Impact of an artificial intelligence deep‐learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study von Greffier, Joël, Si‐Mohamed, Salim, Frandon, Julien, Loisy, Maeliss, Oliveira, Fabien, Beregi, Jean Paul, Dabli, Djamel

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Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study von Dabli, Djamel, Loisy, Maeliss, Frandon, Julien, de Oliveira, Fabien, Meerun, Azhar Mohamad, Guiu, Boris, Beregi, Jean-Paul, Greffier, Joël

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Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study von Greffier, Joël, Durand, Quentin, Frandon, Julien, Si-Mohamed, Salim, Loisy, Maeliss, de Oliveira, Fabien, Beregi, Jean-Paul, Dabli, Djamel

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Phantom task‐based image quality assessment of three generations of rapid kV‐switching dual‐energy CT systems on virtual monoenergetic images von Greffier, Joël, Viry, Anaïs, Barbotteau, Yves, Frandon, Julien, Loisy, Maeliss, Oliveira, Fabien, Beregi, Jean Paul, Dabli, Djamel

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Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study von Greffier, Joël, Frandon, Julien, Durand, Quentin, Kammoun, Tarek, Loisy, Maeliss, Beregi, Jean-Paul, Dabli, Djamel

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Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconst... von Greffier, Joël, Si-Mohamed, Salim, Guiu, Boris, Frandon, Julien, Loisy, Maeliss, de Oliveira, Fabien, Douek, Philippe, Beregi, Jean-Paul, Dabli, Djamel

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