X-ray image reconstruction method, reconstruction system and computer readable storage medium

Abstract

The reconstruction method of X-ray images includes S10 to S50. S10: Obtain the measured light intensity value of each pixel of the detector in each X-ray imaging of the object to be imaged in several X-ray imagings. S40: Calculate the corrected linear attenuation coefficient of each pixel of the detector according to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in each X-ray imaging and the measured light intensity value of each pixel of the detector in each X-ray imaging. S50: Generate an X-ray image according to the corrected linear attenuation coefficient of each pixel of the detector. The reconstruction method can correct the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field. In addition, a computer-readable storage medium and an X-ray image reconstruction system are provided.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging, and in particular to a method for reconstructing an X-ray image, and a reconstruction system and a computer-readable storage medium for implementing the reconstruction method. Background Art

[0002] During the X-ray imaging process, the device generates a cone-shaped X-ray irradiation field. Due to the cone-shaped characteristics of the X-ray irradiation field, if the image is directly reconstructed based on the measured light intensity values ​​of each pixel of the detector, the resulting image will have a magnification effect, and this magnification effect gradually increases from the center to the edge of the object to be imaged, resulting in an inability to accurately reflect the internal structure of the object. Summary of the invention

[0003] The object of the present invention is to provide a method for reconstructing an X-ray image, which can correct the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0004] Another object of the present invention is to provide a computer-readable storage medium that can be used to correct the geometric distortion of an X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0005] Another object of the present invention is to provide an X-ray image reconstruction system that is capable of correcting the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0006] The present invention provides a method for reconstructing an X-ray image, which includes S10 to S50. S10: Obtain the measured light intensity value of each pixel of the detector in each of several X-ray imagings of the object to be imaged, wherein the posture and position of the object to be imaged in the several X-ray imagings remain unchanged, and the positions of the X-ray focal points of the several X-ray imagings are different. S40: According to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in each X-ray imaging and the measured light intensity value of each pixel of the detector in each X-ray imaging, calculate the corrected linear attenuation coefficient of each pixel of the detector, the corrected linear attenuation coefficient of each pixel of the detector is the linear attenuation coefficient of the X-ray beam passing through the analysis area in a direction perpendicular to the detection plane to reach the pixel of the detector. S50: Generate an X-ray image according to the corrected linear attenuation coefficient of each pixel of the detector.

[0007] The X-ray image reconstruction method can correct the geometric distortion of the X-ray image caused by the cone characteristic of the X-ray irradiation field.

[0008] In another exemplary embodiment of the X-ray image reconstruction method, S40 includes S41 and S42. S41: Calculate the actual linear attenuation coefficient of each pixel of the detector in each X-ray imaging according to the measured light intensity value of each pixel of the detector in each X-ray imaging. S42: Calculate the corrected linear attenuation coefficient of each pixel of the detector according to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in each X-ray imaging and the actual linear attenuation coefficient of each pixel of the detector in each X-ray imaging using an algebraic reconstruction algorithm. The corrected linear attenuation coefficient of each pixel of the detector can be calculated using the algebraic reconstruction algorithm when there is less data or the data quality is poor, thereby improving the applicability of the reconstruction method.

[0009] In another exemplary implementation of the X-ray image reconstruction method, S42 includes S421 to S423.

[0010] S421: Establish the following formula (1) for each X-ray imaging:

[0011] Formula (1),

[0012] in, is the coefficient matrix of the algebraic reconstruction algorithm, which is The matrix of the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in the secondary X-ray imaging, It is The matrix of the actual linear attenuation coefficients of each pixel of the detector in the secondary X-ray imaging, is the matrix of unit attenuation factors for each finite element.

[0013] S422: Establish the following formula (2):

[0014] Formula (2),

[0015] in, is the matrix of the propagation distance of the X-ray beam in each finite element of the analysis area in the process of passing through the analysis area in a direction perpendicular to the detection plane to reach each pixel of the detector, and , is the matrix of corrected linear attenuation coefficients for each pixel of the detector, is the matrix of unit attenuation factors for each finite element.

[0016] S423: Define the transformation function according to formula (1) and formula (2): , thus solving ,get This makes calculations easier.

[0017] In another exemplary embodiment of the X-ray image reconstruction method, the X-ray focal points of several X-ray images are located on the same straight line perpendicular to the detection plane, thereby simplifying subsequent geometric operations.

