SU‐E‐I‐39: Metal Artifacts Reduction Using Sinogram Pre‐Processing and Post‐ Processing in Computed Tomography (CT)

B. Meng, J. Wang, L. Xing

Research output: Contribution to journalArticle

Abstract

Purpose: To develop a novel metal artifacts reduction (MAR) technique through sinogram pre‐processing and post‐processing techniques. Methods: The proposed MAR method consists of two steps. In the pre‐processing step, we reconstruct a binary image delineating the locations and shapes of metal objects through a non‐linear amplification of the sinogram. The boundaries of metallic objects are obtained by using a penalized weighted least‐squares (PWLS) algorithm with adequate intensity gradient‐controlled. The obtained metal only image with the associate intensity is the metal image model. Forward‐projection of the metal image model gives us the metal projection pattern. The sinogram post‐processing step includes matching of the metal projection pattern in the projection space, smart subtraction of the metal projection pattern from the original projections and a final smoothing step. The post‐processed projections are used to reconstruct the metal artifacts free image through FDK algorithm. A series of experimental studies of a head phantom with different numbers and shapes of metals are performed to evaluate the proposed approach. Results: The study shows that the proposed method is capable of remarkably reducing metal artifacts, and improving soft‐tissue visibility. Even with multiple metal objects inserted, each one of them is identified after the preprocessing step. The accuracy of the metal artifacts free images are within 3 pixels Conclusions: The proposed method demonstrates to be effective to achieve metal artifacts free images. The combination of sinogram preprocessing and post‐processing provides a viable way of reducing metal artifacts in CT imaging.

Original languageEnglish (US)
Number of pages1
JournalMedical Physics
Volume38
Issue number6
DOIs
StatePublished - 2011

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Artifacts
Metals
Tomography

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

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title = "SU‐E‐I‐39: Metal Artifacts Reduction Using Sinogram Pre‐Processing and Post‐ Processing in Computed Tomography (CT)",
abstract = "Purpose: To develop a novel metal artifacts reduction (MAR) technique through sinogram pre‐processing and post‐processing techniques. Methods: The proposed MAR method consists of two steps. In the pre‐processing step, we reconstruct a binary image delineating the locations and shapes of metal objects through a non‐linear amplification of the sinogram. The boundaries of metallic objects are obtained by using a penalized weighted least‐squares (PWLS) algorithm with adequate intensity gradient‐controlled. The obtained metal only image with the associate intensity is the metal image model. Forward‐projection of the metal image model gives us the metal projection pattern. The sinogram post‐processing step includes matching of the metal projection pattern in the projection space, smart subtraction of the metal projection pattern from the original projections and a final smoothing step. The post‐processed projections are used to reconstruct the metal artifacts free image through FDK algorithm. A series of experimental studies of a head phantom with different numbers and shapes of metals are performed to evaluate the proposed approach. Results: The study shows that the proposed method is capable of remarkably reducing metal artifacts, and improving soft‐tissue visibility. Even with multiple metal objects inserted, each one of them is identified after the preprocessing step. The accuracy of the metal artifacts free images are within 3 pixels Conclusions: The proposed method demonstrates to be effective to achieve metal artifacts free images. The combination of sinogram preprocessing and post‐processing provides a viable way of reducing metal artifacts in CT imaging.",
author = "B. Meng and J. Wang and L. Xing",
year = "2011",
doi = "10.1118/1.3611612",
language = "English (US)",
volume = "38",
journal = "Medical Physics",
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