CN107328798B - Novel ICL system and implementation method - Google Patents

Novel ICL system and implementation method Download PDF

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CN107328798B
CN107328798B CN201710475704.8A CN201710475704A CN107328798B CN 107328798 B CN107328798 B CN 107328798B CN 201710475704 A CN201710475704 A CN 201710475704A CN 107328798 B CN107328798 B CN 107328798B
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刘丰林
王少宇
伍伟文
全超
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Abstract

The invention relates to a novel ICL system, and belongs to the field of computer layered imaging. The system comprises a ray source, a flat panel detector, a sample to be detected and a platform, wherein the ray source is fixed on the platform, the flat panel detector and the sample to be detected move in parallel and linearly along the same direction, and simultaneously the flat panel detector synchronously rotates in the scanning process, so that the flat panel detector is always vertical to a central beam of the ray source in the data acquisition process of the system. The invention also relates to a novel ICL implementation method, which comprises the following steps: establishing an imaging model; an iterative reconstruction algorithm is employed. The invention has simple structure, low cost and high realizability, and can be used for the nondestructive ICL system of large-size plate-shaped components.

Description

Novel ICL system and implementation method
Technical Field
The invention belongs to the field of computer layered imaging, and relates to a novel ICL system and an implementation method thereof.
Background
Computed Tomography (CT) is a relatively mature nondestructive testing technique, can effectively perform imaging analysis on the internal structure of an object, and has been widely applied in the fields of industry, medicine, aviation and the like. In a typical fan-beam industrial CT system, a sample to be measured is placed on a turntable between an X-ray tube and a flat panel detector, and X-rays generated by the X-ray tube are attenuated by an object and collected by the detector for storage. At least 180 degrees of projection data is required to obtain a reconstructed image through the slice of the sample. Due to geometric constraints, acquiring 180 degrees of projection data is nearly impossible for oversized members. Meanwhile, for a platy member with the length and width far larger than the thickness, such as a multilayer printed circuit board, a wing or a satellite solar panel and the like, when a ray bundle is approximately parallel to the member, the transmission intensity is very low, and the detection effect of the member is greatly influenced; on the other hand, to avoid collisions, the distance between the tube and the axis of rotation cannot be too small, thereby limiting the spatial resolution of the CT system. In these cases, Computed tomography (CL) technology has become a potential alternative to CT.
In recent years, the research and development of X-ray computed tomography imaging technology has been spotlighted. A typical CL system consists essentially of three parts: x-ray source, detector and objective table. The method is characterized in that the scanned object is a flat object, the CL system scans in a non-coaxial mode, the X-ray penetrates through the CL system along the direction forming a certain angle with the normal line of the plane of the plate-shaped sample, and the sample is scanned at multiple angles through synchronous rotary motion or simple relative parallel motion of the X-ray source and the detector. The CL technology is essentially a CT technology of non-coaxial scanning limited angle projection, belongs to non-precise reconstruction, and realizes the chromatographic detection of the internal structure form and the defects of the component through incomplete scanning.
Over the past few decades, new CL systems or methods have been proposed in succession for different applications. In 2013, a chest computer tomography (DBT) system applied to the medical field was developed by Sechopoulos and the like; in the industrial field, there are also many different CL systems proposed. In 1995, Zhou et al developed an X-source CL system for testing large or flat components and experimentally tested printed circuit boards and welds with good results; in 2010, Maisl et al introduced the use of CL for light weight component inspection; in 2012, Que and the like establish a set of CL system with a new scanning structure, and research the application of algebraic reconstruction Algorithm (ART) in CL imaging through computer simulation; in the chinese patent application with publication number CN1643371A entitled "system and method for imaging large-view objects", an imaging device is proposed, which realizes "multi-scanning track" scanning of objects by moving the positions of a radiation source and a detector, and finally realizes imaging of objects larger than the view of the detector; yan wrout iron et al solve the problem of imaging large views of long, wide and large objects; in 2015, Liu et al, in chinese patent publication No. CN105319225A, proposed an industrial CL imaging system, which implemented the detection of a large plate-like object with large length, width and thickness. It has some disadvantages: 1) the curvature of the C-shaped arm of the system is determined, and the position of a ray source is fixed, so that the distance S from the ray source of the system to the track of the flat panel detector is caused DNon-adjustable, and thus Field of View (FOV) is not variable, resulting in a system with low flexibility; 2) the high-precision C-shaped arm is complex to manufacture and high in cost; although the application of these systems in both the medical and industrial fields has achieved good resultsHowever, none of them has the structural complexity, cost, etc. of the focusing system.
