Low dose computed tomography (CT) acquisition using reduced radiation or
sparse angle measurements is recommended to decrease the harmful effects of
X-ray radiation. Recent works successfully apply deep networks to the problem
of low dose CT recovery on bench-mark datasets. However, their robustness needs
a thorough evaluation before use in clinical settings. In this work, we
evaluate the robustness of different deep learning approaches and classical
methods for CT recovery. We show that deep networks, including model-based
networks encouraging data consistency, are more susceptible to untargeted
attacks. Surprisingly, we observe that data consistency is not heavily affected
even for these poor quality reconstructions, motivating the need for better
regularization for the networks. We demonstrate the feasibility of universal
attacks and study attack transferability across different methods. We analyze
robustness to attacks causing localized changes in clinically relevant regions.
Both classical approaches and deep networks are affected by such attacks
leading to changes in the visual appearance of localized lesions, for extremely
small perturbations. As the resulting reconstructions have high data
consistency with the original measurements, these localized attacks can be used
to explore the solution space of the CT recovery problem.

评估不同深度学习方法和经典方法在 CT 图像恢复中的鲁棒性，发现深度网络更容易受到未定向攻击的影响，数据一致性不受重建质量影响，需要更好的正则化方法。同时，深度网络和经典方法都容易受到对局部病变产生可见变化的攻击的影响，这些攻击可以用于探索 CT 恢复问题的解空间。

低剂量 CT 图像恢复的对抗鲁棒性评估

Evaluating Adversarial Robustness of Low dose CT Recovery

We propose the Learned Primal-Dual algorithm for tomographic reconstruction.
The algorithm accounts for a (possibly non-linear) forward operator in a deep
neural network by unrolling a proximal primal-dual optimization method, but
where the proximal operators have been replaced with convolutional neural
networks. The algorithm is trained end-to-end, working directly from raw
measured data and it does not depend on any initial reconstruction such as FBP.
We compare performance of the proposed method on low dose CT reconstruction
against FBP, TV, and deep learning based post-processing of FBP. For the
Shepp-Logan phantom we obtain >6dB PSNR improvement against all compared
methods. For human phantoms the corresponding improvement is 6.6dB over TV and
2.2dB over learned post-processing along with a substantial improvement in the
SSIM. Finally, our algorithm involves only ten forward-back-projection
computations, making the method feasible for time critical clinical
applications.

使用学习到的 Primal-Dual 算法通过卷积神经网络替换近端算子来处理前向算子以及低剂量 CT 重建，并且通过与传统方法的比较得到了良好的重建效果，SSIM 明显提高。