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Proceedings Paper

A spatial information incorporation method for irregular sampling CT based on deep learning
Author(s): Zaifeng Shi; Zhongqi Wang; Huilong Li; Jinzhuo Li; Qingjie Cao
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Paper Abstract

Low dose CT is a popular research which focuses to reduce radiation damaging. Inspiring from the aperture coding method in optical imaging, azimuth coding projection method which belongs to the category of incomplete projection is proposed to shorten the exposure time and reduce the projection paths. Based on this coding method, the ROI will inevitably be sampled intensively, the information which is from region of interest (ROI)projection data was modulated by "coding". And the azimuth coding projection methods for the ROI will reflect the spatial continuity of the ROI. The spatial correlation between slice and adjacent slices is strong in human CT image sequences. Deep learning (DL) technology excels in medical image feature extraction. Convolutional neural network(CNN)was used to extract the modulated ROI projection information, and CNN incorporated the spatial information from adjacent slices based on the strong spatial correlation, then the obtained feature map is nonlinearly mapped to the feature map containing less artifacts. After training and testing the CNN, there is one azimuth coding method which are adapted to the corresponding the ROI at least, CT reconstructed images were restored well.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107232 (28 May 2019); doi: 10.1117/12.2534920
Show Author Affiliations
Zaifeng Shi, Tianjin Univ. (China)
Zhongqi Wang, Tianjin Univ. (China)
Huilong Li, Tianjin Univ. (China)
Jinzhuo Li, Tianjin Univ. (China)
Qingjie Cao, Tianjin Normal Univ. (China)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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