Disclosure of Invention
In view of the above, the present invention aims to provide a novel Industrial computer-aided tomography (ICL) system for nondestructive testing of large-sized plate-shaped members, which has a simple structure, low cost and high realizability, and provides an image reconstruction algorithm for the CL system of the present invention.
In order to achieve the purpose, the invention provides the following technical scheme:
a novel ICL system comprises a ray source, a flat panel detector, a sample to be detected and a platform; the ray source, the sample to be detected and the flat panel detector are sequentially placed on the platform; the ray source is fixed on the platform and used for emitting rays; the flat panel detector and the sample to be detected do same-direction linear motion along a linear direction perpendicular to the linear direction of the ray emitted by the ray source; in the scanning process of the system, the ray emitted by the ray source penetrates through the sample to be detected, the flat panel detector synchronously rotates, and the fact that the flat panel detector is perpendicular to the ray emitted by the ray source all the time in the data acquisition process of the system is guaranteed.
A novel ICL implementation method comprises the following steps:
s1: establishing an imaging model;
s2: and adopting an iterative reconstruction algorithm until the image meets the actual requirement.
Further, the imaging model is:
the sample to be detected and the detector synchronously move in parallel along the direction of the x axis, an included angle omega between the detector and the motion track of the detector and the maximum scanning radius r of the sample to be detected are explored at any position in the scanning process, and the calculation formula is as follows:
Figure GDA0002298441570000021
Figure GDA0002298441570000022
establishing a rectangular coordinate system by taking the point of the X-ray source closest to the sample track as an origin and the sample motion direction as the positive direction of the X-axis; the ray source is fixed at the point a and is not moved; the position of the object is x p(P1.. P.) where P is the number of projections acquired by the system and the detector position is x D(ii) a d is half the length of the detector; omega is the included angle between the panel of the flat panel detector and the motion track of the panel; s OIs the distance from the source to the track of the sample to be measured, S DThe distance from the ray source to the flat panel detector track;
by adjusting the distance S from the ray source to the track of the sample to be measured OAnd the distance S from the ray source to the flat panel detector track DChanging the system field angle FOV; changing the magnification of the sample to be detected by moving the object back and forth, and selecting a proper view field and magnification according to the size of the actual sample to be detected; and in the scanning process, the flat panel detector synchronously rotates, so that a detector panel of the system is always vertical to a central beam of the ray source in the data acquisition process, and the length change of the flat panel detector in the scanning process is reduced.
Further, the iterative reconstruction algorithm is as follows: firstly, discretizing a continuous image, dividing all image areas into a limited number of pixels, forming a matrix to be solved by using constants in each pixel, establishing a group of algebraic equations by using measured projection data, and solving an equation set to obtain an unknown image vector; the method specifically comprises the following steps:
s201: inputting projection data p iAnd assigning an initial value: wherein
Figure GDA0002298441570000032
An initial value representing the jth pixel;
s202: the estimated projection values for all rays are calculated:
Figure GDA0002298441570000033
wherein i 1., L denotes the total number of rays; 1, N tableShowing the total number of pixels; p is a radical of iA projection value representing the ith ray; omega ijIs a projection coefficient reflecting the contribution of the jth pixel to the ith ray integral;
s203: calculating a correction value, wherein an average correction term is calculated by using correction terms of all ray projections, and the correction term of the jth pixel is as follows:
wherein W i,+Representing the contribution of all pixels to the integral of the ith ray, W +,jRepresenting the contribution of the jth pixel to all ray integrals,
Figure GDA0002298441570000035
representing projection values of the ith ray of k iterations, and L representing the total number of rays;
s204: and (5) correcting to complete one iteration:
Figure GDA0002298441570000036
wherein x jRepresenting the jth pixel value of the current iteration image;
s205: and finishing a round of iteration after correcting all pixel points of the reconstructed image once, taking the result of the round of iteration as a temporary solution, and repeating the steps of S202, S203 and S204 until the image meets the actual requirement.
