A fast stitching method for CBCT vertical scanning

Abstract

The application belongs to the technical field of image processing, and discloses a fast splicing method for CBCT vertical scanning, which comprises the following steps: step S1, a plurality of scanning positions of a rotating platform are arranged along the Z direction, and a scanning object is scanned at each scanning position to obtain scanning data; step S2, the angle offset of adjacent scanning positions is calculated according to the scanning data; volume reconstruction is performed on each scanning position; step S3, a common reconstructed slice image at the adjacent scanning positions is extracted, and the feature points of the slice image are determined by using a SIFT algorithm; the rotation angle of the slice image is determined by using a RANSAC algorithm; and step S4, the reconstructed volumes of the adjacent scanning positions are spliced after being rotated according to the rotation angle calculated in step S3. The application introduces a high-precision motion calibration technology and an improved image registration algorithm, the image quality is significantly improved, the joint seam artifact is eliminated, and a reliable guarantee is provided for high-resolution three-dimensional reconstruction.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and specifically to a rapid stitching method for CBCT vertical scans. Background Technology

[0002] In the current field of cone-beam computed tomography (CBCT), vertical stitching scanning technology has been used to expand the field of view (FOV) to meet the needs of three-dimensional imaging of larger objects or anatomical regions. The basic principle of traditional CBCT systems is to acquire multi-angle two-dimensional projection data by rotating an X-ray source and detector around the scanned object, and then generate a three-dimensional image using image reconstruction algorithms (such as the Feldkamp-Davis-Kress algorithm). However, limited by detector size and beam coverage, the field of view of a single scan is usually small and cannot completely cover large scanned objects. Therefore, vertical stitching scanning technology has emerged. Its main method is to acquire sub-field of view data through multiple scans and then stitch these data together to form a complete, larger field of view image.

[0003] Traditional vertical stitching scanning generally employs a detector moving up and down to acquire multiple image segments. After performing a set of rotational scans at each position, the detector is controlled to move vertically to a new position to continue acquiring the next set of image data. Finally, the multiple image segments are registered and stitched together. This method suffers from several drawbacks: the detector's movement structure is complex, requires high precision, and stability is difficult to guarantee; the response speed of the movement mechanism limits scanning efficiency; positional errors are prone to occur during movement, affecting the quality of the final stitched image; and the system and maintenance costs are relatively high due to the increased vertical movement axis of the detector.

[0004] Therefore, there is a need to design a rapid stitching method for CBCT vertical scanning that is simpler in structure, more stable in motion, and has higher scanning efficiency. Summary of the Invention

[0005] To address the problems in the prior art, this invention provides a rapid stitching method for CBCT vertical scanning, which solves the problems of low scanning efficiency, insufficient stitching accuracy, and poor equipment flexibility caused by fixed X-ray sources and detectors and moving turntables in existing CBCT vertical stitching scanning technology.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A rapid stitching method for CBCT vertical scans includes the following steps:

[0008] Step S1: Set multiple scanning positions of the turntable along the Z direction, and scan the workpiece to be scanned at each scanning position to obtain scanning data;

[0009] Step S2: Calculate the angular offset between adjacent scanning positions based on the scanning data; perform volume reconstruction for each scanning position;

[0010] Step S3: Extract common reconstructed slice images from adjacent scan positions, use the SIFT algorithm to determine the feature points of the slice images, and use the RANSAC algorithm to determine the rotation angle of the slice images;

[0011] Step S4: Based on the rotation angle calculated in step S3, rotate and stitch the reconstructed volumes of adjacent scanning positions to complete the volume reconstruction of the entire object to be scanned.

[0012] In some embodiments of the present invention, the step S2 of calculating the angular offset of adjacent scan positions based on the scan data includes:

[0013] When the turntable angle at the end of the previous scan position is behind the turntable angle at the start of the scan, the angle offset is the turntable angle at the start of the next scan position minus the turntable angle at the start of the previous scan position.

[0014] When the turntable angle at the end of the previous scan position is ahead of the turntable angle at the start of the scan, the angle offset is the turntable angle at the start of the next scan position minus the turntable angle at the end of the previous scan position.

