mlsanityData to 14 Oct 2026

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

arXiv 1904.05168 ↗First posted 9 Apr 2019physics.med-ph cs.AI cs.LGcited by 16

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Abstract (arXiv)

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 application of 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 a large volume of realistic training data usually required in the deep learning approach.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Apr 2019.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 2; 2025: 6.

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  1. 2022Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonancecited by 22
  2. 2025Protein Large Language Models: A Comprehensive Surveycited by 18
  3. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 15
  4. 2023Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signalcited by 6
  5. 2022A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorizationcited by 1
  6. 2023CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysiscited by 1
  7. 2023Deep Learning as a Method for Inversion of NMR Signalscited by 1
  8. 2024TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopycited by 1
  9. 2025DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signalscited by 1
  10. 2021Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Datacited by 0
  11. 2023Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholdingcited by 0
  12. 2024Beyond traditional Magnetic Resonance processing with Artificial Intelligencecited by 0
4 more
  1. 2025DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertaintycited by 0
  2. 2025Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstructioncited by 0
  3. 2025An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRScited by 0
  4. 2025OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functionscited by 0