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Research progress in material decomposition algorithms for spectral CT
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Zhi JIAO1, 2, Wenyu DING3, Fuqiang YANG1, 2, Kuidong HUANG1, 2
Journal of Materials Engineering | 2025, 53(11) : 30 - 48
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Journal of Materials Engineering | 2025, 53(11): 30-48
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Research progress in material decomposition algorithms for spectral CT
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Zhi JIAO1, 2, Wenyu DING3, Fuqiang YANG1, 2, Kuidong HUANG1, 2
Affiliations
  • 1.School of Mechanical and Electrical Engineering，Northwestern Polytechnical University，Xi’an 710072，China
  • 2.Ningbo Research Institute，Northwestern Polytechnical University，Ningbo 315000，Zhejiang，China
  • 3.Shaanxi Institute of Applied Physics and Chemistry，Xi’an 710061，China
Published: 2025-11-20 doi: 10.11868/j.issn.1001-4381.2025.000126
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Spectral computed tomography （spectral CT） is an emerging detection technology that acquires more comprehensive tissue composition information by measuring an object’s absorption of X-rays of different energies. It plays a pivotal role in various fields such as medical diagnosis， non-destructive testing， material analysis， and security monitoring. Material decomposition algorithms are the core of spectral CT technology， aiming to decompose the composition information of different tissues from multi-energy data. These algorithms are crucial for enhancing the quality and accuracy of decomposed images. This paper reviews the data acquisition methods and mathematical models for material decomposition in spectral CT. It focuses on discussing the research progress of spectral CT material decomposition algorithms in four aspects： projection domain， image domain， direct iteration， and deep learning-based methods. It conducts an in-depth comparative analysis of the theoretical advantages， technical limitations， and current application status of various algorithms. The paper points out that the future research trends in this field include hybrid decomposition optimization in the projection domain， fusion prior constraints and multi-model data in the image domain， convergence stability improvements in direct iteration， and transferability and high generalization in deep learning.

spectral CT  /  material decomposition  /  non destructive testing  /  deep learning
Zhi JIAO, Wenyu DING, Fuqiang YANG, Kuidong HUANG. Research progress in material decomposition algorithms for spectral CT[J]. Journal of Materials Engineering, 2025 , 53 (11) : 30 -48 . DOI: 10.11868/j.issn.1001-4381.2025.000126
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Article Info
doi: 10.11868/j.issn.1001-4381.2025.000126
  • Receive Date：2025-03-04
  • Online Date：2026-01-21
  • Published：2025-11-20
Article Data
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History
  • Received：2025-03-04
  • Accepted：2025-04-14
Funding
Affiliations
    1.School of Mechanical and Electrical Engineering，Northwestern Polytechnical University，Xi’an 710072，China
    2.Ningbo Research Institute，Northwestern Polytechnical University，Ningbo 315000，Zhejiang，China
    3.Shaanxi Institute of Applied Physics and Chemistry，Xi’an 710061，China
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