• Optics and Precision Engineering
  • Vol. 30, Issue 18, 2167 (2022)
刘艳秋1,2, 胡先功2,3, 张衡1,2, 郭红波1,2, and 贺小伟1,2,3,*
Author Affiliations
  • 1School of Information Sciences and Technology, Northwest University, Xi’an7027, China
  • 2The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an71017, China
  • 3Network and Data Center, Northwest University, Xi’an710127, China
  • show less
    DOI: 10.37188/OPE.20223018.2167 Cite this Article
    Yanqiu LIU, Xiangong HU, Heng ZHANG, Hongbo GUO, Xiaowei HE. Application of spectral differential strategy in diffusion approximation model and simplified spherical harmonic approximation model[J]. Optics and Precision Engineering, 2022, 30(18): 2167 Copy Citation Text show less
    References

    [1] L YIN, K WANG, T TONG et al. Adaptive grouping block sparse Bayesian learning method for accurate and robust reconstruction in bioluminescence tomography. IEEE Transactions on Bio-Medical Engineering, 68, 3388-3398(2021).

    [2] G WANG, Y LI, M JIANG. Uniqueness theorems in bioluminescence tomography. Medical Physics, 31, 2289-2299(2004).

    [3] H B GUO, J J YU, Z H HU et al. A hybrid clustering algorithm for multiple-source resolving in bioluminescence tomography. Journal of Biophotonics, 11(2018).

    [4] L YIN, K WANG, T TONG et al. Improved block sparse Bayesian learning method using K-nearest neighbor strategy for accurate tumor morphology reconstruction in bioluminescence tomography. IEEE Transactions on Bio-Medical Engineering, 67, 2023-2032(2020).

    [5] J C FENG, K B JIA, Z LI et al. Bayesian sparse-based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational time. Journal of Biophotonics, 11(2018).

    [6] J J YU, B ZHANG, I I IORDACHITA et al. Systematic study of target localization for bioluminescence tomography guided radiation therapy. Medical Physics, 43, 2619(2016).

    [7] T TARVAINEN, M VAUHKONEN, V KOLEHMAINEN et al. Coupled radiative transfer equation and diffusion approximation model for photon migration in turbid medium with low-scattering and non-scattering regions. Physics in Medicine and Biology, 50, 4913-4930(2005).

    [8] W X CONG, G WANG, D KUMAR et al. Practical reconstruction method for bioluminescence tomography. Optics Express, 13, 6756-6771(2005).

    [9] L WANG, W T ZHU, Y ZHANG et al. Harnessing the power of hybrid light propagation model for three-dimensional optical imaging in cancer detection. Frontiers in Oncology, 11, 750764(2021).

    [10] A D KLOSE, E W LARSEN. Light transport in biological tissue based on the simplified spherical harmonics equations. Journal of Computational Physics, 220, 441-470(2006).

    [11] D F YANG, C G YAN, L YANG et al. An alternative reconstruction framework with optimal permission source region for bioluminescence tomography. Optics Communications, 427, 112-122(2018).

    [12] 12金晨, 郭红波, 侯榆青, 等. 基于变量分离近似稀疏重构和简化球谐近似的生物发光断层成像[J]. 光学学报, 2014, 34(6): 197-205. doi: 10.3788/aos201434.0617001JINC, GUOH B, HOUY Q, et al. Bioluminescence tomography reconstruction based on simplified spherical harmonics approximation model and sparse reconstruction by separable approximation[J]. Acta Optica Sinica, 2014, 34(6): 197-205.(in Chinese). doi: 10.3788/aos201434.0617001

    [13] X L CHEN, F F SUN, D F YANG et al. Hybrid simplified spherical harmonics with diffusion equation for light propagation in tissues. Physics in Medicine and Biology, 60, 6305-6322(2015).

    [14] L WANG, X CAO, Q Y REN et al. Hybrid model based unified scheme for endoscopic Cerenkov and radio-luminescence tomography: simulation demonstration. Journal of Applied Physics, 123, 184701(2018).

    [15] C H QIN, S P ZHU, J C FENG et al. Comparison of permissible source region and multispectral data using efficient bioluminescence tomography method. Journal of Biophotonics, 4, 824-839(2011).

    [16] 16侯榆青, 张文元, 王晓东, 等. 结合流形正则和变量分离近似稀疏重构的荧光分子断层成像[J]. 光学 精密工程, 2018, 26(10): 2592-2604. doi: 10.3788/ope.20182610.2592HOUY Q, ZHANGW Y, WANGX D, et al. Light source reconstruction method in fluorescence molecular tomography based on Laplacian manifold regularization and sparse reconstruction by separable approximation[J]. Opt. Precision Eng., 2018, 26(10): 2592-2604.(in Chinese). doi: 10.3788/ope.20182610.2592

    [17] M A NASER, M S PATTERSON. Algorithms for bioluminescence tomography incorporating anatomical information and reconstruction of tissue optical properties. Biomedical Optics Express, 1, 512-526(2010).

    [18] X Q ZHANG, Y J LU, T CHAN. A novel sparsity reconstruction method from Poisson data for 3D bioluminescence tomography. Journal of Scientific Computing, 50, 519-535(2012).

    [19] 19侯榆青, 曲璇, 张海波, 等. 采用快速贝叶斯匹配追踪的单视图X射线发光断层成像[J]. 光学 精密工程, 2017, 25(5): 1159-1170. doi: 10.3788/ope.20172505.1159HOUY Q, QUX, ZHANGH B, et al. Single-view XLCT imaging based on fast Bayesian matching pursuit[J]. Opt. Precision Eng., 2017, 25(5): 1159-1170.(in Chinese). doi: 10.3788/ope.20172505.1159

    [20] Y Q LIU, M X CHU, H B GUO et al. Multispectral differential reconstruction strategy for bioluminescence tomography. Frontiers in Oncology, 12, 768137(2022).

    [21] Y GAO, K WANG, S X JIANG et al. Bioluminescence tomography based on Gaussian weighted Laplace prior regularization for in vivo morphological imaging of glioma. IEEE Transactions on Medical Imaging, 36, 2343-2354(2017).

    [22] L WANG, H H CAO, X CAO et al. Adaptively hybrid 3rd simplified spherical harmonics with diffusion equation-based multispectral cerenkov luminescence tomography. IEEE Access, 7, 160779-160785(2019).

    [23] H B GUO, Z H HU, X W HE et al. Non-convex sparse regularization approach framework for high multiple-source resolution in Cerenkov luminescence tomography. Optics Express, 25, 28068(2017).

    [24] A BECK, M TEBOULLE. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2, 183-202(2009).

    [25] G ALEXANDRAKIS, F R RANNOU, A F CHATZIIOANNOU. Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study. Physics in Medicine and Biology, 50, 4225-4241(2005).

    [26] R Y YAO, X INTES, Q Q FANG. Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation. Biomedical Optics Express, 7, 171-184(2015).

    Yanqiu LIU, Xiangong HU, Heng ZHANG, Hongbo GUO, Xiaowei HE. Application of spectral differential strategy in diffusion approximation model and simplified spherical harmonic approximation model[J]. Optics and Precision Engineering, 2022, 30(18): 2167
    Download Citation