Calibration and calibration method and system based on radiation imaging system, terminal and storage medium

Abstract

The invention discloses a calibration and calibration method and system based on a radiation imaging system, a terminal and a storage medium. The calibration and calibration method comprises the steps of collecting calibration body projection data; carrying out mark point detection and preprocessing on the calibration body projection data; performing geometric parameter analysis according to the preprocessed calibration body projection data to obtain geometric parameters of the radiation imaging system; and determining the geometric deviation of the radiation imaging system according to the geometric parameters, performing dynamic compensation according to the geometric deviation, and outputting the calibrated geometric parameters. The method combines a novel calibration body, an analytical algorithm and a dynamic compensation mechanism, improves the precision and robustness of a radiation imaging system, and retains the advantages of high precision and low cost of offline calibration.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical imaging technology, in particular to a calibration method and system based on a radiological imaging system, a terminal and a storage medium. BACKGROUND

[0002] CBCT (Cone-Beam Computed Tomography) and C-arm X-ray machines are widely used in medical and industrial non-destructive testing fields. By rotating the X-ray source and the detector, two-dimensional projection data is obtained, thereby realizing three-dimensional reconstruction or real-time two-dimensional imaging. CBCT often uses an offset detector design to expand the field of view, while C-arm X-ray machines are flexible in intraoperative guidance due to their open structure. However, the imaging quality is highly dependent on the accuracy of geometric parameters, including X-ray source position, detector position, puncture point coordinates, and detector rotation angle. If these geometric parameters are not calibrated, it will cause image distortion, artifacts, or resolution degradation, thereby affecting the accuracy of imaging.

[0003] Existing geometric calibration methods mainly include online calibration and offline calibration. Online calibration can dynamically adapt to C-arm deformation or target motion in actual application, and does not require a dedicated calibration body, which can reduce preparation time. However, this calibration method requires additional hardware dependence and additional equipment (such as a camera), and has low robustness, which is not suitable for low-cost CBCT or portable C-arm X-ray machines. Offline calibration has limitations in calibration bodies, poor long-term stability, poor adaptability to offset detectors, and weak dynamic adaptability, which cannot meet the requirements of long-term effective high-precision calibration.

[0004] Therefore, the prior art still needs to be improved. SUMMARY

[0005] The technical problem to be solved by the present application is that, in view of the defects of the prior art, the present application provides a calibration method and system based on a radiological imaging system, a terminal and a storage medium to solve the problem of low precision and robustness of existing geometric calibration methods.

[0006] The technical solution adopted by the present application to solve the technical problem is as follows: In a first aspect, the present application provides a calibration method and system based on a radiological imaging system, comprising: acquiring projection data of a calibration body; detecting and preprocessing marker points of the projection data of the calibration body; analyzing geometric parameters according to the preprocessed projection data of the calibration body to obtain geometric parameters of the radiological imaging system; The geometric deviation of the radiological imaging system is determined according to the geometric parameters, and dynamic compensation is performed according to the geometric deviation, and the calibrated geometric parameters are output.

[0007] In an implementation manner, the projection data of the calibration phantom is acquired, including: The projection images of a preset calibration phantom are acquired at fixed angle intervals, or the projection images of the preset calibration phantom are scanned by a half-FOV scanning method to obtain the projection data of the calibration phantom; wherein the projection data of the calibration phantom includes two-dimensional projection coordinates of multiple marker points in multiple layers.

[0008] In an implementation manner, the projection data of the calibration phantom is acquired, including: An edge detection algorithm is used to extract the marker point centroid coordinates of each projection image from the projection data of the calibration phantom; Based on the extracted marker point centroid coordinates, noise suppression, distortion correction, and contrast enhancement processing are performed on the corresponding projection image to obtain a marker point centroid coordinate set of each projection image; Based on the marker point centroid coordinate set, a projection matrix optimization method and a minimum marker point projection deviation target are used to correct the marker point center to obtain the preprocessed projection data of the calibration phantom.

[0009] In an implementation manner, the geometric parameters include an X-ray source position, a detector position, a puncture point coordinate, and a detector rotation angle.

[0010] In an implementation manner, the geometric parameters are analyzed according to the preprocessed projection data of the calibration phantom to obtain the geometric parameters of the radiological imaging system, including: The multiple marker point coordinates of each projection image in the data are divided into two layers, and the projection trajectories of the marker points in each layer are fitted to obtain corresponding elliptical trajectories; The marker points in the first layer are selected, the selected points are connected with the marker points at corresponding positions in the second layer, and the puncture point coordinate is determined according to the obtained multiple projection connection lines; The Euler angle deflection parameters of the imaging panel of the radiological imaging system are calculated according to the double-layer elliptical trajectories; The X-ray source position and the detector position are calculated according to the puncture point coordinate and the Euler angle deflection parameters, and the detector rotation angle is calculated according to the X-ray source position and the detector position.

[0011] In an implementation manner, the X-ray source position and the detector position are calculated according to the puncture point coordinate and the Euler angle deflection parameters, and the detector rotation angle is calculated according to the X-ray source position and the detector position, including: selecting a parallel mark point pair intersecting at a preset converging point, and converting projection coordinates of the parallel mark point pair to a virtual detector coordinate system according to the puncture point coordinates and the Euler angle deflection parameter; calculating the X-ray source position according to the projection coordinates of the parallel mark point pair and the corresponding position in the real detector coordinate system by using a preset projection formula; solving the detector position according to the puncture point coordinates and the X-ray source position by using a constraint of a line connecting the X-ray source and the puncture point; calculating the detector rotation angle according to the X-ray source position and the detector position by using a coordinate system projection transformation relationship between the virtual detector coordinate system and the real detector coordinate system.

[0012] In an implementation manner, the geometric parameter analysis according to the preprocessed calibration body projection data to obtain the geometric parameters of the radiological imaging system further includes: dividing a plurality of mark point coordinates of each frame of projection image in the data into multiple layers, and fitting projection trajectories of the mark points in each layer to obtain corresponding elliptical trajectories; selecting any two adjacent elliptical trajectories, connecting a mark point in one layer with a mark point at a corresponding position in the other layer, and determining puncture point coordinates corresponding to the two layers according to a plurality of projection connection lines obtained; calculating corresponding Euler angle deflection parameters according to the two layers of elliptical trajectories; calculating geometric parameters corresponding to the two layers of elliptical trajectories according to the puncture point coordinates corresponding to the two layers of elliptical trajectories and the corresponding Euler angle deflection parameters; counting all the geometric parameters calculated according to the two adjacent layers of elliptical trajectories, and obtaining final geometric parameters of the radiological imaging system by taking average values of the X-ray source position, the detector position and the detector rotation angle corresponding to all the calculated geometric parameters.

