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CT local reconstruction method based on truncated data extrapolation network

Due to the limitation of scanning conditions and the emerging clinical application of local imaging, local tomography has become a research hotspot. Traditionally, completing the projection data through interpolation or spatial transformation iteration is popular to overcome the truncation artifact, such as iterative reconstruction algorithms. Recently, deep learning networks have been incorporated in dealing with such problems. Instead of global filtering on truncated data, the proposed work focuses on the full feature extraction using U-net as well as the usage of the redundancy between projection sinogram, by performing extrapolation of truncated projection data through data learning and then filter back projection local reconstruction with high efficiency. During the learning process, 3439 projections are selected as complete projection data , and the corresponding truncated data is simulated according to the actual truncation situation. Then, 150 of the truncated data are randomly selected as the test samples, while the rest 3289 of those as the training samples in the U-net. The output sinogram data is compared with the original complete data by calculating the L2 loss function of both. And the Adam optimizer is used to continuously optimize the parameters of the network. RMSE and NMAD are used to quantitatively evaluate the reconstruction effect. Experimental results show that the proposed method based on truncated data extrapolation network can obviously suppress the ring artifacts and compared with images directly reconstructed using truncated projection data, the RMSE is reduced by an average of 43.185%, and the NMAD is reduced by 44.24%.