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 Q, Coggins BE, Zhou P. Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra. Nat Commun. 2016 Jul 27;7:12281.
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.
Wu Q, Coggins BE, Zhou P. Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra. Nat Commun. 2016 Jul 27;7:12281.
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