论文标题
对抗性的深度学习MRI重建模型的强大培训
Adversarial Robust Training of Deep Learning MRI Reconstruction Models
论文作者
论文摘要
深度学习（DL）表明在加速磁共振图像采集和重建方面具有潜力。然而，缺乏量身定制的方法，以确保重建小型特征是通过高保真实现的。在这项工作中，我们采用对抗性攻击来产生小型合成扰动，这很难重建经过训练的DL重建网络。然后，我们使用强大的培训来提高网络对这些小功能的敏感性，并鼓励其重建。接下来，我们研究了对现实世界特征的上述方法的概括。为此，肌肉骨骼放射科医生注释了膝关节快速MRI数据集中的一组软骨和半月板病变，并设计了一个分类网络来评估特征的重建。实验结果表明，通过将强大的训练引入重建网络，可以降低图像重建中假负特征（4.8 \％）的速率。这些结果令人鼓舞，并强调了图像重建社区对这个问题的关注，这是在临床实践中引入DL重建的一个里程碑。为了支持进一步的研究，我们在https://github.com/fcaliva/fastmri_bb_abnormalisitation_annotation上公开提供注释和代码。
Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee that the reconstruction of small features is achieved with high fidelity. In this work, we employ adversarial attacks to generate small synthetic perturbations, which are difficult to reconstruct for a trained DL reconstruction network. Then, we use robust training to increase the network's sensitivity to these small features and encourage their reconstruction. Next, we investigate the generalization of said approach to real world features. For this, a musculoskeletal radiologist annotated a set of cartilage and meniscal lesions from the knee Fast-MRI dataset, and a classification network was devised to assess the reconstruction of the features. Experimental results show that by introducing robust training to a reconstruction network, the rate of false negative features (4.8\%) in image reconstruction can be reduced. These results are encouraging, and highlight the necessity for attention to this problem by the image reconstruction community, as a milestone for the introduction of DL reconstruction in clinical practice. To support further research, we make our annotations and code publicly available at https://github.com/fcaliva/fastMRI_BB_abnormalities_annotation.
