X-ray imaging device, imaging method, imaging apparatus and readable storage medium

Abstract

The present invention provides an X-ray imaging device, an imaging method thereof, an imaging apparatus, and a readable storage medium. The imaging method includes: controlling the X-ray imaging device to collect at least two images of a part to be diagnosed of a patient; respectively collecting feature points of the at least two images; determining key points according to the feature points of the at least two images and first feature point detection parameters; obtaining associated points between images at two adjacent positions according to the key points of the images at the two adjacent positions in the at least two images; obtaining compensation parameters based on the number of associated points being less than a preset threshold; determining second feature point detection parameters according to the compensation parameters and the first feature point detection parameters; determining key points of the at least two images according to the feature points of the at least two images and the second feature point detection parameters; and splicing images at two adjacent positions according to the matching relationship of the associated points between the images at the two adjacent positions based on the number of associated points being greater than or equal to the preset threshold.

Description

Technical Field

[0001] The present invention relates to the field of X-ray imaging, and in particular to an X-ray imaging device and an imaging method thereof, an imaging apparatus and a readable storage medium. Background Art

[0002] In related technologies, since imaging equipment is limited by its field of view, doctors need to observe and compare more bone tissue in order to assess the patient's condition during certain surgeries that require a larger field of view, such as multi-segment spinal fusion, hip replacement, and long bone fractures.

[0003] In related technologies, the detector used in the X-ray filming system moves along the direction of the patient's spine, and the tube rotates synchronously to collect several images. The focus position of the tube remains unchanged (that is, the optical center remains unchanged). All images have the same acquisition geometric path, and all images are spliced together according to the geometric relationship.

[0004] Conventional X-ray fluoroscopy and photography equipment do not have the functions of focus rotation and electric translation of components. From a cost perspective, how to enable conventional X-ray equipment to obtain large-field images that meet the needs of doctors has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0006] To this end, a first aspect of the present invention provides an imaging method of an X-ray imaging device.

[0007] A second aspect of the present invention provides an imaging device of an X-ray imaging apparatus.

[0008] A third aspect of the present invention provides an imaging device of an X-ray imaging apparatus.

[0009] A fourth aspect of the present invention provides a readable storage medium.

[0010] A fifth aspect of the present invention provides an X-ray imaging device.

[0011] In view of this, a first aspect of the present invention provides an imaging method for an X-ray imaging device, including: controlling the X-ray imaging device to acquire at least two images of a patient's part to be diagnosed; respectively acquiring feature points of the at least two images; determining key points of the at least two images according to the feature points of the at least two images and first feature point detection parameters; obtaining associated points between images at two adjacent positions according to the key points of the images at the two adjacent positions in the at least two images; obtaining a compensation parameter based on the number of associated points between images at the two adjacent positions being less than a preset threshold; determining second feature point detection parameters according to the compensation parameter and the first feature point detection parameters; determining key points of the at least two images according to the feature points of the at least two images and the second feature point detection parameters; and splicing images at the two adjacent positions according to the matching relationship of the associated points between images at the two adjacent positions based on the number of associated points between images at the two adjacent positions being greater than or equal to the preset threshold.

[0012] The imaging method for an X-ray imaging device provided by the present invention includes: controlling the X-ray imaging device to acquire at least two images of a patient's part to be diagnosed, so as to provide materials for image splicing, and respectively acquiring feature points of the at least two images, so as to splice the images according to the feature points of the at least two images. Specifically, the Scale-Invariant-Feature-Transform (SIFT) detection algorithm can be used to extract feature points of the at least two images, so as to utilize the characteristics of the illumination scale and rotation invariance of the SIFT detection algorithm to improve the anti-interference ability of the spliced images to influencing factors such as illumination and noise, and then splice the images taken at different positions of the X-ray imaging device into a panoramic image with a large field of view, enabling the doctor to obtain more patient information.

[0013] Furthermore, based on the feature points of at least two images and the first feature point detection parameters, key points of at least two images are determined, thereby eliminating low-contrast feature points and unstable edge points in the images, improving the stability of key point matching, and enhancing the anti-noise ability of key points; based on the key points of two adjacent-position images among at least two images, associated points between the two adjacent-position images are obtained to establish the connection between at least two images; based on the number of associated points between two adjacent-position images being less than a preset threshold, a compensation parameter is obtained, enabling the stitched image to meet different surgical stitching scenarios, thereby improving the adaptability of the X-ray imaging device; according to the compensation parameter and the first feature point detection parameters, second feature point detection parameters are determined, thereby increasing the number of detected feature points, providing more feature points in at least two images, and enabling the stitched image to meet the usage requirements of the surgical scenario; according to the feature points of at least two images and the second feature point detection parameters, key points of at least two images are determined, thereby determining the associated points in at least two images based on the key points, and thus determining the matching relationship of adjacent-position images based on the associated points; based on the number of associated points between two adjacent-position images being greater than or equal to the preset threshold, the two adjacent-position images are stitched according to the matching relationship of the associated points between the two adjacent-position images, thereby selecting appropriate feature points according to the requirements of different surgical scenarios, ensuring that corresponding feature points exist in the scale space, further enhancing the reliability and matching speed of the matching between associated points, improving the speed and efficiency of stitching the stitched image, forming a large-field panoramic image, enabling the doctor to obtain more patient information, facilitating the doctor's diagnosis of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture, improving the surgical efficiency of the doctor in surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture, shortening the surgical time, accelerating the diagnostic efficiency of the patient, and improving the adaptability of the X-ray imaging device.

