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Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
PublisherIEEE
ISBN (Electronic)978-1-4673-9862-6
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventIEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)1091-0026

Conference

ConferenceIEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE
Period1/01/00 → …

Abstract

High density objects in the field of view (FOV) cause artifacts in medical imaging. In X-ray computed tomography (CT), there are several ways to eliminate the effects of these artifacts. This paper aims to evaluate the performance of a novel reconstruction algorithm which accurately segments the metallic regions and reconstruct sharp metal/tissue boundaries, while reducing the artifacts around the metallic regions. This algorithm uses a multilevel segmentation algorithm based on Otsu’s threshold and adaptive multiresolution maximum a-posteriori expectation maximization (amMAP-EM). The qualities of Gaussian noise contaminated images were evaluated quantitatively using mean squared error and line profile analysis. The reconstructed image were compared with filtered backprojection (FBP) and maximum likelihood expectation maximization (MLEM) methods. According to the results, it is possible to reconstruct the images with more clear and sharper metal/tissue boundaries using amMAP-EM compared to MLEM and FBP, while avoiding the undesired artifacts such as blurring, streak artifacts or ringing.