On the Robustness of deep learning-based MRI Reconstruction to image transformations

Understand

Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities of DL-based MRI reconstruction models.

• However, the approaches of robustifying these models are underdeveloped.
• Compared to image classification, it could be much more challenging to achieve a robust MRI image reconstruction network considering its regression-based learning objective, limited amount of training data, and lack of efficient robustness metrics.
• To circumvent the above limitations, our work revisits the problem of DL-based image reconstruction through the lens of robust machine learning.