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.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the proceedings. They include the speaker's narration with video of the slides and animations. Most include full-text papers. Interactive, searchable transcripts and closed captioning are now available for 2018 presentations, with transcripts for prior recordings added daily.
Search our growing collection of more than 16,000 conference presentations, including many plenaries and keynotes.