Time-domain analysis of magnetic resonance spectra and chemical shift images

The utility of adaptive prediction and filtering algorithms and the autocorrelation-based Yule-Walker algorithm to predict and filter complex NMR (nuclear magnetic resonance) data is demonstrated. The application of these methods improves the available signal-to-noise ratio using time-domain analysi...

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Bibliographic Details
Main Authors: Canady, L.D., Jordan, R., Asgharzadeh, A., Abousleman, G., Koechner, D., Griffey, R.H.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive filters
Autocorrelation
Chemical analysis
Filtering algorithms
Magnetic resonance
Magnetic separation
Nuclear magnetic resonance
Prediction algorithms
Signal resolution
Time domain analysis
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Summary:The utility of adaptive prediction and filtering algorithms and the autocorrelation-based Yule-Walker algorithm to predict and filter complex NMR (nuclear magnetic resonance) data is demonstrated. The application of these methods improves the available signal-to-noise ratio using time-domain analysis, and increases the low resolution via prediction algorithms in data containing phase errors introduced by hardware limitations. The application of the complex least-mean-squares and the modified-least-mean-squares transversal and lattice algorithms to low- and high-resolution NMR data records is demonstrated. The resolution and windowing problems found in the discrete Fourier transform are overcome by these alternative methods.< >
DOI:10.1109/CBMSYS.1990.109430