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Correction of severe beam-hardening artifacts via a high-order linearization function using a prior-image-based

Daejoong Oh1, Sewon Kim1, Doohyun Park1

  • 1School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.

Medical Physics
|July 1, 2018
PubMed
Summary
This summary is machine-generated.

A new method using high-order polynomial correction and prior-image-based linearization effectively corrects severe beam-hardening artifacts and dark bands in computed tomography images. This approach improves image quality without distortion, outperforming conventional techniques.

Keywords:
beam-hardening correctioncomputed tomographystreak artifact

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Area of Science:

  • Medical Imaging
  • Computed Tomography (CT)
  • Image Reconstruction

Background:

  • Polychromatic X-rays in CT scanners cause beam-hardening, degrading image quality and distorting object shapes.
  • Conventional correction methods are insufficient for severe artifacts caused by dense materials (e.g., iron, titanium) and large metal objects.
  • Severe artifacts include streaks and dark bands due to photon starvation and beam-hardening.

Purpose of the Study:

  • To develop a novel method for correcting severe beam-hardening artifacts and dark bands.
  • To utilize a high-order polynomial correction function and a prior-image-based linearization technique.
  • To enhance the accuracy and quality of CT image reconstruction in challenging cases.

Main Methods:

  • Constructed an initial image free of beam-hardening (prior image) from original projection data.
  • Calculated corresponding beam-hardening-free projection data (prior projection) using a projection operator.
  • Determined a new high-order beam-hardening correction function via minimization of projection data differences.
  • Obtained a corrected linearized sinogram for reconstruction of final beam-hardening corrected images.

Main Results:

  • Effectively removed moderate beam-hardening artifacts (streaks) and severe artifacts (dark bands) in simulations and real experiments.
  • Demonstrated no significant structural or contrast distortion in the corrected images.
  • Validated the method's efficacy across various challenging imaging scenarios.

Conclusions:

  • The prior-image-based linearization method offers superior correction performance compared to conventional techniques.
  • The method expedites the correction procedure by avoiding time-consuming iterative reconstruction.
  • Allows for the incorporation of more high-order terms in the linearization correction function for improved accuracy.