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Quantitative material decomposition with dynamic x-ray model in spectral CT.

Chengmin Wang1,2, Zhe Wang1,3, Yuedong Liu1,2

  • 1Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.

Medical Physics
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive image-domain decomposition method (AIDM) for spectral CT material decomposition. AIDM improves accuracy and applicability by dynamically adjusting the x-ray interaction model for better quantitative analysis.

Keywords:
PCDCTmaterial decompositionquantitative imaging

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Area of Science:

  • Medical Imaging
  • Photon-Counting Detector (PCD) CT
  • Quantitative Material Analysis

Background:

  • Photon-counting detectors (PCD) enhance spectral CT flexibility for material decomposition.
  • Traditional methods struggle with accuracy under varied protocols or material ranges.
  • Predefined physical models limit effective atomic number and density estimation.

Purpose of the Study:

  • To develop an adaptive image-domain decomposition method (AIDM) for spectral CT.
  • To enhance material decomposition accuracy and quantitative analysis.
  • To dynamically adjust the x-ray interaction model, compensating for errors.

Main Methods:

  • Introduced correction terms C(E, Zeff) for photoelectric/coherent scattering and D(E, Zeff) for Compton scattering.
  • Formulated correction terms as polynomial expansions fitted with NIST data.
  • Calibrated effective energy and performed decomposition using a nonlinear system on reconstructed images.

Main Results:

  • Validated AIDM with standard materials, minerals, and biological samples.
  • Demonstrated improved accuracy, robustness, and broader applicability compared to existing methods.
  • Achieved enhanced material quantification in spectral CT.

Conclusions:

  • AIDM enables accurate spectral CT-based material estimation.
  • The method offers significant improvements for diverse applications.
  • Enhanced quantitative material analysis is achievable with AIDM.