Maoyu Tian
ABSTRACT
Computed tomography is an advanced imaging technology. It determines the attenuation coefficient distribution of the measured object based on known projection data and reconstruction algorithms. The filtered back-projection algorithm is one of the traditional algorithms commonly used in image reconstruction. It can quickly obtain the image to be reconstructed. The quality of the reconstructed image relies on the selection of the filter designed manually. Besides, the image reconstructed will contain serious artifacts when the projection data is incomplete. In this paper, a fully connected layer is used to simulate the filtered back-projection algorithm by designing a neural network model, it can adaptively learn a suitable filter for the current projection system. Meanwhile, the artifacts generated in the case of limited-angle reconstruction are corrected through the convolution layer. The results indicate that the method proposed in this paper can obtain the image with better quality compared to the traditional filtered back-projection algorithm. Moreover, it is easy to obtain the training set, with only the point source for training, and the network can learn the reconstruction process of the image with any pixel distribution.
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Index Terms
- Neural Network-Based Filtered Back-Projection Reconstruction and Limited-View Artifacts Correction
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