ArticlePatterns (New York, N.Y.)2022
Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- 2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation for Radiation Therapy Using Real Projection Data.IEEE transactions on medical imaging · 2025Article
- Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients.Journal of clinical medicine · 2024Review
- Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.Quantitative imaging in medicine and surgery · 2023Article
- Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- Development of metaverse for intelligent healthcare.Nature machine intelligence · 2022Article
- Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results.Patterns (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Due to lack of the kernel awareness, some popular deep image reconstruction networks are unstable. To address this problem, here we introduce the bounded relative error norm (BREN) property, which is a special case of the Lipschitz continuity. Then, we perform a convergence study consisting of two parts: (1) a heuristic analysis on the convergence of the analytic compressed iterative deep (ACID) scheme (with the simplification that the CS module achieves a perfect sparsification), and (2) a mathematically denser analysis (with the two approximations: [1] A
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.