Motion Compensation Reconstruction Method, Device, Equipment and Medium for Oral Cone Beam CBCT

Abstract

The present invention relates to the field of image processing, and discloses a motion compensation reconstruction method for oral cone beam CBCT, including: using oral cone beam CBCT to scan a moving scanned object to collect projection image data, and performing image reconstruction on the projection image data by using a projection transformation matrix to obtain a motion-to-be-compensated reconstructed image; calculating a forward projection image, performing Gaussian filtering processing and edge feature extraction on the projection image data and the forward projection image, calculating the feature correlation coefficient, adjusting the feature correlation coefficient and optimizing the vector, performing linear interpolation processing on the optimized motion vector, and performing mean filtering processing on the linearly interpolated motion vector; correcting the vector of the projection transformation matrix, performing image compensation reconstruction on the projection image data, and calculating the compensated forward projection; calculating the projection deviation of the compensated forward projection to determine the motion compensation reconstructed image of the oral cone beam CBCT. The present invention can be applied to an oral cone beam CBCT system without additional devices.

Description

Technical Field

[0001] The present invention relates to the field of image processing, and in particular to a motion compensation reconstruction method, device, electronic device and storage medium for oral cone-beam CBCT. Background Art

[0002] Motion compensated reconstruction for oral cone-beam CBCT refers to the process of compensating and reconstructing images with motion artifacts in the scanned oral images during the cone-beam CBCT oral and maxillofacial imaging examination, so as to avoid the adverse effects of motion artifacts in the images on image analysis and improve image clarity.

[0003] At present, during oral examination, it is difficult to avoid the patient's involuntary movement during the scanning process, which makes the collected projection information inconsistent, resulting in motion artifacts such as blurred or dislocated tissue structure in the reconstructed image, which seriously affects the doctor's examination and diagnosis of the patient's oral disease. Relatively speaking, the movement of the patient's mouth during the scanning process usually manifests as rigid movement, so motion compensation for this rigid movement phenomenon becomes the key. In the existing technology, simple fixation devices such as bite bars are generally used to simply fix the head, which cannot avoid the patient's movement during the scanning process; some other technical solutions mainly track the movement of the patient's head through a tracking device and perform motion compensation on the data during the reconstruction process, but they need to rely on additional devices, which may interfere with the equipment scanning and affect the patient's experience; some technical solutions use the idea of ​​iterative registration to compensate for the motion image, but it is usually difficult to meet practical applications due to complex calculations and long time. Therefore, there is still an urgent need for a motion compensation technology that does not require additional devices and can be applied to oral cone-beam CBCT systems. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a motion compensation reconstruction method, device, electronic device and storage medium for oral cone-beam CBCT, which can be applied to an oral cone-beam CBCT system without the need for additional devices.

[0005] In a first aspect, the present invention provides a motion compensation reconstruction method for oral cone-beam CBCT, comprising:

[0006] Scanning a scanning object with motion using an oral cone-beam CBCT to acquire projection image data of the oral cone-beam CBCT, and reconstructing the projection image data using a projection transformation matrix to obtain a reconstructed image to be compensated for motion;

[0007] Perform a forward projection operation on the motion-compensated reconstructed image to obtain a forward projection image. Perform Gaussian filtering on the projection image data to obtain a projection-filtered image, and perform Gaussian filtering on the forward projection image to obtain a forward projection-filtered image. Extract edge features from the projection-filtered image to obtain projection edge features, and extract edge features from the forward projection-filtered image to obtain forward projection edge features. Calculate the feature correlation coefficient between the projection edge features and the forward projection edge features;

[0008] Configure the motion vector of the feature correlation coefficient. According to the motion vector, adjust the coefficient of the feature correlation coefficient to obtain an adjustment coefficient. According to the adjustment coefficient, optimize the motion vector to obtain an optimized motion vector. Perform linear interpolation on the optimized motion vector to obtain a linear interpolation vector, and perform mean filtering on the linear interpolation vector to obtain a mean filtering vector;

[0009] According to the mean filtering vector, correct the vector of the projection transformation matrix to obtain a vector correction matrix. According to the vector correction matrix, perform image compensation reconstruction on the projection image data to obtain a compensated reconstruction image, and calculate the compensated forward projection of the compensated reconstruction image;

[0010] Calculate the projection deviation of the compensated forward projection. According to the projection deviation, determine the motion-compensated reconstructed image of the dental cone beam CBCT.

[0011] In a possible implementation manner of the first aspect, the performing Gaussian filtering on the projection image data to obtain a projection-filtered image includes:

[0012] Configure the Gaussian filtering window of the projection image data;

[0013] Use the following formula to calculate the Gaussian filtering weight of the Gaussian filtering window:

[0014]

[0015] where w u represents the Gaussian filtering weight corresponding to the u-th pixel point in the Gaussian filtering window, h u represents the value corresponding to the u-th pixel point in the Gaussian filtering window, and n represents the number of pixel points in the Gaussian filtering window;

[0016] According to the Gaussian filtering weight, use the following formula to calculate the filtered pixel value of the projection image data:

[0017]

[0018] where mv represents the filtered pixel value of the central point v in the projection image data corresponding to the size of the Gaussian filter window, w u represents the Gaussian filter weight corresponding to the u-th pixel point in the Gaussian filter window, n represents the number of pixel points in the Gaussian filter window, H u represents the value of the pixel point in the projection image data corresponding to the u-th pixel point in the Gaussian filter window;

[0019] Determine the projection filtered image according to the filtered pixel value.

[0020] In a possible implementation of the first aspect, the extracting edge features from the projection filtered image to obtain projection edge features includes:

[0021] Configure the horizontal operator and the vertical operator of the projection filtered image using the following formula:

[0022]

[0023]

[0024] where s x represents the horizontal operator, s y represents the vertical operator;

[0025] According to the horizontal operator and the vertical operator, perform horizontal pixel conversion and vertical pixel conversion on the projection filtered image using the following formula to obtain horizontal converted pixels and vertical converted pixels:

[0026]

[0027] K x =(a2 + 2a3 + a4)-(a0 + 2a7 + a6)

[0028] K y =(a0 + 2a1 + a2)-(a6 + 2a5 + a4)

[0029] where K x represents the horizontal converted pixel, K y represents the vertical converted pixel, [i, j] represents the value of the pixel center point in the 3*3 scale pixel box in the projection filtered image, a0, a1, a2, a7, a3, a4, a5, a6 represent the values of the neighboring pixel points of [i, j];

[0030] Calculate the pixel approximation between the horizontal converted pixel and the vertical converted pixel using the following formula:

[0031]

[0032] Among them, G[i, j] represents the pixel approximation value, and K x represents the horizontally transformed pixel, and K y represents the vertically transformed pixel;

[0033] Determine the projection edge feature according to the pixel approximation value.