[0018] In another exemplary embodiment of the X-ray image reconstruction method, before S40, S20 and S30 are also included. S20: Determine a spatial region including a portion of the object to be imaged that is irradiated in several X-ray imagings as an analysis region. S30: Mesh the analysis region to form several finite elements.

[0019] In another exemplary embodiment of the X-ray image reconstruction method, S20 includes S21 and S22. S21: Acquire the entire or partial boundary of the irradiated portion of the object to be imaged, referred to as the reference boundary. S22: Determine the spatial region as the analysis region according to the reference boundary, and use the reference boundary as at least a part of the boundary of the analysis region. This helps to narrow the scope of the analysis region and reduce the subsequent amount of calculation.

[0020] In another exemplary embodiment of the X-ray image reconstruction method, the method of obtaining all or part of the boundary of the irradiated part of the object to be imaged includes radar imaging, laser scanning, structured light scanning, stereo vision and / or time-of-flight method, which can be easily implemented.

[0021] In another exemplary embodiment of the X-ray image reconstruction method, the grid unit of the grid division is a rectangular body, wherein a set of opposite faces of the rectangular body are parallel to the detection plane, thereby simplifying subsequent geometric operations.

[0022] In another exemplary embodiment of the X-ray image reconstruction method, S50 includes S51 and S52. S51: Calculate the corrected light intensity value of each pixel of the detector according to the corrected linear attenuation coefficient of each pixel of the detector. S52: Generate an X-ray image according to the corrected light intensity value of each pixel of the detector.

[0023] In another exemplary implementation of the X-ray image reconstruction method, in S51, the corrected light intensity value of each pixel of the detector is calculated by the following formula (3):

[0024] Formula (3),

[0025] in, is the matrix of incident light intensity values ​​corresponding to each pixel of the detector, is the matrix of corrected light intensity values ​​for each pixel of the detector, is the matrix of corrected linear attenuation coefficients for each pixel of the detector.

[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned X-ray image reconstruction method can be implemented, thereby correcting the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0027] The present invention also provides an X-ray image reconstruction system, including a storage processing unit. The storage processing unit includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the above-mentioned X-ray image reconstruction method can be implemented. The X-ray image reconstruction system can correct the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The following drawings are only used to schematically illustrate and explain the present invention, and do not limit the scope of the present invention.

[0029] Figure 1 The present invention is a flow chart of an exemplary embodiment of a method for reconstructing an X-ray image.

[0030] Figure 2 Used to schematically illustrate the positions of the X-ray focal points of two X-ray images.

[0031] Figure 3 A schematic method for illustrating the determination of the analysis area.

[0032] Figure 4 for Figure 1 The flowchart of S42 of the reconstruction method is shown.

[0033] Description of symbols

[0034] 10 Object to be imaged

[0035] 30 3D Camera

[0036] S Detection plane

[0037] F1 First X-ray Focus

[0038] F2 Second X-ray focus

[0039] B Reference Boundary

[0040] A Analysis area DETAILED DESCRIPTION

[0041] In order to have a clearer understanding of the technical features, purposes and effects of the invention, the specific embodiments of the present invention are now described with reference to the accompanying drawings. The same reference numerals in the drawings represent components with the same structure or similar structures but the same functions.

[0042] In this document, “exemplary” means “serving as an example, instance or illustration”, and any diagram or implementation described in this document as “exemplary” should not be interpreted as a more preferred or more advantageous technical solution.

[0043] In this article, "first", "second", etc. do not indicate their importance or order, but are only used to indicate the difference between each other for the convenience of document description.

[0044] In order to simplify the drawings, each figure only schematically shows the parts related to the present invention, which do not represent the actual structure of the product.

[0045] Figure 1 FIG. 1 is a flow chart of an exemplary implementation of a method for reconstructing an X-ray image. Figure 1 As shown, the X-ray image reconstruction method includes the following S10 to S50.

[0046] S10: obtaining the measured light intensity value of each pixel of the detector of the object to be imaged in each of the X-ray imaging of the plurality of X-ray imaging, wherein the posture and position of the object to be imaged in the plurality of X-ray imaging are unchanged, and the positions of the X-ray focal points of the plurality of X-ray imaging are different. The unchanged posture and position of the object to be imaged refers to the unchanged posture and position of the object to be imaged relative to the detection plane.

