发表机构
Cardiff University; University of Oxford（卡迪夫大学; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对灰质扩散MRI信号解释难题，引入ConCeG框架，结合拓扑神经元合成与空间约束生长网络，依真实神经胶质形态生成细胞构建三维多细胞灰质基质，经验证可用于扩散MRI模拟。
AI 中文摘要
由于灰质（GM）中复杂、异质且密集的细胞环境，准确解释扩散磁共振成像（dMRI）信号仍然具有挑战性。数值模型为研究微观结构与扩散信号之间的关系提供了一个可控的框架，但现有的方法往往缺乏忠实表示GM组织所需的形态逼真度和多细胞组织。在这项工作中，我们引入了上下文细胞生长（ConCeG），这是一个生成框架，用于创建单个细胞或构建由真实神经元和神经胶质形态学提供信息的密集、三维、多细胞GM基质。该方法将拓扑神经元合成与空间约束生长网络相结合，允许可控地生成具有逼真的细胞内和细胞外隔室的异质细胞环境。使用从生物重建中获得的形态和拓扑特征生成合成细胞。我们通过将结构特征与真实细胞数据进行比较来验证该框架，证明在分支顺序、长度、角度和曲折度分布方面有很强的一致性。功率谱分析进一步表明，两个细胞内隔室都再现了在生物组织中观察到的空间相关性。总之，这些结果表明ConCeG为生成适用于大规模扩散MRI模拟的灰质基质提供了一个基于生物学的框架。
英文摘要
Accurate interpretation of diffusion magnetic resonance imaging (dMRI) signals in grey matter (GM) remains challenging due to the complex, heterogeneous, and densely packed cellular environment. Numerical phantoms provide a controlled framework for investigating the relationship between microstructure and diffusion signals, yet existing approaches often lack the morphological realism and multi-cellular organisation required to faithfully represent GM tissue. In this work, we introduce Contextual Cellular Growth (ConCeG), a generative framework for creating individual cells or constructing dense, three-dimensional, multi-cellular GM substrates informed by real neuronal and glial morphologies. The method combines topological neuron synthesis with a spatially constrained growth network, allowing for the controlled generation of heterogeneous cellular environments with realistic intra- and extracellular compartments. Synthetic cells are generated using morphological and topological characteristics derived from biological reconstructions. We validate the framework through comparisons of structural features with real cellular data, demonstrating strong agreement in branch order, length, angle, and tortuosity distributions. Power spectrum analysis further shows that both intracellular compartments reproduce the spatial correlations observed in biological tissue. Together, these results show ConCeG provides a biologically grounded framework for generating grey matter substrates suitable for large scale diffusion MRI simulation.
