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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.

Xiaokun Liang1,2, Na Li1,2, Zhicheng Zhang1,2

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Quantitative Imaging in Medicine and Surgery
|August 27, 2019
PubMed
Summary

This study introduces a novel deep learning and adaptive filter method to correct shading artifacts in CT scans. The technique significantly improves CT number accuracy, image contrast, and spatial uniformity, enhancing image quality for clinical applications.

Keywords:
Shading artifactadaptive filter (AF)deep convolution neural networkvolumetric CT (VCT)

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Shading artifacts in Computed Tomography (CT) lead to inaccuracies in CT numbers, reduced image contrast, and spatial non-uniformity (SNU).
  • These artifacts are a fundamental limitation for the application of volumetric CT (VCT).

Purpose of the Study:

  • To develop and evaluate a novel method for correcting shading artifacts in CT images.
  • To improve CT number accuracy, image contrast, and spatial uniformity.

Main Methods:

  • A deep convolutional neural network (DCNN) was trained for human tissue segmentation.
  • A template image was generated using segmentation and CT number information.
  • An adaptive filter (AF) was employed to separate shading artifacts from image details.
  • The estimated artifacts were subtracted from the raw image to produce a corrected image.

Main Results:

  • CT number error in the corrected image was reduced from 109 to 11 HU (Catphan©504) and 198 to 10 HU (pelvis study).
  • Image contrast increased by an average factor of 1.46.
  • Spatial non-uniformity (SNU) decreased from 24% to 9%.

Conclusions:

  • The proposed shading correction method, utilizing DCNN and AF, effectively reduces artifacts.
  • This technique shows potential for future clinical practice in CT imaging.