2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-31.6
Paper Title ATTENTION-EMBEDDED DECOMPOSED NETWORK WITH UNPAIRED CT IMAGES PRIOR FOR METAL ARTIFACT REDUCTION
Authors Binyu Zhao, Qianqian Ren, Heilongjiang University, China; Jinbao Li, Qilu University of Technology(Shandong Academy of Science), China; Yafeng Zhao, Heilongjiang University, China
SessionIVMSP-31: Applications 3
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, unsupervised learning is proposed to avoid the performance degrading caused by synthesized paired computed tomography (CT) images. However, existing unsupervised methods for metal artifact reduction (MAR) only use features in image space, which is not enough to restore regions heavily corrupted by metal artifacts. Besides, they lack the distinction and selection for effective features. To address these issues, we propose an attention-embedded decomposed network to reducing metal artifacts in both image space and sinogram space with unpaired images. Specifically, combining with the CT images prior, we decompose the artifact-affected images to artifact images and content images. Besides, normal convolutions are embedded with attention design in pixel-wise and channel-wise to strengthen the representational capacity. Extensive experiments show notable improvements on both synthesized data and clinical data.