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¹³C NMR: ¹H–¹³C Decoupling01:04

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
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A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
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Accelerated high-bandwidth MR spectroscopic imaging using compressed sensing.

Peng Cao1, Peter J Shin1, Ilwoo Park1

  • 1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA.

Magnetic Resonance in Medicine
|May 27, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel compressed sensing (CS) method to accelerate Magnetic Resonance Spectroscopic Imaging (MRSI) with high spectral bandwidth. The technique enhances data acquisition speed while maintaining excellent spectral and spatial fidelity for in vivo studies.

Keywords:
Hankel matrix completionMR spectroscopic imagingcalibrationless parallel imagingcompressed sensinghyperpolarized carbon-13random blip gradients

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Area of Science:

  • Magnetic Resonance Imaging and Spectroscopy
  • Biomedical Engineering
  • Medical Physics

Background:

  • Magnetic Resonance Spectroscopic Imaging (MRSI) provides crucial metabolic information but is often limited by long acquisition times.
  • Accelerating MRSI is essential for improving clinical feasibility and enabling dynamic studies.
  • High spectral bandwidth is critical for resolving a wide range of metabolites, posing a challenge for acceleration techniques.

Purpose of the Study:

  • To develop and validate a compressed sensing (CS) acceleration method for high spectral bandwidth MRSI.
  • To exploit the spatial-spectral sparsity inherent in MRSI data for efficient acceleration.
  • To improve the speed of MRSI acquisition without compromising spectral or spatial resolution.

Main Methods:

  • Implemented a CS acceleration strategy using blip gradients for stochastic random walks in kx-ky-t space.
  • Employed block-Hankel matrix completion for efficient reconstruction of undersampled MRSI data.
  • Applied retrospective and prospective CS accelerations to in vivo (13)C MRSI experiments in rodent brain and liver.

Main Results:

  • Achieved up to 6.6-fold accelerations in retrospective studies, preserving spectral (750 Hz separation) and spatial fidelities (R(2) ≥ 0.96 for pyruvate, ≥ 0.87 for lactate).
  • Demonstrated 3.8-fold acceleration in prospective in vivo experiments with excellent metabolite localization and peak recovery.
  • Successfully imaged pyruvate, lactate, and dihydroxyacetone metabolites with high spectral bandwidth (4.5 kHz at 3.0 T).

Conclusions:

  • The developed CS approach is feasible for accelerating high spectral bandwidth MRSI experiments.
  • This method offers a promising solution for faster and more efficient metabolic imaging.
  • The technique maintains high data quality, paving the way for advanced in vivo MRSI applications.