To reduce the artifacts and improve the image quality in sparse-view CT reconstruction. A novel improved GoogLeNet is proposed to reduce artifacts of the sparse-view CT reconstruction. This paper uses the residual learning for GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

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Shipeng Xie, Pengcheng Zhang, Limin Luo, and Haibo Li, "Sparse-view CT reconstruction with improved GoogLeNet ," Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105780P (Presented at SPIE Medical Imaging: February 12, 2018; Published: 12 March 2018); https://doi.org/10.1117/12.2295345.