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Berman P, Levi O, Parmet Y, et al. Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods. Concepts Magn Reson Part A Bridg Educ Res. 2013;42(3):72-88doi: 10.1002/cmr.a.21263.
Berman, P., Levi, O., Parmet, Y., Saunders, M., & Wiesman, Z. (2013). Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods. Concepts in magnetic resonance. Part A, Bridging education and research, 42(3), 72-88. https://doi.org/10.1002/cmr.a.21263
Berman, Paula, et al. "Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods." Concepts in magnetic resonance. Part A, Bridging education and research vol. 42,3 (2013): 72-88. doi: https://doi.org/10.1002/cmr.a.21263
Berman P, Levi O, Parmet Y, Saunders M, Wiesman Z. Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods. Concepts Magn Reson Part A Bridg Educ Res. 2013 May;42(3):72-88. doi: 10.1002/cmr.a.21263. Epub 2013 May 29. PMID: 23847452; PMCID: PMC3698697.
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Concepts Magn Reson Part A Bridg Educ Res. 2013 May;42(3):72-88. doi: 10.1002/cmr.a.21263. Epub 2013 May 29.

Laplace Inversion of Low-Resolution NMR Relaxometry Data Using Sparse Representation Methods.

Concepts in magnetic resonance. Part A, Bridging education and research

Paula Berman, Ofer Levi, Yisrael Parmet, Michael Saunders, Zeev Wiesman

Affiliations

  1. The Phyto-Lipid Biotechnology Laboratory, Departments of Biotechnology and Environmental Engineering, The Institutes for Applied Research, Ben-Gurion University of the Negev Beer-Sheva, Israel.

PMID: 23847452 PMCID: PMC3698697 DOI: 10.1002/cmr.a.21263

Abstract

Low-resolution nuclear magnetic resonance (LR-NMR) relaxometry is a powerful tool that can be harnessed for characterizing constituents in complex materials. Conversion of the relaxation signal into a continuous distribution of relaxation components is an ill-posed inverse Laplace transform problem. The most common numerical method implemented today for dealing with this kind of problem is based on

Keywords: L1 regularization; convex optimization; low-resolution NMR; sparse reconstruction

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