[0015] In some embodiments of the present invention, step S3, which involves extracting common reconstructed slice images at adjacent scan locations, includes:

[0016] Let the total number of common reconstructed slice images at adjacent scanning positions be N, and divide the distance between adjacent scanning positions into n equal parts along the Z direction;

[0017] Extract slice images at n evenly divided positions, and denote them as sample images, with a quantity of 2n;

[0018] The sample image is downsampled.

[0019] In some embodiments of the present invention, step S3, which uses the SIFT algorithm to determine the feature points of the sliced ​​image, includes:

[0020] Two slices at the same evenly divided position are constructed using Gaussian scale space, and key points are extracted by multi-layer Gaussian pyramids.

[0021] The gradient direction distribution is calculated in the neighborhood of each key point to obtain a feature vector with directional information.

[0022] Candidate matching point pairs are selected by calculating the Euclidean distance between feature points of two slices.

[0023] In some embodiments of the present invention, determining the rotation angle of the sliced ​​image using the RANSAC algorithm in step S3 includes:

[0024] The RANSAC algorithm is used to remove erroneous matching pairs from the candidate matching points.

[0025] Select matching point pairs to calculate the homography matrix;

[0026] The rotation angle is extracted by decomposing the homography matrix.

[0027] In some embodiments of the present invention, the step of using the RANSAC algorithm to remove erroneous matching point pairs from candidate matching points includes:

[0028] Calculate the homography matrix by randomly selecting candidate matching point pairs;

[0029] Substitute all matching points into the validation and count the number of consistent points that conform to the model;

[0030] After multiple iterations, the optimal model is selected, and incorrect matching point pairs are eliminated.

[0031] In some embodiments of the present invention, in step S1, the movement distance of the turntable in the Z direction is less than the scanning field of view of the turntable in the X direction.

[0032] In some embodiments of the present invention, a rapid stitching system for CBCT vertical scanning is provided to implement the above-described method, comprising:

[0033] The image acquisition module is used to scan the document to be scanned at various scanning positions to obtain scan data.

[0034] The image reconstruction module is used to perform the first image reconstruction based on the angular offset of the turntable.

[0035] The rotation angle calculation module is used to calculate the rotation angle of the common reconstruction part according to the SIFT algorithm and the RANSAC algorithm.

[0036] The image stitching module is used to rotate and stitch the first reconstructed image according to the rotation angle.

[0037] Communication module; used for communicating with external devices.

[0038] In some embodiments of the present invention, an electronic device includes:

[0039] A processor, and a memory and a transceiver communicatively connected to the processor;

[0040] The memory stores computer-executed instructions; the transceiver is used for sending and receiving data.

[0041] The processor executes computer execution instructions stored in the memory to implement the above-described method.

[0042] In some embodiments of the present invention, a computer-readable storage medium is provided, wherein computer-executable instructions are stored therein, which, when executed by a processor, are used to implement the above-described method.

[0043] The technical solution of the present invention has the following technical effects compared with the prior art:

[0044] This invention optimizes the scanning path and motion control mechanism, employing a continuous dynamic scanning mode that eliminates the need for frequent pauses and restarts during vertical movement of the turntable, reducing mechanical motion waiting time. This significantly improves equipment efficiency, making it particularly suitable for vertical stitching scanning of tall, long scan pieces in industrial CT. The invention introduces high-precision motion calibration technology and an improved image registration algorithm, using a feature point matching algorithm to perform data fusion on the reconstructed slices, ensuring high spatial consistency of projection data at different heights. Image quality is significantly improved, eliminating seam artifacts and providing reliable assurance for high-resolution 3D reconstruction. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a rapid splicing method according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of a CBCT vertical scanning device according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the scanning device according to an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram illustrating the situation where the turntable angle at the end of data acquisition is behind the turntable angle at the start of data acquisition, as shown in an embodiment of the present invention.

[0050] Figure 5 This is a schematic diagram illustrating the situation where the turntable angle at the end of data acquisition is in front of the turntable angle at the start of data acquisition, as shown in an embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram of the structure of the rapid splicing system.

