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Angle prediction model when the imaging plane is tilted about z-axis.

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  • 1School of Aerospace Engineering, Xiamen University, Xiamen, 361102 China.

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A novel artifact in Computer Tomography (CT) imaging, caused by detector tilt, creates fuzzy half-circles. Machine learning accurately estimates this tilt, enabling artifact correction for improved image quality.

Keywords:
ArtifactCTCone-beamGeometric deviationInceptionV3-RMachine learning

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Computer Tomography (CT) systems demand precise geometric positioning for accurate reconstructions.
  • Geometric deviations, such as detector tilt, can introduce significant artifacts in CT images.
  • Artifacts can obscure details and compromise diagnostic accuracy.

Purpose of the Study:

  • To identify and characterize a specific artifact caused by detector z-axis tilt in cone-beam CT.
  • To develop a method for estimating the detector z-axis tilt angle using machine learning.
  • To propose a formula for recovering geometric deviations and eliminating the identified artifact.

Main Methods:

  • Mathematical modeling was employed to understand the artifact's characteristics.
  • An InceptionV3-R deep learning network was utilized to analyze artifact features.
  • The network was trained to estimate the detector z-axis tilt angle from reconstructed slices.

Main Results:

  • The developed deep learning model achieved high accuracy in estimating the detector z-axis tilt angle.
  • Testing yielded a mean absolute error of 0.08819 degrees and an R-square of 0.99944.
  • A geometric deviation recovery formula was successfully deduced, demonstrating efficient artifact elimination.

Conclusions:

  • This research expands the understanding of CT artifacts, specifically those arising from geometric inaccuracies.
  • Machine learning, exemplified by the InceptionV3-R network, shows significant capability in CT geometric deviation artifact recovery.
  • The proposed method offers an efficient approach to correct detector tilt artifacts, enhancing CT image quality.