A Segmentation-Based Method for Metal Artifact Reduction

Hengyong Yu; Kai Zeng; Deepak K Bharkhada; Ge Wang; Mark T Madsen; Osama Saba; Bruno Policeni; Matthew A Howard; Wendy R.K Smoker

doi:10.1016/j.acra.2006.12.015

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A Segmentation-Based Method for Metal Artifact Reduction

Journal article

Peer reviewed

A Segmentation-Based Method for Metal Artifact Reduction

Hengyong Yu, Kai Zeng, Deepak K Bharkhada, Ge Wang, Mark T Madsen, Osama Saba, Bruno Policeni, Matthew A Howard and Wendy R.K Smoker

Academic radiology, Vol.14(4), pp.495-504

2007

DOI: 10.1016/j.acra.2006.12.015

PMCID: PMC1995751

PMID: 17368220

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https://www.ncbi.nlm.nih.gov/pmc/articles/1995751View

Open Access

Abstract

We propose a novel segmentation-based interpolation method to reduce the metal artifacts caused by surgical aneurysm clips. Our method consists of five steps: coarse image reconstruction, metallic object segmentation, forward-projection, projection interpolation, and final image reconstruction. The major innovations are 2-fold. First, a state-of-the-art mean-shift technique in the computer vision field is used to improve the accuracy of the metallic object segmentation. Second, a feedback strategy is developed in the interpolation step to adjust the interpolated value based on the prior knowledge that the interpolated values should not be larger than the original ones. Physical phantom and real patient datasets are studied to evaluate the efficacy of our method. Compared to the state-of-the-art segmentation-based method designed previously, our method reduces the metal artifacts by 20–40% in terms of the standard deviation and provides more information for the assessment of soft tissues and osseous structures surrounding the surgical clips. Mean shift technique and feedback strategy can help to improve the image quality in terms of reducing metal artifacts.

feedback

projection interpolation

image segmentation

mean shift filtering

Metal artifacts

Details

Title: Subtitle: A Segmentation-Based Method for Metal Artifact Reduction
Creators: Hengyong Yu - Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Science, Virginia Technical, Blacksburg, VA 24061
Kai Zeng - Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242
Deepak K Bharkhada - Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Science, Wake Forest University, Winston-Salem, NC 27157
Ge Wang - Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Science, Virginia Technical, Blacksburg, VA 24061
Mark T Madsen - Department of Radiology, University of Iowa, Iowa City, IA 52242
Osama Saba - SIEMENS Medical Solutions, 51 Valley Stream Parkway, Malvern, PA 19355
Bruno Policeni - Department of Radiology, University of Iowa, Iowa City, IA 52242
Matthew A Howard - Department of Neurosurgery, University of Iowa, Iowa City, IA 52242
Wendy R.K Smoker - Department of Radiology, University of Iowa, Iowa City, IA 52242
Resource Type: Journal article
Publication Details: Academic radiology, Vol.14(4), pp.495-504
DOI: 10.1016/j.acra.2006.12.015
PMID: 17368220
PMCID: PMC1995751
NLM abbreviation: Acad Radiol
ISSN: 1076-6332
eISSN: 1878-4046
Publisher: Elsevier Inc
Language: English
Date published: 2007
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Neurology; Radiology; Oral Pathology, Radiology and Medicine; Iowa Neuroscience Institute; Neurosurgery; Otolaryngology
Record Identifier: 9984020800302771

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