[0034] In a possible implementation manner of the first aspect, the coefficient adjustment of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient includes:

[0035] Obtain the current projection transformation matrix to be adjusted corresponding to the feature correlation coefficient and the current motion-compensated reconstructed image, and calculate the motion transformation matrix of the motion vector using the following formula:

[0036]

[0037] Among them, R represents the motion transformation matrix, and r = {α, β, γ, t x , t y , t z} represents the motion vector, including the motion parameters of six degrees of freedom of rotation and translation along the x, y, and z axes of the scanning object space coordinate system;

[0038] According to the motion transformation matrix, use the following formula to perform vector adjustment on the current projection transformation matrix to be adjusted to obtain the current adjusted projection transformation matrix:

[0039] A k = A k-1 ·R k

[0040] Among them, A k represents the current adjusted projection transformation matrix, A k-1 represents the current projection transformation matrix to be adjusted, and R k represents the current motion transformation matrix, and k represents the current number of corresponding iterations;

[0041] According to the current adjusted projection transformation matrix, perform a forward projection operation on the current motion-compensated reconstructed image to obtain an adjusted forward projection image:

[0042] g = F(A k , I k-1 )

[0043] Among them, g represents the adjusted forward projection image, A k represents the current adjusted projection transformation matrix, and I k-1Denote the current image to be compensated and reconstructed, and \(k\) represents the current iteration number for corresponding execution.

[0044] According to the adjusted forward projection image, the coefficient of the feature correlation coefficient is adjusted by the following formula to obtain an adjustment coefficient:

[0045] \(S = S(I\) k-1 , g)\)

[0046] where \(S\) represents the adjustment coefficient, \(g\) represents the adjusted forward projection image, and \(I\) k-1 represents the current motion compensated and reconstructed image.

[0047] In a possible implementation manner of the first aspect, the vector optimization of the motion vector according to the adjustment coefficient to obtain an optimized motion vector includes:

[0048] Performing iterative adjustment on the motion vector to obtain an iteratively adjusted vector;

[0049] According to the adjustment coefficient and the iteratively adjusted vector, calculate the optimized motion vector by the following formula:

[0050]

[0051] where \(r'\) represents the optimized motion vector, \(S\) represents the adjustment coefficient, indicating adjusting the iteratively adjusted vector to make the value of \(\) the smallest.

[0052] In a possible implementation manner of the first aspect, the linear interpolation processing of the optimized motion vector to obtain a linear interpolation vector includes:

[0053] Performing sorting in the scanning direction on the optimized motion vector to obtain a sorted vector;

[0054] Performing combination pairing on the sorted vector to obtain a paired vector;

[0055] According to the paired vector, calculate the linear interpolation vector by the following formula:

[0056]

[0057] where \((x, r)\) represents the linear interpolation vector, and \((x_0, r_0)\) and \((x_1, r_1)\) represent the paired vector.

[0058] In a possible implementation manner of the first aspect, the calculation of the projection deviation of the compensated forward projection includes:

[0059] Calculate the projection deviation of the compensated forward projection by the following formula:

[0060]

[0061] Among them, d represents the projection deviation of the compensated forward projection, X represents the number of pixels in the region of interest of the compensated forward projection, α represents the serial number of the pixel position in the projection region of interest, Y represents the number of projection scanning angles, β represents the serial number of the projection scanning angle, and g αβk represents the compensated forward projection of the k-th iteration, and g αβk-1 represents the compensated forward projection of the (k - 1)-th iteration.

[0062] In a second aspect, the present invention provides a motion compensation reconstruction device for oral cone beam CBCT, and the device includes:

[0063] A projection image acquisition module, configured to scan a moving scanned object using oral cone beam CBCT to collect projection image data of the oral cone beam CBCT, and perform image reconstruction on the projection image data by using a projection transformation matrix to obtain a motion-to-be-compensated reconstructed image;

[0064] A correlation coefficient calculation module, configured to perform a forward projection operation on the motion-to-be-compensated reconstructed image to obtain a forward projection image, perform Gaussian filtering processing on the projection image data to obtain a projection filtered image, perform Gaussian filtering processing on the forward projection image to obtain a forward projection filtered image, perform edge feature extraction on the projection filtered image to obtain a projection edge feature, perform edge feature extraction on the forward projection filtered image to obtain a forward projection edge feature, and calculate a feature correlation coefficient between the projection edge feature and the forward projection edge feature;

[0065] A vector mean filtering module, configured to configure a motion vector of the feature correlation coefficient, adjust the coefficient of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient, optimize the motion vector according to the adjustment coefficient to obtain an optimized motion vector, perform linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector, and perform mean filtering processing on the linear interpolation vector to obtain a mean filtering vector;

[0066] A compensated projection calculation module, configured to perform vector correction on the projection transformation matrix according to the mean filtering vector to obtain a vector correction matrix, perform image compensation reconstruction on the projection image data according to the vector correction matrix to obtain a compensated reconstructed image, and calculate a compensated forward projection of the compensated reconstructed image;

[0067] A reconstructed image determination module, configured to calculate a projection deviation of the compensated forward projection, and determine a motion compensation reconstructed image of the oral cone beam CBCT according to the projection deviation.

[0068] In a third aspect, the present invention provides an electronic device, comprising:

[0069] at least one processor; and a memory communicatively connected to the at least one processor;

[0070] wherein the memory stores a computer program executable by the at least one processor, so that the at least one processor can execute the motion compensation reconstruction method for oral cone beam CBCT according to any one of the above first aspects.

