Mechanical Engineering, University of Wisconsin, Madison, WI, USA.
Radiology, University of Wisconsin, 1111 Highland Ave, Madison, WI, USA.
Sci Rep. 2021 May 13;11(1):10240. doi: 10.1038/s41598-021-89636-z.
Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.
利用四维（4D）相位对比磁共振成像（MRI）获得的血流参数在临床和实验性脑血管分析中具有重要价值。然而，PC MRI 固有的误差会导致定量和定性分析受到限制。一种在创建低误差、基于物理的速度场方面表现出色的方法是计算流体动力学（CFD）。用 CFD 驱动的神经网络增强脑 4D 血流 MRI 数据，可能为生成高度准确的生理血流场提供一种方法。在这项初步研究中，通过使用高分辨率患者特定 CFD 数据训练卷积神经网络，然后使用训练好的网络增强脑血管数据集的 MRI 衍生速度场，证明了这种方法的潜在效用。通过对模拟图像、体模数据和 20 名患者的脑血管 4D 流数据进行测试，训练好的网络成功地对血流图像进行了去噪、降低了速度误差，并增强了近血管壁速度的量化和可视化。这种图像增强可以改进实验和临床定性和定量脑血管 PC MRI 分析。