[0052] Figure 7 This is a schematic diagram of the structure of the electronic device.

[0053] Reference numerals: 100, rapid stitching system; 110, scanned image acquisition module; 120, image reconstruction module; 130, rotation angle calculation module; 140, image stitching module; 150, communication module; 200, electronic device; 210, processor; 220, memory; 230, transceiver. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0056] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0057] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections, direct connections, or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0058] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0059] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0060] When scanning tall and long objects, it is impossible to complete a single circular scan of the entire object regardless of the position of the detector and turntable. The object must be scanned segment by segment along its height and stitched together. This invention proposes a rapid vertical stitching scanning mode based on turntable lifting, which improves scanning efficiency, enhances stitching accuracy, and provides highly adaptable imaging results. The specific technical solution adopted is as follows.

[0061] Example 1: Refer to Figure 1 As shown, a rapid stitching method for CBCT vertical scans includes the following steps:

[0062] Step S1: Set multiple scanning positions of the turntable along the Z direction, and scan the workpiece to be scanned at each scanning position to obtain scanning data;

[0063] Industrial CT equipment reference Figure 2 As shown, the motion axes are the x-axis, y-axis and z-axis of the turntable, which move in space along three axes. The turntable also has a rotation axis, and the detector has an x-axis, so the entire CT system has a total of five axes of motion.

[0064] In the process of achieving rapid vertical stitching scanning, the X-ray source and detector remain stationary while the tall scanning component is placed on a turntable. The turntable rotates and moves up and down throughout the scanning process. The accuracy of the device's position during the movement is compensated using etalon, achieving an accuracy of up to 0.1µm, which greatly ensures the realization of vertical stitching scanning reconstruction.

[0065] Reference Figure 3As shown, this is a cross-sectional image of the X-ray source, turntable, and detector. In industrial CT, the optical path center of the X-ray source must fall on the center point of the detector after passing through the rotation axis of the turntable. The detector used in this embodiment has an image size of 3072*3072 and a pixel spacing of 0.139mm, so the physical size of the detector is 427.008mm*427.008mm. Figure 3 In the diagram, A is the X-ray source, EG is the rotation axis of the turntable, CD is the detector, AE is the distance from the X-ray source to the turntable (FOD), and AB is the distance from the X-ray source to the detector (FDD). When the turntable is at position E, the maximum object size that can be acquired is FG. FG is calculated using the similarity of triangles: FG = CD * AE / AB. When the turntable moves in the z-direction at position E to perform stitching scans, the moving distance cannot exceed the length of FG; otherwise, a missing segment will appear in the stitching.

[0066] For example, two scanning positions are set along the Z direction, namely Z1 and Z2. When the turntable is at position Z1, the upper half of the tall and long scanning part is seen in the image field of view. When the turntable moves to position Z2, the lower half of the tall and long scanning part is seen in the image field of view.

[0067] The movement distance of the turntable in the Z direction is less than the scanning field of view of the turntable in the X direction. Specifically, the movement distance in the Z direction should be less than the scanning field of view of the turntable at the x position minus a margin of 10mm. If the movement exceeds the scanning field of view minus 10mm at that position, the software will automatically issue a warning and reset. In this embodiment, a margin of 10mm is reserved to facilitate subsequent stitching calculations.

[0068] When the turntable is at position Z1, it performs a rapid circular scan. After acquiring the number of images and the angle, the turntable does not return to zero but continues to move to position Z2 for a rapid circular scan, thus completing the acquisition of the number of images and the angle.

[0069] Step S2: Calculate the angular offset between adjacent scanning positions based on the scanning data; perform volume reconstruction for each scanning position;

[0070] When the turntable performs a rapid circular scan at positions Z1 and Z2, two scenarios arise regarding the angle. Therefore, the calculation of the angle offset also falls into two categories:

[0071] In the first scenario, when the turntable angle at the end of the previous scan position is behind the turntable angle at the start of the scan, the angle offset is the turntable angle at the start of the next scan position minus the turntable angle at the start of the previous scan position.

