A motion artifact reduction method for dental CT based on subpixel-resolution image registration of projection data

• Published in: Computers in biology and medicine 2018-12, Vol.103, p.232-243
• Main Authors: Eldib, Mohamed Elsayed, Hegazy, Mohamed A.A., Cho, Myung Hye, Cho, Min Hyoung, Lee, Soo Yeol
• Format: Article
• Language: English
• Subjects: Algorithms; Computed tomography; Conflicts of interest; Cross-correlation; Dental CT; Dentistry; Digital imaging; Displacement; Entropy; Head movement; Human motion; Image processing; Image reconstruction; Image registration; Image resolution; Medical imaging; Methods; Motion artifact reduction; Orthodontics; Projection; Rigid-body dynamics; Sensors; Spatial resolution; Subpixel image registration; Tracking devices; Translational motion
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.10.028

High-resolution imaging is essential in three-dimensional (3D) CT image-based digital dentistry. A small amount of head motion during a CT scan can degrade the spatial resolution of the images to the extent where the efficacy of 3D image-based digital dentistry is greatly compromised. We introduce a retrospective motion artifact reduction (MAR) method for dental CTs that eliminates the necessity for any external motion tracking devices. Assuming that rigid-body motions are dominant in a dental scan of a human head, we extracted motion information from the projection data. By taking the cross-correlation between two successive projection data for every projection view, we determined the displacement of the projection data at each view. We experimentally found that any motion of the imaging object during the scan resulted in displacement of the projection data proportional to the motion amplitude. We decomposed the displacement into two components, one caused by translational motion and the other caused by rotational motion. The displacement components were used to correct the projection data before CT image reconstruction. We experimentally verified the MAR method using the projection data of a few phantoms acquired through a clinical dental CT machine. When the MAR performance was evaluated by the structural similarity index (SSIM) and the normalized absolute error (NAE) in reference to the motion-less images, the SSIM improved to 99% while the NAE was reduced by 80–90%. [Display omitted] •We introduce a retrospective motion artifact reduction (MAR) method for a dental CT.•A subpixel image registration technique is used to estimate the motion-induced displacements.•The proposed method can estimate all the motion components including translation and rotation.•Actuator-controlled and manual motions have been applied on phantoms using a clinical dental CT.•The experimental results demonstrate the high performance of the proposed method.