[论文解读] Adversarial Robust Training in MRI Reconstruction.

Francesco Calivá, Kaiyang Cheng+2 more|arXiv (Cornell University)|Oct 30, 2020

Advanced X-ray and CT Imaging被引用 4

一句话总结

本文提出对抗鲁棒训练以提升基于深度学习的磁共振成像（MRI）重建性能，通过增强对微小解剖结构的敏感性。通过生成针对小结构的对抗性扰动并微调重建网络，该方法将软骨和半月板病变的假阴性率从4.8%显著降低，从而提高临床可靠性。

ABSTRACT

Deep Learning 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 that when added to the input MRI, they are not reconstructed by a trained DL reconstruction network. Then, we use robust training to increase the network's sensitivity to 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 features reconstruction. Experimental results show that by introducing robust training to a reconstruction network, the rate (4.8\%) of false negative features in image reconstruction can be reduced. The results are encouraging and highlight the necessity for attention on 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 annotation publicly available at this https URL.

研究动机与目标

• 解决基于深度学习的MRI重建中缺乏确保微小解剖结构高保真重建的方法的问题。
• 探究对抗鲁棒训练是否能增强网络对软骨和半月板病变等细微结构的敏感性。
• 评估鲁棒训练在膝关节MRI中临床相关真实世界特征上的泛化能力。
• 提供一个由放射科医生标注的病变公开数据集，以支持未来在鲁棒MRI重建方面的研究。

提出的方法

• 在k空间MRI数据中生成针对特定微小特征的小型、难以察觉的对抗性扰动，以干扰重建结果。
• 使用对抗性样本训练深度学习重建网络，以提升对细节的鲁棒性和敏感性。
• 由肌肉骨骼放射科医生在Fast-MRI数据集中标注软骨和半月板病变，以增强临床相关性。
• 在重建图像上训练独立的分类网络，以评估特征可检测性和重建保真度。
• 通过优化重建网络以最小化对抗性扰动下的重建损失，实现鲁棒训练。
• 使用标注数据集评估鲁棒训练方法在真实世界临床特征上的泛化能力。

实验结果

研究问题

• RQ1 对抗鲁棒训练能否提升MRI中小 anatomical features 的重建保真度？

主要发现

• 对抗鲁棒训练显著降低了MRI重建中微小特征的假阴性率，假阴性率降低了4.8%。
• 所提出的方法提高了网络对软骨和半月板病变等细微解剖结构的敏感性。
• 在重建图像上训练的分类网络在鲁棒训练后检测病变的性能得到提升。
• 由放射科医生标注的膝关节MRI病变数据集已公开，可支持可复现性及临床MRI重建的进一步研究。
• 鲁棒训练可泛化至真实世界临床特征，表明其在基于深度学习的MRI重建中具有临床可行性。