[0013] In an implementation manner, the calculating of the geometric parameters corresponding to the two layers of elliptical trajectories according to the puncture point coordinates corresponding to the two layers of elliptical trajectories and the corresponding Euler angle deflection parameters includes: selecting a parallel mark point pair intersecting at a preset converging point, and converting projection coordinates of the parallel mark point pair to a virtual detector coordinate system according to the puncture point coordinates and the Euler angle deflection parameter; calculating the X-ray source position according to the projection coordinates of the parallel mark point pair and the corresponding position in the real detector coordinate system by using a preset projection formula; solving the corresponding detector position according to the puncture point coordinates and the X-ray source position by using a constraint of a line connecting the X-ray source and the puncture point; According to the X-ray source position and the detector position, a corresponding detector rotation angle is calculated by using a coordinate system projection transformation relationship between the virtual detector coordinate system and the real detector coordinate system.

[0014] In an implementation manner, the determining the geometric deviation of the radiological imaging system according to the geometric parameters and dynamically compensating according to the geometric deviation to output calibrated geometric parameters comprises: comparing the position in the geometric parameters with an expected position of historical calibration data to determine the geometric deviation of the radiological imaging system; optimizing the geometric parameters according to the geometric deviation by using a least square method to output the calibrated geometric parameters.

[0015] In an implementation manner, the comparing the position in the geometric parameters with an expected position of historical calibration data to determine the geometric deviation of the radiological imaging system comprises: fusing the position in the geometric parameters with IMU sensor data and preset key point coordinates, and comparing the fused position with the expected position of the historical calibration data to calculate the geometric deviation of the radiological imaging system; The optimizing the geometric parameters according to the geometric deviation by using a least square method to output the calibrated geometric parameters comprises: dynamically adjusting a deviation detection threshold based on an automatic threshold selection algorithm; comparing the geometric deviation with the deviation detection threshold, and optimizing the geometric parameters greater than the deviation detection threshold by using the least square method to output the calibrated geometric parameters.

[0016] In an implementation manner, the comparing the position in the geometric parameters with an expected position of historical calibration data to determine the geometric deviation of the radiological imaging system comprises: obtaining the IMU sensor data; adopting a corner point detection algorithm or a key point feature extraction algorithm to identify preset key point coordinates in each frame of projection image; fusing and positioning the position in the geometric parameters with the IMU sensor data and the preset key point coordinates to obtain a multi-feature fused position; comparing the fused position with the expected position of the historical calibration data to calculate the geometric deviation of the radiological imaging system.

[0017] In a second aspect, the present application provides a calibration and calibration system based on a radiological imaging system, comprising: a data acquisition module configured to acquire phantom projection data; a detection and preprocessing module configured to perform landmark detection and preprocessing on the phantom projection data; a geometric analysis module configured to perform geometric parameter analysis on the preprocessed phantom projection data to obtain geometric parameters of the radiological imaging system; a dynamic compensation module configured to determine geometric deviation of the radiological imaging system according to the geometric parameters, and perform dynamic compensation according to the geometric deviation to output calibrated geometric parameters.

[0018] In a third aspect, the present application provides a terminal comprising a processor and a memory, wherein the memory stores a radiological imaging system calibration program, and the radiological imaging system calibration program is used to implement the operations of the radiological imaging system calibration method according to the first aspect when executed by the processor.

[0019] In a fourth aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a radiological imaging system calibration program, and the radiological imaging system calibration program is used to implement the operations of the radiological imaging system calibration method according to the first aspect when executed by a processor.

[0020] The technical scheme of the present application has the following effects: The present application acquires phantom projection data, performs landmark detection and preprocessing on the phantom projection data, performs geometric parameter analysis on the preprocessed phantom projection data to obtain geometric parameters of the radiological imaging system, determines geometric deviation of the radiological imaging system according to the geometric parameters, and performs dynamic compensation according to the geometric deviation to output calibrated geometric parameters. The present application combines a new type of phantom, analysis algorithm and dynamic compensation mechanism to improve the precision and robustness of the radiological imaging system, and retains the advantages of high precision and low cost of offline calibration. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained according to the structures shown in these drawings without creative labor.

[0022] Figure 1 is a flowchart of the radiological imaging system calibration method in the present application.

[0023] Figure 2is a schematic diagram of a puncture point in the present application.

[0024] Figure 3 is a schematic diagram of Euler angle deflection of an imaging plate in the present application.

[0025] Figure 4 is a functional schematic diagram of a terminal in an implementation manner of the present application.

[0026] The purposes, technical solutions, and advantages of the present application will be further described with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0027] To make the purposes, technical solutions, and advantages of the present application clearer and more explicit, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0028] Exemplary method In the prior art, the geometric calibration of a radiological imaging system (for example, a CBCT system or a C-arm X-ray machine) is divided into online calibration and offline calibration. The characteristics and disadvantages of the two methods are analyzed below to highlight the advantages of offline calibration.

[0029] 1) Online Calibration: Online calibration adjusts geometric parameters in real time during the imaging process, which is suitable for dynamic scenarios (for example, intraoperative navigation of a C-arm X-ray machine). Common methods mainly include: External tracking system: uses optical or electromagnetic trackers (for example, infrared cameras) to monitor the positions of the source and the detector, generates geometric parameters, and is commonly used for C-arm orthopedic navigation.

[0030] Projection feature analysis: estimates the puncture point or angle based on anatomical markers (for example, bone edges) in the projection data, which is suitable for C-arm fluoroscopy.

[0031] Iterative projection matrix optimization: iteratively calculates the projection matrix through real-time projection data, which is suitable for non-ideal geometry.

[0032] Online calibration can dynamically adapt to intraoperative C-arm deformation or patient motion in actual application, and does not require a dedicated calibration phantom, which can reduce preparation time. However, this calibration method produces additional hardware dependencies, requires additional equipment (for example, cameras), increases cost and complexity, and is not suitable for low-cost CBCT or portable C-arm; due to the large amount of calculation in iterative optimization, the calibration time is often seconds, which is difficult to meet the intraoperative requirement (<1 second), and the real-time performance is also reduced; in low-contrast or occluded scenarios (for example, soft tissue, instruments), the accuracy will decrease, resulting in insufficient robustness; only part of the parameters (for example, the puncture point) is corrected, which is difficult to capture all degrees of freedom of the offset detector or C-arm, and is prone to artifacts.

[0033] 2) Offline Calibration: Offline calibration measures geometric parameters using a calibration phantom before imaging, and stores the results for later use, which is suitable for high-precision calibration of CBCT and C-arm X-ray machines. Common methods include: Single-ball calibration: a single ball bearing (BB) is placed at the rotation center to track the projection to determine the piercing point (P) ), which is simple but low in precision.

