Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA.
Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA.
J Neurosci Methods. 2021 Feb 15;350:109018. doi: 10.1016/j.jneumeth.2020.109018. Epub 2020 Dec 3.
Monte Carlo simulations of diffusion are commonly used as a model validation tool as they are especially suitable for generating the diffusion MRI signal in complicated tissue microgeometries.
Here we describe the details of implementing Monte Carlo simulations in three-dimensional (3d) voxelized segmentations of cells in microscopy images. Using the concept of the corner reflector, we largely reduce the computational load of simulating diffusion within and exchange between multiple cells. Precision is further achieved by GPU-based parallel computations.
Our simulation of diffusion in white matter axons segmented from a mouse brain demonstrates its value in validating biophysical models. Furthermore, we provide the theoretical background for implementing a discretized diffusion process, and consider the finite-step effects of the particle-membrane reflection and permeation events, needed for efficient simulation of interactions with irregular boundaries, spatially variable diffusion coefficient, and exchange.
To our knowledge, this is the first Monte Carlo pipeline for MR signal simulations in a substrate composed of numerous realistic cells, accounting for their permeable and irregularly-shaped membranes.
The proposed RMS pipeline makes it possible to achieve fast and accurate simulations of diffusion in realistic tissue microgeometry, as well as the interplay with other MR contrasts. Presently, RMS focuses on simulations of diffusion, exchange, and T and T NMR relaxation in static tissues, with a possibility to straightforwardly account for susceptibility-induced T effects and flow.
扩散的蒙特卡罗模拟通常被用作模型验证工具，因为它们特别适合在复杂的组织微观结构中产生扩散 MRI 信号。
在这里，我们描述了在显微镜图像的三维（3d）体素化细胞分割中实现蒙特卡罗模拟的细节。我们利用角反射器的概念，大大降低了模拟多个细胞内和细胞间扩散的计算负荷。通过基于 GPU 的并行计算进一步提高了精度。
我们对白质轴突的扩散模拟从老鼠大脑的分割中验证了生物物理模型的价值。此外，我们为离散扩散过程的实现提供了理论背景，并考虑了粒子-膜反射和渗透事件的有限步效应，这对于有效模拟不规则边界、空间变化扩散系数和交换的相互作用是必要的。
据我们所知，这是第一个用于由大量真实细胞组成的基质中 MR 信号模拟的蒙特卡罗流水线，考虑了它们可渗透和不规则形状的膜。
所提出的 RMS 流水线使得在真实组织微观结构中实现快速准确的扩散模拟以及与其他 MR 对比度的相互作用成为可能。目前，RMS 专注于静态组织中扩散、交换和 T 和 T NMR 弛豫的模拟，有可能直接考虑顺磁性诱导的 T 效应和流动。
