材料工程2025，Vol.53Issue(11)：30-48,19.DOI:10.11868/j.issn.1001-4381.2025.000126
能谱CT材料分解算法研究进展
Research progress in material decomposition algorithms for spectral CT
摘要
Abstract
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.关键词
能谱CT/材料分解/无损检测/深度学习Key words
spectral CT/material decomposition/non destructive testing/deep learning分类
信息技术与安全科学引用本文复制引用
焦智,丁文宇,杨富强,黄魁东..能谱CT材料分解算法研究进展[J].材料工程,2025,53(11):30-48,19.基金项目
中国航空发动机集团产学研合作项目(HFZL2022CXY024) （HFZL2022CXY024）
浙江省"尖兵领雁+X"研发攻关计划(2024C01249(SD2)) （2024C01249(SD2）
西北工业大学硕士研究生实践创新能力培育基金项目(PF2025051) （PF2025051）
