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Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra.

Publication ,  Journal Article
Wu, Q; Coggins, BE; Zhou, P
Published in: Nat Commun
July 27, 2016

The application of sparse-sampling techniques to NMR data acquisition would benefit from reliable quality measurements for reconstructed spectra. We introduce a pair of noise-normalized measurements, and , for differentiating inadequate modelling from overfitting. While and can be used jointly for methods that do not enforce exact agreement between the back-calculated time domain and the original sparse data, the cross-validation measure is applicable to all reconstruction algorithms. We show that the fidelity of reconstruction is sensitive to changes in and that model overfitting results in elevated and reduced spectral quality.

Duke Scholars

Published In

Nat Commun

DOI

EISSN

2041-1723

Publication Date

July 27, 2016

Volume

7

Start / End Page

12281

Location

England

Related Subject Headings

  • Magnetic Resonance Spectroscopy
  • Image Processing, Computer-Assisted
  • Entropy
  • Bias
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wu, Q., Coggins, B. E., & Zhou, P. (2016). Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra. Nat Commun, 7, 12281. https://doi.org/10.1038/ncomms12281
Wu, Qinglin, Brian E. Coggins, and Pei Zhou. “Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra.Nat Commun 7 (July 27, 2016): 12281. https://doi.org/10.1038/ncomms12281.
Wu, Qinglin, et al. “Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra.Nat Commun, vol. 7, July 2016, p. 12281. Pubmed, doi:10.1038/ncomms12281.

Published In

Nat Commun

DOI

EISSN

2041-1723

Publication Date

July 27, 2016

Volume

7

Start / End Page

12281

Location

England

Related Subject Headings

  • Magnetic Resonance Spectroscopy
  • Image Processing, Computer-Assisted
  • Entropy
  • Bias
  • Algorithms