Backproject-filter (BPF) CT image reconstruction using convolutional neural network

Qiyang Zhang; Jianwei Chen; Dong Liang; Yongshuai Ge

doi:10.1117/12.2506454

1 March 2019 Backproject-filter (BPF) CT image reconstruction using convolutional neural network

Qiyang Zhang, Jianwei Chen, Dong Liang, Yongshuai Ge

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 1094854 (2019) https://doi.org/10.1117/12.2506454
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Qiyang Zhang, Jianwei Chen, Dong Liang, and Yongshuai Ge "Backproject-filter (BPF) CT image reconstruction using convolutional neural network", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094854 (1 March 2019); https://doi.org/10.1117/12.2506454

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