Study of Parallel Translation Computed Laminography Imaging

Shaoyu Wang; Weiwen Wu; Changcheng Gong; Fenglin Liu

doi:10.3788/AOS201838.1211002

Acta Optica Sinica, Volume. 38, Issue 12, 1211002(2018)

Study of Parallel Translation Computed Laminography Imaging

Shaoyu Wang^1,2、*, Weiwen Wu^1,2, Changcheng Gong^1,2, and Fenglin Liu^1,2、*

¹ Key Lab of Optoelectronic Tech. and Sys. of the Education Ministry of China Chongqing University, Chongqing 400044, China

² Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China

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Figures & Tables(20)

Fig. 1. Imaging geometry and variables of PTCL system

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Fig. 2. Simulation phantom

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Fig. 3. 120° limited angle of global reconstructed images from noise-free cone-beam data. (a) Three-dimensional display; (b) 110th longitudinal profile, the display window is [0 0.8]

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Fig. 4. 130th slice of global reconstructed images from noise-free cone-beam data. (a)-(d) FDK algorithm; (e)-(h) SART algorithm; (i)-(l) SART+TV algorithm, respectively. The first to fourth columns are from 30°, 60°, 90° and 120° limited angle, respectively, the display window is [0 0.8]

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Fig. 5. Central horizontal profiles of global reconstructed images from noise-free cone-beam data by (a) FDK, (b) SART and (c) SART+TV algorithm

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Fig. 6. 130th slice of global reconstructed images from noise cone-beam data. (a)-(d) FDK algorithm; (e)-(h) SART algorithm; (i)-(l) SART+TV algorithm, respectively. The first to fourth columns are from 30°, 60°, 90° and 120° limited angle, respectively, the display window is [0 0.8]

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Fig. 7. Central horizontal profiles of 130th slice global reconstructed images from noise cone-beam data by (a) FDK, (b) SART and (c) SART+TV algorithm

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Fig. 8. 12th slice of local reconstructed images from noise-free cone-beam data. (a)-(d) FDK algorithm; (e)-(h) SART algorithm; (i)-(l) SART+TV algorithm, respectively. The first to fourth columns are from 30°, 60°, 90° and 120° limited angle, respectively, the display window is [0 1.5]

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Fig. 9. Horizontal profiles along y=14 within 12th slice of local reconstructed image from noise-free cone-beam by (a) FDK,(b) SART and (c) SART+TV algorithm

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Fig. 10. Reconstructed images from noise-free cone-beam and 60° projection angle with different magnification ratios by using SART+TV algorithm. (a) Magnification ratio is 8.89; (b) magnification ratio is 6, the display window is [0 1.5]

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Fig. 11. Horizontal profiles along y=14 of 12th slice reconstructed images with 60° limited angle and noise-free cone-beam data by SART+TV algorithm

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Fig. 12. (a) Convergence curve of noise-free cone-beam data with 60° limited angle by using SART+TV algorithm; (b) enlarged image of Fig. (a)

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Fig. 13. (a) Detected chip; (b) PCB

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Fig. 14. 128th slice reconstructed images by using (a) FDK and (b) SART+TV methods

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Fig. 15. PCB 111th slice reconstructed results by using (a) FDK and (b) SART+TV methods

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Table 1. Parameters of the numerical simulation

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Table 1. Parameters of the numerical simulation

Parameters	Value
Source to detector distance S_D /mm	1128
Source to object distance S_O /mm	126.9
Detector mode	Equi-distance
Detector array length /Pixel	512
Translation mode	Equi-angular
Reconstruction matrix	256×256×256
Pixel size /(mm×mm)	1.01×1.01
Number of iterations	50

Table 2. Normalized mean square error of global reconstructed images from noise-free cone-beam data with different angles and different algorithms10-4
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Table 2. Normalized mean square error of global reconstructed images from noise-free cone-beam data with different angles and different algorithms10-4
Method 30° 60° 90° 120°
FDK 7100 2950 875 405
SART 14 5.4 5.0 3.8
SART+TV 11 5.1 2.9 2.1

Table 3. Normalized mean square error of global reconstructed images from noise cone-beam data with different angles and different algorithms10-4
View table
Table 3. Normalized mean square error of global reconstructed images from noise cone-beam data with different angles and different algorithms10-4
Method 30° 60° 90° 120°
FDK 8319 3541 1257 699
SART 15 10.4 9.0 7.1
SART+TV 14 5.6 5.4 4.9

Table 4. Quantitative assessment in terms of local reconstruction images normalized mean square error10-4
View table
Table 4. Quantitative assessment in terms of local reconstruction images normalized mean square error10-4
Method 30° 60° 90° 120°
FDK 8529 4989 2289 1866
SART 88 73 69 63
SART+TV 80 68 68 61

Table 5. Scanning parameters

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Table 5. Scanning parameters

Parameter	Value
Source to detector distance S_D /mm	195
Source to object distance S_O /mm	33
Detector mode	Equi-spatial
Detector array length /(pixel×pixel)	3072×864
Pixel size /(mm×mm)	0.0748×0.0748
Translation mode	equi-angular
Reconstruction matrix	256×256×256
Graduation angle /(°)	60
Number of translate	250

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Shaoyu Wang, Weiwen Wu, Changcheng Gong, Fenglin Liu. Study of Parallel Translation Computed Laminography Imaging[J]. Acta Optica Sinica, 2018, 38(12): 1211002

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Paper Information

Category: Imaging Systems

Received: Jul. 13, 2018

Accepted: Aug. 13, 2018

Published Online: May. 10, 2019

The Author Email:

DOI:10.3788/AOS201838.1211002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Parameters of the numerical simulation

Table 1. Parameters of the numerical simulation

Table 2. Normalized mean square error of global reconstructed images from noise-free cone-beam data with different angles and different algorithms10-4

Table 2. Normalized mean square error of global reconstructed images from noise-free cone-beam data with different angles and different algorithms10-4

Table 3. Normalized mean square error of global reconstructed images from noise cone-beam data with different angles and different algorithms10-4

Table 3. Normalized mean square error of global reconstructed images from noise cone-beam data with different angles and different algorithms10-4

Table 4. Quantitative assessment in terms of local reconstruction images normalized mean square error10-4

Table 4. Quantitative assessment in terms of local reconstruction images normalized mean square error10-4

Table 5. Scanning parameters

Table 5. Scanning parameters