Tuneable Digital Phantoms for Grey Matter Modelling

Motivation: There are several generators of white matter phantoms that have been developed in recent years and have significant potential in developing and validating diffusion MRI techniques through computational simulation. However, no such generator has been proposed for grey matter phantoms. Goal(s): We aim to provide a means of generating grey matter phantoms compatible with diffusion MRI simulations. Approach: Combing the network growth presented in the Contextual Fibre Growth (ConFiG) algorithm with the generative method of topological neuro synthesis (TNS), to create non intersecting morphologically realistic cellular structures. Results: We can show that our algorithm can generate non-intersecting realistic voxels of grey matter. Impact: We have developed a highly versatile algorithm, ConCeG, which can generate realistic digital phantoms of GM. These phantoms are ready to be incorporated into dMRI simulators, such as Camino, DiSimPy and MCDS for testing and validating diffusion MRI techniques