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Supervised Learning for CT Denoising and Deconvolution Without High-Resolution Reference Images
2023-09-02
radiology and imaging
Title + abstract only
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PurposeConvolutional neural networks (CNNs) have been proposed for super-resolution in CT, but training of CNNs requires high-resolution reference data. Higher spatial resolution can also be achieved using deconvolution, but conventional deconvolution approaches amplify noise. We develop a CNN that mitigates increasing noise and that does not require higher-resolution reference images. MethodsOur model includes a noise reduction CNN and a deconvolution CNN that are separately trained. The noise...
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