Patel Neal M, Bartusiak Emily R, Rothenberger Sean M, Schwichtenberg A J, Delp Edward J, Rayz Vitaliy L
Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA.
Ann Biomed Eng. 2025 Feb;53(2):331-347. doi: 10.1007/s10439-024-03606-w. Epub 2024 Sep 2.
To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI.
The div-mDCSRN-Flow network was developed to improve spatial resolution and denoise 4D flow MRI. The network was trained with patches of paired high-resolution and low-resolution synthetic 4D flow MRI data derived from computational fluid dynamic simulations of CSF flow within the cerebral ventricles of five healthy cases and five Alzheimer's disease cases. The loss function combined mean squared error with a binary cross-entropy term for segmentation and a divergence-based regularization term for the conservation of mass. Performance was assessed using synthetic 4D flow MRI in one healthy and one Alzheimer' disease cases, an in vitro study of healthy cerebral ventricles, and in vivo 4D flow imaging of CSF as well as flow in arterial and venous blood vessels. Comparison was performed to trilinear interpolation, divergence-free radial basis functions, divergence-free wavelets, 4DFlowNet, and our network without divergence constraints.
The proposed network div-mDCSRN-Flow outperformed other methods in reconstructing high-resolution velocity fields from synthetic 4D flow MRI in healthy and AD cases. The div-mDCSRN-Flow network reduced error by 22.5% relative to linear interpolation for in vitro core voxels and by 49.5% in edge voxels.
The results demonstrate generalizability of our 4D flow MRI super-resolution and denoising approach due to network training using flow patches and physics-based constraints. The mDCSRN-Flow network can facilitate MRI studies involving CSF flow measurements in cerebral ventricles and association of MRI-based flow metrics with cerebrovascular health.
通过将物理引导神经网络（div-mDCSRN-Flow）应用于四维血流磁共振成像（4D flow MRI），获取高分辨率的脑脊液（CSF）和脑血流速度场。
开发div-mDCSRN-Flow网络以提高空间分辨率并对4D flow MRI进行去噪。该网络使用从五个健康病例和五个阿尔茨海默病病例的脑室CSF流动的计算流体动力学模拟中获得的成对高分辨率和低分辨率合成4D flow MRI数据块进行训练。损失函数将均方误差与用于分割的二元交叉熵项以及用于质量守恒的基于散度的正则化项相结合。使用合成4D flow MRI在一个健康病例和一个阿尔茨海默病病例中、在健康脑室的体外研究以及CSF以及动脉和静脉血管中的血流的体内4D流动成像来评估性能。与三线性插值、无散度径向基函数、无散度小波、4DFlowNet以及我们没有散度约束的网络进行了比较。
所提出的div-mDCSRN-Flow网络在从健康和AD病例的合成4D flow MRI重建高分辨率速度场方面优于其他方法。div-mDCSRN-Flow网络相对于体外核心体素的线性插值将误差降低了22.5%，在边缘体素中降低了49.5%。
结果表明，由于使用流动数据块和基于物理的约束进行网络训练，我们的4D flow MRI超分辨率和去噪方法具有通用性。mDCSRN-Flow网络可以促进涉及脑室CSF流量测量以及基于MRI的流量指标与脑血管健康关联的MRI研究。
