• Chinese Optics Letters
  • Vol. 18, Issue 1, 011701 (2020)
Jiaju Cheng and Jianwen Luo*
Author Affiliations
  • Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
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    DOI: 10.3788/COL202018.011701 Cite this Article Set citation alerts
    Jiaju Cheng, Jianwen Luo. Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction[J]. Chinese Optics Letters, 2020, 18(1): 011701 Copy Citation Text show less
    Paths of 3D examples for (a) m=1 and (b) m=2 solved by zeroing-Tk and paths for (c) m=1 and (d) m=2 solved by PrSP-Tk. Red arrows: the path in the non-negative space. Dark green dashed doted arrows: the projections of the Newton algorithm projecting points into the solution space. Blue dashed dotted arrows: the zeroing steps projecting points into non-negative space from the solution space. Black arrows: the path of points in the solution space. Mauve dashed arrows: the accelerating step in the solution space.
    Fig. 1. Paths of 3D examples for (a) m=1 and (b) m=2 solved by zeroing-Tk and paths for (c) m=1 and (d) m=2 solved by PrSP-Tk. Red arrows: the path in the non-negative space. Dark green dashed doted arrows: the projections of the Newton algorithm projecting points into the solution space. Blue dashed dotted arrows: the zeroing steps projecting points into non-negative space from the solution space. Black arrows: the path of points in the solution space. Mauve dashed arrows: the accelerating step in the solution space.
    (a) Map of the number of iterations needed before the last path could be considered linear for different eigenvalue pairs. (b) The blue curve denotes the maximum number of iterations for given λ1. The black curve denotes the corresponding eigenvalue λi for the maximum number of iterations.
    Fig. 2. (a) Map of the number of iterations needed before the last path could be considered linear for different eigenvalue pairs. (b) The blue curve denotes the maximum number of iterations for given λ1. The black curve denotes the corresponding eigenvalue λi for the maximum number of iterations.
    Reconstruction results of the homogeneous experiment with an EED of 3 mm on the excitation plane by using (a) re-L1-NCG, (b) L1-StOMP, (c) IRL1, (d) zeroing-Tk, and (e) PrSP-Tk, respectively. (f) Profiles along the yellow dashed lines in (a)–(e).
    Fig. 3. Reconstruction results of the homogeneous experiment with an EED of 3 mm on the excitation plane by using (a) re-L1-NCG, (b) L1-StOMP, (c) IRL1, (d) zeroing-Tk, and (e) PrSP-Tk, respectively. (f) Profiles along the yellow dashed lines in (a)–(e).
    Reconstruction results of the homogeneous experiment with an EED of 1.5 mm on the excitation plane by using (a) re-L1-NCG, (b) L1-StOMP, (c) IRL1, (d) zeroing-Tk, and (e) PrSP-Tk, respectively. (f) Profiles along the yellow dashed lines in (a)–(e).
    Fig. 4. Reconstruction results of the homogeneous experiment with an EED of 1.5 mm on the excitation plane by using (a) re-L1-NCG, (b) L1-StOMP, (c) IRL1, (d) zeroing-Tk, and (e) PrSP-Tk, respectively. (f) Profiles along the yellow dashed lines in (a)–(e).
    Reconstruction results of the heterogeneous experiment with an EED of 4 mm on the excitation plane by using (a) IRL1 and (b) PrSP-Tk, respectively. (c) Profiles along the yellow dashed lines in (a) and (b).
    Fig. 5. Reconstruction results of the heterogeneous experiment with an EED of 4 mm on the excitation plane by using (a) IRL1 and (b) PrSP-Tk, respectively. (c) Profiles along the yellow dashed lines in (a) and (b).
    EED Re-L1-NCGL1-StOMPIRL1Zeroing-TkPrSP-Tk
    3 mmPCC0.600.730.760.820.88
    tc(s)4.020.112.9123.391.04
    Ni5010100150044
    1.5 mmPCC0.560.740.660.750.77
    tc(s)4.010.173.2726.561.10
    Ni507100200050
    Table 1. PCC, Computational Time (tc), and Number of Iterations (Ni) of the Homogeneous Experiments
    EED IRL1PrSP-Tk
    4 mmPCC0.520.70
    Computational time (s)4.900.78
    Number of iterations10038
    Table 2. PCC, Computational Time, and Number of Iterations of the Heterogeneous-Target Phantom Experiments
    Jiaju Cheng, Jianwen Luo. Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction[J]. Chinese Optics Letters, 2020, 18(1): 011701
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