ArticlePatterns (New York, N.Y.)2022
Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction.
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 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT.IEEE transactions on medical imaging · 2026Article
- Clinical Metadata-Guided Limited-Angle CT Image Reconstruction.IEEE transactions on medical imaging · 2026Article
- Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.Journal of imaging informatics in medicine · 2026Article
- LoGoReg-Net: local-global feature aggregation regularized unrolling network for functional brain imaging via high-density DOT.Biomedical optics express · 2026Article
- An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.Quantitative imaging in medicine and surgery · 2026Article
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention.Scientific reports · 2025Article
- [A sparse-view cone-beam CT reconstruction algorithm based on bidirectional flow field- guided projection completion].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Dual-Domain Reconstruction Network Incorporating Multi-Level Wavelet Transform and Recurrent Convolution for Sparse View Computed Tomography Imaging.Tomography (Ann Arbor, Mich.) · 2024Article
- Non-invasive arterial blood pressure measurement and SpOBMC medical informatics and decision making · 2023Article
- Vision transformer architecture and applications in digital health: a tutorial and survey.Visual computing for industry, biomedicine, and art · 2023Review
- Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.Medical image analysis · 2023Review
- SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data.IEEE transactions on instrumentation and measurement · 2023Article
- Unsupervised contrastive learning based transformer for lung nodule detection.Physics in medicine and biology · 2022Article
- Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results.Patterns (New York, N.Y.) · 2022Article
- Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks.Patterns (New York, N.Y.) · 2022Article
- Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging.BioMed research international · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors.
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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.