ArticleThe Journal of chemical physics2014
Joint radius-length distribution as a measure of anisotropic pore eccentricity: an experimental and analytical framework.
Article in The Journal of chemical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Review
- Multidimensional correlation MRI.NMR in biomedicine · 2020Article
- Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging.NeuroImage · 2020Article
- Retaining information from multidimensional correlation MRI using a spectral regions of interest generator.Scientific reports · 2020Article
- Water mobility spectral imaging of the spinal cord: Parametrization of model-free Laplace MRI.Magnetic resonance imaging · 2019Article
- Towards clinically feasible relaxation-diffusion correlation MRI using MADCO.Microporous and mesoporous materials : the official journal of the International Zeolite Association · 2018Article
- Article
- Imaging Local Diffusive Dynamics Using Diffusion Exchange Spectroscopy MRI.Physical review letters · 2017Article
- Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing.The Journal of chemical physics · 2016Article
- Use of marginal distributions constrained optimization (MADCO) for accelerated 2D MRI relaxometry and diffusometry.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2016Article
- Article
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2 authors.
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Abstract
In this work, we present an experimental design and analytical framework to measure the nonparametric joint radius-length (R-L) distribution of an ensemble of parallel, finite cylindrical pores, and more generally, the eccentricity distribution of anisotropic pores. Employing a novel 3D double pulsed-field gradient acquisition scheme, we first obtain both the marginal radius and length distributions of a population of cylindrical pores and then use these to constrain and stabilize the estimate of the joint radius-length distribution. Using the marginal distributions as constraints allows the joint R-L distribution to be reconstructed from an underdetermined system (i.e., more variables than equations), which requires a relatively small and feasible number of MR acquisitions. Three simulated representative joint R-L distribution phantoms corrupted by different noise levels were reconstructed to demonstrate the process, using this new framework. As expected, the broader the peaks in the joint distribution, the less stable and more sensitive to noise the estimation of the marginal distributions. Nevertheless, the reconstruction of the joint distribution is remarkably robust to increases in noise level; we attribute this characteristic to the use of the marginal distributions as constraints. Axons are known to exhibit local compartment eccentricity variations upon injury; the extent of the variations depends on the severity of the injury. Nonparametric estimation of the eccentricity distribution of injured axonal tissue is of particular interest since generally one cannot assume a parametric distribution a priori. Reconstructing the eccentricity distribution may provide vital information about changes resulting from injury or that occurred during development.
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