[0047] Specifically, in the exemplary embodiment, the X-ray focal points of several X-ray images are, for example, located on the same straight line perpendicular to the detection plane. Figure 2 In the figure, two X-ray imagings are taken as an example, showing the first X-ray focus F1 of the first X-ray imaging and the second X-ray focus F2 of the second X-ray imaging. The first X-ray focus F1 and the second X-ray focus F2 are located on the same straight line L perpendicular to the detection plane S. This helps to simplify subsequent geometric operations.

[0048] S20: Determine a spatial region including the portion of the object to be imaged that is irradiated in a plurality of X-ray imagings as an analysis region. Specifically, S20 includes, for example, the following S21 and S22.

[0049] S21: Acquire the entire or partial boundary of the irradiated portion of the object to be imaged, which is called a reference boundary. The reference boundary is, for example, a three-dimensional boundary surface, but is not limited thereto.

[0050] S22: Determine a spatial region as an analysis region according to a reference boundary, so that the reference boundary serves as at least a part of a boundary of the analysis region.

[0051] This helps to reduce the scope of the analysis area and the amount of subsequent calculations. In particular, when the entire boundary of the irradiated portion of the object to be imaged can be obtained, the scope defined by the entire boundary can be used as the analysis area, so that the analysis area determined is the smallest.

[0052] In an exemplary embodiment, the method of obtaining the entire or partial boundary of the irradiated part of the object to be imaged is, for example, radar imaging, laser scanning, structured light scanning, stereo vision or time flight method, etc. Of course, these directions can also be used in combination. Radar imaging method: By measuring the time delay and phase information of the signal, combined with complex signal processing technology, the surface shape and position of the object can be effectively obtained. Laser scanning method: Using laser radar or three-dimensional laser scanner, a laser beam is emitted to the surface of the object, and the three-dimensional information of the surface of the object is accurately calculated by measuring the time or angle of the laser reflection back. Structured light scanning method: Using a structured light three-dimensional scanner, a known grating or pattern is projected onto the surface of the object, and a camera is used to capture the deformation of the pattern, and the three-dimensional contour of the object is calculated by an algorithm. Stereo vision method: Using a binocular or multi-camera system, the object is photographed from different angles, and the depth information of the surface of the object is calculated by triangulation to generate a three-dimensional contour. Time-of-Flight (ToF): Using a ToF camera, a light pulse is emitted to the surface of the object, and the distance of each point on the surface of the object is calculated by measuring the time difference of the light pulse returning, thereby generating a three-dimensional contour.

[0053] like Figure 3 As shown, in an exemplary implementation of the stereoscopic vision method, for example, a reference boundary B ( Figure 3 The reference boundary B is used as the analysis area A ( Figure 3 The rest of the boundary of the analysis area A can be determined according to the blind area of ​​the field of view of the 3D camera 30 (i.e., the area blocked by the boundary B on the upper side of the detection plane S), so that the analysis area A includes the blind area of ​​the field of view.

[0054] In other exemplary embodiments, if the boundary of the irradiated portion of the object to be imaged is not considered, the entire X-ray irradiation field may be used as the analysis area, which will increase the amount of calculation.

[0055] S30: Mesh the analysis area to form a plurality of finite elements. The mesh unit of the mesh division is, for example, a rectangular body, wherein a set of opposite faces of the rectangular body are parallel to the detection plane, thereby simplifying subsequent geometric operations, but not limited thereto. In other exemplary embodiments, the mesh unit of the mesh division may also be in other forms.

[0056] S40: According to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area and the measured light intensity value of each pixel of the detector in each X-ray imaging, the corrected linear attenuation coefficient of each pixel of the detector is calculated. The corrected linear attenuation coefficient of each pixel of the detector is the linear attenuation coefficient of the X-ray beam passing through the analysis area in a direction perpendicular to the detection plane to reach the pixel of the detector.

[0057] For the convenience of calculation, for example, the incident X-ray beam of each pixel of the detector can be represented by a straight line from the X-ray focus to the center point of the pixel. On this basis, the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area can be obtained through geometric calculation.

[0058] Specifically, S40 includes, for example, the following S41 and S42.