[0014] In addition, the imaging method of the above X-ray imaging device provided by the present invention may further have the following additional technical features:

[0015] In the above technical solution, controlling the X-ray imaging device to collect at least two images of the patient's part to be diagnosed includes: obtaining the initial position, termination position, and moving distance of the X-ray imaging device; determining the moving step size of the X-ray imaging device according to the initial position, termination position, and moving distance; and controlling the X-ray imaging device to collect at least two images of the patient's part to be diagnosed according to the moving step size.

[0016] In this technical solution, the initial position, the termination position, and the moving distance of the X-ray imaging device are obtained. According to the initial position, the termination position, and the moving distance, the moving step of the X-ray imaging device is determined. The doctor moves the X-ray imaging device according to the moving step and acquires at least two images of the part of the patient to be diagnosed. At the same time, the images can be exposed, so as to completely capture all the images of the part of the patient to be diagnosed, facilitating the subsequent stitching of the images.

[0017] In any of the above technical solutions, controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed includes: obtaining the initial position, the termination position, and the moving distance of the X-ray imaging device; determining the moving speed of the X-ray imaging device according to the initial position, the termination position, and the moving distance; and controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed according to the moving speed.

[0018] In this technical solution, the initial position, the termination position, and the moving distance of the X-ray imaging device are obtained; the moving speed of the X-ray imaging device is determined according to the initial position, the termination position, and the moving distance, so as to control the movement of the X-ray imaging device according to the moving speed. During the entire moving process, no manual operation by the doctor is required, improving the automation and intelligence of the X-ray imaging device. At the same time, low-frame-rate pulsed exposure can be used for at least two images acquired of the part of the patient to be diagnosed, reducing the radiation dose of the X-ray imaging device, reducing the radiation to the patient caused by the X-ray, reducing the risk that the patient is damaged by high-dose radiation to their health, obtaining more image overlap areas, and further ensuring the effect of image stitching, enabling the doctor to obtain a panoramic image with a large field of view, understand more patient information, and facilitating the doctor's diagnosis of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0019] In any of the above technical solutions, the image includes a first region and a second region; the contrast of the first region is less than the contrast of the second region, and the intensity of the feature points in the first region is less than the intensity of the feature points in the second region; the feature points at the junction of the first region and the second region are edge points; obtaining the compensation parameter includes: obtaining the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid; and determining the compensation parameter according to the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid.

[0020] In this technical solution, the image includes a first region and a second region. The contrast of the first region is less than that of the second region, and the intensity of the feature points in the first region is less than that of the feature points in the second region. It can be understood that the first region is the low-contrast region in the image, and the second region is the high-contrast region in the image. The fact that the intensity of the feature points in the first region is less than that of the feature points in the second region can be used as a weak feature parameter, and the feature points at the junction of the first region and the second region are unstable edge points.

[0021] Furthermore, according to the weak feature threshold parameter of the low-contrast region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid, a first compensation parameter is determined, so as to determine the first compensation parameter, increase the number of matched associated points, ensure the reliability of the stitched image, meet the usage requirements of surgical scenarios such as multi-segment spinal fusion, hip replacement, and long bone fracture, and further enable the doctor to obtain a panoramic image with a large field of view, allowing the doctor to obtain more patient information and facilitating the doctor's diagnosis of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0022] In any of the above technical solutions, the imaging method further includes: determining the distance between the associated points of the images at adjacent positions; determining a distance threshold according to the distance between the associated points of the images at adjacent positions; retaining the associated points based on the fact that the distance between the associated points of the images at adjacent positions is less than the distance threshold; determining the slope of the associated points of the images at adjacent positions; determining a slope threshold according to the slope of the associated points of the images at adjacent positions; and retaining the associated points based on the fact that the slope of the associated points of the images at adjacent positions is less than the slope threshold.

[0023] In this technical solution, by determining the distance and slope of the associated points between the images at adjacent positions, the associated points with a distance not less than the distance threshold are excluded, and the associated points with a slope not less than the slope threshold are excluded, thereby improving the matching degree between the associated points, enhancing the reliability and matching speed of the associated point matching, and then quickly stitching the images, enabling the doctor to obtain a panoramic image with a large field of view and improving the surgical efficiency of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0024] In any of the above technical solutions, before determining the key points of at least two images according to the feature points of at least two images and the first feature point detection parameter, the imaging method further includes: reducing the weight of the feature points for the key points according to the Gaussian descent function; the Gaussian descent function includes:

[0025]

[0026] Among them, x represents the abscissa of the feature point in the image, y represents the ordinate of the feature point in the image, x0 represents the abscissa of the key point in the image, y0 represents the ordinate of the key point in the image, e represents the base of the natural logarithm, and σ represents the standard deviation of the Gaussian decay function, which can be set according to the actual situation and empirical values.

[0027] In this technical solution, the weight of the feature point relative to the key point is reduced by the Gaussian decay function. Specifically, the weight of the feature point far from the key point is reduced, so as to control the distribution of the weight through the Gaussian decay function and improve the reliability of the key point matching.

[0028] The second aspect of the present invention provides an imaging device for an X-ray imaging device, including: a moving unit for controlling the movement of the X-ray imaging device; an acquisition unit for acquiring at least two images of the part to be diagnosed of the patient; and a splicing unit for splicing at least two images of the part to be diagnosed.

[0029] In this technical solution, the imaging device of the X-ray imaging device includes a moving unit, an acquisition unit, and a splicing unit. The moving unit is used to control the movement of the X-ray imaging device, the acquisition unit is used to acquire at least two images of the part to be diagnosed of the patient, and the splicing unit is used to receive a swapping instruction, swap the positional relationship of at least two images, and splice at least two images of the part to be diagnosed, thereby realizing automatic splicing of the images, enabling the doctor to obtain a panoramic image with a large field of view, enabling the doctor to obtain more patient information, and facilitating the doctor to diagnose surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0030] The third aspect of the present invention provides an imaging device for an X-ray imaging device, including: a memory and a processor. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the imaging method of the X-ray imaging device in any of the above technical solutions are implemented, and thus it has all the beneficial effects of the imaging method of the X-ray imaging device in any of the above technical solutions.

