DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signals

• URL: http://arxiv.org/abs/2505.20367v1
• Date: Mon, 26 May 2025 11:06:11 GMT
• Title: DiffNMR: Advancing Inpainting of Randomly Sampled Nuclear Magnetic Resonance Signals
• Authors: Sen Yan, Fabrizio Gabellieri, Etienne Goffinet, Filippo Castiglione, Thomas Launey,
• Abstract summary: Nuclear Magnetic Resonance spectroscopy is used to probe molecules' chemical environment, structure, and dynamics.<n>Non-Uniform sampling (NUS) is widely employed as a sub-sampling method to address these challenges.<n>We propose the use of deep learning techniques to enhance the reconstruction quality of NUS spectra.
• Score: 1.2582887633807602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy leverages nuclear magnetization to probe molecules' chemical environment, structure, and dynamics, with applications spanning from pharmaceuticals to the petroleum industry. Despite its utility, the high cost of NMR instrumentation, operation and the lengthy duration of experiments necessitate the development of computational techniques to optimize acquisition times. Non-Uniform sampling (NUS) is widely employed as a sub-sampling method to address these challenges, but it often introduces artifacts and degrades spectral quality, offsetting the benefits of reduced acquisition times. In this work, we propose the use of deep learning techniques to enhance the reconstruction quality of NUS spectra. Specifically, we explore the application of diffusion models, a relatively untapped approach in this domain. Our methodology involves applying diffusion models to both time-time and time-frequency NUS data, yielding satisfactory reconstructions of challenging spectra from the benchmark Artina dataset. This approach demonstrates the potential of diffusion models to improve the efficiency and accuracy of NMR spectroscopy as well as the superiority of using a time-frequency domain data over the time-time one, opening new landscapes for future studies.

Related papers

• DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation [9.321270922757442]
  Nuclear Magnetic Resonance (NMR) spectroscopy is a central characterization method for molecular structure elucidation.<n>We introduce DiffNMR, a novel end-to-end framework that leverages a conditional discrete diffusion model for de novo molecular structure elucidation from NMR spectra.
  arXiv  Detail & Related papers  (2025-07-09T06:21:36Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Simulating near-infrared spectroscopy on a quantum computer for enhanced chemical detection [0.0]
  Near-infrared (NIR) spectroscopy is a non-invasive, low-cost, reagent-less, and rapid technique to measure chemical concentrations.<n>We propose a method for simulating NIR spectra on a quantum computer, as part of a larger workflow to improve NIR-based chemical detection.
  arXiv  Detail & Related papers  (2025-04-14T18:02:48Z)
• DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra [60.39311767532607]
  We present DiffMS, a formula-restricted encoder-decoder generative network that achieves state-of-the-art performance on this task.<n>To develop a robust decoder that bridges latent embeddings and molecular structures, we pretrain the diffusion decoder with fingerprint-structure pairs.<n>Experiments on established benchmarks show that DiffMS outperforms existing models on de novo molecule generation.
  arXiv  Detail & Related papers  (2025-02-13T18:29:48Z)
• DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits [1.1844780810044506]
  High-field NMR instruments provide high-resolution spectra but are prohibitively expensive, whereas lower-field instruments offer more accessible, but lower-resolution, results.<n>This paper introduces an AI-driven approach that enhances the frequency resolution of NMR spectra through super-resolution techniques.
  arXiv  Detail & Related papers  (2025-02-06T21:00:35Z)
• DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model Uncertainty [2.4634393035848494]
  We propose a novel sub-sampling strategy based on a diffusion model trained on protein NMR data.<n>Our method iteratively reconstructs under-sampled spectra while using model uncertainty to guide subsequent sampling, significantly reducing acquisition time.<n>This advancement holds promise for many applications, from drug discovery to materials science, where rapid and high-resolution spectral analysis is critical.
  arXiv  Detail & Related papers  (2025-02-06T20:10:28Z)
• Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging [4.784326786161368]
  The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance imaging (MRSI) data.
  arXiv  Detail & Related papers  (2024-03-01T09:46:41Z)
• Digital noise spectroscopy with a quantum sensor [57.53000001488777]
  We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process. Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters. We experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond.
  arXiv  Detail & Related papers  (2022-12-19T02:19:35Z)
• Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise [52.77024349608834]
  We implement a deep neural network to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field. These results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.
  arXiv  Detail & Related papers  (2022-08-01T19:18:26Z)
• Supernova Light Curves Approximation based on Neural Network Models [53.180678723280145]
  Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. We study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve.
  arXiv  Detail & Related papers  (2022-06-27T13:46:51Z)
• Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks [46.751292014516025]
  Deep neural networks have been applied to reconstruct undersampled k-space data and have shown improved reconstruction performance. This work proposes a novel framework to learn k-space sampling trajectories by considering it as an Ordinary Differential Equation (ODE) problem. Experiments were conducted on different in-viv datasets (textite.g., brain and knee images) acquired with different sequences.
  arXiv  Detail & Related papers  (2022-04-05T20:28:42Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• Real-time estimation of the optically detected magnetic resonance shift in diamond quantum thermometry [47.50219326456544]
  We investigate the real-time estimation protocols for the frequency shift of optically detected magnetic resonance (ODMR) of nitrogen-vacancy centers in nanodiamonds (NDs) Efficiently integrating multipoint ODMR measurements and ND particle tracking into fluorescence microscopy has recently demonstrated stable monitoring of the temperature inside living animals.
  arXiv  Detail & Related papers  (2020-06-12T01:44:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.