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Accelerated NMR Spectroscopy: Merge Optimization with Deep Learning
Multi-dimensional NMR spectroscopy is an invaluable biophysical tool in ...
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Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
Since the concept of deep learning (DL) was formally proposed in 2006, i...
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Molecular Quantum Electrodynamics at Finite Temperatures: Applications to Nuclear Magnetic Resonance
In this document it is shown that the chemical shift, spin-spin coupling...
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A deep learning approach for Magnetic Resonance Fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recover...
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NMR Assignment through Linear Programming
Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used te...
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Context-endcoding for neural network based skull stripping in magnetic resonance imaging
Skull stripping is usually the first step for most brain analysisprocess...
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An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
We propose a novel numerical approach to separate multiple tissue compar...
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Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of harnessing deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for large volume of realistic training data usually required in the deep learning approach.
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