Many methods have been developed to reduce radiation dose in computed tomography (CT) scans without sacrificing image quality. Recently, deep learning-based methods have shown promising results on the task of CT image denoising. However, they remain difficult to interpret, and thus safety concerns have been raised. In this work we develop a method to reconstruct the invariances of arbitrary denoising methods with an approach inspired by the optimization schemes commonly used to generate adversarial examples. We apply our method to one proof-of-principle algorithm as well as to two previously proposed denoising networks and show that it can successfully reconstruct their invariances.
Long lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for CT acquisitions without severe deterioration of image quality. To this end, different reconstruction and noise reduction algorithms have been developed, many of which are based on iterative reconstruction techniques, incorporating prior knowledge in the image domain. Recently, deep learning-based methods have shown impressive performance, outperforming many of the previously proposed CT denoising approaches both visually and quantitatively. However, with most neural networks being black boxes they remain notoriously difficult to interpret and concerns about the robustness and safety of such denoising methods have been raised. In this work we want to lay the fundamentals for a post-hoc interpretation of existing CT denoising networks by reconstructing their invariances.
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