Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
Department of Radiology, The Fifth People's Hospital of Shanghai, Fudan University, 801 Heqing Road, Shanghai, 200240, China.
Med Biol Eng Comput. 2020 Nov;58(11):2835-2844. doi: 10.1007/s11517-020-02262-1. Epub 2020 Sep 21.
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.
基于磁共振成像的脑有限元模型构建及其在创伤性脑损伤研究中的应用
计算大脑模型对于研究创伤性脑损伤至关重要。具有精细细节的解剖学上准确的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更长的计算时间，从而阻碍模型的临床转化。因此，需要一种构建计算成本低但计算精度可与高保真模型相媲美的模型的方法。在这项研究中，我们构建了基于磁共振成像的老鼠大脑有限元模型，用于皮质撞击的模拟。通过将每个图像体素映射到相应的有限元网格元素，保留了解剖细节。我们构建了一个超分辨率神经网络，可以从粗网格有限元模型（网格尺寸为 280μm）生成具有 70μm 网格尺寸的精细有限元模型的计算结果。重建结果的峰值信噪比为 33.26dB，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，基于磁共振成像的计算模型可以及时应用和评估。这为基于磁共振图像的快速有限元建模和计算铺平了道路。研究结果还支持基于磁共振成像的人类大脑计算模型在各种场景中的潜在临床应用，如脑冲击和干预。
