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CT Image Reconstruction for Defective Detector Based On ART-TV Algorithm

C Yuan1*, Z Chen2 , H Qi3 , B Li1 , J Ma1 , N Luo1 , S Wu3 , L Zhou1 , Y Xu1 , (1) Southern Medical University, Guangzhou, Guangdong, (2) Shantou Central Hospital, Shantou, Guangdong, (3) Guangzhou Huaduan Technology Co. Ltd., Guangzhou, Guangdong Guangdong


(Monday, 7/30/2018) 9:30 AM - 10:00 AM

Room: Exhibit Hall | Forum 1

Purpose: Iterative CT image reconstruction method is effective to reduce radiation dose. However, when some bins of CT detector are damaged, especially located in the central area of the projection, artifacts are generated in the reconstructed image. To solve this problem, two new methods based on ART-TV algorithm are proposed to reconstruct CT images.

Methods: The first new method, in each iteration, applies sinogram completion to correct and update the field of bad pixels being in the center of projection and performs ART-TV reconstruction. When the width of defective bins in the middle region of projection is wider, the second new approach of detector offset is proposed before employing ART-TV reconstruction.

Results: Simulation studies of digital Shepp-Logan phantom are carried out to verify the effectiveness of the two proposed methods. A region of interest in the center of image is chosen to calculate SNR and MAE. The first method produces higher SNR (16.71dB>14.38dB) and lower MAE (0.0079<0.0132) of reconstructed image than traditional ART-TV method, quantitatively. The second method of detector offset reconstructs image with higher SNR (20.99dB>8.13dB) and lower MAE (0.0039<0.0274) than detector non-offset ART-TV algorithm.

Conclusion: Two practical methods based on ART-TV algorithm for defective detector are proposed, and reconstruct images with no artifacts compared to traditional ART-TV method.

Funding Support, Disclosures, and Conflict of Interest: National Natural Science Foundation of China (81301940 and 81428019), National Key Research and Development Program (2016YFA0202003), Guangdong Natural Science Foundation of China (2016A030310388 and2017A030313692),and Southern Medical University Startup fund (LX2016003).


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