[0034] Multi-ball calibration phantom: two planar BB arrays are used to calculate the source position, detector position, and angle through projection analysis.

[0035] Spiral calibration phantom: spiral markers are used to calculate complex geometric parameters, which is suitable for high-precision CBCT.

[0036] Offline calibration has high precision with errors reaching sub-pixel level because it can capture all degrees of freedom, is not affected by imaging objects, is suitable for CBCT, C-arm navigation, and industrial detection in various fields, and has strong robustness. It does not require additional hardware, is easy to integrate into existing devices, supports offset detector CBCT and C-arm, and has strong versatility. However, offline calibration has limitations of calibration phantom, poor long-term stability, poor adaptability to offset detectors, and weak dynamic adaptability.

[0037] To solve the above technical problems, the present application provides a calibration and calibration method based on a radiological imaging system, which comprises the following steps:

[0038] As shown in Figure 1 , the present application provides a calibration and calibration method based on a radiological imaging system, which comprises the following steps: Step S100, acquiring calibration phantom projection data.

[0039] In the embodiment, by means of a novel calibration body design and an efficient analysis algorithm, the imaging accuracy of a mobile C-arm X-ray machine or a cone beam CT (CBCT) system with an offset detector is significantly improved to meet the high-quality three-dimensional imaging requirements in imaging, intraoperative guidance, and other scenarios. The core process of the technical solution includes five main steps: calibration body projection data acquisition, marker point detection and preprocessing, geometric parameter analysis and calculation, dynamic compensation and verification, and final three-dimensional image reconstruction.

[0040] In the embodiment, the input data is a projection image sequence of the calibration body, the calibration body is a cylindrical structure containing multiple layers of asymmetrically distributed marker points, and the projection data is acquired in real time by the CBCT system in a specific rotation trajectory. The output is a high-precision geometric parameter set for subsequent three-dimensional image reconstruction.

[0041] As an example, during the calibration body projection data acquisition process, a calibration body containing double-layer symmetric spherical markers (Ball Bearing, BB) can be used to uniformly project and sample the markers within a 180° range with a sampling interval of 1°. In addition, a calibration body with multiple layers of symmetric spherical markers can also be used for data acquisition, such as a three-layer or more calibration body.

[0042] Specifically, in one implementation manner of the embodiment, step S100 includes the following steps: Step S101: acquiring projection images of a preset calibration body at fixed angular intervals or scanning the projection images of the preset calibration body by a half-field scanning method to obtain the calibration body projection data; wherein the calibration body projection data includes two-dimensional projection coordinates of multiple marker points in multiple layers.

[0043] In the embodiment, taking a CBCT system as an example, the projection data (i.e. projection images) of the calibration body is acquired by the CBCT system to provide a basis for subsequent geometric parameter calculation. As an example, the adopted calibration body is an acrylic glass cylinder with double-layer embedded marker points, each layer containing 8 steel balls (BB) with a diameter of 5mm; the diameter of the calibration body is 100mm, and the layer spacing is 120mm. The marker points in each layer are distributed at asymmetric angles, for example, the marker points in each layer are sequentially increased by a specified angle (i.e. the interval between two marker points is fixed, such as 15°, 30°, 45°, etc.), or the interval between the marker points in each layer is set according to an increasing rule (such as the first interval is 15°, the second interval is 30°, and the third interval is 45°); and the starting angle of each layer is offset by a specified angle, for example, the offset angle is 15°, to ensure the uniqueness of the projection trajectory. The calibration body is placed near the rotation center of the system without the need for accurate alignment, reducing the operation complexity.

[0044] Based on the above calibration body structure, the acquisition process of the projection image of the calibration body is: The CBCT system is used to collect projection images at fixed angle intervals, wherein the collected projection images cover a rotation range of about 180°, and for a detector system with a large offset, a half-field scanning method is used to ensure that the projection of the calibration phantom can cover the entire detector area.

[0045] Output: 200-frame projection image sequence, containing two-dimensional projection coordinates of three layers of 24 marker points Due to the asymmetric design of the calibration phantom, the marker points in the projection images form unique elliptical trajectories, providing high-robustness data for geometric parameter solving.

[0046] The embodiment proposes a calibration phantom containing double-layer asymmetrically distributed marker points, which is used for CBCT system or C-arm geometry calibration. Compared with the traditional single-ball or symmetric array calibration phantom, the design ensures the uniqueness and coverage of the projection trajectory through the asymmetric angle distribution and the multi-layer structure, significantly improving the solving robustness. This calibration phantom does not need to be placed accurately, reducing the operation difficulty, and is particularly suitable for offset detector systems and C-arm systems with large mechanical flexibility (intraoperative imaging), effectively controlling the calibration error.

[0047] As shown in Figure 1 , the embodiment of the present application provides a calibration and calibration method based on a radiological imaging system, comprising the following steps: Step S200, marker point detection and preprocessing of the calibration phantom projection data.

[0048] In the embodiment, the detection accuracy is enhanced through preprocessing to provide reliable input for subsequent geometric solving; in the process of marker point detection and preprocessing, the centroid coordinates of the steel balls (marker points) can be extracted by edge detection method and circular fitting method , and adaptive filtering method is applied to correct the noise and distortion in the projection image, so as to obtain the preprocessed calibration phantom projection image.

[0049] Specifically, in one implementation manner of the embodiment, step S200 comprises the following steps: Step S201, using an edge detection algorithm to extract the marker point centroid coordinates of each projection image from the calibration phantom projection data.

[0050] In the embodiment, an edge detection algorithm is used to identify the edges of the marker points in each projection image, and for each marker point region, a Gaussian filter is used to process and smooth the noise, then a least squares method is used to fit the contour of each marker point, and an elliptical fitting correction is performed on all projection points to determine the centroid coordinates of each marker point ) position. In the calculation process, the expected geometric distribution of the marker points (known double-layer asymmetric layout) is marked, the abnormal points (for example, scattering artifacts) are automatically removed, and it is ensured that the detected 16 centroid coordinates can accurately correspond to the calibration body structure, avoiding detection errors in the calculation.

[0051] In step S202, based on the extracted marker point centroid coordinates, noise suppression, distortion correction and contrast enhancement processing are performed on the corresponding frame projection image, to obtain a set of marker point centroid coordinates of each frame projection image.

[0052] In this embodiment, after extracting the marker point centroid coordinates of each frame projection image, noise suppression, distortion correction and contrast enhancement processing are performed, and the specific process is as follows: Noise suppression: adaptive median filtering method is applied to remove high-frequency noise in the projection image, and the marker point edge details are retained.

[0053] Distortion correction: based on the detector calibration parameters (i.e. the pre-measured spatial distortion map), the non-linear distortion of the detector is corrected to ensure the geometric consistency of the centroid coordinates.