[0071] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the motion compensation reconstruction method for oral cone beam CBCT according to any one of the above first aspects.

[0072] Compared with the prior art, the technical principle and beneficial effects of the present solution are as follows:

[0073] In an embodiment of the present invention, a scanning object with movement is scanned using an oral cone beam CBCT to collect projection image data of the oral cone beam CBCT, which is used to collect projection image data corresponding to the blurred images in the moving state captured by the oral cone beam CBCT, so as to ensure the subsequent correction of the blurred images. Further, in an embodiment of the present invention, the projection transformation matrix is configured to perform vector transformation on the projections in different movement directions using the direction vectors in the projection transformation matrix, so as to achieve the purpose of correcting the movement projections. Further, in an embodiment of the present invention, image reconstruction is performed on the projection image data according to the projection transformation matrix, which is used to perform translation, rotation, and scaling on the projection image data using the projection transformation matrix including multiple direction angles. Further, in an embodiment of the present invention, Gaussian filtering processing is performed on the projection image data to eliminate Gaussian noise in the image and reduce the adverse impact of Gaussian noise in the image on subsequent image analysis. Further, in an embodiment of the present invention, Gaussian filtering processing is performed on the forward projection image to eliminate Gaussian noise in the image and reduce the adverse impact of Gaussian noise in the image on subsequent image analysis. Further, in an embodiment of the present invention, edge feature extraction is performed on the projection filtered image to extract the parts with significant brightness changes in local regions of the projection image, converting the analysis of all contents of the image into the analysis of partial contents of the image, and reducing the workload of image analysis while ensuring the accuracy rate of image analysis. Further, in an embodiment of the present invention, the feature correlation coefficient between the projection edge feature and the forward projection edge feature is calculated to determine whether the compensated forward projection is similar to the projection image data, avoiding the adverse impact of projection compensation errors on the subsequent correction of actual movement images. Further, in an embodiment of the present invention, the motion vector of the feature correlation coefficient is configured to perform degree-of-freedom conversion on the artifacts in the image, and adjust the image artifacts by transforming each degree of freedom, ensuring that the subsequent motion vector with the best image adjustment effect is used as the final vector for adjusting the image artifacts, and improving the accuracy rate of image adjustment. Further, in an embodiment of the present invention, the coefficient of the feature correlation coefficient is adjusted according to the motion vector, which is used to perform vector adjustment on the above projection transformation matrix using the constructed initial motion vector to obtain the correlation coefficient after vector adjustment, ensuring that the motion vector is adjusted according to the correlation coefficient subsequently to obtain a better motion vector for adjusting the projection transformation matrix. Further, in an embodiment of the present invention, linear interpolation processing is performed on the optimized motion vector to calculate the optimized motion vectors at all scanning angles using the linear interpolation method. Further, in an embodiment of the present invention, mean filtering processing is performed on the linear interpolation vector to suppress the interference caused by calculation errors. Further, in an embodiment of the present invention, image compensation reconstruction is performed on the reconstructed image according to the vector correction matrix.For motion compensation reconstruction of an image using a corrected matrix to eliminate motion artifacts in the reconstructed image and improve the image quality. Further, in the embodiments of the present invention, the projection deviation of the compensated forward projection is calculated to detect whether the projection compensation is stable during the iterative process based on the magnitude of the deviation, so as to control the stopping of the iterative process and determine the final compensated reconstructed image. Therefore, a motion compensation reconstruction method, device, electronic device, and storage medium for oral cone beam CBCT proposed in the embodiments of the present invention can be applied to an oral cone beam CBCT system without additional devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0075] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0076] Figure 1 It is a schematic flowchart of a motion compensation reconstruction method for oral cone beam CBCT provided by an embodiment of the present invention;

[0077] Figure 2 In an embodiment of the present invention Figure 1 It is a schematic flowchart of one of the steps of a motion compensation reconstruction method for oral cone beam CBCT provided;

[0078] Figure 3 In an embodiment of the present invention Figure 1 It is a schematic flowchart of another step of a motion compensation reconstruction method for oral cone beam CBCT provided;

[0079] Figure 4 It is a schematic module diagram of a motion compensation reconstruction device for oral cone beam CBCT provided by an embodiment of the present invention;

[0080] Figure 5 It is a schematic internal structure diagram of an electronic device for implementing a motion compensation reconstruction method for oral cone beam CBCT provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0082] An embodiment of the present invention provides a motion compensation reconstruction method for dental cone beam CBCT. The execution subject of the motion compensation reconstruction method for dental cone beam CBCT includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present invention. In other words, the motion compensation reconstruction method for dental cone beam CBCT can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0083] Referring to Figure 1 As shown, it is a flowchart of a motion compensation reconstruction method for dental cone beam CBCT provided by an embodiment of the present invention. Among them, Figure 1 The motion compensation reconstruction method for dental cone beam CBCT described in

[0084] S1. Use a dental cone beam CBCT to scan a scanning object with motion to collect the projection image data of the dental cone beam CBCT, and perform image reconstruction on the projection image data by using a projection transformation matrix to obtain a reconstruction image to be motion-compensated.

[0085] In the embodiment of the present invention, by using a dental cone beam CBCT to scan a scanning object with motion to collect the projection image data of the dental cone beam CBCT, it is used to collect the projection image data corresponding to the blurred image in the motion state captured by the dental cone beam CBCT, so as to ensure the subsequent correction of the blurred image. Among them, the dental cone beam CBCT refers to a cone beam computed tomography imaging device, where CBCT refers to cone beam CT, which is composed of an X-ray emitter and a flat panel detector. The principle of the dental cone beam CBCT is that the X-ray emitter makes a circular DR (digital radiography) around the object to be projected with a relatively low radiation dose (usually the tube current is about 10 milliamperes), and emits the rays through the object to be projected, and the voxel point passing through a certain point on the object falls on the detector unit. The projection image data refers to the projection of all rays emitted from the point where the ray source is located at a certain angle passing through a certain voxel point on the object and falling on the detector unit.

[0086] In an embodiment of the present invention, the collection of the projection image data of the dental cone beam CBCT is realized by querying in the flat panel detector of the dental cone beam CBCT.

