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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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Coil combination for receive array spectroscopy: Are data-driven methods superior to methods using computed field

Christopher T Rodgers1, Matthew D Robson1

  • 1Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom.

Magnetic Resonance in Medicine
|March 31, 2015
PubMed
Summary
This summary is machine-generated.

Data-driven methods for combining spectra from MRI receive arrays, especially X-nuclear spectra with low signal-to-noise ratios (SNRs), are superior to computed coil sensitivities. Advanced whitened singular-value decomposition (WSVD) techniques offer the best performance for improved spectral SNR.

Keywords:
MR spectroscopyWSVDWSVD+ApodWSVD+Apod+Bluradaptive combination theoryarraycoil combination

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Spectroscopy
  • Signal Processing

Background:

  • Combining spectra from MRI receive arrays, particularly X-nuclear spectra with low signal-to-noise ratios (SNRs), presents significant challenges.
  • Existing combination algorithms can be unified under Roemer's formula, highlighting differences in coil sensitivity estimation.
  • Extensions of the whitened singular-value decomposition (WSVD) algorithm, incorporating temporal or spatio-temporal apodization, aim to enhance coil sensitivities and spectral SNR.

Purpose of the Study:

  • To evaluate whether data-driven spectral combination methods outperform traditional computed coil sensitivities for low-SNR X-nuclear MRI.
  • To compare the efficacy of various spectral combination algorithms, including novel apodized WSVD approaches.

Main Methods:

  • Simulated radiofrequency fields were used to generate synthetic spectra for combination algorithm testing.
  • Ten different combination algorithms were applied to the synthetic data.
  • Combined spectra were quantitatively assessed for signal-to-noise ratio (SNR), with validation using phantom and human cardiac (31)P MRI data at 3T.

Main Results:

  • Spectral SNR trends were consistent across simulations, phantoms, and human subjects.
  • In phantom studies, WSVD combination with apodization (especially temporal + spatial) yielded higher SNRs than computed coil sensitivities when initial WSVD SNR exceeded specific thresholds (e.g., >9).
  • Apodized WSVD methods demonstrated superior SNR performance compared to other tested data-driven techniques.

Conclusions:

  • Data-driven spectral combination methods are preferable to computed coil sensitivities for human torso imaging at frequencies ≥49 MHz.
  • Apodized WSVD algorithms represent an advancement in spectral combination for improving SNR in MRI.
  • The findings support the use of advanced data-driven techniques for enhanced spectral quality in challenging MRI applications.