计算机与现代化 ›› 2022, Vol. 0 ›› Issue (05): 96-101.

• 图像处理 • 上一篇    下一篇

一种基于改进多光谱的稀疏角CB-XLCT成像方法

  

  1. (1.西安航空职业技术学院航空维修工程学院,陕西西安710089;2.西北大学信息科学与技术学院,陕西西安710127)
  • 出版日期:2022-06-08 发布日期:2022-06-08
  • 作者简介:张文元(1992—),男,陕西咸阳人,助教,硕士,研究方向:图像处理,信息技术,E-mail: 2454176182@qq.com; 海琳琦(1999—),男,硕士研究生,研究方向:计算机辅助设计,E-mail: 202032947 @ stumail.nwu.edu.cn; 刘英杰(2000—),男,本科生,研究方向:医学图像处理,E-mail: 215738520@qq.com; 张海波(1985—),男,副教授,博士,研究方向:稀疏感知,医学影像分析,E-mail: zhanghb@nwu.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(61902317); 陕西省自然科学基础研究计划项目(2019JQ-166)

An Improved Multispectral Reconstruction for Sparse-view CB-XLCT Imaging

  1. (1. School of Aviation Maintenance Engineering, Xi’an Aeronautical Polytechnic Institute, Xi’an 710089, China;
    2. School of Information Science and Technology, Northwest University, Xi’an 710127, China)
  • Online:2022-06-08 Published:2022-06-08

摘要: 稀疏角锥束X射线发光断层成像(Sparse-view Cone-Beam X-ray Luminescence Computed Tomography, Sparse-view CB-XLCT)是一种新型的多模光学断层成像技术,在早期肿瘤的实时检测方面展现出了良好的应用潜力。然而,受限于有限投影数据的限制,稀疏角CB-XLCT成像的逆问题相对于传统多角度CB-XLCT,其病态性更为严重。针对上述问题,本文提出一种改进多光谱的稀疏角CB-XLCT成像方法,首先,基于多光谱策略构建系统矩阵并建模逆问题;接着,利用谱回归方法对上一步逆问题中的高维系统矩阵进行特征学习;随后,采用一种矩阵预处理方法有效降低系统矩阵的列相关性并建模为新的逆问题进行准确重建。分别设计多组仿体实验以及噪声测试实验,验证本文所提方法的有效性和鲁棒性。实验结果表明,所提方法不仅可有效求解稀疏角CB-XLCT成像逆问题,还具有良好的鲁棒性。

关键词: 生物光学, X射线发光断层成像, 三维重建, 多光谱策略

Abstract: Sparse-view Cone-beam X-ray luminescence computed tomography (CB-XLCT) is a novel multimode optical tomography technique, which has shown good potential for real-time detection of early tumor. However, the inverse problem of sparse-view CB-XLCT is more serious than that of traditional multi-angle CB-XLCT, because of the limited projective data. Aiming at above problem, this paper proposes a spectral regression (SR) and preconditioning method to improve multispectral reconstruction for sparse-view CB-XLCT. Firstly, the multispectral strategy is used to construct the system matrix and model the inverse problem; Then, the spectral regression method is used to learn the features of the high-dimensional system matrix in the previous inverse problem; After that, a preconditioning approach is used to reduce the coherence of the new system matrix, and further form a new inverse problem. Numerical simulations and robustness tests were performed to verify the effectiveness and robustness of the proposed method. The experimental results indicated that our proposed method not only can significantly improve the imaging quality of multispectral reconstruction for sparse-view CB-XLCT, but also has good robutsness.

Key words: biotechnology, X-ray luminescence computed tomography, 3D reconstruction, multispectral strategy