A spatial information incorporation method for irregular sampling CT based on deep learning

Zaifeng Shi; Zhongqi Wang; Huilong Li; Jinzhuo Li; Qingjie Cao

doi:10.1117/12.2534920

28 May 2019 A spatial information incorporation method for irregular sampling CT based on deep learning

Zaifeng Shi, Zhongqi Wang, Huilong Li, Jinzhuo Li, Qingjie Cao

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107232 (2019) https://doi.org/10.1117/12.2534920
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

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.

Citation Download Citation

Zaifeng Shi, Zhongqi Wang, Huilong Li, Jinzhuo Li, and Qingjie Cao "A spatial information incorporation method for irregular sampling CT based on deep learning", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107232 (28 May 2019); https://doi.org/10.1117/12.2534920

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