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Proceedings Paper

Sparse view-CT based on photon counting detector using multi-level wavelet-CNN
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Paper Abstract

Sparse view - computed tomography (CT) for low dose and photon counting detector (PCD) for spectral imaging have been studied for improvement of image quality and quantification in medical imaging. The sparse view–CT can reduce dose, but there is a limitation that cannot be completely restored yet and PCD with physical phenomena such as charge sharing, K-escape and material characteristic can be difficult to material quantification due to different distribution of noise characteristics in a specific energy band. In this study, we propose a deep running-based wavelet-CNN for the efficient reduction of physical factors such as noise and streak artifact generated by fusion of sparse view-CT and PCD. The physical phenomena of the spatio-energetic cross-talks were reflected in PCD. We obtained images with a total of four energy thresholds with limited angles and trained through the proposed method. The proposed method was evaluated for the image quality by the peak signal to noise ratio (PSNR), the normalized mean square error (NMSE), the structural similarity (SSIM), the multi-scale SSIM (MS-SSIM), and the feature similarity (FSIM). The experimental results demonstrated that the sparse view-CT with PCD using proposed deep running structure effectively removes the streak artifacts and improves the image quality.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124S (16 March 2020); doi: 10.1117/12.2549188
Show Author Affiliations
Minjae Lee, Yonsei Univ. (Korea, Republic of)
Hyemi Kim, Yonsei Univ. (Korea, Republic of)
Dohyeon Kim, Yonsei Univ. (Korea, Republic of)
Hee-Joung Kim, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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