[0072] For example, refer to Figure 4As shown, because the fast scan mode requires the turntable to accelerate from 0 to a constant speed before starting image acquisition, the turntable does not begin acquiring images when it is at 0°. Assuming OB is the initial 0° turntable position, OC is the angle at which the turntable begins acquiring images (i.e., the first image acquired after the turntable reaches a constant speed), OD is the angle at which the turntable stops after image acquisition is complete, and OE is the angle at which the second image acquisition begins. This results in the turntable angle at which acquisition ends after completion at position Z1 potentially being after the turntable angle at which acquisition began at position Z1. Figure 4 The situation is as follows: the turntable moves to position Z2 for rapid circular scanning, and the angular offset between the two is the angle at which the acquisition begins at position Z2 minus the turntable angle at which the acquisition begins at position Z1.

[0073] In the second scenario, when the turntable angle at the end of the previous scan position is ahead of the turntable angle at the start of the scan, the angle offset is the turntable angle at the start of the next scan position minus the turntable angle at the end of the previous scan position.

[0074] For example, refer to Figure 5 As shown, when the turntable finishes acquiring data at position Z1, the turntable angle at the end of acquisition at position Z1 is ahead of the turntable angle at the start of acquisition at position Z1. The angular offset between the two is the angle at the start of acquisition at position Z2 minus the turntable angle at the end of acquisition at position Z1.

[0075] After the scan is completed, reconstruction is performed separately. After the reconstruction of position Z1 is completed, reconstruction of position Z2 is continued.

[0076] Step S3: Extract common reconstructed slice images from adjacent scan positions, use the SIFT algorithm to determine the feature points of the slice images, and use the RANSAC algorithm to determine the rotation angle of the slice images.

[0077] Step S31: Let the total number of common reconstructed slice images at adjacent scanning positions be N, and divide the distance between adjacent scanning positions into n equal parts along the Z direction; for example, the number of common reconstructed slice images at positions Z1 and Z2 is 50, and the distance between positions Z1 and Z2 is 5 parts each.

[0078] Step S32: Extract slice images at n evenly divided positions respectively, and denot them as sample images, with a quantity of 2n; for example, at 5 evenly divided positions, extract 5 images that have common overlap from the scan data at Z1 position and Z2 position respectively, and denot them as sample data, and downsample the sample images.

[0079] Step S33: Use the SIFT algorithm to determine the feature points of the sliced ​​image. Specifically, for the second three-dimensional spatial volume rotation, use the SIFT (Scale-Invariant Feature Transform) based feature point matching method to accurately determine the common overlapping positions between different slices.

[0080] S331. Construct Gaussian scale space for two slices at the same evenly divided position, and extract key points through multi-layer Gaussian pyramids. These feature points are invariant in scale and rotation, and can remain stable under different imaging conditions.

[0081] DoG(x,y,σ)=L(x,y,kσ)- L(x,y,σ);

[0082] DoG(x, y, σ) is a difference Gaussian image used to detect extreme points in an image. Keypoints are obtained by DoG local extremum detection (neighboring space-scale point comparison).

[0083] Where L(x,y,σ) is the Gaussian blurred image at scale σ; σ is the scale parameter of the Gaussian kernel, which represents the degree of blurring. The larger σ is, the more blurred the image is.

[0084] L(x,y,σ)= G(x,y,σ)* I(x,y);

[0085] I(x, y) represents the pixel intensity value at coordinate point (x, y) in the input image; G(x,y,σ) is a two-dimensional Gaussian function used to blur and smooth the image.

[0086] in, .

[0087] S332. Calculate the gradient direction distribution in the neighborhood of each key point to obtain a feature vector with directional information; this is used to uniquely describe the local region.

[0088] ;

[0089] m(x, y) is the gradient magnitude, representing the edge intensity. Lx and Ly are the first-order partial derivatives (gradient components) of the image at point (x, y) along the x and y directions, respectively.

[0090] ;

[0091] θ(x, y) represents the gradient direction, and y represents the direction of the edge.

