Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks

“…We had shown the convergence of ACID with respect to PSNR in Part A of our work, 3 and here we show the convergence of ACID in terms of the Lipschitz constant with respect to the number of iterations. As representative examples, the convergence curves in the C3 and M4 cases (see the supplemental information of Wu et al.…”

“…As representative examples, the convergence curves in the C3 and M4 cases (see the supplemental information of Wu et al. 3 for more details) are given in Figure 5 . It can be observed that the Lipschitz constant of ACID for both CT and MRI are monotonically decreasing and finally converge to a constant scale.…”

“…Although some examples about the insensitivity of ACID against noise are reported in Part A of Wu et al., 3 here we followed up the study in Koonjoo et al. 30 and performed a similar local stability test on ACID with DAGAN and Ell-50 respectively built in.…”

“… 2 To address these instabilities, we design an analytic compressed iterative deep (ACID) network. 3 The key idea behind ACID is to combine data-driven priors and sparsity constraints to outperform either simple-minded deep reconstruction networks or established compressed sensing-based reconstruction methods. In our study, we have not only experimentally shown the merits of ACID 3 but also theoretically analyzed the rationale of ACID in terms of its converging behavior and solution characteristics.…”

“… 3 The key idea behind ACID is to combine data-driven priors and sparsity constraints to outperform either simple-minded deep reconstruction networks or established compressed sensing-based reconstruction methods. In our study, we have not only experimentally shown the merits of ACID 3 but also theoretically analyzed the rationale of ACID in terms of its converging behavior and solution characteristics. In the following, we put our analysis on ACID in the perspective of others’ analyses on general computational optimization in general and existing representative image reconstruction networks in particular.…”