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Deep learning based adaptive filtering for projection data noise reduction in x-ray computed tomography
Author(s): Tzu-Cheng Lee; Jian Zhou; Zhou Yu
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Paper Abstract

In conventional x-ray CT imaging, noise reduction is often applied on raw data to remove noise while improving reconstruction quality. Adaptive data filtering is one noise reduction method that suppresses data noise using a local smooth kernel. The design of the local kernel is important and can greatly affect the reconstruction quality. In this report we develop a deep learning convolutional neural network to help predict the local kernel automatically and adaptively to the data statistics. The proposed network is trained to directly generate kernel parameters and hence allow fast data filtering. We compare our method to the existing filtering method. The results shows that our deep learning based method is more efficient and robust over a variety of scan conditions.

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, 110721D (28 May 2019); doi: 10.1117/12.2534838
Show Author Affiliations
Tzu-Cheng Lee, Canon Medical Research USA, Inc (United States)
Jian Zhou, Canon Medical Research USA, Inc. (United States)
Zhou Yu, Canon Medical Research USA, Inc. (United States)


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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