[0059] S41: Calculate the actual linear attenuation coefficient of each pixel of the detector in each X-ray imaging according to the measured light intensity value of each pixel of the detector in each X-ray imaging.

[0060] S42: According to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area and the actual linear attenuation coefficient of each pixel of the detector in each X-ray imaging, the corrected linear attenuation coefficient of each pixel of the detector is calculated using an algebraic reconstruction algorithm.

[0061] Specifically, Figure 4 As shown, in the exemplary embodiment, S42 includes, for example, the following S421 to S423.

[0062] S421: Establish the following formula (1) for each X-ray imaging:

[0063] Formula (1),

[0064] in, is the coefficient matrix of the algebraic reconstruction algorithm, which is The matrix of the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in the secondary X-ray imaging, It is The matrix of the actual linear attenuation coefficients of each pixel of the detector in the secondary X-ray imaging, is the matrix of unit attenuation factors for each finite element.

[0065] Taking two X-ray images as an example, we can establish and .

[0066] S422: Establish the following formula (2):

[0067] Formula (2),

[0068] in, is the matrix of the propagation distance of the X-ray beam in each finite element of the analysis area in the process of passing through the analysis area in a direction perpendicular to the detection plane to reach each pixel of the detector, and , is the matrix of corrected linear attenuation coefficients for each pixel of the detector, is the matrix of unit attenuation factors for each finite element.

[0069] S423: Define the transformation function according to formula (1) and formula (2): , thus solving ,get .

[0070] Taking two X-ray images as an example, the transformation function is defined according to formula (1) and formula (2): , thus solving ,get .

[0071] It can be understood that in the X-ray image reconstruction method, the more times the X-ray imaging is used, the more The more accurate the value.

[0072] However, it is not limited to this. In other exemplary embodiments, the unit attenuation factor of each finite element can be calculated based on the propagation distance of the incident X-ray beam of each pixel of the detector in each X-ray imaging within each finite element of the analysis area and the measured light intensity value of each pixel of the detector in each X-ray imaging, and then the corrected linear attenuation coefficient of each pixel of the detector can be calculated based on the unit attenuation factor of each finite element.

[0073] S50: Generate an X-ray image according to the corrected linear attenuation coefficient of each pixel of the detector.

[0074] Specifically, S50 includes, for example, the following S51 and S52.

[0075] S51: Calculate the corrected light intensity value of each pixel of the detector according to the corrected linear attenuation coefficient of each pixel of the detector.

[0076] Specifically, in S51, the corrected light intensity value of each pixel of the detector is calculated by, for example, the following formula (3):

[0077] Formula (3),

[0078] in, is a matrix of incident light intensity values ​​(i.e., incident light intensity values) corresponding to each pixel of the detector. is the matrix of corrected light intensity values ​​for each pixel of the detector, is the matrix of corrected linear attenuation coefficients for each pixel of the detector.

[0079] S52: Generate an X-ray image according to the corrected light intensity value of each pixel of the detector. Generating an X-ray image according to the light intensity value can be achieved using existing methods, which will not be described in detail here.

[0080] The X-ray image reconstruction method calculates the corrected linear attenuation coefficient of each pixel of the detector according to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in each X-ray imaging and the measured light intensity value of each pixel of the detector in each X-ray imaging, and generates an X-ray image according to the corrected linear attenuation coefficient of each pixel of the detector, thereby correcting the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0081] The present invention also provides a computer-readable storage medium, in one exemplary embodiment of which a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned X-ray image reconstruction method can be implemented, thereby correcting the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0082] The present invention also provides an X-ray image reconstruction system. In an exemplary embodiment thereof, the reconstruction system includes a storage processing unit. The storage processing unit includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the above-mentioned X-ray image reconstruction method can be implemented. The X-ray image reconstruction system can correct the geometric distortion of the X-ray image caused by the conical characteristics of the X-ray irradiation field.

[0083] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0084] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation scheme or changes that do not deviate from the technical spirit of the present invention, such as combination, division or repetition of features, should be included in the scope of protection of the present invention.