[0087] Furthermore, in the embodiment of the present invention, image reconstruction is performed on the projection image data by using a projection transformation matrix for vector mapping of projections in different motion directions by using the direction vectors in the projection transformation matrix. The projection transformation matrix refers to the geometric transformation relationship from the object space to the detector space, which is obtained by calculating through the three-dimensional geometric system of CBCT. The reconstructed image refers to the reconstructed image of the slice sequence obtained by performing three-dimensional reconstruction on the projection image data.

[0088] In one embodiment of the present invention, referring to Figure 2 as shown, the image reconstruction of the projection image data by using the projection transformation matrix to obtain a motion to be compensated reconstructed image includes:

[0089] S201. Determine the projection transformation form of the projection image data by using the projection transformation matrix;

[0090] S202. Determine the motion to be compensated reconstructed image by using the projection transformation form.

[0091] Furthermore, in the embodiment of the present invention, the forward projection image of the reconstructed image is calculated to identify the projection pixels in the image after reconstruction.

[0092] In one embodiment of the present invention, the calculation of the forward projection image of the reconstructed image is realized by using the downsampling method.

[0093] The downsampling method is a process of reducing the sampling rate of a specific signal, which is usually used to reduce the data transmission rate or the data size.

[0094] S2. Perform a forward projection operation on the motion to be compensated reconstructed image to obtain a forward projection image, perform Gaussian filtering on the projection image data to obtain a projection filtered image, perform Gaussian filtering on the forward projection image to obtain a forward projection filtered image, extract edge features from the projection filtered image to obtain projection edge features, extract edge features from the forward projection filtered image to obtain forward projection edge features, and calculate the feature correlation coefficient between the projection edge features and the forward projection edge features.

[0095] In the embodiment of the present invention, Gaussian filtering is performed on the projection image data to eliminate the Gaussian noise of the image and reduce the adverse impact of the Gaussian noise of the image on subsequent image analysis.

[0096] In one embodiment of the present invention, the Gaussian filtering process for the projection image data to obtain a projection filtered image includes: configuring a Gaussian filtering window for the projection image data; calculating the Gaussian filtering weights of the Gaussian filtering window using the following formula:

[0097]

[0098] where w u represents the Gaussian filtering weight corresponding to the u-th pixel point in the Gaussian filtering window, h u represents the value corresponding to the u-th pixel point in the Gaussian filtering window, and n represents the number of pixel points in the Gaussian filtering window;

[0099] According to the Gaussian filtering weights, calculate the filtered pixel values of the projection image data using the following formula:

[0100]

[0101] where m v represents the filtered pixel value of the center point v in the projection image data corresponding to the size of the Gaussian filtering window, w u represents the Gaussian filtering weight corresponding to the u-th pixel point in the Gaussian filtering window, n represents the number of pixel points in the Gaussian filtering window, and H u represents the value of the pixel point in the projection image data corresponding to the u-th pixel point in the Gaussian filtering window;

[0102] Determine the projection filtered image according to the filtered pixel values.

[0103] Among them, the Gaussian filtering window is similar to a convolution kernel and has various sizes, such as a 3*3 window.

[0104] Further, in the embodiment of the present invention, the forward projection image is subjected to Gaussian filtering to eliminate the Gaussian noise in the image and reduce the adverse impact of the Gaussian noise in the image on subsequent image analysis.

[0105] In one embodiment of the present invention, the steps of performing Gaussian filtering on the forward projection image to obtain a forward projection filtered image are similar to the principle of performing Gaussian filtering on the projection image data to obtain a projection filtered image as described above, and will not be further elaborated here.

[0106] Further, in the embodiment of the present invention, edge feature extraction is performed on the projection filtered image to extract the part with significant brightness change in the local area of the projection image, converting the analysis of all contents of the image into the analysis of part of the image content, and reducing the workload of image analysis on the premise of ensuring the accuracy of image analysis. The projection edge feature refers to the part with significant brightness change in the local area of the image, and the gray profile of this area can generally be regarded as a step, that is, it changes sharply from one gray value to another gray value with a large difference in a very small buffer area.

[0107] In an embodiment of the present invention, the edge feature extraction of the projection filtered image to obtain the projection edge feature includes: configuring the horizontal operator and the vertical operator of the projection filtered image by using the following formula:

[0108]

[0109]

[0110] where s x represents the horizontal operator, and s y represents the vertical operator;

[0111] According to the horizontal operator and the vertical operator, perform horizontal pixel conversion and vertical pixel conversion on the projection filtered image by using the following formula to obtain horizontal conversion pixels and vertical conversion pixels:

[0112]

[0113] K x =(a2 + 2a3 + a4)-(a0 + 2a7 + a6)

[0114] K y =(a0 + 2a1 + a2)-(a6 + 2a5 + a4)

[0115] where K x represents the horizontal conversion pixel, K y represents the vertical conversion pixel, [i, j] represents the value of the pixel center point in the pixel frame with a 3*3 ratio size in the projection filtered image, and a0, a1, a2, a7, a3, a4, a5, a6 represent the values of the neighboring pixel points of [i, j];

[0116] Calculate the pixel approximation value between the horizontal conversion pixel and the vertical conversion pixel by using the following formula:

[0117]

[0118] where G[i, j] represents the pixel approximation value, Kx Denote the horizontally converted pixels as K y Denote the vertically converted pixels;

[0119] Determine the projection edge features according to the pixel approximation values.

[0120] Optionally, the determining of the projection edge features according to the pixel approximation values may be performed by comparing the pixel approximation values with a pre-configured threshold, and according to the comparison result, taking the pixel center point corresponding to the pixel approximation value as the projection edge feature.

[0121] Furthermore, in an embodiment of the present invention, edge feature extraction is performed on the forward projection filtered image to extract the parts with significant brightness changes in the local areas of the projection image, converting the analysis of all contents of the image into the analysis of partial contents of the image, and reducing the workload of image analysis on the premise of ensuring the accuracy of image analysis. Among them, the forward projection edge feature refers to the parts with significant brightness changes in the local areas of the image, and the gray profile of this area can generally be regarded as a step, that is, rapidly changing from one gray value to another gray value with a large difference in a very small buffer area.

