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In recent years, the in vivo study of the central nervous system has undergone significant advancement through the implementation of magnetic resonance imaging (MRI)-based neuroimaging techniques. In particular, the advent of high- and ultra-high-field MRI systems has substantially contributed to a deeper understanding of the pathophysiological mechanisms underlying neurological diseases and has supported the development of personalized approaches in medicine. In the field of neuroimaging, the application of MRI techniques to investigate brain function and connectivity in vivo is increasingly expanding at ultra-high magnetic field strength (7T). This research project aims to develop, implement, and optimize advanced techniques to improve image quality through deep learning-based approaches and real-time monitoring of magnetic field fluctuations, with the goal of achieving functional neu
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