[0031] The fourth aspect of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the imaging method of the X-ray imaging device in any of the above technical solutions are implemented, and thus it has all the beneficial effects of the imaging method of the X-ray imaging device in any of the above technical solutions.

[0032] The fifth aspect of the present invention provides an X-ray imaging device, including the imaging device of the X-ray imaging device in the second aspect; or the imaging device of the X-ray imaging device in the third aspect; or the readable storage medium in the fourth aspect, and thus it has all the beneficial effects of the imaging method of the X-ray imaging device in the first aspect and the imaging device of the X-ray imaging device in the second aspect.

[0033] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, in which:

[0035] Figure 1 FIG. 1 shows one of the schematic flowcharts of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0036] Figure 2 FIG. 2 shows the splicing schematic diagram of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0037] Figure 3 FIG. 3 shows the schematic diagram of constructing a scale space of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0038] Figure 4 FIG. 4 shows the schematic diagram of detecting extreme points of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0039] Figure 5 FIG. 5 shows the schematic diagram of filtering extreme points of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0040] Figure 6 FIG. 6 shows one of the schematic diagrams of generating descriptors of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0041] Figure 7 FIG. 7 shows another schematic diagram of generating descriptors of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0042] Figure 8 FIG. 8 shows yet another schematic diagram of generating descriptors of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0043] Figure 9 FIG. 9 shows still another schematic diagram of generating descriptors of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0044] Figure 10 FIG. 10 shows another schematic flowchart of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0045] Figure 11 FIG. 11 shows yet another schematic flowchart of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0046] Figure 12Shows a schematic diagram of the movement of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0047] Figure 13 Shows the fourth schematic flow chart of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0048] Figure 14 Shows the compensation parameter variation diagram of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0049] Figure 15 Shows the schematic diagram of the associated points of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0050] Figure 16 Shows the splicing schematic diagram of the imaging method of the X-ray imaging device in an embodiment of the present invention;

[0051] Figure 17 Shows the first schematic structural diagram of the imaging device of the X-ray imaging device in an embodiment of the present invention;

[0052] Figure 18 Shows the schematic diagram of the operation interface of the splicing unit of the X-ray imaging device in an embodiment of the present invention;

[0053] Figure 19 Shows the schematic diagram of the display interface of the display unit of the X-ray imaging device in an embodiment of the present invention;

[0054] Figure 20 Shows the second schematic structural diagram of the imaging device of the X-ray imaging device in an embodiment of the present invention.

[0055] Among them, Figures 1 to 20 The corresponding relationship between the reference numerals in the drawings and the component names is as follows:

[0056] 100 Imaging device of the X-ray imaging device, 102 Moving unit, 104 Acquisition unit, 106 Splicing unit, 1062 Spliced image display area, 1064 Image database, 1066 Spliced image list, 1068a Splicing button, 1068b Deletion button, 1068c Export button, 108 Display unit, 200 Imaging device of the X-ray imaging device, 202 Processor, 204 Memory. Detailed implementation manners

[0057] In order to be able to more clearly understand the above aspects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0058] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0059] The following refers to Figures 1 to 20 Describe an imaging method of an X-ray imaging device, an imaging device 100 of the X-ray imaging device, an imaging device 200 of the X-ray imaging device, a readable storage medium, and an X-ray imaging device according to some embodiments of the present invention.

[0060] As Figure 1 shown, an embodiment of the present invention provides an imaging method of an X-ray imaging device, including:

[0061] S102, controlling the X-ray imaging device to collect at least two images of the patient's part to be diagnosed;

[0062] S104, respectively collecting the feature points of at least two images;

[0063] S106, determining the key points of at least two images according to the feature points of at least two images and the first feature point detection parameter;

[0064] S108, obtaining the associated points between the images at two adjacent positions according to the key points of the images at two adjacent positions in at least two images;

[0065] S110, determining whether the number of associated points between the images at two adjacent positions is less than a preset threshold. If so, execute S112; if not, execute S118;

[0066] S112, obtaining a compensation parameter;

[0067] S114, determining a second feature point detection parameter according to the compensation parameter and the first feature point detection parameter;

[0068] S116, determining the key points of at least two images according to the feature points of at least two images and the second feature point detection parameter, and returning to S108;

[0069] S118, splicing the images at two adjacent positions according to the matching relationship of the associated points between the images at two adjacent positions.

[0070] In this embodiment, the X-ray imaging device is controlled to acquire at least two images of the part of the patient to be diagnosed, so as to provide materials for image stitching. Feature points of at least two images are respectively acquired, and then the images are stitched according to the feature points of at least two images. Specifically, the Scale-Invariant-Feature-Transform (SIFT) detection algorithm can be used to extract the feature points of at least two images. Thus, by virtue of the characteristics of the SIFT detection algorithm, such as illumination scale and rotation invariance, the anti-interference ability of the stitched image against influencing factors such as illumination and noise can be improved. Furthermore, the images taken at different positions by the X-ray imaging device are stitched together to form a panoramic image with a large field of view, enabling the doctor to obtain more patient information. It can be understood that the first feature point detection parameter and the second feature point detection parameter are the quantities of feature point detection.

[0071] Further, as Figure 2 shown, image stitching includes three steps, namely image registration, image transform, and blending.

[0072] Specifically, the SIFT detection algorithm can perform image registration. The steps of the SIFT detection algorithm specifically include: constructing a scale space, detecting extreme points, filtering extreme points, generating descriptors, and matching feature points by the Random Sample Consensus (RANSAC) algorithm.

