Denoising neural networks for magnetic resonance spectroscopy

• URL: http://arxiv.org/abs/2211.00080v1
• Date: Mon, 31 Oct 2022 18:26:21 GMT
• Title: Denoising neural networks for magnetic resonance spectroscopy
• Authors: Natalie Klein, Amber J. Day, Harris Mason, Michael W. Malone, Sinead A. Williamson
• Abstract summary: In many scientific applications, measured time series are corrupted by noise or distortions. In this work, we demonstrate that deep learning-based denoising methods can outperform traditional techniques. Our motivating example is magnetic resonance spectroscopy, in which a primary goal is to detect the presence of short-duration, low-amplitude radio frequency signals.
• Score: 2.397411219508639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many scientific applications, measured time series are corrupted by noise or distortions. Traditional denoising techniques often fail to recover the signal of interest, particularly when the signal-to-noise ratio is low or when certain assumptions on the signal and noise are violated. In this work, we demonstrate that deep learning-based denoising methods can outperform traditional techniques while exhibiting greater robustness to variation in noise and signal characteristics. Our motivating example is magnetic resonance spectroscopy, in which a primary goal is to detect the presence of short-duration, low-amplitude radio frequency signals that are often obscured by strong interference that can be difficult to separate from the signal using traditional methods. We explore various deep learning architecture choices to capture the inherently complex-valued nature of magnetic resonance signals. On both synthetic and experimental data, we show that our deep learning-based approaches can exceed performance of traditional techniques, providing a powerful new class of methods for analysis of scientific time series data.

Related papers

• Underwater Acoustic Signal Recognition Based on Salient Feature [9.110359213246825]
  This paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals.
  arXiv  Detail & Related papers  (2023-12-20T16:04:02Z)
• Spatio-temporal correlations of noise in MOS spin qubits [0.2981781876202281]
  We show how to decompose signals into both frequency and time components to gain a deeper insight into the sources of noise in our systems. We apply the analysis to a long feedback experiment performed on a state-of-the-art two-qubit system in a pair of SiMOS quantum dots.
  arXiv  Detail & Related papers  (2023-09-21T23:45:14Z)
• Learning Provably Robust Estimators for Inverse Problems via Jittering [51.467236126126366]
  We investigate whether jittering, a simple regularization technique, is effective for learning worst-case robust estimators for inverse problems. We show that jittering significantly enhances the worst-case robustness, but can be suboptimal for inverse problems beyond denoising.
  arXiv  Detail & Related papers  (2023-07-24T14:19:36Z)
• RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network [7.5123109191537205]
  We propose a new non-stationary signal decomposition method under the framework of deep learning. We use the convolutional neural network, residual structure and nonlinear activation function to compute in an innovative way the local average of the signal. In the experiments, we evaluate the performance of the proposed model from two points of view: the calculation of the local average and the signal decomposition.
  arXiv  Detail & Related papers  (2023-07-04T13:53:01Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• 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)
• Weak-signal extraction enabled by deep-neural-network denoising of diffraction data [26.36525764239897]
  We show how data can be denoised via a deep convolutional neural network. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data.
  arXiv  Detail & Related papers  (2022-09-19T14:43:01Z)
• 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)
• Unsupervised Image Denoising with Frequency Domain Knowledge [2.834895018689047]
  Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale datasets. In this study we propose a frequency-sensitive unsupervised denoising method. Results using natural and synthetic datasets indicate that our unsupervised learning method augmented with frequency information achieves state-of-the-art denoising performance.
  arXiv  Detail & Related papers  (2021-11-29T07:41:32Z)
• Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders [62.997667081978825]
  We propose a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals.
  arXiv  Detail & Related papers  (2021-09-18T14:51:24Z)
• Frequency fluctuations of ferromagnetic resonances at milliKelvin temperatures [50.591267188664666]
  Noise is detrimental to device performance, especially for quantum coherent circuits. Recent efforts have demonstrated routes to utilizing magnon systems for quantum technologies, which are based on single magnons to superconducting qubits. Researching the temporal behavior can help to identify the underlying noise sources.
  arXiv  Detail & Related papers  (2021-07-14T08:00:37Z)

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.