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Related Experiment Video

Updated: May 20, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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Polychromatic sinogram based material decomposition and reconstruction methods: integrate standard reference database

David Shih-Chun Jin1,2, Wei-Lin Li1,3, Chieh Shen1,4

  • 1Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Biomedical Physics & Engineering Express
|May 18, 2026
PubMed
Summary

This study introduces a novel sinogram-based material decomposition method for dual-energy micro-CT. This technique enables quantitative spectral imaging and material characterization without specialized detectors, advancing preclinical research.

Keywords:
Compton scatter images (CSIs)cone-beam micro-computed tomography (micro-CT)material decomposition (MD)photoelectric effect images (PEIs)virtual monochromatic images (VMIs)

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Area of Science:

  • Medical Imaging
  • Materials Science
  • Computational Imaging

Background:

  • Quantitative spectral imaging is crucial for material characterization in preclinical research.
  • Conventional micro-CT systems often lack specialized detectors for advanced material analysis.
  • Existing methods face challenges in accurate material decomposition, especially in vivo.

Purpose of the Study:

  • To develop a sinogram-based material decomposition (MD) framework for dual-energy cone-beam micro-CT.
  • To enable quantitative spectral imaging and material characterization without photon-counting detectors.
  • To provide a practical tool for advanced preclinical in vivo imaging.

Main Methods:

  • Acquired dual-energy projections (50 and 80 kVp) for calibration.
  • Utilized a three-material basis model solved in the sinogram domain via Moore-Penrose pseudoinverse.
  • Synthesized virtual monochromatic images, photoelectric effect images (PEIs), and Compton scatter images (CSIs).

Main Results:

  • The framework demonstrated high generalizability across different phantoms and in vivo rat scans.
  • Achieved accurate, automated segmentation of skeletal and soft tissues with high structural similarity.
  • Successfully separated physical attenuation mechanisms, isolating bone and soft tissue signals.

Conclusions:

  • Validated an accessible and robust sinogram-based MD method for conventional micro-CT systems.
  • Enables quantitative multi-material analysis, crucial for preclinical research.
  • Offers a practical solution for material characterization in challenging imaging scenarios.