[0054] Contrast enhancement: histogram equalization is applied to low-contrast projection (for example, high scattering scene) to improve the discrimination of the marker points and the background.

[0055] The above processing process outputs a set of 16 marker point centroid coordinates of each frame projection image in this embodiment. The preprocessing step significantly improves the detection robustness and adapts to the noise, scattering and mechanical jitter problems commonly found in the offset detector and C-arm X-ray machine system.

[0056] In step S203, based on the set of marker point centroid coordinates, the marker point center is corrected by using a projection matrix optimization method and minimizing the marker point projection deviation target, to obtain the preprocessed calibration body projection data.

[0057] In this embodiment, in addition to noise suppression, distortion correction and contrast enhancement processing on each frame projection image, the marker point center also needs to be corrected; since the steel ball is not circular in projection at different angles of the C-arm due to perspective distortion or non-ideal imaging, it is necessary to optimize based on the projection matrix, that is, by minimizing the marker point projection deviation, the estimated value of the internal and external parameters of the C-arm is optimized, the perspective distortion at different angles is compensated, and the steel ball projection is ensured to be close to a circle, so as to correct the center marker point.

[0058] As shown in Figure 1 , the present embodiment provides a calibration and correction method based on a radiological imaging system, comprising the following steps: Step S300: Perform geometric parameter analysis based on the preprocessed calibration body projection data to obtain the geometric parameters of the radiographic imaging system.

[0059] In this embodiment, after correcting the center of the marker point, the geometric parameters of the CBCT system are analytically calculated based on the projected trajectory of the marker point; wherein, the geometric parameters include: X-ray source position ( ), detector location ( ), piercing point coordinates ( ) and detector rotation angle ( The solution process is divided into five parts: ellipse trajectory fitting, puncture point calculation, imaging plate Euler angle calculation, X-ray source and detector position calculation, and projection rotation angle calculation.

[0060] Specifically, in one implementation of this embodiment, step S300 includes the following steps: Step S311: Divide the coordinates of multiple marker points in each frame of the projected image in the data into two layers, and fit the projection trajectory of the marker points in each layer to obtain the corresponding elliptical trajectory.

[0061] In this embodiment, during the elliptical trajectory fitting process, it is necessary to use the coordinates of 16 marker points in each frame of the projected image ( The points are grouped into two layers (8 points per layer), and the projected trajectories of the marked points in each layer are fitted with elliptical equations; the form of the elliptical trajectory fitting is as follows: ; in,( ( ) is the center of the ellipse, The parameters are for the shape of the ellipse. This is an overdetermined system of equations, and the parameters are analytically determined using linear least squares. Based on two sets of elliptical parameters (one set for each layer), describe the projection geometry of the marker points onto the detector plane.

[0062] Step S312: Select a marker point in the first layer, connect the selected point with the corresponding marker point in the second layer, and determine the coordinates of the puncture point based on the obtained multiple projection connection lines.

[0063] In this embodiment, after fitting an ellipse on both sides based on the positions of multiple steel balls, the alignment intersection point can be calculated based on the corresponding ellipse angles, thus determining the coordinates of the puncture point. Specifically, in the process of calculating the puncture point, the geometric constraints of the double-layered marker points are mainly used to calculate the puncture point. This involves calculating the projection of the world coordinate system origin (center of the calibration body) onto the detector plane. For example... Figure 2As shown, by connecting the corresponding points in each layer of the ellipse at the corresponding angle position, a plurality of projection connection lines are obtained, and the intersection points of the plurality of projection connection lines are determined based on the overall distance optimization target, and the intersection points of the plurality of projection connection lines are taken as the puncture points (green points in FIG. 8) Figure 2 Therefore, based on the symmetrical distribution of the phantom steel balls, the uniqueness of the intersection points is ensured.

[0064] In step S313, the Euler angle deflection parameters of the imaging plate of the radiographic imaging system are calculated according to the double-layer elliptical trajectory.

[0065] In this embodiment, in the process of calculating the Euler angles of the imaging plate, the Euler angle deflection parameters of the detector are analytically calculated based on the elliptical projection trajectory of the double-layer BB (as shown in FIG. 9) ), and the actual Euler angle deflection parameters of the detector are as shown in FIG. 10. Figure 3 Specifically, based on the fitting elliptical curve equation of the double-layer calibration plate and the coordinate point distribution angle η, the farthest point (pole) of the projection ellipse can be calculated, and the pole of the projection ellipse corresponds to the maximum value of the X direction on the virtual detector plane. Connecting the two poles of the ellipse forms two lines (denoted as ), and the calculation formula of η is as follows: ; By combining the following four formulas, and and the inclination angle can be obtained by a nonlinear solving method: ; ; ; ; wherein, is an elliptical equation parameter, is the direction angle between the p and q vectors, is the distance from point m to point n, and the height direction convergence point , is the estimated distance of the object to the detector plane, is an intermediate parameter in the calculation process, and k is one of the two circular bb groups.

[0066] In step S314, the X-ray source position and the detector position are calculated according to the puncture point coordinates and the Euler angle deflection parameters, and the detector rotation angle is calculated according to the X-ray source position and the detector position.

[0067] In this embodiment, in the process of calculating the X-ray source and detector positions, an Euler angle rotation matrix can be established, and the puncture point (as shown in FIG. 11) ​By transforming the projected coordinates to the virtual detector coordinate system, the X-ray source can be calculated. ) and detector position ( ).

[0068] In one implementation of this embodiment, step S314 includes the following steps: Step S314a: Select a pair of parallel marker points where the projection lines intersect at a preset convergence point, and transform the projection coordinates of the pair of parallel marker points to the virtual detector coordinate system according to the puncture point coordinates and the Euler angle deflection parameters. Step S314b: The position of the X-ray source is calculated using a preset projection formula based on the projected coordinates of the parallel marker point pair and the corresponding positions in the real detector coordinate system. Step S314c: Based on the coordinates of the puncture point and the position of the X-ray source, the position of the detector is obtained by using the constraint of the line connecting the X-ray source to the puncture point.

[0069] Specifically, in this embodiment, when calculating the positions of the X-ray source and the detector, a plane (i.e., a divergence plane) is defined by the emission source and a pair of steel balls. Each divergence plane intersects the detector plane with a line. The divergence planes of multiple pairs of steel balls intersect at an axis (i.e., a divergence axis), which intersects the detector at a convergence point. .

[0070] Take a pair of parallel steel balls along the Z-axis of the calibration body; their projection lines intersect at the convergence point. Based on puncture point ( ) and Euler angle deflection parameters Transform the projection coordinates of the puncture point to the virtual detector coordinate system. ): ; in, Here is the Euler angle deflection parameter matrix: First line: ; Second line: ; Third line: ; Based on the projection formula: ; Substitute the known steel ball projection ( ), the object position vector in the real detector coordinate system ( ), the position vector of the X-ray source ( The X-ray source location is calculated.

