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Proceedings Paper

Backproject-filter (BPF) CT image reconstruction using convolutional neural network
Author(s): Qiyang Zhang; Jianwei Chen; Dong Liang; Yongshuai Ge
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Paper Abstract

In this work, we realize the image-domain backproject-filter (BPF) CT image reconstruction using the convolutional neural network (CNN) method. Within this new CT image reconstruction framework, the acquired sinogram data is backprojected first to generate the highly blurred laminogram. Afterwards, the laminogram is feed into the CNN to retrieve the desired sharp CT image. Both numerical and experimental results demonstrate that this new CNN-based image reconstruction method is feasible to reconstruct CT images with maintained high spatial resolution and accurate pixel values from the laminogram as of from the conventional FBP method. The experimental results also show that the performance of this new CT image reconstruction network does not rely on the used radiation dose level. Due to these advantages, this proposed innovative CNN-based image-domain BPF type image reconstruction strategy provides promising prospects in generating high quality CT images for future clinical applications.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094854 (1 March 2019); doi: 10.1117/12.2506454
Show Author Affiliations
Qiyang Zhang, Shenzhen Institutes of Advanced Technology (China)
Jianwei Chen, Shenzhen Institutes of Advanced Technology (China)
Dong Liang, Shenzhen Institutes of Advanced Technology (China)
Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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