[0092] S333. By calculating the Euclidean distance between feature points of two slices, the point pair with the smallest distance and satisfying a certain ratio test condition is taken as the candidate matching point to ensure the robustness of the matching result.

[0093] ;

[0094] d represents the descriptor vector of the feature point.

[0095] ;

[0096] d1 and d2 are the nearest neighbor distance (d1) and second nearest neighbor distance (d2) of the candidate matching point during the feature point matching process, respectively; τ represents the matching ratio threshold. If d1 / d2 < τ, the matching is considered valid.

[0097] The above Euclidean distance is L2 normalized and a ratio test is performed. The ratio τ is set to a threshold of 0.75.

[0098] Step S34: Determine the rotation angle of the sliced ​​image using the RANSAC algorithm, including:

[0099] S341. Since direct matching will contain a certain proportion of erroneous point pairs, the RANSAC (Random Sample Consensus) algorithm is introduced, that is, the RANSAC algorithm is used to remove erroneous point pairs from the candidate matching points.

[0100] 1. Randomly select candidate matching point pairs and calculate the homography matrix;

[0101] 2. Substitute all matching points into the validation and count the number of consistent points that conform to the model;

[0102] 3. After multiple iterations, select the optimal model and eliminate incorrect matching point pairs.

[0103] The following calculations are performed to identify most of the incorrect matching points, and the reprojection error and threshold are defined as follows:

[0104]

[0105]

[0106] : Reprojection error of the i-th pair of matching points (in pixels).

[0107] t: RANSAC interior point decision threshold.

[0108] Pixel size, representing the length of a pixel in physical space.

[0109] : The upper limit of allowed pixel error, with a value of 1.

[0110] S342. Select matching point pairs and calculate the homography matrix;

[0111] Create the following homography matrix H:

[0112]

[0113] x and x′ represent the coordinates of the same feature point in the two images, respectively, where x = [x, y, 1]. T x′=[x′,y′,1] T (Homogeneous coordinate form). The Direct Linear Method (DLT) solves for H using the minimum singular vectors of the SVD.

[0114] S35. Extract the rotation angle by decomposing the homography matrix.

[0115] The homography matrix describes the geometric transformation relationship between two slices, including translation, scaling, and rotation information. By decomposing the homography matrix, the rotation angle can be directly extracted and used for subsequent rotation alignment.

[0116] The singular value decomposition of matrix M is as follows:

[0117]

[0118]

[0119]

[0120] Where M: the first 2×2 submatrix of the homography matrix H, used for approximate rotation and scaling.

[0121] U, Σ, The matrix obtained through SVD decomposition is used to extract the rotation components.

[0122] R: Rotation matrix, 2×2 orthogonal matrix.

[0123] θ: Rotation angle, in radians or degrees.

[0124] Step S4: Based on the rotation angle calculated in step S3, rotate and stitch the reconstructed volumes of adjacent scanning positions to complete the volume reconstruction of the entire object to be scanned.

[0125] After obtaining the rotation angle, the slice sequence is rotated and registered before volumetric stitching is performed. Due to the use of a high-precision alignment method based on SIFT + RANSAC, this method achieves a measurement accuracy within 10 μm after volumetric stitching, significantly reducing artifact errors from stitching seams. Furthermore, compared to traditional fine-tuning stop-and-go stitching scanning, this method saves more than 70% of scanning time, improving overall detection efficiency.

[0126] The technical solution of the present invention has the following technical effects compared with the prior art:

[0127] I. This invention significantly improves scanning efficiency. In traditional CBCT vertical stitching scanning, the X-ray source and detector are fixed, and the turntable needs to complete a full rotation at each height, resulting in a long scanning time. This invention, by optimizing the scanning path and motion control mechanism and employing a continuous dynamic scanning mode, eliminates the need for frequent pauses and restarts of the turntable during vertical movement, reducing the waiting time for mechanical motion. Compared to existing technologies, the scanning time of this invention can be reduced by more than 70%, thereby significantly improving the equipment's working efficiency, and is particularly suitable for vertical stitching scanning of tall and long scan pieces in industrial CT.

