Contextual fibre growth to generate realistic axonal packing for diffusion MRI simulation

This paper presents ConFiG, a method for generating white matter (WM) numerical phantoms with more realistic orientation dispersion and packing density. Numerical phantoms are commonly used in the validation of diffusion MRI (dMRI) techniques so it is important that they are as realistic as possible. Current numerical phantoms either oversimplify the complex morphology of WM or are unable to produce realistic orientation dispersion at high packing density. The highest packing density and orientation dispersion achieved so far is only 20% at 10∘. ConFiG takes advantage of a shift of paradigm: rather than ‘packing fibres’, our algorithm ‘grows fibres’ contextually and efficiently, attempting to produce a substrate with desired morphological priors (orientation dispersion, packing density and diameter distribution), whilst avoiding intersection between fibres. The potential of ConFiG is demonstrated by reaching the highest packing density and orientation dispersion ever, to our knowledge (25% at 35∘). The algorithm is compared with a ‘brute force’ growth approach showing that it is much more efficient, being O(n) compared to the O(n2) brute-force method. The application of the method to dMRI is demonstrated with simulations of diffusion-weighted MR signal in three example substrates with differing orientation-dispersions, packing-densities and permeabilities

Paramyxida

Paramyxidans are obligate parasites of marine invertebrates. They produce a characteristic delicate spore with one cell within a second cell. In some genera, these are enclosed within a third cell and even a fourth cell. Life cycles are generally unknown. In two Marteilla species that are major pathogens of oysters, there could be an alternate host in the life cycle

An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms

International audienceIn diffusion-weighted MRI, general gradient waveforms became of interest for their sensitivity to microstructure features of the brain white matter. However, the design of such waveforms remains an open problem. In this work, we propose a framework for generalized gradient waveform design with optimized sensitivity to selected microstruc-ture features. In particular, we present a rotation-invariant method based on a genetic algorithm to maximize the sensitivity of the signal to the intra-axonal volume fraction. The sensitivity is evaluated by computing a score based on the Fisher information matrix from Monte-Carlo simulations , which offer greater flexibility and realism than conventional analytical models. As proof of concept, we show that the optimized waveforms have higher scores than the conventional pulsed-field gradients experiments. Finally, the proposed framework can be generalized to optimize the waveforms for to any microstructure feature of interest