Nuclear Magnetic Resonance (NMR) spectroscopy is regarded as an important tool in bio-engineering while often suffers from its time-consuming acquisition. Non-Uniformly Sampling (NUS) method can speed up the acquisition, but the missing FID signals need to be reconstructed with proper method.. In this work, we proposed a deep learning reconstruction method based on unrolling the iterative process of a state-of-the-art model-based low rank Hankel matrix method. Experimental results show that the proposed method provides a better approximation of low rank and preserves the low-intensity signals much better.
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