[0128] II. This invention effectively enhances stitching accuracy. In existing technologies, mechanical errors and vibrations of the turntable often lead to difficulties in aligning sub-field-of-view data, and artifacts easily appear at the stitching points. This invention introduces high-precision motion calibration technology and an improved image registration algorithm, using a feature point matching algorithm to perform data fusion on the reconstructed result slices, ensuring that the projection data at different heights are spatially highly consistent. Experimental results show that the stitching error of this invention can be controlled within 10μm, compared to the 1mm error of traditional methods, significantly improving image quality, eliminating seam artifacts, and providing a reliable guarantee for high-resolution 3D reconstruction.

[0129] Third, this invention enhances the flexibility and adaptability of the equipment. Existing technologies with fixed X-ray sources and detectors limit adaptability to objects of different sizes, while this invention, by dynamically adjusting the z-axis movement range of the turntable, enables flexible scanning of ultra-large or irregularly shaped objects. Compared to traditional methods that are only applicable to fixed fields of view, this invention can cover a wide range of applications, from small parts to large anatomical structures, expanding the practicality of CBCT systems and meeting the needs of various fields such as industrial inspection.

[0130] Example 2: This example will be based on Figure 6 and Figure 7 A rapid stitching system 100 for CBCT vertical scanning and an electronic device 200 are described to implement the above-described method.

[0131] For a CBCT vertical scanning rapid stitching system 100, refer to Figure 6 As shown, it includes:

[0132] The image acquisition module 110 is used to scan the document to be scanned at various scanning positions to obtain scan data.

[0133] Image reconstruction module 120 is used to perform the first image reconstruction based on the angular offset of the turntable;

[0134] The rotation angle calculation module 130 is used to calculate the rotation angle of the common reconstruction part according to the SIFT algorithm and the RANSAC algorithm.

[0135] Image stitching module 140 is used to stitch the first image reconstruction after rotation according to the rotation angle.

[0136] Communication module 150; used for communicating with external devices.

[0137] It should be understood that the rapid stitching system 100 described here is embodied in the form of functional modules. The term "module" here can refer to application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors, etc.) and memories for executing one or more software or firmware programs, integrated logic circuits, and / or other suitable components supporting the described functions. In an alternative example, those skilled in the art will understand that the rapid stitching system 100 may be specifically the electronic device 200 in the above embodiments, or the functions of the electronic device 200 in the above embodiments may be integrated into the rapid stitching system 100. The rapid stitching system 100 may be used to execute the various processes and / or steps corresponding to the electronic device 200 in the above method embodiments; to avoid repetition, these will not be described again here.

[0138] The aforementioned rapid stitching system 100 has the function of implementing the corresponding steps performed by the electronic device 200 in Embodiments 1 and 2; the aforementioned functions can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the aforementioned functions. For example, the aforementioned acquisition module can be a communication interface, such as a transceiver interface.

[0139] In the embodiments of this application, Figure 6 The fast splicing system 100 in the text can also be a chip or a chip system, such as a system on chip (SoC).

[0140] Reference Figure 7 As shown, in this embodiment, an electronic device 200 is provided, including:

[0141] Processor 210, and memory 220 and transceiver 230 communicatively connected to said processor;

[0142] The memory 220 stores computer-executed instructions; the transceiver 230 is used for sending and receiving data.

[0143] The processor 210 executes the computer execution instructions stored in the memory 220 to implement the fast splicing method in Embodiment 1.

[0144] It should be understood that the electronic device 200 can be used to perform the corresponding steps and / or processes in the above method embodiments. Optionally, the memory 220 may include read-only memory and random access memory, and provide instructions and data to the processor. A portion of the memory 220 may also include non-volatile random access memory. For example, the memory 220 may also store device type information. The processor 210 can be used to execute instructions stored in the memory 220, and when the processor 210 executes the instructions, the processor 210 can perform the corresponding steps and / or processes in the above method embodiments.

