Phase-contrast magnetic resonance imaging (MRI) provides time-resolved quantification of blood flow dynamics that can aid clinical diagnosis. Long in vivo scan times due to repeated three-dimensional (3D) volume sampling over cardiac phases and breathing cycles necessitate accelerated imaging techniques that leverage data correlations. Standard compressed sensing reconstruction methods require tuning of hyperparameters and are computationally expensive, which diminishes the potential reduction of examination times. We propose an efficient model-based deep neural reconstruction network and evaluate its performance on clinical aortic flow data. The network is shown to reconstruct undersampled 4D flow MRI data in under a minute on standard consumer hardware. Remarkably, the relatively low amounts of tunable parameters allowed the network to be trained on images from 11 reference scans while generalizing well to retrospective and prospective undersampled data for various acceleration factors and anatomies.

相衬磁共振成像（MRI）提供时间分辨的血流动力学定量分析，可帮助临床诊断。由于在心脏相位和呼吸周期上进行了重复的三维（3D）体积采样，因此体内扫描时间较长，因此需要利用数据相关性的加速成像技术。标准的压缩感测重建方法需要调整超参数，并且计算量大，从而减少了检查时间的潜在减少。我们提出了一个有效的基于模型的深度神经重建网络，并评估其在临床主动脉血流数据上的性能。该网络显示可在一分钟内在标准消费类硬件上重建欠采样的4D流MRI数据。值得注意的是