[0122] In an embodiment of the present invention, the step of performing edge feature extraction on the forward projection filtered image to obtain the forward projection edge features is similar to the principle of performing edge feature extraction on the projection filtered image to obtain the projection edge features as described above, and will not be further elaborated here.

[0123] Furthermore, in an embodiment of the present invention, the feature correlation coefficient between the projection edge features and the forward projection edge features is calculated to determine whether the compensated forward projection image is similar to the acquired projection image, avoiding the adverse impact of projection compensation errors on the subsequent correction of actual motion images. Among them, the feature correlation coefficient refers to the similarity between the projection edge features and the forward projection edge features.

[0124] In an embodiment of the present invention, the following formula is used to calculate the feature correlation coefficient between the projection edge features and the forward projection edge features:

[0125]

[0126] Among them, S(p,q) represents the feature correlation coefficient between the projection edge features and the forward projection edge features, N represents the number of pixels of the projection edge features and the forward projection edge features in the region of interest, p i represents the value of the i-th pixel point of the projection edge features in the region of interest, q i represents the i-th pixel value of the forward projection edge features in the region of interest, represents the average pixel value of the projection edge feature within the region of interest, represents the average pixel value of the forward projection edge feature within the region of interest.

[0127] S3. Configure the motion vector of the feature correlation coefficient. According to the motion vector, adjust the coefficient of the feature correlation coefficient to obtain an adjustment coefficient. According to the adjustment coefficient, optimize the motion vector to obtain an optimized motion vector. Perform linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector. Perform mean filtering processing on the linear interpolation vector to obtain a mean filtering vector.

[0128] In the embodiment of the present invention, by configuring the motion vector of the feature correlation coefficient for performing degree-of-freedom conversion on the artifacts in the image, and adjusting the image artifacts by transforming each degree of freedom, it is ensured that the subsequent motion vector with the best adjustment effect on the image is used as the final vector for adjusting the image artifacts, thereby improving the accuracy of image adjustment.

[0129] In an embodiment of the present invention, the step of configuring the motion vector of the feature correlation coefficient is similar to the principle of configuring the projection transformation matrix described above, and will not be further elaborated here.

[0130] Further, in the embodiment of the present invention, by adjusting the coefficient of the feature correlation coefficient according to the motion vector, it is used to perform vector adjustment on the above projection transformation matrix by using the constructed initial motion vector to obtain the correlation coefficient after vector adjustment, ensuring that the motion vector is adjusted according to the correlation coefficient subsequently to obtain a better motion vector for adjusting the projection transformation matrix.

[0131] In an embodiment of the present invention, the adjusting the coefficient of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient includes: obtaining the current projection transformation matrix to be adjusted corresponding to the feature correlation coefficient and the current motion-compensated reconstructed image, and calculating the motion transformation matrix of the motion vector by using the following formula:

[0132]

[0133] where, R represents the motion transformation matrix, and r = {α, β, γ, t x , t y , t z} represents the motion vector, including the motion parameters of six degrees of freedom of rotation and translation along the x, y, and z axes of the scanning object space coordinate system;

[0134] According to the motion transformation matrix, perform vector adjustment on the current projection transformation matrix to be adjusted by using the following formula to obtain the current adjusted projection transformation matrix:

[0135] A k = A k-1 ·R k

[0136] wherein, A k represents the current adjusted projection transformation matrix, A k-1 represents the current projection transformation matrix to be adjusted, R k represents the current motion transformation matrix, and k represents the current number of corresponding iterations performed;

[0137] According to the current adjusted projection transformation matrix, perform a forward projection operation on the current motion to be compensated reconstructed image by using the following formula to obtain an adjusted forward projection image:

[0138] g = F(A k , I k-1 )

[0139] wherein, g represents the adjusted forward projection image, A k represents the current adjusted projection transformation matrix, I k-1 represents the current motion to be compensated reconstructed image, and k represents the current number of corresponding iterations performed;

[0140] According to the adjusted forward projection image, adjust the coefficient of the feature correlation coefficient by using the following formula to obtain an adjusted coefficient:

[0141] S = S(I k-1 , g)

[0142] wherein, S represents the adjusted coefficient, g represents the adjusted forward projection image, I k-1 represents the current motion to be compensated reconstructed image, and k represents the current number of corresponding iterations performed.

[0143] It should be noted that the principle of adjusting the coefficient of the feature correlation coefficient to obtain an adjusted coefficient is similar to the principle of calculating the feature correlation coefficient between the projection edge feature and the forward projection edge feature described above.

[0144] Furthermore, in an embodiment of the present invention, by according to the adjusted coefficient, perform vector optimization on the motion vector to be used for performing vector adjustment on the above projection transformation matrix by using the constructed initial motion vector to obtain a correlation coefficient after vector adjustment, so as to ensure subsequent adjustment of the motion vector according to the correlation coefficient to obtain a better motion vector for adjusting the projection transformation matrix.

[0145] In an embodiment of the present invention, performing vector optimization on the motion vector according to the adjusted coefficient to obtain an optimized motion vector includes:

[0146] Iteratively adjust the motion vector to obtain an iteratively adjusted vector;

[0147] According to the adjustment coefficient and the iteratively adjusted vector, use the following formula to calculate the optimized motion vector:

[0148]

[0149] where r′ represents the optimized motion vector, S represents the adjustment coefficient, represents adjusting the iteratively adjusted vector such that the value is minimized.

[0150] It should be noted that the vector optimization of the motion vector according to the adjustment coefficient to obtain the optimized motion vector is achieved by repeatedly adjusting the motion vector and calculating the similarity between the forward projection image of the corresponding motion change and the projection image data according to the adjusted motion vector, so that the objective function is optimized. Finally, the Nelder-Mead method is used to optimize and solve the above motion estimation objective function. The Nelder-Mead method can solve multi-variable functions, does not require the function to be differentiable, and can quickly converge to the local minimum. By optimizing and solving the motion estimation objective function, the motion factors that cause the projection information mismatch, that is, the motion vector, can be estimated and calculated, and a suitable motion vector r can be obtained. Preferably, the forward projection operation process in this optimization and solution process can be performed in a downsampling manner to optimize the calculation efficiency of the algorithm.