[0145] It should be understood that, in the embodiments of this application, the processor 210 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0146] In implementation, each step of the above method can be completed by the integrated logic circuitry of the hardware in the processor 210 or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution by the hardware processor, or by a combination of hardware and software modules in the processor 210. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor executes the instructions in the memory, combining them with its hardware to complete the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0147] Example 3: In this example, a computer-readable storage medium is provided, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the fast splicing method of Example 1.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0151] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0153] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A rapid stitching method for CBCT vertical scans, characterized in that, Includes the following steps: Step S1: Set multiple scanning positions of the turntable along the Z direction, and scan the workpiece to be scanned at each scanning position to obtain scanning data; Step S2: Calculate the angular offset between adjacent scanning positions based on the scanning data; perform volume reconstruction for each scanning position; Step S3: Extract common reconstructed slice images from adjacent scan positions, use the SIFT algorithm to determine the feature points of the slice images, and use the RANSAC algorithm to determine the rotation angle of the slice images; Step S4: Based on the rotation angle calculated in step S3, rotate and stitch the reconstructed volumes of adjacent scanning positions to complete the volume reconstruction of the entire object to be scanned.

2. The rapid splicing method according to claim 1, characterized in that, The step S2, which calculates the angular offset between adjacent scan positions based on the scan data, includes: When the turntable angle at the end of the previous scan position is behind the turntable angle at the start of the scan, the angle offset is the turntable angle at the start of the next scan position minus the turntable angle at the start of the previous scan position. When the turntable angle at the end of the previous scan position is ahead of the turntable angle at the start of the scan, the angle offset is the turntable angle at the start of the next scan position minus the turntable angle at the end of the previous scan position.

3. The rapid splicing method according to claim 1, characterized in that, The step S3 of extracting common reconstructed slice images at adjacent scan locations includes: Let the total number of common reconstructed slice images at adjacent scanning positions be N, and divide the distance between adjacent scanning positions into n equal parts along the Z direction; Extract slice images at n evenly divided positions, and denote them as sample images, with a quantity of 2n; The sample image is downsampled.

4. The rapid splicing method according to claim 1, characterized in that, The step S3, which uses the SIFT algorithm to determine the feature points of the sliced ​​image, includes: Two slices at the same evenly divided position are constructed using Gaussian scale space, and key points are extracted by multi-layer Gaussian pyramids. The gradient direction distribution is calculated in the neighborhood of each key point to obtain a feature vector with directional information. Candidate matching point pairs are selected by calculating the Euclidean distance between feature points of two slices.

5. The rapid splicing method according to claim 1, characterized in that, The step S3, which uses the RANSAC algorithm to determine the rotation angle of the sliced ​​image, includes: The RANSAC algorithm is used to remove erroneous matching pairs from the candidate matching points. Select matching point pairs to calculate the homography matrix; The rotation angle is extracted by decomposing the homography matrix.

6. The rapid splicing method according to claim 5, characterized in that, The process of using the RANSAC algorithm to remove erroneous matching point pairs from candidate matching points includes: Calculate the homography matrix by randomly selecting candidate matching point pairs; Substitute all matching points into the validation and count the number of consistent points that conform to the model; After multiple iterations, the optimal model is selected, and incorrect matching point pairs are eliminated.

7. The rapid splicing method according to claim 1, characterized in that, In step S1, the movement distance of the turntable in the Z direction is less than the scanning field of view of the turntable in the X direction.

8. A rapid stitching system for CBCT vertical scanning to implement the rapid stitching method according to any one of claims 1-7, comprising: The image acquisition module is used to scan the document to be scanned at various scanning positions to obtain scan data. The image reconstruction module is used to perform the first image reconstruction based on the angular offset of the turntable. The rotation angle calculation module is used to calculate the rotation angle of the common reconstruction part according to the SIFT algorithm and the RANSAC algorithm. The image stitching module is used to rotate and stitch the first reconstructed image according to the rotation angle. Communication module; used for communicating with external devices.

9. An electronic device, characterized in that, include: A processor, and a memory and a transceiver communicatively connected to the processor; The memory stores computer-executed instructions; the transceiver is used for sending and receiving data. The processor executes computer execution instructions stored in the memory to implement the fast splicing method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the fast splicing method as described in any one of claims 1-7.

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