SciPost Submission Page
Real-Time Motion Correction in Magnetic Resonance Spectroscopy: AI solution inspired by fundamental science
by Benedetta Argiento, Alberto Annovi, Silvia Capuani, Matteo Cacioppo, Andrea Ciardiello, Roberto Coccurello, Stefano Giagu, Federico Giove, Alessandro Lonardo, Francesca Lo Cicero, Alessandra Maiuro, Carlo Mancini Terracciano, Mario Merola, Marco Montuori, Emilia Nisticò, Pierpaolo Perticaroli, Biagio Rossi, Cristian Rossi, Elvira Rossi, Francesco Simula, Cecilia Voena
Submission summary
| Authors (as registered SciPost users): | Benedetta Argiento |
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| Preprint Link: | https://arxiv.org/abs/2509.24676v1 (pdf) |
| Date submitted: | Sept. 30, 2025, 9:43 a.m. |
| Submitted by: | Benedetta Argiento |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
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| Academic field: | Physics |
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| Approach: | Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
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Abstract
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.