NMR signal enhancement via a new time-frequency transform

Ahmed, O.A.; Fahmy, M.M.

doi:10.1109/42.959299

Cited by 19 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet transforms are powerful methods for time-frequency analysis due to their flexible window length and applicability to multi-dimensional signals. The advantages of using wavelet-based spectral analysis in both ESR and nuclear magnetic resonance (NMR) spectroscopic studies in one-dimensional experiments have been established, − where one can reliably decouple different spectral components, including noise, in separating individual peaks. In this work, we present a 2D undecimated discrete wavelet transform , (UDWT)-based approach that can effectively identify and separate overlapping peaks in 2D ESR signals.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals

Sahoo,

Roy,

Srivastava

2023

J. Phys. Chem. A

View full text Add to dashboard Cite

Two-dimensional electron spin resonance (2D ESR) spectroscopy is a unique experimental technique for probing protein structure and dynamics, including processes that occur at the microsecond time scale. While it provides significant resolution enhancement over the one-dimensional experimental setup, spectral broadening and noise make extraction of spectral information highly challenging. Traditionally, two-dimensional Fourier transform (2D FT) is applied for the analysis of 2D ESR signals, although its efficiency is limited to stationary signals. In addition, it often fails to resolve overlapping peaks in 2D ESR. In this work, we propose a time-frequency analysis of 2D time-domain signals, which identifies all frequency peaks by decoupling a signal into its distinct constituent components via projection on the time-frequency plane. The method utilizes 2D undecimated discrete wavelet transform (2D UDWT) as an intermediate step in the analysis, followed by signal reconstruction and 2D FT. We have applied the method to a simulated 2D double quantum coherence (DQC) signal for validation and a set of experimental 2D ESR signals, demonstrating its efficiency in resolving overlapping peaks in the frequency domain, while displaying frequency evolution with time in case of non-stationary data.

show abstract

Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals

Sahoo,

Roy,

Srivastava

2023

J. Phys. Chem. A

View full text Add to dashboard Cite

show abstract

“…In order to get accurate parameters and structure estimation from the magnetic resonance spectrum, several de-noising methods have been developed. Most of them are implemented by transforming the signal and noise into another domain, such as Fourier [15], time-frequency transform [16], or wavelet transforms [17,18], and then remove the noise whose amplitudes are below a pre-defined threshold, which can be set based on the distribution of noise amplitudes [19], or be set by introducing an improved hybrid threshold function based on SNR and mean square error [20]. However, when the frequencies of the noise peaks overlap with the frequencies of the NMR signal and when the SNR is very low, the noise peaks may introduce erroneous information into the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Sensor Configuration and Algorithms for Power-Line Interference Suppression in Low Field Nuclear Magnetic Resonance

Huang

Dong

Tao

et al. 2019

Sensors

View full text Add to dashboard Cite

Low field (LF) nuclear magnetic resonance (NMR) shows potential advantages to study pure heteronuclear J-coupling and observe the fine structure of matter. Power-line harmonics interferences and fixed-frequency noise peaks might introduce discrete noise peaks into the LF-NMR spectrum in an open environment or in a conductively shielded room, which might disturb J-coupling spectra of matter recorded at LF. In this paper, we describe a multi-channel sensor configuration of superconducting quantum interference devices, and measure the multiple peaks of the 2,2,2-trifluoroethanol J-coupling spectrum. For the case of low signal to noise ratio (SNR) < 1, we suggest two noise suppression algorithms using discrete wavelet analysis (DWA), combined with either least squares method (LSM) or gradient descent (GD). The de-noising methods are based on spatial correlation of the interferences among the superconducting sensors, and are experimentally demonstrated. The DWA-LSM algorithm shows a significant effect in the noise reduction and recovers SNR > 1 for most of the signal peaks. The DWA-GD algorithm improves the SNR further, but takes more computational time. Depending on whether the accuracy or the speed of the de-noising process is more important in LF-NMR applications, the choice of algorithm should be made.

show abstract

“…Ahmed [8] proposed consecutive projections of the noisy MRS data in different domains, in conjunction with noise filtration for each domain. For these projections, a set of stable, linear, time-frequency transforms was applied with different resolutions [9] and improvements over the wavelet shrinkage method for 31 P single voxel data were reported [8]. However, for consideration of this method to volumetric MRSI data that can have on the order of 10 5 spectra, the computational cost of using multiple projections must also be considered.…”

Section: Introductionmentioning

confidence: 99%

Denoising of MR spectroscopic imaging data using statistical selection of principal components

Abdoli

Stoyanova

Maudsley

2016

Magn Reson Mater Phy

View full text Add to dashboard Cite

Objectives To evaluate a new denoising method for MR Spectroscopic Imaging (MRSI) data based on Selection of signal-related Principal Components (SSPC)from principal components analysis (PCA). Materials and Methods A PCA-based method was implemented for selection of signal-related PCs and denoising achieved by reconstructing the original dataset utilizing only these PCs. Performance was evaluated using simulated MRSI data and two volumetric in vivo MRSI of human brain from normal subject and patient with brain tumor using variable signal to noise ratios (SNR), metabolite peak areas, Cramer-Rao Bounds (CRB) of fitted metabolite peak areas and metabolite linewidth. Results In simulated data SSPC determined the correct number of signal-related PCs. For in vivo studies, the SSPC denoising resulted in improved SNR and reduced metabolite quantification uncertainty compared to the original data and two other methods for denoising. The method also performed very well in preserving the spectral linewidth and peak areas. However, this method performs better for regions that have larger numbers of similar spectra. Conclusion The proposed SSPC denoising improved the SNR and metabolite quantification uncertainty in MRSI, with minimal compromise of the spectral information, and can result in increased accuracy.

show abstract

NMR signal enhancement via a new time-frequency transform

Cited by 19 publications

References 22 publications

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals

Time-Frequency Analysis of Two-Dimensional Electron Spin Resonance Signals

Sensor Configuration and Algorithms for Power-Line Interference Suppression in Low Field Nuclear Magnetic Resonance

Denoising of MR spectroscopic imaging data using statistical selection of principal components

Contact Info

Product

Resources

About