[0073] As Figure 3 and Figure 4 shown, constructing a scale space and detecting extreme points specifically include: The basis for constructing the scale space is the Difference of Gaussian (DOG) pyramid. Specifically, a Gaussian pyramid is constructed. The pyramid is divided into O groups, and each group is further divided into S layers. The image of each layer is the result of filtering by Gaussian filters with different variances. It can be understood that the variance of the Gaussian filter can be set in advance by the doctor, so as to suppress the noise and secondary details in the image through Gaussian filtering, filter out some unnecessary details, and retain important image features, thus ensuring the scale invariance of the image features.

[0074] Further, Figure 3 in which octave represents a layer, scale represents a scale, first octave represents the initial layer, next octave represents the next layer. Taking the difference between two adjacent layers of the Gaussian pyramid to obtain the Difference of Gaussian pyramid, thus constituting the Gaussian scale space. Figure 4 in which scale represents a scale.

[0075] It is understandable that in an image of the same scale, the layers are distributed vertically to form a shape similar to a "pyramid", so that the image of the layers within the same scale is regarded as a "Gaussian pyramid".

[0076] Furthermore, in the Gaussian scale space, taking the "X" point in Figure 4 as an example, the pixel point of the "X" point and its eight neighborhood points in the image of the same scale, the points at the same positions in the previous image, and its eight neighborhood points in the image of the same scale, the points at the same positions in the next image, and its eight neighborhood points in the image of the same scale, a total of 26 neighborhood points. If the current X point is the maximum or minimum value compared with the 26 neighborhood points, the current X point is marked as an extreme point.

[0077] As Figure 5 shown, the filtering of extreme points includes interpolating the marked extreme points in the continuous scale space to obtain the exact positions of the extreme points, setting a given pixel threshold according to the empirical value. When the pixel value corresponding to the extreme point is less than the given pixel threshold, it is considered that the point is unstable and the unstable point is removed. The unstable points mainly include weak feature points with low contrast and unstable edge points.

[0078] The SIFT detection algorithm has a poor response to edge points, and it is difficult to locate edge points in the image and they are easily affected by noise. Specifically, edge points have a large curvature where they cross the edge in the image and a small curvature in the direction perpendicular to the edge. The curvature of edge points can be obtained through a 2×2 Hessian matrix:

[0079]

[0080] where H represents the Hessian matrix, Dxx represents the second-order partial derivative of Dx with respect to x, Dxy represents the second-order partial derivative of Dx with respect to y, and Dyy represents the second-order partial derivative of Dy with respect to y. Thus, the curvature of edge points where they cross the edge in the image and the curvature where they are perpendicular to the edge are obtained through the Hessian matrix, and the unstable edge points are removed, thereby avoiding the influence of the edge effect on the scale space.

[0081] Specifically, the place where it crosses the edge can be the place where it crosses the image feature in the horizontal direction of the image, and the place where it is perpendicular to the edge can be the place where it crosses the image feature in the vertical direction of the image. Figure 5 The abscissa x in Figure 5 can represent a horizontal dimension, a vertical dimension, or a scale.

[0082] As Figure 6 and Figure 7As shown, among the feature points with maximum or minimum values, the edge points are removed, and the remaining feature points are key points. Generating a descriptor includes: sampling in a neighborhood window centered on the key point, and using a histogram to statistically analyze the gradient directions of the neighborhood pixels. The range of the gradient direction is from 0 degrees to 360 degrees. Dividing the histogram into eight directions with a 45-degree boundary. It can be understood that Figure 6 the Image gradients of Figure 6 represent the image gradient. The direction pointed by the arrow in the image gradient represents the gradient direction, and the length of the arrow represents the magnitude of the gradient vector. The largest gradient vector indicates the presence of a gradient peak, and the gradient direction with the gradient peak is the main direction of the key point. The directions that reach 80% of the main direction vector among the eight directions can be used as auxiliary directions. The direction of the key point is determined jointly by the main direction and the auxiliary directions, thereby increasing the matching robustness of the SIFT detection algorithm, and further improving the stability of key point matching. Rotating the coordinate axis to the direction of the key point, thereby ensuring the rotational invariance of the SIFT detection algorithm. Figure 7 The Keypoint descirptor of Figure 7 represents the key point direction description.

[0083] Furthermore, as Figure 8 and Figure 9 shown, calculate the gradient direction histograms of eight directions on the 4×4 small grids in the neighborhood of the key point, and draw the cumulative values of each direction, thereby calculating the gradients of each pixel point in the 16×16 window (window) around the key point. Use the Gaussian decay function to reduce the weights of the pixel points farther from the key point. Specifically:

[0084]

[0085] where f(x, y) represents the Gaussian decay function, x represents the abscissa of the neighborhood point in the image, y represents the ordinate of the neighborhood point in the image, x0 represents the abscissa of the key point in the image, y0 represents the ordinate of the key point in the image, e represents the base of the natural logarithm, specifically an infinite non-repeating decimal, approximately 2.71828, and σ represents the standard deviation of the Gaussian function, which can be set according to the actual situation and empirical values.

[0086] Controlling the weight distribution of neighborhood points through the Gaussian decay function, thereby forming a descriptor of a 128-dimensional vector. Specifically, in the 4×4 grids, each grid has 8 dimensions of scale and direction, thereby normalizing the dimensional vector and removing the influence of illumination scale on the key point.