Claims

1. A method for reconstructing an X-ray image, characterized in that: include: S10: obtaining a measured light intensity value of each pixel of the detector of the object to be imaged in each of the multiple X-ray imagings, wherein the posture and position of the object to be imaged in the multiple X-ray imagings remain unchanged, and the positions of the X-ray focal points of the multiple X-ray imagings are different; S40: Calculate the corrected linear attenuation coefficient of each pixel of the detector according to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in each X-ray imaging and the measured light intensity value of each pixel of the detector in each X-ray imaging, wherein the corrected linear attenuation coefficient of each pixel of the detector is the linear attenuation coefficient of the X-ray beam passing through the analysis area in a direction perpendicular to the detection plane to reach the pixel of the detector; as well as S50: generating an X-ray image according to the corrected linear attenuation coefficient of each pixel of the detector; Wherein, the S40 includes: S41: Calculating the actual linear attenuation coefficient of each pixel of the detector in each X-ray imaging according to the measured light intensity value of each pixel of the detector in each X-ray imaging; and S42: According to the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in each X-ray imaging and the actual linear attenuation coefficient of each pixel of the detector in each X-ray imaging, the corrected linear attenuation coefficient of each pixel of the detector is calculated using an algebraic reconstruction algorithm.

2. The X-ray image reconstruction method according to claim 1, characterized in that: The S42 includes: S421: Establish the following formula (1) for each X-ray imaging: A n · μ = P n Formula (1) Among them, A n is the coefficient matrix of the algebraic reconstruction algorithm, which is the matrix of the propagation distance of the incident X-ray beam of each pixel of the detector in each finite element of the analysis area in the nth X-ray imaging, P n is the matrix of the actual linear attenuation coefficients of each pixel of the detector in the nth X-ray imaging, and μ is the matrix of the unit attenuation factors of each finite element; S422: Establish the following formula (2): A·μ=P Formula (2) Where A is the matrix of propagation distances of the X-ray beam in each finite element of the analysis area when it passes through the analysis area in a direction perpendicular to the detection plane and reaches each pixel of the detector, and P is the matrix of the corrected linear attenuation coefficients of each pixel of the detector, μ is the matrix of the unit attenuation factors of each finite element; and S423: Define the transformation function according to formula (1) and formula (2): F(A1, A2, ..., A n ) = f -1 (A), and thus solve P = F (P1, P2, ..., P n ), and get P.

3. The X-ray image reconstruction method according to claim 1, characterized in that: The X-ray focal points of several X-ray images are located on the same straight line perpendicular to the detection plane.

4. The X-ray image reconstruction method according to claim 1, characterized in that: Before the S40, the method further includes: S20: determining a spatial region including a portion of the object to be imaged that is irradiated in a plurality of X-ray imagings as an analysis region; and S30: Meshing the analysis area to form a plurality of finite elements.

5. The X-ray image reconstruction method according to claim 4, characterized in that: The S20 includes: S21: Acquire the entire or partial boundary of the illuminated portion of the object to be imaged, referred to as a reference boundary; and S22: Determine a spatial region as the analysis region according to the reference boundary, so that the reference boundary serves as at least a part of a boundary of the analysis region.

6. The X-ray image reconstruction method according to claim 5, characterized in that: Methods for acquiring all or part of the boundary of the illuminated portion of the object to be imaged include radar imaging, laser scanning, structured light scanning, stereo vision and / or time-of-flight methods.

7. The X-ray image reconstruction method according to claim 6, characterized in that: The grid units of the grid division are rectangular bodies, wherein a set of opposite faces of the rectangular bodies are parallel to the detection plane.

8. The X-ray image reconstruction method according to claim 1, characterized in that: The S50 includes: S51: Calculating the corrected light intensity value of each pixel of the detector according to the corrected linear attenuation coefficient of each pixel of the detector; and S52: Generate an X-ray image according to the corrected light intensity value of each pixel of the detector.

9. The X-ray image reconstruction method according to claim 8, characterized in that: In S51, the corrected light intensity value of each pixel of the detector is calculated by the following formula (3): I = I0e -P Formula (3) Among them, I0 is the matrix of incident light intensity values ​​corresponding to each pixel of the detector, I is the matrix of the corrected light intensity values ​​of each pixel of the detector, and P is the matrix of the corrected linear attenuation coefficients of each pixel of the detector.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the X-ray image reconstruction method according to any one of claims 1 to 9 can be implemented.

11. A system for reconstructing an X-ray image, characterized in that It comprises a storage processing unit, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it can implement the X-ray image reconstruction method described in any one of claims 1 to 9.

Images