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Related Experiment Video

Updated: May 8, 2026

Contrast Enhanced Vessel Imaging using MicroCT
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Air artifact suppression in phase contrast micro-CT using conditional generative adversarial networks.

Md Motiur Rahman Sagar1, Lorenzo D'Amico2, Elena Longo2

  • 1Translational Molecular Imaging, Max-Plank-Institute for Multidisciplinary Sciences, Germany.

Journal of Synchrotron Radiation
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using conditional generative adversarial networks (cGANs) to remove air artifacts in 3D virtual histology. This technique enhances the visualization and analysis of formalin-fixed, paraffin-embedded (FFPE) lung tissue scanned with phase contrast micro-computed tomography (micro-CT).

Keywords:
FFPE lung tissueair artifact suppressionconditional GANsphase contrast micro-CTvirtual histology

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Area of Science:

  • Biomedical Imaging
  • Computational Pathology
  • Histology

Background:

  • Phase contrast micro-computed tomography (micro-CT) enables 3D virtual histology of formalin-fixed, paraffin-embedded (FFPE) tissues without heavy ion staining.
  • Trapped air within FFPE tissues poses significant challenges for 3D visualization and structural analysis in micro-CT due to edge effects.

Purpose of the Study:

  • To develop and validate a novel workflow to eliminate air artifacts in 3D phase contrast micro-CT scans of FFPE tissues.
  • To improve the 3D visualization and facilitate structural analysis of lung tissue affected by air inclusions.

Main Methods:

  • A conditional generative adversarial network (cGAN) based workflow was developed to replace air artifact regions with generated tissue.
  • The generative model was trained to synthesize tissue structures influenced by the surrounding morphology.
  • The approach was tested on FFPE lung tissue and validated for transferability to porcine lung specimens.

Main Results:

  • The cGAN-based workflow effectively removed air artifacts from phase contrast micro-CT scans of FFPE lung tissue.
  • The method significantly improved the visualization of lung tissue architecture.
  • Structural analysis of the air artifact-suppressed scans was notably eased.

Conclusions:

  • The proposed cGAN workflow offers a powerful solution for mitigating air artifacts in 3D virtual histology of FFPE tissues.
  • This technique enhances the utility of phase contrast micro-CT for detailed morphological studies and structural analysis, particularly in challenging soft tissues like the lung.
  • The demonstrated transferability suggests broad applicability across different FFPE tissue types.