[0151] Furthermore, the embodiment of the present invention performs linear interpolation processing on the optimized motion vector for all scan angles obtained by using the linear interpolation method

[0152] In an embodiment of the present invention, the linear interpolation processing of the optimized motion vector to obtain a linear interpolation vector includes: sorting the optimized motion vector in the scanning direction to obtain a sorted vector; combining and pairing the sorted vector to obtain a paired vector; according to the paired vector, using the following formula to calculate the linear interpolation vector:

[0153]

[0154] where (x, r) represents the linear interpolation vector, and (x0, r0) and (x1, r1) represent the paired vectors.

[0155] Furthermore, the embodiment of the present invention performs mean filtering processing on the linear interpolation vector to suppress the interference caused by calculation errors.

[0156] In one embodiment of the present invention, the mean filtering process is performed on the linear interpolation vector to obtain a mean filtering vector. The one-dimensional mean filtering process can be performed on the parameter of each degree of freedom component in the motion vector of the linear interpolation along the scanning angle direction, so as to ensure that the motion parameters of each degree of freedom component are continuous and smooth along the scanning direction, and avoid introducing secondary artifacts that affect the image quality.

[0157] S4. According to the mean filtering vector, the vector correction is performed on the projection transformation matrix to obtain a vector correction matrix. According to the vector correction matrix, the image compensation reconstruction is performed on the projection image data to obtain a compensated reconstructed image, and the compensated forward projection of the compensated reconstructed image is calculated.

[0158] In the embodiment of the present invention, the vector correction is performed on the projection transformation matrix according to the mean filtering vector, so as to adjust the parameters of each degree of freedom in the projection transformation matrix to be corrected, so as to adapt to the projection images of different degrees of freedom movements, and improve the accuracy of the image compensation reconstruction.

[0159] In one embodiment of the present invention, the step of performing vector correction on the projection transformation matrix according to the mean filtering vector to obtain a vector correction matrix is similar to the principle of performing vector adjustment on the current projection transformation matrix to be adjusted according to the motion transformation matrix by using the following formula to obtain the current adjusted projection transformation matrix, and will not be further described herein.

[0160] Further, in the embodiment of the present invention, the image compensation reconstruction is performed on the projection image data according to the vector correction matrix, so as to perform motion compensation reconstruction on the image by using the corrected matrix, so as to eliminate the motion artifacts of the reconstructed image and improve the image quality.

[0161] In one embodiment of the present invention, the step of performing image compensation reconstruction on the reconstructed image according to the vector correction matrix to obtain a compensated reconstructed image is similar to the principle of performing image reconstruction on the projection image data according to the projection transformation matrix to obtain a reconstructed image, and will not be further described herein.

[0162] Further, in the embodiment of the present invention, the compensated forward projection of the compensated reconstructed image is calculated, so as to calculate the forward projection of the image after motion compensation reconstruction, and ensure the subsequent detection of whether the projection compensation is stable during the iteration process.

[0163] In one embodiment of the present invention, the step of calculating the compensated forward projection of the compensated reconstructed image is similar to the principle of performing a forward projection operation on the motion to be compensated reconstructed image to obtain a forward projection image, and will not be further described herein.

[0164] S5. Calculate the projection deviation of the compensated forward projection, and determine the motion-compensated reconstruction image of the oral cone-beam CBCT according to the projection deviation.

[0165] In an embodiment of the present invention, by calculating the projection deviation of the compensated forward projection, it is used to detect whether the projection compensation is stable during the iterative process by using the magnitude of the deviation, so as to control the stop of the iterative process and determine the final compensated reconstruction image. Among them, the projection deviation refers to the displacement of the image point caused by the plane undulation. When the plane has undulation, it is the displacement between the conformation points of the vertical projection points above or below a certain reference plane on the photo.

[0166] In an embodiment of the present invention, the following formula is used to calculate the projection deviation of the compensated forward projection:

[0167]

[0168] Where d represents the projection deviation of the compensated forward projection, X represents the number of pixels in the region of interest of the compensated forward projection, α represents the serial number of the pixel position in the projection region of interest, Y represents the number of projection scanning angles, β represents the serial number of the projection scanning angle, g αβk represents the compensated forward projection of the k-th iteration, and g αβk-1 represents the compensated forward projection of the (k - 1)-th iteration.

[0169] Furthermore, in an embodiment of the present invention, by determining the motion-compensated reconstruction image of the oral cone-beam CBCT according to the projection deviation, it is used to determine whether the projection compensation is stable during the iterative process corresponding to the projection deviation.

[0170] In an embodiment of the present invention, as shown in Figure 3 the determination of the motion-compensated reconstruction image of the oral cone-beam CBCT according to the projection deviation includes:

[0171] S301. Configure the deviation threshold of the projection deviation;

[0172] S302. When the projection deviation is not less than the deviation threshold, return to the above step of configuring the motion vector of the feature correlation coefficient for iterative execution;

[0173] S303. When the projection deviation is less than the deviation threshold, use the compensated reconstruction image corresponding to the projection deviation as the motion-compensated reconstruction image of the oral cone-beam CBCT.

[0174] Among them, the deviation threshold can be set to 0.0001, or can be set according to the actual scenario.