[0071] Detector position ( ) defines the translation of the virtual detector origin to the world origin, is defined as 0, the detector position is constrained in the geometric relationship of the source-needle point line. Based on the needle point and the source position, the detector is constrained on the source-needle point line, and the detector position ( ) can be solved.

[0072] In step S314d, according to the X-ray source position and the detector position, the coordinate system projection transformation relationship between the virtual detector coordinate system and the real detector coordinate system is used to calculate the detector rotation angle.

[0073] In this embodiment, after the X-ray source and detector positions are calculated, the gantry angle (t) can be calculated, that is, the projection rotation angle is solved, and the specific process is as follows: The X-ray source ( ) reflects the projection position rotation in the world coordinate system. When the gantry tilt angle t is calculated, the coordinate system projection transformation relationship is determined through the transformation matrix between the virtual detector coordinate system and the real detector coordinate system, and then the gantry rotation angle (t) is solved through trigonometric functions (part of the formula is carried over from the previous text, and the variables are assumed to be known): ; ; ; ; ; ; In the formula, is the transformation relationship from the virtual detector coordinate system to the real coordinate system, is the X-ray source coordinate, is the world coordinate system, are intermediate variables, and the actual value of the gantry tilt angle t can be obtained through the above calculation.

[0074] The above scheme divides the multiple marker point coordinates of each frame of projection image in the data into two layers to obtain a needle point coordinate, and then calculates the X-ray source position, the detector position, and the detector rotation angle under the two-layer trajectory condition by using the double-layer elliptical trajectory and the needle point coordinate.

[0075] In order to improve the analytical accuracy of the geometric deviation of the radiological imaging system, in this embodiment, the multiple marker point coordinates of each frame of projection image in the data can be divided into multiple layers (three layers or more), multiple needle point coordinates are obtained, and then the X-ray source position, the detector position, and the detector rotation angle under the multi-layer trajectory condition are calculated by using the multi-layer elliptical trajectory and the multiple needle point coordinates. ​

[0076] Specifically, in one implementation form of the embodiment, step S300 further comprises the following steps: In step S321, the plurality of marker point coordinates of each frame of projection image in the data are divided into a plurality of layers, and the projection trajectories of the marker points in each layer are fitted to obtain corresponding elliptical trajectories; In step S322, the plurality of elliptical trajectories are traversed, any two adjacent elliptical trajectories are selected, the marker points in one of the two layers are connected with the marker points at the corresponding positions in the other layer, and the piercing point coordinates corresponding to the two layers of elliptical trajectories are determined according to the plurality of projection connection lines obtained; In step S323, the corresponding Euler angle deflection parameters are calculated according to the two layers of elliptical trajectories; In step S324, the corresponding geometric parameters of the two layers of elliptical trajectories are calculated according to the piercing point coordinates corresponding to the two layers of elliptical trajectories and the corresponding Euler angle deflection parameters; In step S325, all the geometric parameters calculated according to the two layers of adjacent elliptical trajectories are counted, and the corresponding X-ray source position average value, detector position average value and detector rotation angle average value are taken according to all the calculated geometric parameters to obtain the final geometric parameters of the radiological imaging system.

[0077] In one implementation form of the embodiment, step S324 comprises the following steps: In step S324a, a parallel marker point pair whose projection lines intersect at a preset converging point is selected, and the projection coordinates of the parallel marker point pair are converted to a virtual detector coordinate system according to the piercing point coordinates and the Euler angle deflection parameters; In step S324b, the corresponding X-ray source position is calculated by using a preset projection formula according to the projection coordinates of the parallel marker point pair and the corresponding positions in the real detector coordinate system; In step S324c, the corresponding detector position is solved by using the constraint of the X-ray source to piercing point connection line according to the piercing point coordinates and the X-ray source position; In step S324d, the corresponding detector rotation angle is calculated by using the coordinate system projection transformation relationship between the virtual detector coordinate system and the real detector coordinate system according to the X-ray source position and the detector position.

[0078] Compared with the scheme of dividing the multiple marker point coordinates of each frame of projection image in the data into two layers, the scheme of dividing the multiple marker point coordinates of each frame of projection image in the data into multiple layers is as follows: any two adjacent layers are selected from the multiple layers of elliptical trajectories to obtain multiple groups of elliptical trajectories, for example, the first layer and the second layer are selected as a group of elliptical trajectories, the second layer and the third layer are selected as a group of elliptical trajectories, and so on until the last layer is selected. For any group of elliptical trajectories, the scheme of two layers is used to calculate a puncture point coordinate and Euler angle deflection parameters, and then the puncture point coordinate and Euler angle deflection parameters are used to calculate the geometric parameters corresponding to the group of elliptical trajectories, so that the geometric parameters corresponding to the multiple groups of elliptical trajectories can be obtained; finally, when the final geometric parameters of the radiological imaging system are calculated, the corresponding values are selected from the data groups to take the mean value, and the final X-ray source position, detector position and detector rotation angle can be obtained.

[0079] In the embodiment, an analytical calibration algorithm based on marker point projection trajectories is developed, and all geometric parameters (X-ray source position, detector position, puncture point coordinate and rotation angle) are directly calculated by fitting multiple layers of elliptical trajectories. Compared with the traditional iterative method, the algorithm does not require initial parameter guessing, avoids local optimal problem, and the calculation time is shortened to about 10 ms per frame. The algorithm combines projection angle correction marker point detection, and significantly improves the calibration accuracy.

[0080] As shown in Figure 1 The embodiment of the present application provides a calibration and calibration method based on a radiological imaging system, which comprises the following steps: Step S400, determining the geometric deviation of the radiological imaging system according to the geometric parameters, and dynamically compensating according to the geometric deviation, and outputting the calibrated geometric parameters.

[0081] In the embodiment, after the geometric parameter calculation, the deviation of the projection data and the historical calibration is analyzed, a compensation model is generated, and the geometric parameters calculated in step S300 are dynamically compensated based on the compensation model, so that the compensated geometric parameters can adapt to the C-arm deformation or CBCT device support relaxation scene.

[0082] Specifically, in an implementation manner of the embodiment, step S400 comprises the following steps: Step S401, comparing the position in the geometric parameters with the expected position of the historical calibration data to determine the geometric deviation of the radiological imaging system.

[0083] In the embodiment, geometric deviations caused by mechanical flexibility are detected by comparing the actual coordinates of the marker points in the current projection image with the expected positions of the historical calibration data. Based on the deviation vector, the geometric parameters are optimized using the least squares method, focusing on compensating for the puncture point coordinates and the in-plane coordinates of the detector. For C-arm systems, the gravitational deformation is corrected; for offset detectors, the asymmetry of the scanning trajectory is compensated.

