Feature Guided Motion Artifact Reduction with Structure-Awareness in 4D CT Images

In this paper, we propose a novel method to reduce the magnitude of 4D CT artifacts by stitching two images with a data-driven regularization constrain, which helps preserve the local anatomy structures. Our method first computes an interface seam for the stitching in the overlapping region of the first image, which passes through the “smoothest” region, to reduce the structure complexity along the stitching interface. Then, we compute the displacements of the seam by matching the corresponding interface seam in the second image. We use sparse 3D features as the structure cues to guide the seam matching, in which a regularization term is incorporated to keep the structure consistency. The energy function is minimized by solving a multiple-label problem in Markov Random Fields with an anatomical structure preserving regularization term. The displacements are propagated to the rest of second image and the two image are stitched along the interface seams based on the computed displ...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Dongfeng Han, John Bayouth, Qi Song, sudershan Bhatia, Milan Sonka, Xiaodong Wu
• Keywords: Computer Vision, CVPR 2011, Images, Interface Seam, Regularization Term