通过正则化最优质量传输可视化流体流动及其在神经科学中的应用
Visualizing fluid flows via regularized optimal mass transport with applications to neuroscience
摘要：
正则化最优质量输运（rOMT）问题在Benamou和Brenier提出的最优质量输运（OMT）问题的原动力学方程中增加了扩散项。我们表明，rOMT模型作为一个强大的工具，在计算流体动力学（CFD）可视化流体流动的胶质淋巴系统。在目前的工作中，我们描述了如何修改以前的数值方法的有效实施，从而显着减少计算时间。数值结果适用于合成和实际数据。
英文摘要：
Regularized optimal mass transport (rOMT) problem adds a diffusion term to the continuity equation in the original dynamic formulation of the optimal mass transport (OMT) problem proposed by Benamou and Brenier. We show that the rOMT model serves as a powerful tool in computational fluid dynamics (CFD) for visualizing fluid flows in the glymphatic system. In the present work, we describe how to modify the previous numerical method for efficient implementation, resulting in a significant reduction in computational runtime. Numerical results applied to synthetic and real-data are provided.
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