Sparse-View Cone-Beam X-Ray Luminescence Computed Tomography Imaging for Optimized Regional Prior Knowledge

Haibo Zhang; Jiaojiao Kou; Qichen Huang; Yingjie Liu; Yuqing Hou; Xiaowei He; Mingquan Zhou; Rui Zhang

doi:10.3788/AOS202242.0117001

Acta Optica Sinica, Volume. 42, Issue 1, 0117001(2022)

Sparse-View Cone-Beam X-Ray Luminescence Computed Tomography Imaging for Optimized Regional Prior Knowledge

Haibo Zhang¹, Jiaojiao Kou¹, Qichen Huang¹, Yingjie Liu¹, Yuqing Hou¹, Xiaowei He¹, Mingquan Zhou¹, and Rui Zhang^2、*

Author Affiliations

¹School of Information Science & Technology, Northwest University, Xi′an, Shaanxi 710127, China;

²State Key Laboratory of Military Stomatology, National Clinical Research Center for Oral Diseases, Clinical Research Center of Oral Diseases of Shaanxi Province, Department of Orthodontics, Stomatological Hospital of Fourth Military Medical University, Xian, Shaanxi 710032, China

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Figures & Tables(9)

Fig. 1. Sparse-view CB-XLCT imaging algorithm framework based on optimized region knowledge prior

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Fig. 2. Digital rat trunk model and forward simulation results. (a) Digital rat trunk model; (b) sparse 4-angle forward simulation result; (c) full 24-angle forward simulation result

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Fig. 3. Reconstruction results with sparse 4-view projection. (a) Proposed method+SB-L₁; (b) proposed method+GPSR-L₁; (c) proposed method+CoSaMP-L₀; (d) proposed method+OMP-L₀; (e) SB-L₁;(f) GPSR-L₁; (g) CoSaMP-L₀; (h) OMP-L₀

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Fig. 4. Reconstruction results with full 24-view projection. (a) Proposed method+SB-L₁; (b) proposed method+GPSR-L₁; (c) proposed method+CoSaMP-L₀; (d) proposed method+OMP-L₀; (e) SB-L₁;(f) GPSR-L₁; (g) CoSaMP-L₀; (h) OMP-L₀

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Fig. 5. Reconstruction results of robust test. (a) Test results for different depth levels; (b) test results for different noise levels

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Fig. 6. Surface light signal distribution and sparse 4-view reconstruction results obtained by real simulant experiments. (a) Surface light signal distribution obtained by real simulant experiments; (b) reconstruction results of xy plane and CT slice superposition; (c) reconstruction results of xz plane and CT slice superposition; (d) reconstruction results of yz plane and CT slice superposition

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Table 1. Quantitative reconstruction results obtained by sparse 4-view projection

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Table 1. Quantitative reconstruction results obtained by sparse 4-view projection

Method	LE /mm	PNZ /%
Proposed method+SB-L₁	0.641	0.47
Proposed method+GPSR-L₁	0.744	0.51
Proposed method+CoSaMP-L₀	0.792	0.52
Proposed method+OMP-L₀	0.870	0.61
SB-L₁	1.522	2.33
GPSR-L₁	2.103	2.29
CoSaMP-L₀	2.713	2.34
OMP-L₀	2.827	2.25

Table 2. Quantitative reconstruction results obtained by full 4-view projection

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Table 2. Quantitative reconstruction results obtained by full 4-view projection

Method	LE /mm	PNZ /%
Proposed method+SB-L₁	0.486	0.26
Proposed method+GPSR-L₁	0.749	0.29
Proposed method+CoSaMP-L₀	0.771	0.43
Proposed method+OMP-L₀	0.794	0.51
SB-L₁	1.897	1.77
GPSR-L₁	1.937	1.77
CoSaMP-L₀	2.007	2.25
OMP-L₀	2.586	2.12

Table 3. Reconstruction quantitative results of real simulant experiments
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Table 3. Reconstruction quantitative results of real simulant experiments
Algorithm LE /mm PNZ /%
Proposed method+SB 0.92 0.76
SB 2.67 3.36

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Haibo Zhang, Jiaojiao Kou, Qichen Huang, Yingjie Liu, Yuqing Hou, Xiaowei He, Mingquan Zhou, Rui Zhang. Sparse-View Cone-Beam X-Ray Luminescence Computed Tomography Imaging for Optimized Regional Prior Knowledge[J]. Acta Optica Sinica, 2022, 42(1): 0117001

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