ArticleMagma (New York, N.Y.)2018
Toward faster inference of micron-scale axon diameters using Monte Carlo simulations.
Article in Magma (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Validating the standard model of diffusion MRI in white matter with numerical substrates.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Alpha_Mesh_Swc: automatic and robust surface mesh generation from the skeleton description of brain cells.Briefings in bioinformatics · 2025Article
- Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.NeuroImage · 2019Article
- Inferring diameters of spheres and cylinders using interstitial water.Magma (New York, N.Y.) · 2018Article
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Authors and funding
2 authors.
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Abstract
objectRecent advances have allowed oscillating gradient (OG) diffusion MRI to infer the sizes of micron-scale axon diameters. Here the effects on the precision of the inferred diameters are studied when reducing the number of images collected to reduce imaging time for clinical feasibility. MATERIALS AND
methodsMonte Carlo simulations of cosine OG sequences (50-1000 Hz) using a two-compartment model on a parallel cylinder (diameters 1-5 μm) geometry were conducted. Temporal diffusion spectroscopy was used to infer axon diameters. Three different gradient sets were simulated with different combinations of gradient strengths.
resultsFive frequencies were adequate for d = 3-5 μm with single-sized cylinders and for effective mean axon diameters greater than 2 μm for cylinders with a distributions of diameters. There was some improvement in precision for d = 1-2 μm with 10 frequencies. It is better to repeat measurements at higher gradient strengths than to use a range of gradient strengths. The improvement tended to be greatest when using fewer frequencies and was especially noticeable at very high gradient strengths.
conclusionImages can be collected with fewer gradient strengths and frequencies without sacrificing the precision of the measurements. This could be useful in reducing imaging time so that OG techniques can be used in clinical settings.
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