A correlation and LS-SVM based approach to mitigate motion artifacts in FDK based 3D cone-beam tomography

Head motion during brain CT studies can degrade the reconstructed image by introducing distortion and loss of resolution, thereby contributing to misdiagnosis of diseases. In this paper, we have proposed a correlation coefficient and Least Squares Support Vector Machines (LS-SVM) based approach to detect and mitigate motion artifacts in FDK based three-dimensional cone-beam tomography. Motion is detected using correlation between adjacent x-ray projections. Artifacts, caused by motion, are mitigated either by replacing motion corrupted projections with their counterpart 180° apart projections under certain conditions, or by estimating motion corrupted projections using LS-SVM based time series prediction. The method has been evaluated on 3D Shepp-Logan phantom. Simulation results validate our claims.