The invention has the beneficial effects that:
(1) however, the CL system proposed by the present invention solves some of the disadvantages of the Liu system and the like: 1) the design of the arc guide rail is changed into a relatively simple linear and swinging scanning mode, so that the scanning structure and the movement mode are simplified, and the system cost is reduced. 2) The curvature of a C-shaped arm in a CL system of Liu et al is determined, the position of a ray source is fixed, and the distance S from the ray source of the system to the track of a flat panel detector is caused DIs not adjustable so that the Field of View (FOV) is not variable. The system provided by the invention not only can adjust the distance from the ray source to the detected object, but also can adjust the distance S DCan be changed at will according to the needs of the detected object, thereby improving the system flexibilityThe activity is suitable for different detection requirements.
(2) The detector selected by the invention has smaller size, reduces the system cost, but has limited field radius, only can partially reconstruct an object, and needs to scan for many times for large flat panel detection to sacrifice the detection efficiency. For this problem, the present invention rotates synchronously during the scanning process of the detector, so that the panel is always vertical to the central beam of the ray source. Compared with a system without a rotary detector, the system can obtain a larger view field under the same detector length, thereby exponentially reducing the scanning times of a large flat plate and greatly improving the detection efficiency.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic diagram of a novel ICL system architecture;
FIG. 2 is a novel ICL system imaging geometry model;
FIG. 3 is a spatial resolution test card for reconstruction;
FIG. 4 is a noise-free fan beam 90, 120, 150 finite angle image reconstruction;
fig. 5 is a cross-sectional view of the direction of y-0 in fig. 4;
FIG. 6 is a noisy fan beam 90, 120, 150 finite angle image reconstruction;
a cross-sectional view in the direction of y-0 in fig. 6 is shown as 7;
FIG. 8 is a 150 degree finite angle image reconstruction of noiseless fan beams 200, 300, 400 projections;
fig. 9 is a cross-sectional view of fig. 8 taken along the direction of y-0.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
1. Imaging model
The data acquisition mode is based on the synchronous parallel linear motion of the detector and the sample to be detected. As shown in fig. 1, the radiation source is fixed on the platform and stationary, the flat panel detector and the sample to be measured move in parallel and linearly in the same direction, and the flat panel detector rotates synchronously in the scanning process, so that the detector of the system is always perpendicular to the central beam of the radiation source in the data acquisition process.
As shown in fig. 2. And establishing a rectangular coordinate system by taking the point of the X-ray source closest to the sample track as an origin and the sample motion direction as the positive direction of the X-axis. The ray source is fixed at the point a and does not move. The position of the object is x p(P1.., P), where P is the number of projections acquired by the system. The position of the detector being x DAnd d is half the length of the detector. Omega is the included angle between the panel of the flat panel detector and the motion track of the panel. S OIs the distance from the source to the track of the sample to be measured, S DIs the distance from the ray source to the flat panel detector trajectory. The sample to be detected and the detector synchronously move in parallel along the direction of the x axis, and the size of an included angle omega between the detector and the motion track of the detector and the size of the maximum scanning radius r of the sample to be detected are researched by taking any position in the scanning process.
Figure GDA0002298441570000051
Figure GDA0002298441570000052
The system has the advantages of simple motion mode, low cost, easy realization and adjustable view field and magnification. On one hand, the distance S from the ray source to the track of the sample to be measured can be adjusted OAnd the distance S from the ray source to the flat panel detector track DThe FOV of the system is changed, and on the other hand, the magnification of the sample to be detected can be changed by moving the object back and forth, so that the proper field of view and magnification can be selected according to the size of the actual sample to be detected. And in the scanning process, the flat panel detector synchronously rotates, so that a detector panel of the system is always vertical to a central beam of the ray source in the data acquisition process. The method ensures that the length change of the detector is small in the scanning process, thereby solving the problem that the detector needs too much at the edge position of a finite angle in linear motion, greatly improving the utilization rate of the detector and reducing the cost. With simultaneous symmetrical ray structure, the projection matrix in iterative reconstruction algorithm is simplifiedAnd the reconstruction speed is improved by calculation.