[0175] It can be seen that in the embodiments of the present invention, a dental cone beam CBCT is used to scan a scanning object with movement to collect projection image data of the dental cone beam CBCT, so as to collect projection image data corresponding to a blurred image in a moving state, ensuring subsequent correction of the blurred image. Further, in the embodiments of the present invention, the projection transformation matrix is configured to perform vector transformation on projections in different movement directions using the direction vectors in the projection transformation matrix, achieving the purpose of correcting the movement projection. Further, in the embodiments of the present invention, according to the projection transformation matrix, image reconstruction is performed on the projection image data to move, rotate, and scale the projection image data using a projection transformation matrix including multiple direction angles. Further, in the embodiments of the present invention, Gaussian filtering processing is performed on the projection image data to eliminate Gaussian noise in the image and reduce the adverse impact of Gaussian noise in the image on subsequent image analysis. Further, in the embodiments of the present invention, Gaussian filtering processing is performed on the forward projection image to eliminate Gaussian noise in the image and reduce the adverse impact of Gaussian noise in the image on subsequent image analysis. Further, in the embodiments of the present invention, edge feature extraction is performed on the projection filtered image to extract parts with significant brightness changes in local areas of the projection image, converting the analysis of all contents of the image into the analysis of partial contents of the image, and reducing the workload of image analysis while ensuring the accuracy rate of image analysis. Further, in the embodiments of the present invention, the feature correlation coefficient between the projection edge feature and the forward projection edge feature is calculated to determine whether the calculated compensated forward projection is similar to the collected projection image, avoiding the adverse impact of incorrect forward projection compensation on subsequent correction of actual movement images. Further, in the embodiments of the present invention, the motion vector of the feature correlation coefficient is configured to perform degree-of-freedom conversion on artifacts in the image, and adjust the image artifacts by transforming each degree of freedom, ensuring that the motion vector with the best image adjustment effect is used as the final vector for adjusting the image artifacts, improving the accuracy rate of image adjustment. Further, in the embodiments of the present invention, according to the motion vector, coefficient adjustment is performed on the feature correlation coefficient to perform vector adjustment on the above projection transformation matrix using the constructed initial motion vector, obtaining the correlation coefficient after vector adjustment, ensuring subsequent adjustment of the motion vector according to the correlation coefficient to obtain a better motion vector for adjusting the projection transformation matrix. Further, in the embodiments of the present invention, linear interpolation processing is performed on the optimized motion vector to obtain optimized motion vectors at all scanning angles calculated using the linear interpolation method. Further, in the embodiments of the present invention, mean filtering processing is performed on the linear interpolation vector to suppress interference caused by calculation errors. Further, in the embodiments of the present invention, according to the vector correction matrix, image compensation reconstruction is performed on the reconstructed image to perform motion compensation reconstruction on the image using the corrected matrix.To achieve the purpose of eliminating motion artifacts in the reconstructed image and improving the image quality. Further, in the embodiments of the present invention, the projection deviation of the compensated forward projection is calculated to detect whether the projection compensation is stable during the iterative process by using the magnitude of the deviation, so as to control the stopping of the iterative process and determine the final compensated reconstructed image. Therefore, a motion compensation reconstruction method for dental cone-beam CBCT proposed in the embodiments of the present invention can be applied to a dental cone-beam CBCT system without additional devices.

[0176] As Figure 4 shown, it is a functional module diagram of the motion compensation reconstruction device for dental cone-beam CBCT of the present invention.

[0177] The motion compensation reconstruction device 400 for dental cone-beam CBCT of the present invention can be installed in an electronic device. According to the functions achieved, the motion compensation reconstruction device for dental cone-beam CBCT may include a projection image acquisition module 401, a correlation coefficient calculation module 402, a vector mean filtering module 403, a compensated projection calculation module 404, and a reconstructed image determination module 405. The modules of the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0178] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0179] The projection image acquisition module 401 is configured to scan a moving scanned object using dental cone-beam CBCT to collect the projection image data of the dental cone-beam CBCT, and perform image reconstruction on the projection image data using a projection transformation matrix to obtain a motion-to-be-compensated reconstructed image;

[0180] The correlation coefficient calculation module 402 is configured to perform a forward projection operation on the motion-to-be-compensated reconstructed image to obtain a forward projection image, perform Gaussian filtering on the projection image data to obtain a projection filtered image, perform Gaussian filtering on the forward projection image to obtain a forward projection filtered image, extract edge features from the projection filtered image to obtain projection edge features, extract edge features from the forward projection filtered image to obtain forward projection edge features, and calculate the feature correlation coefficient between the projection edge features and the forward projection edge features;

[0181] The vector mean filtering module 403 is configured to configure the motion vectors of the feature correlation coefficients, adjust the coefficients of the feature correlation coefficients according to the motion vectors to obtain adjustment coefficients, optimize the motion vectors according to the adjustment coefficients to obtain optimized motion vectors, perform linear interpolation processing on the optimized motion vectors to obtain linear interpolation vectors, and perform mean filtering processing on the linear interpolation vectors to obtain mean filtering vectors;

[0182] The compensation projection calculation module 404 is configured to correct the projection transformation matrix according to the mean filtering vectors to obtain a vector correction matrix, perform image compensation reconstruction on the projection image data according to the vector correction matrix to obtain a compensated reconstructed image, and calculate the forward projection before compensation of the compensated reconstructed image;

[0183] The reconstructed image determination module 405 is configured to calculate the projection deviation of the forward projection before compensation, and determine the motion compensation reconstructed image of the dental cone beam CBCT according to the projection deviation.

[0184] Specifically, each module in the motion compensation reconstruction device 400 for dental cone beam CBCT in the embodiments of the present invention uses the same technical means as those in the above-mentioned Figures 1 to 3 motion compensation reconstruction method for dental cone beam CBCT, and can produce the same technical effects, which will not be elaborated here.

[0185] As Figure 5 shown, it is a schematic structural diagram of an electronic device for implementing the motion compensation reconstruction method for dental cone beam CBCT according to the present invention.

[0186] The electronic device may include a processor 50, a memory 51, a communication bus 52, and a communication interface 53, and may further include a computer program stored in the memory 51 and executable on the processor 50, such as a motion compensation reconstruction program for dental cone beam CBCT.

[0187] Among them, in some embodiments, the processor 50 may be composed of an integrated circuit. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 50 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and circuits. By running or executing programs or modules stored in the memory 51 (such as executing a motion compensation reconstruction program for oral cone beam CBCT, etc.), and calling data stored in the memory 51, it performs various functions of the electronic device and processes data.

[0188] The memory 51 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, magnetic disks, optical disks, etc. In some embodiments, the memory 51 may be an internal storage unit of the electronic device, such as the mobile hard disk of the electronic device. In some other embodiments, the memory 51 may also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 51 may also include both an internal storage unit and an external storage device of the electronic device. The memory 51 can not only be used to store application software installed on the electronic device and various types of data, such as the code of a database configuration connection program, etc., but can also be used to temporarily store data that has been output or will be output.

