An imaging geometry online calibration method

Abstract

The application discloses an imaging geometry online calibration method, which comprises the following steps: scanning a marker phantom by a high-degree-of-freedom CT to obtain a first group of projection images, wherein the marker phantom comprises an upper half and a bottom part; processing the projection data based on the first group of projection images to obtain initial geometric information; removing the upper half of the marker phantom, scanning the bottom part of the marker phantom and a measured object by the high-degree-of-freedom CT to obtain a second group of projection images; correcting the initial geometric information based on a global optimization algorithm and the second group of projection images to obtain final optimized geometric information; and performing three-dimensional reconstruction based on the final optimized geometric information. The marker phantom device used in the application occupies very little imaging field and is located at the bottom of the field, so that the imaging of the measured object is not disturbed, and the engineering applicability is strong.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of X-ray computed tomography (CT) imaging technology, and particularly relates to an imaging geometry online calibration method. BACKGROUND

[0002] The X-ray based computed tomography (CT) is one of the mainstream non-destructive testing technologies at present. As shown in FIG. 1, the basic principle is that the X-ray penetrates the detected object, and due to the different absorption rates of different materials, the X-ray passing through the object will leave different counts on the pixels of the detector at different positions. All the detector pixel counts are arranged together to form a projection image. The CT obtains the projection images at different angles through the rotation of the ray source and the detector relative to the measured object on the object table, and then reconstructs the internal information of the object by using a reconstruction algorithm (such as FDK). Figure 1

[0003] The high degree of freedom CT is a new type of CT structure, which mainly uses flexible motion devices to load imaging components such as X-ray sources and flat panel detectors. During imaging, the driving device drives the X-ray source and the detector to rotate around the measured object to obtain projection data at different angles, so as to reconstruct the tomographic profile of the measured object, and to non-destructively observe the fine structure inside the measured object. The high degree of freedom CT system has the following characteristics: 1. high flexibility and high degree of freedom, which can perform multiple mode imaging; 2. it can perform large field of view imaging of large size objects, and also can realize high resolution imaging of local position of complex structure objects, and is suitable for scanning more types of objects.

[0004] Due to the flexibility of the structure, the high degree of freedom CT may have non-circular acquisition trajectory, asynchronous motion of the detector and the X-ray source, and low motion accuracy of each axis, which affects the quality of the reconstructed image, causes quality decline, produces distortion or artifacts, and cannot achieve the detection purpose. Accurate and effective imaging geometry calibration is one of the key technologies of high degree of freedom CT imaging, which can ensure the accuracy of data acquisition.

[0005] The existing calibration method is mainly an offline correction method based on a marker phantom, as shown in FIG. 2. A standard marker phantom (such as a marker ball) is placed in the field of view for a CT scan, and the correction method based on the projection matrix of the correction phantom is used to express the projection geometry parameters at a single angle as a homogeneous matrix. Through the one-to-one correspondence between the marker points in the phantom and the corresponding two-dimensional projection points at a single angle, the projection matrix at the corresponding angle is calculated. In the process of back projection reconstruction, the geometric information contained in the projection matrix is used to calculate the corresponding back projection position information, so that the position information is used for the reconstruction of the actual measured object. Figure 2

[0006] ​​The above-mentioned offline calibration method is suitable for situations where the repetitive positioning accuracy of the high-degree-of-freedom CT acquisition system's motion axis is very high. However, in reality, when the high-degree-of-freedom CT acquisition system's motion axis repeats, the geometric information of the two repeated movements is unlikely to be completely consistent. The deviation between the two repetitive movements depends on the repetitive positioning accuracy of the high-degree-of-freedom CT acquisition system's motion axis. When this accuracy fails to meet the requirements of high-resolution imaging accuracy, that is, when the positioning accuracy error differs significantly from the size of the reconstructed pixel, it may be impossible to use the geometric information obtained from a single marker scan to reconstruct the projection data for each subsequent scan. In this case, the offline calibration method is no longer applicable, and it is necessary to develop an online correction method that can be applied to real-time acquisition. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides an online calibration method for imaging geometry, which for the first time proposes a feasible solution to the problem of online calibration of high-degree-of-freedom CT imaging geometry, and obtains good correction effects through algorithm verification.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for online calibration of imaging geometry, comprising:

[0010] Step 1: Scanning a marker phantom using a high-degree-of-freedom CT scan to obtain a first set of projection images, wherein the marker phantom includes an upper portion and a bottom portion, each of which includes a standard small ball made of a metal material;

[0011] Step 2: Process the projection data based on the first set of projection images to obtain initial geometric information;

[0012] Step 3: Remove the upper half of the marker phantom, and scan the bottom of the marker phantom and the object under test using the high-degree-of-freedom CT to obtain a second set of projection images;

[0013] Step 4: Based on the global optimization algorithm and the second set of projection images, the initial geometric information is corrected to obtain the final optimized geometric information;

[0014] Step 5: Perform 3D reconstruction based on the final optimized geometric information.

[0015] Furthermore, the upper part and the bottom part of the marker phantom are mounted together by a rotating buckle, and the standard balls are distributed at equal intervals along a spiral line.

[0016] Furthermore, the step 2 includes: identifying the center of the standard sphere on the first set of projection images, and obtaining the position coordinates pt of the center of each standard sphere in the projection image in the detector coordinate system:

[0017] ,

[0018] It represents the coordinates of the center of the standard ball on one of the projections in the first set of projections;

[0019] Define the geometric information calculated for each projection as twelve-dimensional:

[0020] ,

[0021] Where S, D, U, and V are three-dimensional geometric parameters defined in the three-dimensional reconstruction coordinate system. U and V are two vectors that determine the detector posture and have a modulus of one detector pixel length. S is the coordinate of the ray source, and D is the coordinate of the lower left pixel of the detector.

[0022] Define vpos as the three-dimensional coordinates of the center of the standard ball in the marker phantom in the three-dimensional reconstruction coordinate system:

[0023] ,

[0024] The projection matrix P of the center of the standard ball projected onto the detector coordinate system is defined as:

[0025] ,

[0026] ,

[0027] Where, the superscript T represents the transpose of the matrix;

[0028] Set the intermediate vector w to:

[0029] ,

[0030] Calculate the projection coordinates pt_proj of the center of the standard ball in the detector coordinate system:

[0031] ,

[0032] Based on the assumption that the projection point coordinate pt_proj and the position coordinate pt are the same point, initial geometric information is obtained by calculating each projection image of the first group of projection images.

[0033] Furthermore, step 3 includes: separating the upper part of the marker phantom and moving it out of the field of view, placing the object to be measured on the bottom part of the marker phantom, and performing a second scan with the high-degree-of-freedom CT to obtain a second set of projection images.

[0034] Furthermore, the step 4 includes:

[0035] Obtain the center position pt' of each standard ball in the bottom part of the phantom in the second group of projections projected onto the detector coordinate system;

[0036] Take the geometric information of the second set of projections that are generated in the same time order as the projections in the first set of projections as input, and process them again according to the data processing method in step 2 to calculate the projection point coordinates pt_proj' of the center of the standard ball of the bottom part of the phantom on the detector coordinate system, and calculate the Euclidean distance between pt' and pt_proj' :

[0037] ,

[0038] The target function ObjectFunc is obtained by summing the Euclidean distance pterror calculated for all standard balls in the bottom part:

[0039] ,

[0040] For each projection image in the second group of projection images, the initial value of the geometric information dimension with poor repeatability of the high-degree-of-freedom CT system is selected as the input variable of the optimization algorithm, and the variation range of each variable is specified. The initial values ​​of other geometric information dimensions are used as input constants. The value of the variable is changed in each iteration, and the objective function value is calculated. When the objective function value converges below a certain value, the changed geometric information is output to obtain the final optimized geometric information.

[0041] Furthermore, step 5 includes performing three-dimensional reconstruction on the second set of projection images using the FDK algorithm according to the final optimized geometric information and the initial geometric information.

