ArticleIEEE transactions on medical imaging2026
Deep Few-View High-Resolution Photon-Counting CT at Halved Dose for Extremity Imaging.
Article in IEEE transactions on medical imaging, 2026. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- CitrusGS: 3D Gaussian splatting for sparse-view CT reconstruction and precise morphological phenotyping of citrus fruit.Plant phenomics (Washington, D.C.) · 2026Article
- Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT.IEEE transactions on medical imaging · 2026Article
- Review
- Patch-based dual-domain photon-counting CT data correction with residual-based WGAN-ViT.Physics in medicine and biology · 2025Article
- Coronary atherosclerotic plaque characterization with silicon-based photon-counting computed tomography (CT): A simulation-based feasibility study.Medical physics · 2024Article
- Contrast-enhanced photon-counting micro-CT of tumor xenograft models.Physics in medicine and biology · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed validated in a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value.
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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.