Abstract
CT plays an important role in medical diagnosis and industrial nondestructive testing. How to reduce the radiation to patients in the process of CT scanning has become a hot spot. An effective way is to use sparse sampling projection data to reconstruct the CT image. However, the image obtained by using the traditional CT reconstruction algorithm to deal with the sparse projection data has serious image distortion and artifact. This paper presents a CT reconstruction method based on CNN. This method learns the mapping relationship between the sparse projection data and the complete projection data in the training database, and uses the learned result to process the sparse sampling projection data. FBP algorithm is used to get the high resolution CT image. This paper uses CNN to carry out an end-to-end learning to repair the projection sinusoidal image with missing angle and improve the quality of CT image reconstruction.
Published Version
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