title: A correlation and LS-SVM based approach to mitigate motion artifacts in FDK based 3D cone-beam tomography.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering The University of Alabama in Huntsville Huntsville, AL-35899, USA. ukb0001@uah.edu
authors.forename: Ujjal Kumar
authors.initials: UK
authors.lastname: Bhowmik
authors.forename: Reza R
authors.initials: RR
authors.lastname: Adhami
mesh_terms: D000465:Algorithms; D016477:Artifacts; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016018:Least-Squares Analysis; D009038:Motion; D019047:Phantoms, Imaging; D012399:Rotation; D060388:Support Vector Machine