[0087] The matching feature points of the Random Sample Consensus algorithm include: randomly extracting four pairs of matching points from two sets of key point samples to be matched, forming a 3x3 transformation matrix based on the positional relationships of the four pairs of matching points, calculating the error metric of the stitched image according to the sample set and the transformation matrix of the SIFT detection algorithm, calculating the set of key points in the sample set that is consistent with the transformation matrix, obtaining the number of elements in the consistent set of key points, taking the number of the current consistent set of key points as the optimal consistent set or the maximum consistent set, updating the current optimal consistent set or the current maximum consistent set in the SIFT detection algorithm, setting an error probability threshold which can be set according to the actual situation and empirical values, calculating the matching probability of the current key point being incorrect, and when the matching probability of the current key point being incorrect is greater than the error probability threshold, re-extracting pairs of matching points, re-determining the error metric according to the transformation matrix, re-updating the current optimal consistent set or the current maximum consistent set, and performing iteration until the matching probability of the current key point being incorrect is less than the error probability threshold.

[0088] As Figure 10 shown, an embodiment of the present invention provides an imaging method for an X-ray imaging device, including:

[0089] S1002, obtaining the initial position, the termination position, and the moving distance of the X-ray imaging device;

[0090] S1004, determining the moving step size of the X-ray imaging device according to the initial position, the termination position, and the moving distance;

[0091] S1006, controlling the X-ray imaging device to collect at least two images of the part of the patient to be diagnosed according to the moving step size;

[0092] S1008, respectively collecting the feature points of at least two images;

[0093] S1010, determining the key points of at least two images according to the feature points of at least two images and the first feature point detection parameters;

[0094] S1012, obtaining the associated points between the images at two adjacent positions according to the key points of the images at two adjacent positions in at least two images;

[0095] S1014, determining whether the number of associated points between the images at two adjacent positions is less than a preset threshold. If so, execute S1016; if not, execute S1022;

[0096] S1016, obtaining the compensation parameters;

[0097] S1018, determining the second feature point detection parameters according to the compensation parameters and the first feature point detection parameters;

[0098] S1020. Determine the key points of at least two images according to the feature points of at least two images and the second feature point detection parameters, and return to S1012;

[0099] S1022. Stitch the images at two adjacent positions according to the matching relationship of the associated points between the images at two adjacent positions.

[0100] In this embodiment, obtain the initial position, the termination position, and the moving distance of the X-ray imaging device. According to the initial position, the termination position, and the moving distance, determine the moving step size of the X-ray imaging device. The doctor moves the X-ray imaging device according to the moving step size and acquires at least two images of the part of the patient to be diagnosed. At the same time, the images can be exposed, so as to completely capture all the images of the part of the patient to be diagnosed, which is convenient for subsequent stitching of the images.

[0101] As shown, an embodiment of the present invention provides an imaging method for an X-ray imaging device, including:

[0102] S1102. Obtain the initial position, the termination position, and the moving distance of the X-ray imaging device;

[0103] S1104. Determine the moving step size speed of the X-ray imaging device according to the initial position, the termination position, and the moving distance;

[0104] S1106. Control the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed according to the moving speed;

[0105] S1108. Acquire the feature points of at least two images respectively;

[0106] S1110. Determine the key points of at least two images according to the feature points of at least two images and the first feature point detection parameters;

[0107] S1112. Obtain the associated points between the images at two adjacent positions according to the key points of the images at two adjacent positions in at least two images;

[0108] S1114. Determine whether the number of the associated points between the images at two adjacent positions is less than a preset threshold. If so, execute S1116; if not, execute S1122;

[0109] S1116. Obtain the compensation parameters;

[0110] S1118. Determine the second feature point detection parameters according to the compensation parameters and the first feature point detection parameters;

[0111] S1120. Determine the key points of at least two images according to the feature points of at least two images and the second feature point detection parameters, and return to S1112;

[0112] S1122, splice the images of two adjacent positions according to the matching relationship of the correlation points between the images of two adjacent positions.

[0113] In this embodiment, obtain the initial position, the termination position and the moving distance of the X-ray imaging device; determine the moving speed of the X-ray imaging device according to the initial position, the termination position and the moving distance, so as to control the movement of the X-ray imaging device according to the moving speed. During the whole moving process, manual operation by doctors is not required, which improves the automation and intelligence level of the X-ray imaging device. At the same time, low-frame-rate pulse exposure can be used for at least two images collected from the part of the patient to be diagnosed, reducing the radiation dose of the X-ray imaging device, reducing the radiation caused by the X-ray to the patient, reducing the patient from receiving a large dose of radiation, avoiding the risk of damage to the patient's health, obtaining more image overlapping areas, and further ensuring the effect of image splicing, so that doctors can obtain panoramic images with a large field of view, understand more patient information, and facilitate doctors to diagnose surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0114] Further, as Figure 11 shown, the doctor places the patient on the flat plate, sets the moving speed, and pre-sets the moving direction of the X-ray imaging device (as shown by arrow a), which can be set according to the joint direction of the part of the patient to be diagnosed, so as to control the X-ray imaging device to move along the part of the patient to be diagnosed through electric control, and at the same time, exposure imaging can be performed.

[0115] As Figure 12 shown, an embodiment of the present invention provides an imaging method for an X-ray imaging device, including:

[0116] S1302, control the X-ray imaging device to collect at least two images of the part of the patient to be diagnosed;

[0117] S1304, receive a swapping instruction;

[0118] S1306, according to the swapping instruction, swap the positional relationship of at least two images;

[0119] S1308, respectively collect the feature points of at least two images;

[0120] S1310, determine the key points of at least two images according to the feature points of at least two images and the first feature point detection parameters;

[0121] S1312, according to the key points of the images of two adjacent positions in at least two images, obtain the correlation points between the images of two adjacent positions;

[0122] S1314, determine the distance between the correlation points of the images of adjacent positions;

[0123] S1316, determine a distance threshold according to the distance between the associated points in the images at adjacent positions;

[0124] S1318, determine whether the distance between the associated points in the images at adjacent positions is less than the distance threshold. If so, execute S1320; if not, execute S1322;

