ArticleBJR open2023
Pseudo dual-energy CT-derived iodine mapping using single-energy CT data based on a convolution neural network.
Article in BJR open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
4 authors.
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Abstract
Objective: The objectives of this study are: (1) to develop a convolutional neural network model that yields pseudo high-energy CT (CT Methods: Eighty patients who underwent dual-energy CT (DECT) examinations were enrolled. The data obtained from 55, 5, and 20 patients were used for training, validation, and testing, respectively. The ResUnet model was used for image generation model and was trained using CT Results: The mean difference in the CT values between CT Conclusions: Our results indicated that the proposed model enables to obtain the DECT images and material-specific images from only single-energy CT images. Advances in knowledges: We constructed the CNN-based model which can generate pseudo DECT image and DECT-derived material-specific image using only simple image-processed CT
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Registered trials
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