Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 66 references that are in the map. First posted on arXiv 19 Mar 2024.
Builds on
16 of 66 references.
References · 16
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4 more
- 2020X-ray Photon-Counting Data Correction through Deep Learningcited by 3
- 2022Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equationscited by 3
- 2021Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methodscited by 2
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Citing papers per year: 2026: 1.
1 paper.