[0042] The beneficial effects of the present invention are:

[0043] The present invention proposes an online calibration method for imaging geometry. This method is based on global iterative optimization of key parameters of high-degree-of-freedom CT. The marker phantom device used occupies very little of the imaging field of view and is located at the bottom of the field of view, without any interference with the imaging of the object being measured. The method is suitable for online calibration of high-precision imaging of CT systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the structure of a CT system in the prior art;

[0045] Figure 2 Schematic diagram of the principle of the marker-based CT geometric information analysis method in the prior art;

[0046] Figure 3 Schematic diagram of the standard marker motif;

[0047] Figure 4A flow chart of a CT imaging geometry online calibration method of the present application;

[0048] Figure 5 A schematic diagram of a phantom projection image;

[0049] Figure 6 A schematic diagram of a detector coordinate system;

[0050] Figure 7 A schematic diagram of a posture of deflection of the detector in different directions;

[0051] Figure 8 A schematic diagram of a reconstruction result comparison. DETAILED DESCRIPTION

[0052] The present application is further described below in conjunction with the accompanying drawings and examples.

[0053] The present application provides a specific structure of a marker phantom used in an imaging geometry online calibration method as shown in Figure 3 The marker phantom includes an upper half and a bottom part, both of which are cylindrical bodies containing standard balls, and the two cylindrical bodies are mounted together by a rotating buckle. The cylindrical bodies are generally made of resin material, and the standard balls are made of metal material, as shown in Figure 3 The upper cylindrical body contains 7-9 metal balls, and the bottom part cylindrical body contains 2-4 metal balls. The distribution requirement of the metal balls is that any two circles in the projection image obtained by CT scanning cannot block each other, and the specific arrangement is determined according to the CT field of view. Generally, the metal balls are distributed in a spiral and equally spaced upward manner. Without loss of generality, the external structure shape loaded with the metal balls is not limited to a cylindrical body, and the external structure does not affect the specific implementation of the present application. Before imaging, the marker phantom needs to be three-dimensionally calibrated to establish a three-dimensional coordinate system of the marker phantom and obtain the ball center coordinates of all the standard balls in the marker phantom. The calibration process is as follows: the marker phantom is scanned by a high degree of freedom CT during imaging, and then the ball center coordinates of all the standard balls calibrated and the circle center coordinates in the projection image are combined to obtain the imaging geometry information. During online calibration, the bottom part of the phantom is kept stationary, the upper half is removed, and the measured object is placed above the bottom part. The high degree of freedom CT scans the bottom part and the measured object at the same time. The ball center coordinates of the standard balls in the bottom part and the circle center coordinates in the projection are used as input data to perform online correction through a corresponding algorithm. The advantages of the device are as follows: the entire marker phantom is scanned only once before calibration, which does not bring about tedious calibration steps; during online real-time calibration, only the bottom part participates in imaging, which occupies a very small field of view and does not interfere with the imaging of the measured object, and the engineering applicability is stronger.

[0054] As shown in Figure 4As shown, the present invention provides a schematic diagram of an online calibration method for imaging geometry. First, the entire marker phantom is scanned and analyzed to obtain a set of initial geometric information. The geometric information includes: the coordinates of a certain pixel of the detector in the three-dimensional coordinate system of the marker phantom calibration, the coordinates of the ray source, and two vertical vectors that determine the surface of the detector pixel array. During online correction, the upper half of the phantom is separated, and the object to be measured can be placed on the bottom part. Based on the previous initial geometric information, the projection of this scan and the coordinates of the center of the standard sphere in the bottom part are used as input to set the optimized objective function value. The initial geometric information is modified by the corresponding algorithm to obtain imaging geometric information that is more suitable for this scan. The algorithm used is a global optimization algorithm, which aims to find the global optimal value of the function and is usually used to deal with optimization problems with multiple local minima. Specifically:

[0055] Step 1: Scan the entire marker phantom using high-degree-of-freedom CT.

[0056] The geometry of a high-degree-of-freedom CT imaging system allows for flexible and precise imaging of the object being measured. The X-ray source and detector complete projection data acquisition with a single rotation around the object. CT reconstruction algorithms then generate three-dimensional data of the object. Using this CT system, the aforementioned marker phantom can be scanned to produce the first set of projection images.

