Inter-module gap filling method for photon counting detectors based on dual acquisition

Zhuo Chen; Xiaoqi Xi; Yu Han; Siyu Tan; Lei Li; Xuejing Lu; Bin Yan

doi:10.1364/AO.508066

Applied Optics
Vol. 63,
Issue 10,
pp. A106-A114
(2024)
•https://doi.org/10.1364/AO.508066

Inter-module gap filling method for photon counting detectors based on dual acquisition

Zhuo Chen, Xiaoqi Xi, Yu Han, Siyu Tan, Lei Li, Xuejing Lu, and Bin Yan

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Zhuo Chen, Xiaoqi Xi, Yu Han, Siyu Tan, Lei Li, Xuejing Lu, and Bin Yan, "Inter-module gap filling method for photon counting detectors based on dual acquisition," Appl. Opt. 63, A106-A114 (2024)

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- Terahertz and X-ray Optics
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The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: October 27, 2023
- Revised Manuscript: February 26, 2024
- Manuscript Accepted: February 28, 2024
- Published: March 18, 2024
Virtual Issues
Applied Optics Radiography, Applied Optics, and Data Science 2023 (2024)

Abstract

The use of photon counting detectors in X-ray imaging missions can effectively improve the signal-to-noise ratio and image resolution. However, the stitching of photon counting detector modules leads to large-size localized information loss in the acquired projected image, which seriously affects the regional observation. In this paper, we propose a method to fill the inter-module gap based on dual acquisition, referred to as the GFDA algorithm, which is divided into three main steps: (i) acquire the main projection by short-exposure scanning, and then scan again by vertically moving the carrier table to acquire the reference projection; (ii) use the alignment method to locate the projected region of interest; (iii) use image stitching and image fusion to recover the missing information. We analyzed the gray value of the region of interest of the Siemens star projection and the reconstructed conch slice data, and proved that the proposed method can recover the information more smoothly and perfectly. The GFDA algorithm is able to achieve a better image restoration effect without additional scanning time and better retain image details. In addition, the GFDA algorithm is scalable, which is demonstrated in the task of filling the stitching of multiple types of photonic technology detectors.

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Data availability

The data and the code used for the paper are available for researchers upon request from the corresponding author.

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Fig. 1. Geometric model of the PCD-CT system.

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Fig. 2. Parameter calibration flow of the proposed method.

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Fig. 3. Schematic diagram of a numerical simulation strategy.

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Fig. 4. Alignment results obtained for different types of samples.

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Fig. 5. Relationship between the area in which the pixel point is located and the weighting factor

$w$

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Fig. 6. Siemens star projection results obtained by different weighted methods.

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Fig. 7. Local results of Fig. 6 and the gray scale values of the locations marked with the red line.

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Fig. 8. Slices of conch 3D data obtained by different image inpainting methods.

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Fig. 9. Partial results of Fig. 8 and the gray scale values of the locations marked with the red line.

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Fig. 10. Splice stitching results for different types of photon counting detectors.

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Tables (3)

Table 1. Digital-Analog Strategy to Obtain the Results of the Registration

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Table 2. Experimental Objects and Projected Imaging Parameters

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Table 3. Detector Specifications

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Equations (2)

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[\begin{array}{l} x_{m a i n} \\ y_{m a i n} \end{array}] = [\begin{array}{l} x_{r e f} \\ y_{r e f} \end{array}] + [\begin{array}{l} d x \\ d y \end{array}],

F (m, n) = w A (m, n) + (1 - w) B (m, n),

Number	Sample	Method	Horizontal Offset (pixels)	Vertical Offset (pixels)	( $Δ x$ , $Δ y$ )
1	Tongyi (Bronze)	Truth	0	66	(0, 0)
		MI	12	34	(12, −32)
		DFT	0	66	(0, 0)
		SIFT	0.0026	66.0113	(0.0026,0.0113)
2	Industrial grids	Truth	0	140	(0,0)
		MI	0	140	(0,0)
		DFT	0	117	(0, −37)
		SIFT	0.0919	139.5977	(0.0919, −0.4023)
3	Frog skeleton	Truth	0	100	(0, 0)
		MI	0	79	(0, −21)
		DFT	0	0	(0, −100)
		SIFT	0.161	100.7553	(0.1610, 0.7553)

Parameter Type		Parameter Name	Numeric Value
System	X-ray source	Voltage (kV)	60
parameters	X-ray source	Current (mA)	0.3
	Detector	Pixel size (µm)	75
	Detector	Detector size (pixels)	$1030 \times 1065$
Conch	Experiment 1	Number of projections	$360^{*} 2$
	Experiment 1	Exposure time(s)	10
	Experiment 2	Number of projections	360
	Experiment 2	Exposure time(s)	20

Type	EIGER R 1M	PILATUS3 R 300 K	PIXIRAD-8
Pixel array ( $W \times H$ )	$1065^{*} 1030 p i x e l$	$619^{*} 487 p i x e l$	$4608^{*} 476 p i x e l$
Number of modules ( $W \times H$ )	$1 \times 2$	$1 \times 3$	$8 \times 1$
Sensor material	Silicon (Si)	Silicon (Si)	Silicon (Si)
Pixel size ( $W \times H$ ) [ ${µ m}^{2}$ ]	$75 \times 75$	$172 \times 172$	$60 \times 60$
Inter-module gap	hor. - pixel, vert. 37 pixel	hor. 17 pixel, vert. - pixel	hor. 3 pixel, vert. - pixel
Active area ( $W \times H$ ) [ ${m m}^{2}$ ]	$79.9 \times 77.2$	$83.8 \times 106.5$	$250 \times 25$

Number	Sample	Method	Horizontal Offset (pixels)	Vertical Offset (pixels)	( $Δ x$ , $Δ y$ )
1	Tongyi (Bronze)	Truth	0	66	(0, 0)
		MI	12	34	(12, −32)
		DFT	0	66	(0, 0)
		SIFT	0.0026	66.0113	(0.0026,0.0113)
2	Industrial grids	Truth	0	140	(0,0)
		MI	0	140	(0,0)
		DFT	0	117	(0, −37)
		SIFT	0.0919	139.5977	(0.0919, −0.4023)
3	Frog skeleton	Truth	0	100	(0, 0)
		MI	0	79	(0, −21)
		DFT	0	0	(0, −100)
		SIFT	0.161	100.7553	(0.1610, 0.7553)

Parameter Type		Parameter Name	Numeric Value
System	X-ray source	Voltage (kV)	60
parameters	X-ray source	Current (mA)	0.3
	Detector	Pixel size (µm)	75
	Detector	Detector size (pixels)	$1030 \times 1065$
Conch	Experiment 1	Number of projections	$360^{*} 2$
	Experiment 1	Exposure time(s)	10
	Experiment 2	Number of projections	360
	Experiment 2	Exposure time(s)	20

Abstract

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Tables (3)

Equations (2)

Applied Optics