2. Reconstruction algorithm
2.1 tomosynthesis
In the system, an X-ray source is kept static, a flat panel detector and a sample to be detected synchronously move in parallel, the detector collects and stores single projection data at each angle, and then images of all layers of an object are obtained through processing. The essence of the method is back projection reconstruction, which is incomplete data reconstruction under the condition of limited angles, except that the back projection reconstruction method operates each point, and the fault fusion operates each layer (each line).
The tomosynthesis method, although simple and rapid, has many disadvantages. Because it mimics classical fuzzy hierarchical imaging techniques, during scanning, only points in the object's focal plane can be projected sharply onto the same region of the detector, while the portions of the object above and below the focal plane will be projected onto different regions of the detector, inevitably causing blurring and artifacts in the reconstructed image. And the method requires that the magnification cannot be changed in the scanning process, thereby bringing limitation to the scanning structure. Although the image quality can be improved by filtering techniques similar to those in CT back-projection reconstruction methods, even so the image quality of tomosynthesis is inferior to CT. However, the method can be used in object measurements with high contrast in X-ray projections, such as printed circuit boards.
2.2 iterative reconstruction Algorithm
In order to further improve the image quality, improve the system resolution and improve the limitation that the magnification is not changeable in the scanning process, an iterative reconstruction algorithm can be adopted. Compared with a fault fusion technology for simply superposing projection data, the iterative reconstruction algorithm firstly discretizes continuous images, divides all image areas into a limited number of pixels, and a constant is arranged in each pixel, so that a matrix to be solved is formed, a group of algebraic equations are established by using the measured projection data, and unknown image vectors are obtained by solving the equation set. The system presented herein can be modeled as the following linear matrix equation:
AX＝b (3)
b＝(b 1,b 2,...,b M)∈R Mfor projection data where M is the total amount of data, X ═ X 1,...,X N)∈R NFor reconstructing the object, where N is the total number of pixels, and A ═ a mn) Is a system measurement matrix where M1.
A classical iterative Reconstruction algorithm is an Algebraic Reconstruction Algorithm (ART) that corrects the value of each pixel by adding a correction term during iterative computation of image Reconstruction. The SIRT algorithm, namely a joint algebraic reconstruction technology, is an improved method for the ART algorithm. Similarly, the SIRT algorithm updates the temporary solution by combining correction terms under a specific projection angle. The joint correction term is the correction term that is generated jointly by all rays at a particular projection angle. The basic process of implementation of the SART algorithm is as follows:
(1) and calculating correction terms of the equation corresponding to the first ray for each pixel point, and storing the correction terms in an array. And calculating the correction term of the equation corresponding to the second ray for each pixel point, and adding the correction terms into the array. And repeating the steps until the correction term of the equation corresponding to the last ray to each pixel point is calculated and the correction terms are added into the array, so that the updating processing of the iterative solution under a projection angle is completed.
(2) And (3) applying the step in the step (1) to the condition of other projection angles until the reconstructed image meets the actual requirement.
The iterative formula of the SART algorithm is as follows:
Figure GDA0002298441570000061
wherein λ kIs a relaxation factor for suppressing overcorrection, k being the number of iterations. L, L is the total number of rays. j 1.. N, N is the total number of pixels. p is a radical of iIs the projection value of the ith ray. Omega ijIs the projection coefficient which reflects the contribution of the jth pixel to the ith ray. It is clear that the projection coefficients are critical in the equation solving process, they will not beThe known image is associated with the known projection values. The whole iterative process is as follows:
inputting projection data: p is a radical of iAssigning an initial value:
Figure GDA0002298441570000062
the estimated projection values for all rays are calculated.
Figure GDA0002298441570000063
A correction value is calculated. The correction term for the jth pixel is as follows:
Figure GDA0002298441570000064
the correction term is an average correction term calculated using the correction terms of all ray projections.
And correcting to finish one iteration.
Figure GDA0002298441570000071
And finishing a round of iteration after correcting all pixel points of the reconstructed image once, and repeating the steps 2, 3 and 4 by taking the result of the round of iteration as a temporary solution until the image meets the actual requirement.
