ArticleNMR in biomedicine2020
Parsimonious discretization for characterizing multi-exponential decay in magnetic resonance.
Article in NMR in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Short T1 Fraction as a Marker of Myelin Content: Evidence from Postmortem MRI and Histology.bioRxiv : the preprint server for biology · 2026Article
- Age-related changes in myelin and myelin water quantified with short-TR adiabatic inversion-recovery (STAIR) sequences.NeuroImage. Clinical · 2025Article
- Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: A review.Journal of neurochemistry · 2024Review
- Span of regularization for solution of inverse problems with application to magnetic resonance relaxometry of the brain.Scientific reports · 2022Article
- Stabilization of parameter estimates from multiexponential decay through extension into higher dimensions.Scientific reports · 2022Article
- Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging.NeuroImage · 2021Article
- Circadian Variation of Root Water Status in Three Herbaceous Species Assessed by Portable NMR.Plants (Basel, Switzerland) · 2021Article
- Nonlinear associations of neurite density and myelin content with age revealed using multicomponent diffusion and relaxometry magnetic resonance imaging.NeuroImage · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
We address the problem of analyzing noise-corrupted magnetic resonance transverse decay signals as a superposition of underlying independently decaying monoexponentials of positive amplitude. First, we indicate the manner in which this is an ill-conditioned inverse problem, rendering the analysis unstable with respect to noise. Second, we define an approach to this analysis, stabilized solely by the nonnegativity constraint without regularization. This is made possible by appropriate discretization, which is coarser than that often used in practice. Thirdly, we indicate further stabilization by inspecting the plateaus of cumulative distributions. We demonstrate our approach through analysis of simulated myelin water fraction measurements, and compare the accuracy with more conventional approaches. Finally, we apply our method to brain imaging data obtained from a human subject, showing that our approach leads to maps of the myelin water fraction which are much more stable with respect to increasing noise than those obtained with conventional approaches.
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