Callaghan, R; Alexander, DC; Zhang, H; Palombo, M; (2019) Contextual fibre growth to generate realistic axonal packing for diffusion MRI simulation. In: Chung, A and Gee, J and Yushkevich, P and Bao, S, (eds.) IPMI 2019: Information Processing in Medical Imaging. (pp. 429-440). Springer, Cham: Cham, Switzerland.

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Abstract

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(n 2 ) 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.

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