[0125] S1320, retain the associated points and execute S1324;

[0126] S1322, delete the associated points and execute S1324;

[0127] S1324, determine the slope of the associated points between the images at adjacent positions;

[0128] S1326, determine a slope threshold according to the slope of the associated points between the images at adjacent positions;

[0129] S1328, determine whether the slope of the associated points between the images at adjacent positions is less than the slope threshold. If so, execute S1330; if not, execute S1332;

[0130] S1330, retain the associated points and execute S1334;

[0131] S1332, delete the associated points and execute S1334;

[0132] S1334, obtain the associated points between the images at two adjacent positions according to the key points of the images at two adjacent positions in at least two images;

[0133] S1336, determine whether the number of the associated points between the images at two adjacent positions is less than a preset threshold. If so, execute S1338; if not, execute S1346;

[0134] S1338, obtain the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid;

[0135] S1340, determine a compensation parameter according to the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid;

[0136] S1342, determine a second feature point detection parameter according to the compensation parameter and the first feature point detection parameter;

[0137] S1344, determine the key points of at least two images according to the feature points of at least two images and the second feature point detection parameter, and return to S1334;

[0138] S1346. Stitch the images of two adjacent positions according to the matching relationship of the associated points between the images of the two adjacent positions.

[0139] In this embodiment, the image includes a first region and a second region. The contrast of the first region is less than that of the second region, and the intensity of the feature points in the first region is less than that of the feature points in the second region. It can be understood that the first region is the low-contrast region in the image, and the second region is the high-contrast region in the image. The fact that the intensity of the feature points in the first region is less than that of the feature points in the second region can be used as a weak feature parameter, and the feature points at the junction of the first region and the second region are unstable edge points.

[0140] Furthermore, as Figure 13 shown, determine the first compensation parameter according to the weak feature threshold parameter of the low-contrast region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid, so as to determine the first compensation parameter, increase the number of matches of the associated points, ensure the reliability of the stitched image, meet the usage requirements of surgical scenarios such as multi-segment spinal fusion, hip replacement, and long bone fracture, and further enable the doctor to obtain a panoramic image with a large field of view, so that the doctor can obtain more patient information and facilitate the diagnosis of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture. Among them, Contrast threshold represents the weak feature threshold parameter of the low-contrast region, Edge threshold represents the threshold parameter of the edge points, and Initial noise represents the noise parameter of the Gaussian pyramid. It can be understood that according to empirical values, in the SIFT detection algorithm, the weak feature threshold parameter of the low-contrast region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid can be preset in advance. There is a quantitative relationship stored in advance between the weak feature threshold parameter of the low-contrast region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid and the number of iterations. Thus, determine the weak feature threshold parameter of the low-contrast region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid according to the number of iterations, and determine the compensation parameter according to the weak feature threshold parameter of the low-contrast region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid determined by the number of iterations, and jointly determine the second feature point detection parameter with the first feature point detection parameter to detect appropriate feature points.

[0141] In this embodiment, as Figure 14 shown, by determining the distance and slope of the associated points between the images of adjacent positions, exclude the associated points whose distance is not less than the distance threshold and whose slope is not less than the slope threshold, so as to improve the matching degree between the associated points, improve the reliability and matching speed of the associated point matching, and then quickly stitch the images, enabling the doctor to obtain a panoramic image with a large field of view and improving the surgical efficiency of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0142] Specifically, determining the distance threshold according to the distances between the associated points of the images at adjacent positions includes determining the distances between the associated points of the images at all adjacent positions, calculating the average value of the distances between all adjacent associated points, using the average value as the distance threshold, deleting the associated points with distances between the associated points of the images at adjacent positions not less than the average value, and retaining the associated points with distances between the associated points of the images at all adjacent positions not greater than the average value, thereby improving the reliability of the matching of the associated points.

[0143] Specifically, determining the distance threshold according to the distances between the associated points of the images at adjacent positions includes determining the distances between the associated points of the images at all adjacent positions, calculating the mode of the distances between all adjacent associated points, using the mode as the distance threshold, deleting the associated points with distances between the associated points of the images at adjacent positions greater than the mode, and retaining the associated points with distances between the associated points of the images at all adjacent positions not greater than the mode, thereby improving the reliability of the matching of the associated points.

[0144] Specifically, determining the slope threshold according to the slopes of the associated points of the images at adjacent positions includes determining the slopes of the associated points of the images at all adjacent positions, calculating the average value of the values of the slopes of all adjacent associated points, using the average value as the slope threshold, deleting the associated points with values of the slopes of the associated points of the images at adjacent positions greater than the average value, and retaining the associated points with values of the slopes of the associated points of the images at all adjacent positions not greater than the average value, thereby improving the reliability of the matching of the associated points.

[0145] Specifically, determining the slope threshold according to the slopes of the associated points of the images at adjacent positions includes determining the slopes of the associated points of the images at all adjacent positions, calculating the mode of the values of the slopes of all adjacent associated points, using the mode as the slope threshold, deleting the associated points with values of the slopes of the associated points of the images at adjacent positions greater than the mode, and retaining the associated points with values of the slopes of the associated points of the images at all adjacent positions not greater than the mode, thereby improving the reliability of the matching of the associated points.

[0146] It can be understood that this application does not limit the sequence of statistically calculating the distances of the associated point pairs and the slopes of the associated point pairs. Specifically, the distances of the associated point pairs can be statistically calculated first and then the slopes of the associated point pairs, or the slopes of the associated point pairs can be statistically calculated first and then the distances of the associated point pairs, or the distances of the associated point pairs and the slopes of the associated point pairs can be statistically calculated simultaneously.

