Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

中文翻译：

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一种用于基于磁共振图像的小鼠大脑建模和受控皮层影响快速计算的机器学习方法。

大脑的计算建模对于创伤性脑损伤的研究至关重要。具有精细细节的解剖学准确模型可以提供最准确的计算结果。然而，具有精细网格细节的计算模型可能需要较长的计算时间，从而阻碍模型的临床转化。因此，需要一种以低计算成本构建模型同时保持与高保真模型相当的计算精度的方法。在这项研究中，我们构建了小鼠大脑的基于磁共振 (MR) 图像的有限元 (FE) 模型，用于模拟受控皮质影响。通过将每个图像体素映射到相应的 FE 网格元素来保留解剖细节。我们构建了一个超分辨率神经网络，该网络可以从网格尺寸为 280 μm 的粗略有限元模型生成网格尺寸为 70 μm 的精细有限元模型的计算结果。重建结果的峰值信噪比为 33.26 dB，而计算速度提高了 50 倍。这项概念验证研究表明，使用机器学习技术，可以及时应用和评估基于 MR 图像的计算建模。这为基于 MR 图像的快速有限元建模和计算铺平了道路。结果还支持基于 MR 图像的人脑计算建模在各种场景中的潜在临床应用，例如大脑影响和干预。为 CCI 生成了具有不同网格大小的基于图形抽象 MR 图像的有限元模型。训练和测试数据集是用 5 个不同的撞击位置和 3 个不同的撞击速度计算的。使用 SR 神经网络估计高分辨率应变图，大大降低了计算成本。