[0084] In an implementation manner of the embodiment, the step S401 comprises the following steps: In the step S401a, the position in the geometric parameters is fused with the IMU sensor data and the preset key point coordinates, and the fused position is compared with the expected position of the historical calibration data to calculate the geometric deviation of the radiological imaging system.

[0085] In the step S402, the geometric parameters are optimized using the least squares method according to the geometric deviation, and the calibrated geometric parameters are output.

[0086] Specifically, in the embodiment, the dynamic compensation scheme comprises the following steps: 1) Multi-feature fusion: IMU sensor data is obtained, and the IMU sensor data (such as angular velocity and acceleration) is compared with the marker point coordinates to improve the robustness of deviation detection. Moreover, Harris corner detection or SIFT feature extraction method is adopted to identify the key points of the projection image, which makes up for the problem of insufficient or occluded marker points. In the embodiment, the multi-feature fusion scheme is to fuse and position the position in the geometric parameters with the IMU sensor data and the preset key point coordinates, and to compare the fused position with the expected position of the historical calibration data, so as to improve the positioning accuracy of the position in the geometric parameters.

[0087] 2) Dynamic threshold adjustment: According to the projection angle and the surgical scene, the deviation detection threshold is dynamically adjusted to reduce the misjudgment caused by noise or low contrast. In the embodiment, Otsu method (i.e. automatic threshold selection algorithm) is used to realize adaptive threshold adjustment based on local image statistics, that is, to dynamically adjust the deviation detection threshold, and then to optimize the geometric parameters according to the dynamically adjusted deviation detection threshold, so as to improve the detection accuracy.

[0088] In the embodiment, after the C-arm X-ray machine or CBCT system is calibrated and calibrated according to the above scheme, the calibration verification is performed by the following method: Based on Grangeat formula, the Radon plane data is generated by the weighted integral of the projection image, and the relationship between cone beam projection and three-dimensional Radon transform is established. Under ideal geometry, the polar line data of different projection angles should be consistent in the specific polar plane of the Radon domain. The geometric deviation (such as source-detector distance, rotation axis offset) will cause the inconsistency of the polar line. G-ECC uses this feature to optimize the geometric parameters by minimizing the inconsistency.

[0089] The calibration accuracy is evaluated by using the calibration board for projection and reconstruction. The method includes comparing the actual imaging marker point data, the camera calibration result of the calibration board, and measuring the resolution and artifact degree of the reconstructed image. The optimized geometric parameter set is stored in the system database after verification. Thanks to the dynamic compensation mechanism, the system does not need to be frequently recalibrated in long-term use, and maintains high stability.

[0090] The dynamic compensation mechanism of real-time projection deviation analysis is introduced in this embodiment. By comparing the difference between the current projection and the historical calibration data, the geometric parameters are dynamically adjusted to adapt to the mechanical flexibility changes (such as C-arm sag or detector jitter). This mechanism does not require additional hardware support, ensuring real-time intraoperative. Experiments show that the system does not need to be recalibrated during the use cycle, maintains high-precision imaging, and significantly reduces maintenance costs, which is superior to the frequent calibration requirement of traditional methods.

[0091] Based on the calibration and calibration scheme of the C-arm X-ray machine or CBCT system described above, the following changes can also be made in this embodiment: To deal with intraoperative scattering artifacts, marker point occlusion or mechanical jitter and other abnormal situations, a multi-level fault tolerance mechanism is designed, including: a simplified backup calibration mode based on a small number of marker points, a real-time anomaly detection algorithm (such as fitting residual analysis) and a regional calibration strategy, to ensure that geometric parameters can be stably output even in complex environments. For the visualization needs of specific imaging targets or tools, a dynamic target segmentation algorithm based on high-contrast characteristics is proposed, and combined with the three-dimensional model output by the geometric parameters, the superimposed display is carried out through the augmented reality (AR) device, so as to improve the intuitiveness and precision of the on-site operation.

[0092] At the same time, in order to overcome the limitations of CBCT in soft tissue identification and functional information, this embodiment explores a multi-modal imaging fusion strategy, including registering intraoperative ultrasound high-resolution data with CBCT bone structure, using preoperative MRI to provide functional priori (such as nerve distribution) to assist intraoperative planning, and fusing optical imaging signals (such as fluorescence or infrared) to enhance target recognition accuracy. These fusion methods not only enrich the intraoperative information, but also improve the accuracy of clinical judgment and the safety of operation.

[0093] In terms of application expansion, the core algorithm of the present application and the calibration body design have high portability and are suitable for various needs such as industrial non-destructive testing (such as imaging of internal defects of aerospace materials), portable CBCT equipment (to meet emergency and remote medical scenarios), dynamic navigation (real-time imaging compensation related to breathing / heartbeat), and robot-assisted surgery (such as precise implantation guidance in orthopedics), etc. Through the adjustable calibration body structure and the highly adaptive algorithm, the method can flexibly match different system parameters (such as field of view, energy, and resolution), realize extensive deployment across devices and scenarios while maintaining high precision and efficiency, and become an integrated geometric calibration solution for CBCT and related radiological imaging systems.

[0094] In summary, the present embodiment proposes a highly adaptive and high-precision geometric calibration scheme suitable for various CBCT and related radiological imaging systems, mainly including three aspects of calibration body structure optimization, calibration algorithm upgrade, and function expansion.

[0095] Firstly, a modular and adjustable calibration body design is adopted to replace the traditional asymmetric cylindrical structure, so that the marker point layer is detachable and the layer spacing is adjustable, thereby flexibly adapting to different specifications of the projection system and enhancing the stability of geometric solution under high noise and scattering conditions.

[0096] Secondly, in terms of calibration algorithm, a deep learning model is introduced to realize sub-pixel level automatic detection of marker points, which significantly improves the robustness in low contrast and complex environments; a hybrid algorithm structure is formed by combining the analytical solution and lightweight iterative optimization to further improve the geometric parameter precision while maintaining high efficiency; and a self-adaptive parameter adjustment mechanism is developed to automatically adjust the threshold setting according to the noise level and marker point distribution characteristics, thereby improving the universality of the algorithm for different hardware conditions.

[0097] In addition, the system supports multi-modal imaging geometric calibration, including fluoroscopy X-ray and fluorescence imaging, and is extended to dynamic imaging scenarios such as respiratory or heart-related CBCT through trajectory modeling, thereby enhancing the multi-task capability of the device. For industrial non-destructive testing scenarios, high-energy compatible marker materials (such as tungsten balls) can be replaced and field of view parameters can be optimized to meet material analysis requirements.