[0057] Step 2: Processing the projection data based on the first set of projection images;

[0058] The center of the standard ball is identified on the first set of projections, where the projections are as follows: Figure 5 As shown. Get the position coordinate pt of the center of each standard ball in the projection image in the detector coordinate system:

[0059] ,

[0060] It represents the coordinates of the center of the standard ball on one of the projections in the first set of projections;

[0061] The detector coordinate system diagram is as follows Figure 6 As shown, each point represents a pixel in the detector.

[0062] The geometric information calculated for each projection is defined as twelve-dimensional, specifically:

[0063] ,

[0064] Among them, S, D, U, V are three-dimensional geometric parameters defined in the three-dimensional reconstruction coordinate system, U, V are two vectors that determine the detector posture and whose modulus is the length of one detector pixel (expressed as u, v in the detector coordinate system, such as Figure 7 S is the coordinate of the source and D is the coordinate of the left bottom pixel of the detector.

[0065] Define vpos as the 3D coordinate of the center of the standard ball in the phantom in the 3D reconstruction coordinate system:

[0066] ,

[0067] Define the projection matrix P of the center of the ball onto the detector coordinate system as:

[0068] ,

[0069] ,

[0070] In the formula, the superscript T represents the transpose of the matrix.

[0071] Set the intermediate vector w as:

[0072] ,

[0073] Calculate the projection point coordinate pt_proj of the center of the ball in the detector coordinate system:

[0074] ,

[0075] For seven or more balls in the phantom, jointly establish an analytical formula, wherein it is assumed that the projection point coordinates pt_proj and pt are the same point, i.e., the geometric information (S, D, U, V) corresponding to the projection diagram can be calculated through the projection matrix relationship between the projection diagram center and the center of the ball. Calculate all the projection diagrams in the first step to obtain the initial geometric information.

[0076] Step 3, scan the bottom part of the marker phantom and the measured object by the CT with high degree of freedom;

[0077] Separate the upper part of the marker phantom and move it out of the field of view, while the bottom part of the phantom remains stationary. Place the measured object to be detected on the bottom part of the marker phantom, and the CT performs a second scan again to obtain a second set of projection diagrams.

[0078] Step 4, correct the initial geometric information based on a global optimization algorithm;

[0079] Select the dimensions of geometric information that need to be changed: From the twelve-dimensional geometric information, select the dimensions that may change from the initial geometric information in this scan and have a significant impact on the quality of the reconstructed image. The selection criteria are the dimensions with the most significant statistical changes in the geometric information experiments performed on the CT system. The geometric information dimensions with poor repeatability may be different for each CT system, depending on the specific equipment. The changes in the two posture vectors U and V of the detector are abstracted into changes in three posture angles, such as Figure 7 As shown in the figure, the first attitude angle θ is the angle of rotation of the U vector around the V vector; the second attitude angle : The angle of rotation of the V vector around the U vector; the third attitude angle : The angle of rotation of the U and V vectors around the normal vector of the face they are on.

[0080] Get the position pt' of each center of the bottom part of the phantom in the detector coordinate system in the second set of projections,

[0081] Take the geometric information of the second set of projections and the first set of projections that are generated in the same time order as the input, and process them again according to the data processing method in step 2 to calculate the projection point pt_proj' of the sphere center of the bottom part of the phantom on the detector coordinate system. Calculate the Euclidean distance between pt' and pt_proj' :

[0082] ,

[0083] The objective function ObjectFunc is obtained by summing the pterrors calculated for the n standard balls at the bottom. The ultimate goal of the optimization is to make the value of the objective function closer to 0:

[0084] ,

[0085] The optimization algorithm is a global one. Initial values ​​for geometric information dimensions with poor repeatability in the CT system are used as input variables, with a specified range for each variable. Initial values ​​for the other dimensions are used as constant inputs. The variable values ​​are modified with each iteration, and the objective function value is calculated. During the iteration, when the objective function value converges below a certain value, the modified geometric information is output. This process is repeated for each projection in the second set of projections to obtain the final optimized geometric information.

