STEADY STATE FREE PRECESSION RELAXATION MAPPING WITH DEEP LEARNING IN HIGH-RESOLUTION NMR

This research project aims to develop a novel deep learning (DL)-driven framework for accurate and efficient relaxation time mapping using Steady-State Free Precession (SSFP) techniques in high-resolution NMR spectroscopy. Recognizing the limitations of traditional methods, such as sensitivity to experimental parameters and the complexity of data analysis, this project seeks to leverage the power of DL to overcome these challenges.