[0098] Finally, a dynamic compensation mechanism is constructed by combining environmental sensors and historical calibration data, which can sense the device deformation caused by temperature and gravity and predict its evolution trend through a machine learning model, thereby realizing high-precision compensation and long-term stable operation. The overall scheme greatly improves the adaptability, robustness, and multi-scene application capability of the system while ensuring precision and efficiency.

[0099] The technical effects achieved by the technical solutions of the present embodiment are as follows: The embodiment introduces a deep learning model to realize sub-pixel level automatic detection of the marker points, significantly improves the robustness in low contrast and complex environment, combines the analytical solution and light iterative optimization to form a hybrid algorithm structure, further improves the geometric parameter precision while retaining high efficiency, develops a self-adaptive parameter adjustment mechanism to automatically adjust the threshold setting according to the noise level and marker point distribution characteristics, and improves the universality of the algorithm for different hardware conditions. Moreover, combined with the environmental sensor and historical calibration data, a dynamic compensation mechanism is constructed to perceive the device deformation caused by temperature and gravity and predict the evolution trend through a machine learning model, realizes high-precision compensation and long-term stable operation. On the basis of ensuring precision and efficiency, the adaptability, robustness and multi-scene application ability of the system are greatly improved.

[0100] Exemplary device Based on the above embodiment, the application also provides a calibration and calibration system based on a radiological imaging system, comprising: A data acquisition module is configured to acquire calibration body projection data. A detection and preprocessing module is configured to detect and preprocess the marker points in the calibration body projection data. A geometric analysis module is configured to analyze the geometric parameters of the radiological imaging system according to the preprocessed calibration body projection data. A dynamic compensation module is configured to determine the geometric deviation of the radiological imaging system according to the geometric parameters, and to perform dynamic compensation according to the geometric deviation, and to output the calibrated geometric parameters.

[0101] The above technical solution achieves the following technical effects: The embodiment introduces a deep learning model to realize sub-pixel level automatic detection of the marker points, significantly improves the robustness in low contrast and complex environment, combines the analytical solution and light iterative optimization to form a hybrid algorithm structure, further improves the geometric parameter precision while retaining high efficiency, develops a self-adaptive parameter adjustment mechanism to automatically adjust the threshold setting according to the noise level and marker point distribution characteristics, and improves the universality of the algorithm for different hardware conditions. Moreover, combined with the environmental sensor and historical calibration data, a dynamic compensation mechanism is constructed to perceive the device deformation caused by temperature and gravity and predict the evolution trend through a machine learning model, realizes high-precision compensation and long-term stable operation. On the basis of ensuring precision and efficiency, the adaptability, robustness and multi-scene application ability of the system are greatly improved.

[0102] Based on the above embodiment, the application also provides a terminal, and the principle block diagram can be as shown in Figure 4 .

[0103] The terminal comprises a processor, a memory, an interface, a display screen and a communication module connected through a system bus; the processor of the terminal is configured to provide computing and control capabilities; the memory of the terminal comprises a computer readable storage medium and an internal memory; the computer readable storage medium stores an operating system and a computer program; the internal memory provides an environment for the operating system and the computer program in the computer readable storage medium to run; the interface is configured to connect external devices; the display screen is configured to display corresponding information; and the communication module is configured to communicate with a cloud server or other devices.

[0104] The computer program is configured to implement the operations of the method for calibrating and calibrating a radiological imaging system when executed by the processor.

[0105] Those skilled in the art can understand that, Figure 4 The principle block diagram shown in the figure is only a block diagram of part of the structure related to the present application, and does not constitute a limitation on the terminal to which the present application is applied. The specific terminal can comprise more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0106] In one embodiment, a terminal is provided, comprising a processor and a memory, wherein the memory stores a program for calibrating and calibrating a radiological imaging system, and the program for calibrating and calibrating a radiological imaging system is configured to implement the operations of the method for calibrating and calibrating a radiological imaging system as described above when executed by the processor.

[0107] In one embodiment, a computer readable storage medium is provided, wherein the computer readable storage medium stores a program for calibrating and calibrating a radiological imaging system, and the program for calibrating and calibrating a radiological imaging system is configured to implement the operations of the method for calibrating and calibrating a radiological imaging system as described above when executed by the processor.

[0108] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to the memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and volatile memories.

[0109] In conclusion, the present application provides a kind of calibration method and system based on radiological imaging system, terminal and storage medium, comprising: acquisition calibration body projection data;Marking point detection and preprocessing are carried out to the calibration body projection data;According to the geometric parameter analysis of the calibration body projection data after preprocessing, the geometric parameter of the radiological imaging system is obtained;According to the geometric parameter, the geometric deviation of the radiological imaging system is determined, and dynamic compensation is carried out according to the geometric deviation, and the calibrated geometric parameter is output.The present application combines new calibration body, analysis algorithm and dynamic compensation mechanism, improves the precision and robustness of radiological imaging system, retains the advantages of high precision, low cost of offline calibration.

[0110] It should be understood that the application of the present application is not limited to the above examples, and those skilled in the art can improve or change according to the above description, and all these improvements and changes shall belong to the protection scope of the appended claims of the present application.

Claims

1. A method for calibration and calibration of a radiological imaging system, characterized in that, The method comprises the following steps: Collecting calibration body projection data; Performing marker point detection and preprocessing on the calibration body projection data; Analyzing geometric parameters according to the preprocessed calibration body projection data to obtain geometric parameters of a radiation imaging system; Determining geometric deviations of the radiation imaging system according to the geometric parameters, and performing dynamic compensation according to the geometric deviations to output calibrated geometric parameters.

2. The method for calibration and calibration of radiological imaging systems according to claim 1, characterized in that, The step of collecting calibration body projection data comprises the following steps: Collecting projection images of a preset calibration body at fixed angle intervals, or scanning projection images of the preset calibration body by a half-FOV scanning method to obtain the calibration body projection data; wherein the calibration body projection data comprises two-dimensional projection coordinates of multiple marker points in multiple levels; The step of performing marker point detection and preprocessing on the calibration body projection data comprises the following steps: Extracting marker point centroid coordinates of each projection image from the calibration body projection data by using an edge detection algorithm; Performing noise suppression, distortion correction and contrast enhancement processing on the corresponding projection image based on the extracted marker point centroid coordinates to obtain a marker point centroid coordinate set of each projection image; Correcting the marker point center by using a projection matrix optimization method and a minimum marker point projection deviation target based on the marker point centroid coordinate set to obtain the preprocessed calibration body projection data.

3. The method for calibration and calibration of radiological imaging systems according to claim 1, characterized in that, The geometric parameters comprise an X-ray source position, a detector position, a puncture point coordinate and a detector rotation angle.

