# Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

arXiv: https://arxiv.org/abs/1904.05168 · HTML: https://mlsanity.com/paper/1904.05168/

## Key facts

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

## Built on by (most cited first, 16 of 16)

- [Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance](https://mlsanity.com/paper/2203.11178/) (2022)
- [Protein Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.17504/) (2025)
- [One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction](https://mlsanity.com/paper/2307.13220/) (2023)
- [Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal](https://mlsanity.com/paper/2306.09681/) (2023)
- [A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization](https://mlsanity.com/paper/2212.14150/) (2022)
- [CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis](https://mlsanity.com/paper/2309.07178/) (2023)
- [Deep Learning as a Method for Inversion of NMR Signals](https://mlsanity.com/paper/2311.13722/) (2023)
- [TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy](https://mlsanity.com/paper/2405.11064/) (2024)
- [DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signals](https://mlsanity.com/paper/2505.20367/) (2025)
- [Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data](https://mlsanity.com/paper/2101.11442/) (2021)
- [Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding](https://mlsanity.com/paper/2304.06959/) (2023)
- [Beyond traditional Magnetic Resonance processing with Artificial Intelligence](https://mlsanity.com/paper/2405.07657/) (2024)
- [DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty](https://mlsanity.com/paper/2502.05230/) (2025)
- [Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction](https://mlsanity.com/paper/2503.03475/) (2025)
- [An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS](https://mlsanity.com/paper/2503.04469/) (2025)
- [OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions](https://mlsanity.com/paper/2509.11499/) (2025)

## 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.