论文标题
用于加速MRI重建的端到端变异网络
End-to-End Variational Networks for Accelerated MRI Reconstruction
论文作者
论文摘要
磁共振成像（MRI）的缓慢采集速度导致了两种互补方法的发展：同时获得解剖学的多种视图（并行成像），并获得比传统信号处理方法（压缩传感）所必需的样品更少的样品。尽管这些方法的组合有可能允许更快的扫描时间，但此类采样的多线圈数据的重建仍然是一个空旷的问题。在本文中，我们提出了一种解决此问题的新方法，该方法通过完全端到端学习来扩展先前提出的变分方法。我们的方法在大脑和膝盖MRI的FastMRI数据集上获得了新的最新结果。
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). While the combination of these methods has the potential to allow much faster scan times, reconstruction from such undersampled multi-coil data has remained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset for both brain and knee MRIs.