3. Numerical simulation
In order to verify the effectiveness of the system, a space resolution test card is used as a sample to be tested to perform some preliminary simulation experiments. Since the projection data obtained by one straight line scan is incomplete finite angle data, there is no accurate reconstruction method in theory, and the reconstruction results have some artifacts actually caused by the data. In order to improve the quality of the reconstructed image, a number of methods have been proposed. The M-SART algorithm is adopted to reconstruct the data obtained by the system. FIG. 3 is a spatial resolution test card for reconstruction with an image size of 256 × 1024 and a pixel size of 1 × 1mm 2. The scan parameters are shown in table 1.
TABLE 1 simulation parameters
Parameter(s) Value of
Distance SDD (mm) from ray source to detector 1800
Distance from source to object SOD (mm) 1500
Detector array Length (mm) 350
Detector pixel size (mm) 1
Projection graduation 200300400
Projection division number P of ray source for one-time scanning 350
Scanning model Equiangular scanning
Reconstructed image size 256×1024
Pixel size (mm) 2) 1×1
Number of iterations 2000
3.1 image reconstruction of different finite Angle data
Fig. 4 shows that M-SIRT algorithm is used to perform 90-degree, 120-degree, 150-degree finite-angle noiseless image reconstruction on the spatial resolution test card under fan beam, where the first row is the original image and the red circle mark part is the effective reconstruction area of the local scan. Fig. 5 shows a comparison of the section gray values of the reconstructed image of the noiseless data at different angles on the line y equal to 0. To further evaluate the quality of the reconstructed images, the root mean square error and the peak signal to noise ratio in the red region at each angle are listed in tables 2 and 3.
TABLE 2 reconstructed image RMS error and peak SNR under different limited angles without noise in ICL system
Angle of rotation RMSE PSNR
90 degree 68.1281 11.4643
120 degrees 67.6595 11.5242
150 degree 58.7665 12.7482
To test the noise characteristics of the system and algorithm, we added gaussian noise with a variance of 5% of the maximum value of the noiseless data to the simulated projection data. And performing 90-degree, 120-degree and 150-degree finite angle noise image reconstruction on the spatial resolution test card by using an M-SIRT algorithm under a fan beam as shown in FIG. 6, wherein the first row is an original image. Fig. 7 shows a comparison of the section gray values of the reconstructed images at different angles on the line where y is 0.
TABLE 3 ICL System reconstructed image RMS error and Peak SNR under different limited angles with noise
Angle of rotation RMSE PSNR
90 degree 68.4445 11.4240
120 degrees 67.9287 11.54897
150 degree 59.3292 12.6654
From the above results, as the limited angle increases, the more projection data the system acquires, and thus the smaller the reconstructed image artifact is obtained, the better the image quality is. While the reconstruction results for the lateral part are always quite ambiguous, which is caused by the absence of projection data in the horizontal direction for limited angle scans. Our field of view is inside the red region and a small number of rays pass outside the red region, and although the projection data obtained in this region is small, this small amount of data still appears in the reconstruction as the limited angle increases. Meanwhile, the system and the algorithm have good noise characteristics.
3.2 image reconstruction of different projection graduation data
The M-SIRT algorithm was used as in fig. 8 to perform a noise- free fan beam 200, 300, 400 projection graduation image reconstruction on the spatial resolution test card, where the first row is the original image. Fig. 9 shows the gray scale values of the cross-sectional view of the reconstructed image without noise data at the projection graduation on the line y equal to 0. To further evaluate the quality of the reconstructed image, the root mean square error and peak signal to noise ratio in the red region at each projection division are listed in table 4.
TABLE 4 reconstructed image RMS error and peak SNR under noiseless different projection graduations of ICL system
Figure GDA0002298441570000091
From the above results, as the projection graduation increases, the more projection data the system acquires, and thus the less the reconstructed image artifact is obtained, the better the image quality is. Similarly, the reconstruction results of the lateral part are always quite ambiguous, which is caused by the absence of projection data in the horizontal direction for limited angle scans. Meanwhile, the set of experiments are performed under a limited angle of 150 degrees, so that a small amount of projection data in the outer region of the field of view appears in the reconstruction result.
