Application of low‐rank approximation using truncated singular value decomposition for noise reduction in hyperpolarized 13C NMR spectroscopy

Dissolution dynamic nuclear polarization allows in vivo studies of metabolic flux using 13C‐hyperpolarized tracers by enhancing signal intensity by up to four orders of magnitude. The T1 for in vivo applications is typically in the range of 10–50 s for the different 13C‐enriched metabolic substrates...

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Veröffentlicht in:NMR in biomedicine 2021-05, Vol.34 (5), p.n/a
Hauptverfasser: Francischello, R., Geppi, M., Flori, A., Vasini, E.M., Sykora, S., Menichetti, L.
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container_issue 5
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container_title NMR in biomedicine
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creator Francischello, R.
Geppi, M.
Flori, A.
Vasini, E.M.
Sykora, S.
Menichetti, L.
description Dissolution dynamic nuclear polarization allows in vivo studies of metabolic flux using 13C‐hyperpolarized tracers by enhancing signal intensity by up to four orders of magnitude. The T1 for in vivo applications is typically in the range of 10–50 s for the different 13C‐enriched metabolic substrates; the exponential loss of polarization due to various relaxation mechanisms leads to a strong reduction of the signal‐to‐noise ratio (SNR). A common solution to the problem of low SNR is the accumulation/averaging of consecutive spectra. However, some limitations related to long delays between consecutive scans occur: in particular, following biochemical kinetics and estimate apparent enzymatic constants becomes time critical when measurement scans are repeated with the typical delay of about 3 T1. Here we propose a method to dramatically reduce the noise, and therefore also the acquisition times, by computing, via truncated singular value decomposition, a low‐rank approximation to the individual complex time‐domain signals. Moreover, this approach has the additional advantage that the phase correction can be applied to the spectra already denoised, thus greatly reducing phase correction errors. We have tested the method on (1) simulated data; (2) performing dissolution of hyperpolarized 1‐13C‐pyruvate in standard conditions and (3) in vivo data sets, using a porcine model injected with hyperpolarized Na‐1‐13C‐acetate. It was shown that the presented method reduces the noise level in all the experimental data sets, allowing the retrieval of signals from highly noisy data without any prior phase correction pre‐processing. The effects of the proposed approach on the quantification of metabolic kinetics parameters have to be shown by full quantification studies. The signal‐to‐noise‐ratio for hyperpolarized 13C‐Pyruvate (41mM). In blue the original data, in orange the denoised data. We propose a method to denoise NMR spectra without phase correction. We apply our method to two d‐DNP 13C experiment: hyperpolarization decay monitoring in a phantom 13C‐Pyruvate and an in‐vivo metabolic flux measurement in porcine model with Na‐[1‐13C]‐Acetate. We improve the SNR up to two order of magnitude with a median value of 40 for the 13C‐Pyruvate and 36 for the Na‐[1‐13C]‐Acetate.
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The T1 for in vivo applications is typically in the range of 10–50 s for the different 13C‐enriched metabolic substrates; the exponential loss of polarization due to various relaxation mechanisms leads to a strong reduction of the signal‐to‐noise ratio (SNR). A common solution to the problem of low SNR is the accumulation/averaging of consecutive spectra. However, some limitations related to long delays between consecutive scans occur: in particular, following biochemical kinetics and estimate apparent enzymatic constants becomes time critical when measurement scans are repeated with the typical delay of about 3 T1. Here we propose a method to dramatically reduce the noise, and therefore also the acquisition times, by computing, via truncated singular value decomposition, a low‐rank approximation to the individual complex time‐domain signals. Moreover, this approach has the additional advantage that the phase correction can be applied to the spectra already denoised, thus greatly reducing phase correction errors. We have tested the method on (1) simulated data; (2) performing dissolution of hyperpolarized 1‐13C‐pyruvate in standard conditions and (3) in vivo data sets, using a porcine model injected with hyperpolarized Na‐1‐13C‐acetate. It was shown that the presented method reduces the noise level in all the experimental data sets, allowing the retrieval of signals from highly noisy data without any prior phase correction pre‐processing. The effects of the proposed approach on the quantification of metabolic kinetics parameters have to be shown by full quantification studies. The signal‐to‐noise‐ratio for hyperpolarized 13C‐Pyruvate (41mM). In blue the original data, in orange the denoised data. We propose a method to denoise NMR spectra without phase correction. 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subjects 13C hyperpolarization
Acetic acid
Approximation
Biological products
Datasets
Decomposition
denoising methods
Dissolution
d‐DNP
In vivo methods and tests
Kinetics
low‐rank approximation
Magnetic resonance spectroscopy
Metabolic flux
Metabolism
MRS
NMR
NMR spectroscopy
Noise
Noise levels
Noise reduction
Nuclear magnetic resonance
Polarization
Pyruvic acid
Signal processing
Singular value decomposition
Spectrum analysis
Substrates
SVD
Time measurement
title Application of low‐rank approximation using truncated singular value decomposition for noise reduction in hyperpolarized 13C NMR spectroscopy
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