论文标题
快速4D流MRI重建的深度变分网络
Deep variational network for rapid 4D flow MRI reconstruction
论文作者
论文摘要
相对比对比度磁共振成像（MRI）提供了可以有助于临床诊断的血流动力学的时间分辨定量。由于反复的三维（3D）体积在心脏相和呼吸周期上进行了长时间的体内扫描时间，因此需要加速的成像技术，以利用数据相关性。标准压缩感测重建方法需要调整超参数，并且在计算上昂贵，这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神经重建网络，并评估其在临床主动脉流数据上的性能。该网络被证明可在不到一分钟的标准消费者硬件上重建未经采样的4D流MRI数据。值得注意的是，相对较低的可调参数允许对网络进行11次参考扫描的图像进行训练，同时将其推广到回顾性和前瞻性下采样数据，以获取各种加速因子和解剖学。
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.