In summary, the invention provides a novel industrial computer layered Imaging (ICL) system which is simple in motion mode, low in cost and easy to implement. And the other detector, which is innovative in the design of the system, is synchronously rotated during the scanning process, so that the panel of the other detector is always vertical to the central beam of the ray source. The problem that the requirement of a detector for the edge position of a linear motion at a limited angle is overlarge is solved, the utilization rate of the detector and the detection efficiency of a system are greatly improved, the cost is reduced, the calculation of an iterative reconstruction algorithm is simplified, and the reconstruction speed is improved. An improved M-SIRT algorithm is proposed for use in the present system. Meanwhile, an improved M-SIRT algorithm is used for carrying out a preliminary fan-beam two-dimensional simulation experiment on the system, and the feasibility of the system is verified. In the future research, the system is further improved, and three-dimensional simulation experiments and actual experimental research are carried out.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (3)

1. A novel ICL implementation method is characterized in that: the method comprises the following steps:
s1: establishing an imaging model;
s2: adopting an iterative reconstruction algorithm until the image meets the actual requirement;
the imaging model is as follows:
the sample to be detected and the detector synchronously move in parallel along the direction of the x axis, an included angle omega between the detector and the motion track of the detector and the maximum scanning radius r of the sample to be detected are explored at any position in the scanning process, and the calculation formula is as follows:
Figure FDA0002298441560000011
establishing a rectangular coordinate system by taking the point of the X-ray source closest to the sample track as an origin and the sample motion direction as the positive direction of the X-axis; the ray source is fixed at the point a and is not moved; the position of the object is x pP1, P, where P is the number of projections acquired by the system and the detector position is x D(ii) a d is half the length of the detector; omega is the included angle between the panel of the flat panel detector and the motion track of the panel; s OIs the distance from the source to the track of the sample to be measured, S DThe distance from the ray source to the flat panel detector track;
by adjusting the distance S from the ray source to the track of the sample to be measured OAnd the distance S from the ray source to the flat panel detector track DChanging the system field angle FOV; changing the magnification of the sample to be detected by moving the object back and forth, and selecting a proper view field and magnification according to the size of the actual sample to be detected; and in the scanning process, the flat panel detector synchronously rotates, so that a detector panel of the system is always vertical to a central beam of the ray source in the data acquisition process, and the length change of the flat panel detector in the scanning process is reduced.
2. A novel ICL implementation method as claimed in claim 1 wherein: the iterative reconstruction algorithm is as follows: firstly, discretizing a continuous image, dividing all image areas into a limited number of pixels, forming a matrix to be solved by using constants in each pixel, establishing a group of algebraic equations by using measured projection data, and solving an equation set to obtain an unknown image vector; the method specifically comprises the following steps:
s201: inputting projection data p iAnd assigning an initial value:
Figure FDA0002298441560000013
wherein
Figure FDA0002298441560000014
An initial value representing the jth pixel;
s202: the estimated projection values for all rays are calculated: wherein L represents the total number of rays; j 1.. N, N denotes the total number of pixels; p is a radical of iA projection value representing the ith ray; omega ijIs a projection coefficient reflecting the contribution of the jth pixel to the ith ray integral;
s203: calculating a correction value, wherein an average correction term is calculated by using correction terms of all ray projections, and the correction term of the jth pixel is as follows:
Figure FDA0002298441560000021
wherein W i,+Representing the contribution of all pixels to the integral of the ith ray, W +,jRepresenting the contribution of the jth pixel to all ray integrals,
Figure FDA0002298441560000022
representing projection values of the ith ray of k iterations, and L representing the total number of rays;
s204: and (5) correcting to complete one iteration:
Figure FDA0002298441560000023
wherein x jRepresenting the jth pixel value of the current iteration image;
s205: and finishing a round of iteration after correcting all pixel points of the reconstructed image once, taking the result of the round of iteration as a temporary solution, and repeating the steps of S202, S203 and S204 until the image meets the actual requirement.
3. An ICL implementation system based on the method of claim 1 or 2, characterized in that: the device comprises a ray source, a flat panel detector, a sample to be detected and a platform; the ray source, the sample to be detected and the flat panel detector are sequentially placed on the platform; the ray source is fixed on the platform and used for emitting rays; the flat panel detector and the sample to be detected do same-direction linear motion along a linear direction perpendicular to the linear direction of the ray emitted by the ray source; in the scanning process of the system, the ray emitted by the ray source penetrates through the sample to be detected, the flat panel detector synchronously rotates, and the fact that the flat panel detector is perpendicular to the ray emitted by the ray source all the time in the data acquisition process of the system is guaranteed.
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