A generative model of realistic brain cells with application to numerical simulation of diffusion-weighted MR signal

TL;DR

This paper introduces a novel generative model for realistic brain cells that enables detailed numerical simulations of diffusion-weighted MR signals, advancing the understanding of brain tissue microstructure.

Contribution

The paper presents a new computational framework inspired by neuroscience that simulates molecular diffusion in realistic digital brain cells with high accuracy.

Findings

Excellent match between simulated and real brain cell morphology.

Simulated DW-MR signals closely match those from real brain cells.

Framework demonstrates versatility for diffusion MRI research.

Abstract

In this work, we introduce a novel computational framework that we developed to use numerical simulations to investigate the complexity of brain tissue at a microscopic level with a detail never realised before. Directly inspired by the advances in computational neuroscience for modelling brain cells, we propose a generative model that enables us to simulate molecular diffusion within realistic digitalised brain cells, such as neurons and glia, in a completely controlled and flexible fashion. We validate our new approach by showing an excellent match between the morphology and simulated DW-MR signal of the generated digital model of brain cells and those of digital reconstruction of real brain cells from available open-access databases. We demonstrate the versatility and potentiality of the framework by showing a select set of examples of relevance for the DW-MR community. Further development is ongoing, which will support even more realistic conditions like dense packing of numerous 3D complex cell structures and varying cell surface permeability.