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.
翻译:深层学习(DL) 显示在加速磁共振图像获取和重建方面的潜力。 然而,缺乏量身定制的方法来保证小特征的重建能够以高度忠诚的方式实现。 在这项工作中,我们使用对抗性攻击来制造小型合成扰动,很难重建经过训练的DL重建网络。然后,我们利用强力培训来提高网络对这些小特征的敏感性并鼓励其重建。接着,我们调查上述方法对真实世界特征的概括性。为此,一个肌肉骨骼放射学家对一套膝盖快速磁共振数据集的软体和皮肤损伤进行附加说明,并设计了一个分类网络来评估这些特征的重建。实验结果显示,通过对重建网络进行强有力的培训,图像重建中的虚假负面特征(4.8 ⁇ )的速率可以降低。这些结果令人鼓舞,并突出了图像重建界关注这一问题的必要性,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 http://givas/mabral_stalalalalal_mabastivas_stalmental_mabastation.