This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches.

中文翻译：

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ConFiG：上下文纤维生长，为扩散 MRI 模拟生成逼真的轴突堆积

本文介绍了上下文纤维生长 (ConFiG)，这是一种通过模仿天然纤维起源来生成白质数值幻影的方法。ConFiG 遵循由真实轴突引导机制驱动的简单规则，一根一根地生长纤维。这些简单的规则使 ConFiG 能够通过生长纤维同时尝试满足用户指定的密度和方向分布等形态目标来生成具有可调节微观结构特征的体模。我们通过在一系列纤维配置（包括交叉纤维束和取向色散）中生成幻影，将 ConFiG 与基于将纤维打包在一起的最先进方法进行比较。结果表明，ConFiG 产生的体模密度比最先进的技术高 20%，尤其是在具有交叉纤维的复杂配置中。此外，我们还表明 ConFiG 体模的微观结构形态与真实组织相当，产生的直径和方向分布接近真实组织的电子显微镜估计，并捕获复杂的纤维横截面。从 ConFiG 体模模拟的信号与真实的扩散 MRI 数据匹配良好，表明 ConFiG 体模可用于生成真实的扩散 MRI 数据。这证明了 ConFiG 生成逼真的合成扩散 MRI 数据以开发和验证微结构建模方法的可行性。从 ConFiG 体模模拟的信号与真实的扩散 MRI 数据匹配良好，表明 ConFiG 体模可用于生成真实的扩散 MRI 数据。这证明了 ConFiG 生成逼真的合成扩散 MRI 数据以开发和验证微结构建模方法的可行性。从 ConFiG 体模模拟的信号与真实的扩散 MRI 数据匹配良好，表明 ConFiG 体模可用于生成真实的扩散 MRI 数据。这证明了 ConFiG 生成逼真的合成扩散 MRI 数据以开发和验证微结构建模方法的可行性。