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.
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.
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