Reconstruction of fluorescence molecular tomography with a cosinoidal level set method

被引:2
|
作者
Zhang, Xuanxuan [1 ]
Cao, Xu
Zhu, Shouping
机构
[1] Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian 710071, Shaanxi, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Fluorescence molecular tomography; Level set method; Levenberg-Marquardt method; DIFFUSE OPTICAL TOMOGRAPHY; IN-VIVO; IMAGE-RECONSTRUCTION; ILLUMINATION; MICROSCOPY; DOMAIN; LIGHT; SCATTERING; TUMORS; CELLS;
D O I
10.1186/s12938-017-0377-0
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Background: Implicit shape-based reconstruction method in fluorescence molecular tomography (FMT) is capable of achieving higher image clarity than image-based reconstruction method. However, the implicit shape method suffers from a low convergence speed and performs unstably due to the utilization of gradient-based optimization methods. Moreover, the implicit shape method requires priori information about the number of targets. Methods: A shape-based reconstruction scheme of FMT with a cosinoidal level set method is proposed in this paper. The Heaviside function in the classical implicit shape method is replaced with a cosine function, and then the reconstruction can be accomplished with the Levenberg-Marquardt method rather than gradient- based methods. As a result, the priori information about the number of targets is not required anymore and the choice of step length is avoided. Results: Numerical simulations and phantom experiments were carried out to validate the proposed method. Results of the proposed method show higher contrast to noise ratios and Pearson correlations than the implicit shape method and image-based reconstruction method. Moreover, the number of iterations required in the proposed method is much less than the implicit shape method. Conclusions: The proposed method performs more stably, provides a faster convergence speed than the implicit shape method, and achieves higher image clarity than the image-based reconstruction method.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Method for improving the accuracy of fluorescence molecular tomography based on multi-wavelength concurrent reconstruction
    Wang, Huiquan
    Feng, Tianzi
    Dong, Xinming
    Zhao, Zhe
    Han, Guang
    Wang, Jinhai
    Ma, Wenjuan
    Wang, Rong
    Liu, Minghu
    Miao, Jinghong
    REVIEW OF SCIENTIFIC INSTRUMENTS, 2022, 93 (04):
  • [42] Application of kernel method in fluorescence molecular tomography
    Zhao, Yue
    Baikejiang, Reheman
    Li, Changqing
    MULTIMODAL BIOMEDICAL IMAGING XII, 2017, 10057
  • [43] Inertial gradient method for fluorescence molecular tomography
    Wang, Lei
    Huang, Hui
    JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES, 2021, 14 (02)
  • [44] Improved level set method for particle reconstruction from X-ray computed tomography images
    Wang, Tingxuan
    Fei, Wenbin
    Ehinger, Krista A.
    Drummond, Tom W.
    Narsilio, Guillermo A.
    POWDER TECHNOLOGY, 2025, 455
  • [45] Novel Regularized Sparse Model for Fluorescence Molecular Tomography Reconstruction
    Liu, Yuhao
    Liu, Jie
    An, Yu
    Jiang, Shixin
    INTERNATIONAL CONFERENCE ON INNOVATIVE OPTICAL HEALTH SCIENCE, 2017, 0245
  • [46] Image reconstruction for synchronous data acquisition in fluorescence molecular tomography
    Zhang, Xuanxuan
    Liu, Fei
    Zuo, Siming
    Bai, Jing
    Luo, Jianwen
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2015, 23 (04) : 463 - 472
  • [47] Fluorescence molecular-tomography reconstruction with a priori anatomical information
    Zhou, Lu
    Yazici, Birsen
    Ntziachristos, Vasilis
    SMALL ANIMAL WHOLE-BODY OPTICAL IMAGING BASED ON GENETICALLY ENGINEERED PROBES, 2008, 6868
  • [48] Weighted depth compensation algorithm for fluorescence molecular tomography reconstruction
    Liu, Fei
    Li, Mingze
    Zhang, Bin
    Luo, Jianwen
    Bai, Jing
    APPLIED OPTICS, 2012, 51 (36) : 8883 - 8892
  • [49] Reconstruction of fluorescence molecular tomography based on graph convolution networks
    Li, Dongsheng
    Chen, Chunxiao
    Li, Jianfei
    Yan, Qiang
    JOURNAL OF OPTICS, 2020, 22 (04)
  • [50] Performance Evaluation of a Priori Information on Reconstruction of Fluorescence Molecular Tomography
    Liu, Xin
    Yan, Zhuangzhi
    Lu, Hongbing
    IEEE ACCESS, 2015, 3 : 64 - 72