Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging.
Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging.
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DOI：
10.1109/tmi.2022.3161653
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发表时间：
2022-09
影响因子：
10.6
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中科院分区：
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测定脑血流动力学在各种脑血管疾病的诊断和治疗中起着至关重要的作用。在这项工作中，我们提出了一种基于物理的深度学习框架，该框架通过一维（1D）降阶模型（ROM）模拟增强稀疏临床测量，以生成具有高时空分辨率的物理一致的大脑血流动力学参数。经颅多普勒 (TCD) 超声是当前临床工作流程中最常用的技术之一，可以无创、即时评估脑动脉内的血流速度。然而，由于通过头骨声窗的可达性受到限制，它在空间上仅限于脑血管系统中的少数几个位置。我们的深度学习框架使用大脑多个位置的体内实时 TCD 速度测量，结合从 3D 血管造影图像获取的基线血管横截面积，并提供整个大脑血管系统中速度、面积和压力的高分辨率图。我们根据通过四维 (4D) 流磁共振成像 (MRI) 扫描获得的体内速度测量结果验证了模型的预测。然后，我们通过基于相应的稀疏速度测量成功预测血管痉挛局部血管直径的变化，展示了该技术在诊断脑血管痉挛（CVS）方面的临床意义。我们通过在不同程度的狭窄脑血管痉挛后生成合成血流数据来展示这种能力。在这里，我们证明基于物理的深度学习方法可以高精度地估计和量化特定于受试者的脑血流动力学变量，尽管缺乏入口和出口边界条件的知识，这对传统的纯基于物理的计算模型的准确性是一个重大限制。
Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with one-dimensional (1D) reduced-order model (ROM) simulations to generate physically consistent brain hemodynamic parameters with high spatiotemporal resolution. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables noninvasive and instantaneous evaluation of blood flow velocity within the cerebral arteries. However, it is spatially limited to only a handful of locations across the cerebrovasculature due to the constrained accessibility through the skull’s acoustic windows. Our deep learning framework uses in vivo real-time TCD velocity measurements at several locations in the brain combined with baseline vessel cross-sectional areas acquired from 3D angiography images and provides high-resolution maps of velocity, area, and pressure in the entire brain vasculature. We validate the predictions of our model against in vivo velocity measurements obtained via four-dimensional (4D) flow magnetic resonance imaging (MRI) scans. We then showcase the clinical significance of this technique in diagnosing cerebral vasospasm (CVS) by successfully predicting the changes in vasospastic local vessel diameters based on corresponding sparse velocity measurements. We show this capability by generating synthetic blood flow data after cerebral vasospasm at various levels of stenosis. Here, we demonstrate that the physics-based deep learning approach can estimate and quantify the subject-specific cerebral hemodynamic variables with high accuracy despite lacking knowledge of inlet and outlet boundary conditions, which is a significant limitation for the accuracy of the conventional purely physics-based computational models.