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Ring artifact removal for differential phase-contrast X-ray computed tomography using a conditional generative

Zhuoran Huang1, Naoki Sunaguchi2, Daisuke Shimao3

  • 1Department of Radiological and Medical Laboratory Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan.

International Journal of Computer Assisted Radiology and Surgery
|October 15, 2021
PubMed
Summary

A new conditional generative adversarial network (cGAN) method effectively removes ring artifacts (RA) from differential phase-contrast X-ray CT (d-PCCT) images. This technique preserves biological structures, improving soft tissue observation.

Keywords:
Generative adversarial networkMachine learningPhase-contrast X-ray CTRing artifactX-ray dark-field imaging

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Processing

Background:

  • Differential phase-contrast X-ray CT (d-PCCT) reconstruction involves an integration pre-processing step.
  • This step propagates noise, leading to ring artifacts (RA) in reconstructed images.
  • Conventional RA removal methods are ineffective for d-PCCT due to its unique properties.

Purpose of the Study:

  • To develop an effective method for removing ring artifacts (RA) from d-PCCT images.
  • To address the limitations of existing RA removal techniques in the context of d-PCCT.
  • To improve the quality of d-PCCT images for better biological soft tissue observation.

Main Methods:

  • A conditional generative adversarial network (cGAN) was employed for RA removal.
  • The method utilizes Laplacian images derived from second-derivative projections of d-PCCT.
  • The cGAN's loss function was enhanced with L1- and L2-norm, trained on simulated d-PCCT data.

Main Results:

  • Numerical validation demonstrated superior RA removal compared to conventional methods.
  • The proposed method successfully removed RA from simulated and actual experimental d-PCCT data.
  • Visual evaluation confirmed that the method preserved original structures in biological images.

Conclusions:

  • A novel cGAN-based method effectively removes RA from d-PCCT images by leveraging physical properties.
  • The method achieved complete RA removal in both simulated and biological d-PCCT datasets.
  • This technique holds significant potential for enhanced observation of biological soft tissues.