High-fidelity meshes from tissue samples for diffusion MRI simulations.
High-fidelity meshes from tissue samples for diffusion MRI simulations.
复制标题
用于扩散 MRI 模拟的组织样本的高保真网格。
DOI：
10.1007/978-3-642-15745-5_50
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Panagiotaki E
中科院分区：
文献类型：
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作者：
Panagiotaki E
本文提出了一种构造组织微观结构精细几何模型的方法，用于合成真实弥散MRI数据。我们使用行进立方体算法从共聚焦显微镜图像堆栈中构建三维网格模型。结果网格内的随机行走模拟提供了合成扩散MRI测量。实验优化仿真参数和网格的复杂性，以实现精度和再现性，同时最小化计算时间。最后，我们通过与扫描仪数据以及简单几何模型和仅在二维上变化的简化网格的合成数据进行比较，来评估网格模型合成数据的质量。尽管对网格分辨率的敏感性相当强，但与简单模型相比，结果支持三维网格的额外复杂性。
This paper presents a method for constructing detailed geometric models of tissue microstructure for synthesizing realistic diffusion MRI data. We construct three-dimensional mesh models from confocal microscopy image stacks using the marching cubes algorithm. Random-walk simulations within the resulting meshes provide synthetic diffusion MRI measurements. Experiments optimise simulation parameters and complexity of the meshes to achieve accuracy and reproducibility while minimizing computation time. Finally we assess the quality of the synthesized data from the mesh models by comparison with scanner data as well as synthetic data from simple geometric models and simplified meshes that vary only in two dimensions. The results support the extra complexity of the three-dimensional mesh compared to simpler models although sensitivity to the mesh resolution is quite robust.