[0086] Step 5: Perform 3D reconstruction based on the final optimized geometric information;

[0087] The second set of projection images is reconstructed using the FDK algorithm based on the final optimized geometric information and the initial geometric information. The reconstruction results before and after correction are compared. Figure 8As shown, the left picture is the initial geometry information reconstruction before correction, and the right picture is the geometry information reconstruction after correction. It can be seen that the edge part of the reconstructed image of the corrected geometry information is clearer.

[0088] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are merely examples of the present application and are not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An imaging geometry online calibration method, characterized in that: The method comprises: Step 1: Scanning a marker phantom using a high-degree-of-freedom CT scan to obtain a first set of projection images, wherein the marker phantom includes an upper portion and a bottom portion, each of which includes a standard small ball made of a metal material; Step 2: Process the projection data based on the first set of projection images to obtain initial geometric information; Step 3: removing the upper portion of the marker phantom, and scanning the bottom portion of the marker phantom and the object under test using the high-degree-of-freedom CT to obtain a second set of projection images; Step 4: Based on the global optimization algorithm and the second set of projection images, the initial geometric information is corrected to obtain the final optimized geometric information; Step 5: Perform 3D reconstruction based on the final optimized geometric information; Wherein, in step 1, the upper part and the bottom part of the marker phantom are mounted together by a rotating buckle, and the standard balls are distributed at equal intervals along a spiral line; The step 2 includes: identifying the center of the standard sphere on the first set of projection images, and obtaining the position coordinates pt of the center of each standard sphere in the projection image in the detector coordinate system: ， It represents the coordinates of the center of the standard ball on one of the projections in the first set of projections; Define the geometric information calculated for each projection as twelve-dimensional: ， Where S, D, U, and V are three-dimensional geometric parameters defined in the three-dimensional reconstruction coordinate system. U and V are two vectors that determine the detector posture and have a modulus of one detector pixel length. S is the coordinate of the ray source, and D is the coordinate of the lower left pixel of the detector. Define vpos as the three-dimensional coordinates of the center of the standard ball in the marker phantom in the three-dimensional reconstruction coordinate system: ， The projection matrix P of the center of the standard ball projected onto the detector coordinate system is defined as: ， ， Where, the superscript T represents the transpose of the matrix; Set the middle vector w to: ， Calculate the projection coordinates pt_proj of the center of the standard ball in the detector coordinate system: ， Based on the assumption that the projection point coordinate pt_proj and the position coordinate pt are the same point, the initial geometric information is calculated for each projection image of the first group of projection images; The step 3 includes: separating the upper portion of the marker phantom and moving it out of the field of view, placing the object to be measured on the bottom portion of the marker phantom, and performing a second scan with the high-degree-of-freedom CT to obtain a second set of projection images; The step 4 comprises: Obtain the center position pt' of each standard ball in the bottom part of the phantom in the second group of projections projected onto the detector coordinate system; Take the geometric information of the second set of projections that are generated in the same time order as the projections in the first set of projections as input, and process them again according to the data processing method in step 2 to calculate the projection point coordinates pt_proj' of the center of the standard ball of the bottom part of the phantom on the detector coordinate system, and calculate the Euclidean distance between pt' and pt_proj' : ， The target function ObjectFunc is obtained by summing the Euclidean distance pterror calculated for all standard balls in the bottom part: ， For each projection image in the second group of projection images, the initial value of the geometric information dimension with poor repeatability of the high-degree-of-freedom CT system is selected as the input variable of the optimization algorithm, and the variation range of each variable is specified. The initial values ​​of other geometric information dimensions are used as input constants. The value of the variable is changed in each iteration, and the objective function value is calculated. When the objective function value converges below a certain value, the changed geometric information is output to obtain the final optimized geometric information.

2. The method for online calibration of imaging geometry according to claim 1, characterized in that: The step 5 includes performing three-dimensional reconstruction on the second set of projection images using the FDK (Feldkamp, ​​Davis and Kress) algorithm according to the final optimized geometric information and the initial geometric information.

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