeline based on optimized GRU networks, inspired by tagging and fast-trigger algorithms from high-energy physics. Models evaluated on held-out test sets achieve good predictive performance and overall positive framewise displacement (FD) gains. These results demonstrate feasibility for prospective scanner integration; future work will complete in-vivo validation.
@article{arxiv.2509.24676,
title = {Real-Time Motion Correction in Magnetic Resonance Spectroscopy: AI solution inspired by fundamental science},
author = {Benedetta Argiento and Alberto Annovi and Silvia Capuani and Matteo Cacioppo and Andrea Ciardiello and Roberto Coccurello and Stefano Giagu and Federico Giove and Alessandro Lonardo and Francesca Lo Cicero and Alessandra Maiuro and Carlo Mancini Terracciano and Mario Merola and Marco Montuori and Emilia Nisticò and Pierpaolo Perticaroli and Biagio Rossi and Cristian Rossi and Elvira Rossi and Francesco Simula and Cecilia Voena},
journal= {arXiv preprint arXiv:2509.24676},
year = {2025}
}