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Latent space reconstruction for missing data problems in CT
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DOI：10.1002/mp.17910.png)
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Abstract
En 中文
The reconstruction of a computed tomography (CT) image can be compromised by artifacts, which, in many cases, reduce the diagnostic value of the image. These artifacts often result from missing or corrupt regions in the projection data, for example, by truncation, metal, or limited angle acquisitions.
Keywords:
computed tomography
deep learning
missing data
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