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

Ujjal Kumar Bhowmik, Reza R. Adhami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages8471-8474
Number of pages4
DOIs
StatePublished - Dec 26 2011
Externally publishedYes
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

Keywords

  • FDK
  • LS-SVM
  • Motion Artifacts
  • Motion Detection
  • Three-dimensional CT
  • Time series prediction

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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