Projection super-resolution based on convolutional neural network for computed tomography

Chao Tang; Wenkun Zhang; Ziheng Li; Ailong Cai; Linyuan Wang; Lei Li; Ningning Liang; Bin Yan

doi:10.1117/12.2533766

28 May 2019 Projection super-resolution based on convolutional neural network for computed tomography

Chao Tang, Wenkun Zhang, Ziheng Li, Ailong Cai, Linyuan Wang, Lei Li, Ningning Liang, Bin Yan

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107233 (2019) https://doi.org/10.1117/12.2533766
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)- based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projectiondomain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of low- and high-resolution projection. The network label is high-resolution projection and the input is its corresponding interpolation data after down sampling. FDK algorithm is utilized for three-dimensional image reconstruction and one slice of reconstruction image is taken as an example to evaluate the performance of the proposed method. Qualitative and quantitative results show that the proposed method is potential to improve the resolution of projection and enables the reconstructed image with higher quality.

Citation Download Citation

Chao Tang, Wenkun Zhang, Ziheng Li, Ailong Cai, Linyuan Wang, Lei Li, Ningning Liang, and Bin Yan "Projection super-resolution based on convolutional neural network for computed tomography", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107233 (28 May 2019); https://doi.org/10.1117/12.2533766

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