论文标题
无监督的MRI重建与生成对抗网络
Unsupervised MRI Reconstruction with Generative Adversarial Networks
论文作者
论文摘要
基于深度学习的图像重建方法已在多个MRI应用中取得了有希望的结果。但是，大多数方法都需要大规模采样的地面真相数据来监督培训。获取完全采样的数据通常是困难或不可能的，尤其是对于动态对比度增强（DCE），3D心脏电影和4D流。我们为MRI重建提供了深度学习框架，而无需使用生成对抗网络进行任何完全采样的数据。我们在两种情况下测试了所提出的方法：回顾性地采样快速自旋回声膝关节检查和前瞻性下采样的腹部DCE。与常规方法相比，该方法恢复了更多的解剖结构。
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow. We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE. The method recovers more anatomical structure compared to conventional methods.