[0147] Such as Figure 15As shown in the figure, the image transformation and fusion specifically include: According to the matching relationship of the correlation points between the images at two adjacent positions, stitching the images at two adjacent positions includes: forming a 3x3 transformation matrix according to the matching relationship of the correlation points between the images at adjacent positions, projecting one of the adjacent images, Image1, into the other image, Image2, according to the transformation matrix to form a fused image, Image21, which can also be represented by BlendImage. Select the fusion points in the overlapping part of Image1 and Image2. The fusion points can be selected based on the actual situation combined with empirical values. A certain key point of the part to be diagnosed of the patient can be used as the fusion point. Measure the distance from the fusion point to the overlapping boundary. Specifically, set the distance from the fusion point to the overlapping boundary of Image1 as d1, and set the distance from the fusion point to the overlapping boundary of Image2 as d2. Then, add Image1 and Image2 according to different weights to achieve a smooth transition of the fused image. The weight formula can be expressed as:

[0148] BlendImage = d2 / (d1 + d2)×Image1 + d1 / (d1 + d2)×Image2;

[0149] Among them, BlendImage represents the 3x3 transformation matrix of the fused image, Image1 represents the 3x3 transformation matrix of Image1, and Image2 represents the 3x3 transformation matrix of Image2.

[0150] As Figure 16 shown, an embodiment of the present invention provides an imaging device 100 of an X-ray imaging device, including: a moving unit 102 for controlling the movement of the X-ray imaging device; an acquisition unit 104 for acquiring at least two images of the part to be diagnosed of the patient; a stitching unit 106 for stitching at least two images of the part to be diagnosed.

[0151] In this technical solution, the imaging device 100 of the X-ray imaging device includes a moving unit 102, an acquisition unit 104, and a stitching unit 106. The moving unit 102 is used to control the movement of the X-ray imaging device. The acquisition unit 104 is used to acquire at least two images of the part to be diagnosed of the patient. The stitching unit 106 is used to stitch at least two images of the part to be diagnosed, thereby realizing automatic stitching of the images, enabling the doctor to obtain a panoramic image with a large field of view, allowing the doctor to obtain more patient information, and facilitating the doctor's diagnosis of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0152] Furthermore, Figure 17The operation interface of the splicing unit 106 is shown. On the operation interface, there are a spliced image display area 1062, an image database 1064, a list of images to be spliced 1066, a splicing button 1068a, a deletion button 1068b, and an export button 1068c. Doctors can import the images in the previous image database 1064 into the list of images to be spliced 1066 for splicing, or can expose the currently acquired images and splice the currently exposed images or the images imported into the list of images to be spliced 1066. The spliced images are saved in the image database 1064 list. After the user long-presses the thumbnail in the list of images to be spliced 1066 for two seconds, the thumbnail becomes in a draggable state, and doctors can adjust the splicing order, thereby improving the accuracy of the spliced images.

[0153] Furthermore, the splicing unit 106 can receive a splicing instruction, and thus splice images according to the received instruction. The splicing unit 106 can also receive a deletion instruction to delete images or the spliced images. The splicing unit 106 can also receive an export instruction to export images or the spliced images.

[0154] As Figure 18 shown, the imaging device 100 of the X-ray imaging device further includes a display unit 108 for displaying the spliced panoramic image. As Figure 17 shown, the display unit 108 can use a 16:9 widescreen monitor, which is closer to the ratio of the spliced images, so as to present the panoramic image, enabling doctors to more intuitively understand the information of the patient's part to be diagnosed, thus facilitating doctors to diagnose surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture.

[0155] As Figure 19 Figure 20 shown, an embodiment of the present invention provides an imaging device 200 of an X-ray imaging device, including: a memory 204 and a processor 202. The memory 204 stores programs or instructions that can run on the processor 202. When the programs or instructions are executed by the processor 202, the steps of the imaging method of the X-ray imaging device in any of the above embodiments are implemented, and thus all the beneficial effects of the imaging method of the X-ray imaging device in any of the above embodiments are achieved.

[0156] An embodiment of the present invention provides a readable storage medium, on which programs or instructions are stored. When the programs or instructions are executed by the processor 202, the steps of the imaging method of the X-ray imaging device in any of the above embodiments are implemented, and thus all the beneficial effects of the imaging method of the X-ray imaging device in any of the above embodiments are achieved.

[0157] An embodiment of the present invention provides an X-ray imaging device, including the imaging device 100 of the X-ray imaging device in the above embodiment; or the imaging device 200 of the X-ray imaging device in the above embodiment; or the readable storage medium in the above embodiment, thus having all the beneficial effects of the imaging method of the X-ray imaging device in any of the above embodiments and the imaging device 100 of the X-ray imaging device in any of the above embodiments.

[0158] Specifically, the X-ray fluoroscopy device in the related art does not have the function of electric translation. In this application, by setting the moving step length and moving speed, the movement of the X-ray fluoroscopy device is controlled, thereby expanding the application range of the X-ray fluoroscopy device and enabling it to meet the usage requirements of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture. By controlling the SIFT detection algorithm, compensation parameters, distance threshold, and slope threshold, the robustness of the algorithm is improved, enabling it to adapt to the surgical scenario requirements in different situations. The operation interface provides a method for convenient splicing, browsing, and management of the spliced data, thus enhancing the usage experience of doctor interaction, reducing the usage difficulty of the X-ray fluoroscopy device, improving the simplicity of doctor operation, and facilitating the popularization of the X-ray fluoroscopy device. The 16:9 widescreen display can present the spliced panoramic image, facilitating the doctor to evaluate the patient's condition. Images taken in different positions can be spliced together to form a large-field panoramic image, enabling the doctor to obtain more patient information, facilitating the diagnosis of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture, improving the surgical efficiency of surgeries such as multi-segment spinal fusion, hip replacement, and long bone fracture, shortening the surgical time, and accelerating the diagnostic efficiency of the patient.