4. The method for calibration and calibration of radiological imaging systems according to claim 3, characterized in that, The step of analyzing geometric parameters according to the preprocessed calibration body projection data to obtain geometric parameters of a radiation imaging system comprises the following steps: Dividing multiple marker point coordinates of each projection image in the data into two layers, and fitting the projection trajectories of the marker points in each layer to obtain corresponding elliptical trajectories; Selecting marker points in the first layer, connecting the selected points with the marker points at corresponding positions in the second layer, and determining the puncture point coordinate according to the obtained multiple projection connection lines; Calculating Euler angle deflection parameters of an imaging panel of the radiation imaging system according to the double-layer elliptical trajectories; Calculating the X-ray source position and the detector position according to the puncture point coordinate and the Euler angle deflection parameters, and calculating the detector rotation angle according to the X-ray source position and the detector position.

5. The method for calibration and calibration of radiological imaging systems according to claim 4, characterized in that, The step of calculating the X-ray source position and the detector position according to the puncture point coordinate and the Euler angle deflection parameters, and calculating the detector rotation angle according to the X-ray source position and the detector position comprises the following steps: Selecting parallel marker point pairs whose projection lines intersect at a preset convergence point, and converting the projection coordinates of the parallel marker point pairs to a virtual detector coordinate system according to the puncture point coordinate and the Euler angle deflection parameters; Calculating the X-ray source position by using a preset projection formula according to the projection coordinates of the parallel marker point pairs and the corresponding positions in a real detector coordinate system; Solving the detector position by using the constraint of the X-ray source to puncture point connection line according to the puncture point coordinate and the X-ray source position; According to the X-ray source position and the detector position, a detector rotation angle is calculated by using a coordinate system projection transformation relationship between the virtual detector coordinate system and the real detector coordinate system.

6. The method for calibration and calibration of radiological imaging systems according to claim 3, characterized in that, The geometric parameter analysis according to the pre-processed calibration body projection data to obtain the geometric parameter of the radiological imaging system further includes: The multiple marker point coordinates of each frame of projection image in the data are divided into multiple layers, and the projection trajectories of the marker points in each layer are fitted to obtain corresponding elliptical trajectories; The multiple layers of elliptical trajectories are traversed, any two adjacent layers of elliptical trajectories are selected, the marker points of one layer are connected with the marker points at corresponding positions in another layer, and the piercing point coordinates corresponding to the two layers of elliptical trajectories are determined according to the multiple projection connection lines obtained; The corresponding Euler angle deflection parameters are calculated according to the two layers of elliptical trajectories; The geometric parameters corresponding to the two layers of elliptical trajectories are calculated according to the piercing point coordinates and the corresponding Euler angle deflection parameters of the two layers of elliptical trajectories; All the geometric parameters calculated according to the two layers of adjacent elliptical trajectories are counted, and the average value of the X-ray source position, the average value of the detector position and the average value of the detector rotation angle are taken according to all the calculated geometric parameters to obtain the final geometric parameter of the radiological imaging system.

7. The method for calibration and calibration of radiological imaging systems according to claim 6, characterized in that, The geometric parameters corresponding to the two layers of elliptical trajectories are calculated according to the piercing point coordinates and the corresponding Euler angle deflection parameters of the two layers of elliptical trajectories, including: A parallel marker point pair whose projection lines intersect at a preset convergence point is selected, and the projection coordinates of the parallel marker point pair are converted to a virtual detector coordinate system according to the piercing point coordinates and the Euler angle deflection parameters; According to the projection coordinates of the parallel marker point pair and the corresponding positions in the real detector coordinate system, a corresponding X-ray source position is calculated by using a preset projection formula; According to the piercing point coordinates and the X-ray source position, a corresponding detector position is solved by using the constraint of the X-ray source to piercing point connection line; According to the X-ray source position and the detector position, a corresponding detector rotation angle is calculated by using a coordinate system projection transformation relationship between the virtual detector coordinate system and the real detector coordinate system.

8. The method for calibration and calibration of radiological imaging systems according to claim 1, characterized in that, The geometric deviation of the radiological imaging system is determined by comparing the position in the geometric parameter with the expected position of the historical calibration data, and the calibrated geometric parameter is output by dynamically compensating according to the geometric deviation, including: The geometric deviation of the radiological imaging system is determined by comparing the position in the geometric parameter with the expected position of the historical calibration data; The calibrated geometric parameter is output by optimizing the geometric parameter by using the least square method according to the geometric deviation.

9. The method for calibration and calibration of radiological imaging systems according to claim 8, characterized in that, The geometric deviation of the radiological imaging system is determined by comparing the position in the geometric parameter with the expected position of the historical calibration data, including: The position in the geometric parameter is fused with the IMU sensor data and the preset key point coordinates, and the fused position is compared with the expected position of the historical calibration data to calculate the geometric deviation of the radiological imaging system; The calibrated geometric parameter is output by optimizing the geometric parameter by using the least square method according to the geometric deviation. The deviation detection threshold is dynamically adjusted based on an automatic threshold selection algorithm. The geometric deviation is compared with the deviation detection threshold, the geometric parameter greater than the deviation detection threshold is optimized by using the least square method, and the calibrated geometric parameter is output.

10. The method for calibration and calibration of radiological imaging systems according to claim 9, characterized in that, The position in the geometric parameter is fused with the IMU sensor data and the preset key point coordinates, and the fused position is compared with the expected position of the historical calibration data to calculate the geometric deviation of the radiological imaging system, including: The IMU sensor data is acquired. An angle point detection algorithm or a key point feature extraction algorithm is used to identify the preset key point coordinates in each frame of projection image. The position in the geometric parameter is fused with the IMU sensor data and the preset key point coordinates to obtain a multi-feature fused position. The fused position is compared with the expected position of the historical calibration data to calculate the geometric deviation of the radiological imaging system.

11. A system for calibration and calibration of a radiological imaging system, characterized in that, It comprises: A data acquisition module is configured to acquire calibration body projection data. A detection and preprocessing module is configured to detect and preprocess the calibration body projection data. A geometric analysis module is configured to analyze geometric parameters of a radiological imaging system according to the preprocessed calibration body projection data. A dynamic compensation module is configured to determine the geometric deviation of the radiological imaging system according to the geometric parameters, and to perform dynamic compensation according to the geometric deviation, and to output the calibrated geometric parameters.

12. A terminal, characterized by comprising: It comprises: A processor and a memory, the memory stores a radiological imaging system calibration and calibration program, and the radiological imaging system calibration and calibration program is executed by the processor to implement the operation of the radiological imaging system calibration and calibration method in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a radiological imaging system calibration and calibration program, and the radiological imaging system calibration and calibration program is executed by the processor to implement the operation of the radiological imaging system calibration and calibration method in any one of claims 1-10.

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