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Proceedings Paper

A hybrid ring artifact reduction algorithm based on CNN in CT images
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Paper Abstract

In flat-panel based cone beam computed tomography (CBCT), ring artifacts always exist and degrade the quality of reconstructed images. In this work, we propose a convolutional neural network (CNN) based ring artifact reduction algorithm in CT images, which fuses the information from the original and corrected images to eliminate the artifacts. The proposed method consists of two steps. First, we establish a database consisting of three types of images for training, artifact-free, ring artifact and pre-corrected images. Second, the original and pre-corrected images are input to the trained CNN to generate an image with less artifacts. To further reduce the artifacts, by using image mutual correlation, pixels in the pre-corrected image and the CNN output image, which are less sensitive to artifacts, are combined to generate a hybrid corrected image. Both simulated and real data experiments were performed to verify the proposed method. Experimental results show that the proposed method can effectively suppress the ring artifacts without introducing processing distortion to the image structure.

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, 1107226 (28 May 2019);
Show Author Affiliations
Shaojie Chang, Xi'an Jiaotong Univ. (China)
Xi Chen, Xi'an Jiaotong Univ. (China)
Jiayu Duan, Xi'an Jiaotong Univ. (China)
Xuanqin Mou, Xi'an Jiaotong 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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