[0159] The terms "first" and "second" in the description and claims of this application may explicitly or implicitly include one or more of such features. In the written description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0160] In the description of the present invention, the descriptions of terms such as "an embodiment", "some embodiments", "specific embodiments", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiment or instance. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0161] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An imaging method for an X-ray imaging device, characterized in that, Including: Controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed; Respectively acquiring the feature points of the at least two images; Determining the key points of the at least two images according to the feature points of the at least two images and the first feature point detection parameter; Obtaining the correlation points between the images at two adjacent positions according to the key points of the images at two adjacent positions among the at least two images; Obtaining a compensation parameter based on the number of correlation points between the images at two adjacent positions being less than a preset threshold; Determining a second feature point detection parameter according to the compensation parameter and the first feature point detection parameter, where the first feature point detection parameter and the second feature point detection parameter are the number of feature points detected; Determining the key points of the at least two images according to the feature points of the at least two images and the second feature point detection parameter; Based on the number of correlation points between the images at two adjacent positions being greater than or equal to the preset threshold, splicing the images at two adjacent positions according to the matching relationship of the correlation points between the images at two adjacent positions; The image includes a first region and a second region; The contrast of the first region is less than the contrast of the second region, and the intensity of the feature points in the first region is less than the intensity of the feature points in the second region; The feature points at the junction of the first region and the second region are edge points; Obtaining the compensation parameter includes: Obtaining the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid; Determining the compensation parameter according to the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid.

2. The imaging method of the X-ray imaging device according to claim 1, wherein The controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed includes: Obtaining the initial position, the termination position, and the moving distance of the X-ray imaging device; Determining the moving step of the X-ray imaging device according to the initial position, the termination position, and the moving distance; Controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed according to the moving step.

3. The imaging method of the X-ray imaging device according to claim 1, characterized in that, The controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed includes: Obtaining the initial position, the termination position, and the moving distance of the X-ray imaging device; Determining the moving speed of the X-ray imaging device according to the initial position, the termination position, and the moving distance; Controlling the X-ray imaging device to acquire at least two images of the part of the patient to be diagnosed according to the moving speed.

4. The imaging method of the X-ray imaging device according to claim 1, characterized in that Before obtaining the compensation parameter, the imaging method further includes: Determining the distance between the correlation points between the images at the adjacent positions; Determining a distance threshold according to the distance between the correlation points between the images at the adjacent positions; Based on the distance between the correlation points between the images at the adjacent positions being less than the distance threshold, retaining the correlation points; Determining the slope of the correlation points between the images at the adjacent positions; Determining a slope threshold according to the slope of the correlation points between the images at the adjacent positions; Based on that the slope of the correlation points between the images at the adjacent positions is less than the slope threshold, the correlation points are retained.

5. The imaging method of the X-ray imaging device according to claim 1, characterized in that, Before determining the key points of the at least two images according to the feature points of the at least two images and the first feature point detection parameter, the imaging method further includes: According to the Gaussian descent function, reducing the weight of the feature points for the key points; The Gaussian descent function includes: Where x represents the abscissa of the feature point in the image, y represents the ordinate of the feature point in the image, x0 represents the abscissa of the key point in the image, y0 represents the ordinate of the key point in the image, e represents the base of the natural logarithm, and σ represents the standard deviation of the Gaussian descent function.

6. An imaging device of an X-ray imaging apparatus, characterized in that, Including: A moving unit for controlling the movement of the X-ray imaging device; An acquisition unit for acquiring at least two images of the part to be diagnosed of the patient; A splicing unit for splicing at least two images of the part to be diagnosed; Wherein, splicing at least two images of the part to be diagnosed includes: Respectively acquiring the feature points of the at least two images; Determining the key points of the at least two images according to the feature points of the at least two images and the first feature point detection parameter; Obtaining the correlation points between the images at two adjacent positions according to the key points of the images at two adjacent positions in the at least two images; Based on that the number of the correlation points between the images at two adjacent positions is less than a preset threshold, obtaining a compensation parameter; Determining a second feature point detection parameter according to the compensation parameter and the first feature point detection parameter, wherein the first feature point detection parameter and the second feature point detection parameter are the numbers of the feature point detections; Determining the key points of the at least two images according to the feature points of the at least two images and the second feature point detection parameter; Based on that the number of the correlation points between the images at two adjacent positions is greater than or equal to the preset threshold, splicing the images at two adjacent positions according to the matching relationship of the correlation points between the images at two adjacent positions; The image includes a first region and a second region; The contrast of the first region is less than the contrast of the second region, and the intensity of the feature points in the first region is less than the intensity of the feature points in the second region; The feature points at the junction of the first region and the second region are edge points; Obtaining the compensation parameter includes: Obtaining the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid; Determining the compensation parameter according to the threshold parameter of the feature points in the first region, the threshold parameter of the edge points, and the noise parameter of the Gaussian pyramid.

7. An imaging device of an X-ray imaging apparatus, characterized in that, Including: A memory and a processor, the memory stores the programs or instructions running on the processor, and when the programs or the instructions are executed by the processor, the steps of the imaging method of the X-ray imaging device according to any one of claims 1 to 5 are implemented.

8. A readable storage medium having a program or instructions stored thereon, characterized in that, When the programs or the instructions are executed by the processor, the steps of the imaging method of the X-ray imaging device according to any one of claims 1 to 5 are implemented.

9. An X-ray imaging device, characterized in that, Including: The imaging device of the X-ray imaging apparatus according to claim 6; or The imaging device of the X-ray imaging apparatus according to claim 7; or The readable storage medium according to claim 8.

Images