FlowMRI-Net：一种可泛化的自监督4D血流MRI重建网络
FlowMRI-Net: A Generalizable Self-Supervised 4D Flow MRI Reconstruction network
AI总结：
FlowMRI-Net利用自监督复值循环神经网络实现高度欠采样4D血流MRI的快速准确重建，在主动脉和脑血管应用中优于现有方法，且泛化性强。
摘要：
背景：从高度欠采样的4D血流MRI数据中进行图像重建可能非常耗时，并且根据正则化方式的不同，可能导致速度的显著低估，从而限制了该方法的适用性。本研究的目标是开发一种可泛化的自监督深度学习框架，用于快速、准确地重建高度欠采样的4D血流MRI，并展示该框架在主动脉和脑血管应用中的实用性。方法：所提出的深度学习框架名为FlowMRI-Net，采用基于物理的展开优化，使用复值卷积循环神经网络，并以自监督方式进行训练。该框架的泛化性通过使用来自两个不同厂商的系统采集的主动脉和脑血管4D血流MRI数据，在不同欠采样因子（R=8、16、24）下进行评估，并与最先进的压缩感知（CS-LLR）和基于深度学习的（FlowVN）重建方法进行比较。评估包括消融研究以及对图像和速度幅度的定性和定量分析。结果：在主动脉4D血流MRI重建中，FlowMRI-Net优于CS-LLR和FlowVN，在胸主动脉速度的矢量归一化均方根误差和平均方向误差方面显著降低。此外，FlowMRI-Net的泛化性在脑血管4D血流MRI重建中得到了验证，而由于缺乏高质量参考数据，FlowVN无法在该场景下训练。重建时间在普通CPU/GPU硬件上为3至7分钟。结论：FlowMRI-Net能够快速、准确地重建高度欠采样的主动脉和脑血管4D血流MRI，并可能应用于其他血管区域。
英文摘要：
Background: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications. Methods: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R=8,16,24) and compared to state-of-the-art compressed sensing (CS-LLR) and deep learning-based (FlowVN) reconstructions. Evaluation includes an ablation study and a qualitative and quantitative analysis of image and velocity magnitudes. Results: FlowMRI-Net outperforms CS-LLR and FlowVN for aortic 4D flow MRI reconstruction, resulting in significantly lower vectorial normalized root mean square error and mean directional errors for velocities in the thoracic aorta. Furthermore, the feasibility of FlowMRI-Net's generalizability is demonstrated for cerebrovascular 4D flow MRI reconstruction, where no FlowVN can be trained due to the lack of high-quality reference data. Reconstruction times ranged from 3 to 7 minutes on commodity CPU/GPU hardware. Conclusion: FlowMRI-Net enables fast and accurate reconstruction of highly undersampled aortic and cerebrovascular 4D flow MRI, with possible applications to other vascular territories.
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