[0189] The communication bus 52 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is set to realize the connection and communication between the memory 51 and at least one processor 50, etc.

[0190] The communication interface 53 is used for communication between the above-mentioned electronic device 5 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between this electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device and to display a visual user interface.

[0191] Figure 5 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 5 the shown structure does not constitute a limitation on the electronic device, and it may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0192] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering each component. Preferably, the power source may be logically connected to the at least one processor 50 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or an inverter, and a power status indicator. The electronic device may also include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0193] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure within the scope of the patent invention.

[0194] The database configuration connection program stored in the memory 51 of the electronic device is a combination of multiple computer programs. When running in the processor 50, it can achieve:

[0195] Scanning a moving scanned object using oral cone beam CBCT to collect projection image data of the oral cone beam CBCT, and performing image reconstruction on the projection image data using a projection transformation matrix to obtain a motion-to-be-compensated reconstructed image;

[0196] Perform a forward projection operation on the motion-compensated reconstructed image to obtain a forward projection image, perform Gaussian filtering on the projection image data to obtain a projection-filtered image, and perform Gaussian filtering on the forward projection image to obtain a forward projection-filtered image. Extract edge features from the projection-filtered image to obtain projection edge features, and extract edge features from the forward projection-filtered image to obtain forward projection edge features, and calculate the feature correlation coefficient between the projection edge features and the forward projection edge features;

[0197] Configure the motion vector of the feature correlation coefficient, adjust the coefficient of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient, optimize the motion vector according to the adjustment coefficient to obtain an optimized motion vector, perform linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector, and perform mean filtering processing on the linear interpolation vector to obtain a mean filtering vector;

[0198] Correct the projection transformation matrix according to the mean filtering vector to obtain a vector correction matrix, perform image compensation reconstruction on the projection image data according to the vector correction matrix to obtain a compensated reconstructed image, and calculate the compensated forward projection of the compensated reconstructed image;

[0199] Calculate the projection deviation of the compensated forward projection, and determine the motion-compensated reconstructed image of the oral cone-beam CBCT according to the projection deviation.

[0200] Specifically, for the specific implementation method of the above computer program by the processor 50, reference can be made to Figure 1 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0201] Furthermore, if the modules / units integrated in the electronic device are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium. The storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0202] The present invention also provides a storage medium, the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can implement:

[0203] Scanning a scanning object with motion using an oral cone beam CBCT to collect projection image data of the oral cone beam CBCT, and performing image reconstruction on the projection image data using a projection transformation matrix to obtain a motion-to-be-compensated reconstructed image;

[0204] Performing a forward projection operation on the motion-to-be-compensated reconstructed image to obtain a forward projection image, performing Gaussian filtering on the projection image data to obtain a projection filtered image, performing Gaussian filtering on the forward projection image to obtain a forward projection filtered image, performing edge feature extraction on the projection filtered image to obtain projection edge features, performing edge feature extraction on the forward projection filtered image to obtain forward projection edge features, and calculating a feature correlation coefficient between the projection edge features and the forward projection edge features;

[0205] Configuring a motion vector of the feature correlation coefficient, adjusting the coefficient of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient, optimizing the motion vector according to the adjustment coefficient to obtain an optimized motion vector, performing linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector, and performing mean filtering processing on the linear interpolation vector to obtain a mean filtering vector;

[0206] Performing vector correction on the projection transformation matrix according to the mean filtering vector to obtain a vector correction matrix, performing image compensation reconstruction on the projection image data according to the vector correction matrix to obtain a compensated reconstructed image, and calculating a compensated forward projection of the compensated reconstructed image;

[0207] Calculating a projection deviation of the compensated forward projection, and determining a motion compensation reconstructed image of the oral cone beam CBCT according to the projection deviation.

[0208] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0209] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] In addition, in each embodiment of the present invention, each functional module can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0211] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0212] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any associated drawing marks in the claims should not be regarded as limiting the claimed rights.

[0213] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0214] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A motion compensation reconstruction method for dental cone beam CBCT, characterized in that, The method includes: Scanning a scanning object with motion using an oral cone-beam CBCT to collect projection image data of the oral cone-beam CBCT, and performing image reconstruction on the projection image data using a projection transformation matrix to obtain a motion-to-be-compensated reconstructed image; Performing a forward projection operation on the motion-to-be-compensated reconstructed image to obtain a forward projection image, performing Gaussian filtering on the projection image data to obtain a projection filtered image, performing Gaussian filtering on the forward projection image to obtain a forward projection filtered image, extracting edge features from the projection filtered image to obtain projection edge features, extracting edge features from the forward projection filtered image to obtain forward projection edge features, and calculating a feature correlation coefficient between the projection edge features and the forward projection edge features; Configuring a motion vector of the feature correlation coefficient, adjusting the coefficient of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient, optimizing the motion vector according to the adjustment coefficient to obtain an optimized motion vector, performing linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector, and performing mean filtering processing on the linear interpolation vector to obtain a mean filtering vector; Correcting the vector of the projection transformation matrix according to the mean filtering vector to obtain a vector correction matrix, performing image compensation reconstruction on the projection image data according to the vector correction matrix to obtain a compensated reconstructed image, and calculating a compensated forward projection of the compensated reconstructed image; Calculating a projection deviation of the compensated forward projection, and determining a motion compensation reconstructed image of the oral cone-beam CBCT according to the projection deviation, wherein calculating the projection deviation of the compensated forward projection includes: Calculating the projection deviation of the compensated forward projection using the following formula: Where, d represents the projection deviation of the compensated forward projection, X represents the number of pixels within the region of interest of the compensated forward projection, α represents the pixel position serial number within the projection region of interest, Y represents the number of projection scanning angles, β represents the serial number of the projection scanning angle, and g αβk represents the compensated forward projection of the k-th iteration, and g αβk-1 represents the compensated forward projection of the (k-1)-th iteration.

2. The method according to claim 1, characterized in that, The performing Gaussian filtering on the projection image data to obtain a projection filtered image includes: Configuring a Gaussian filtering window of the projection image data; Calculating a Gaussian filtering weight of the Gaussian filtering window using the following formula: where w u represents the Gaussian filtering weight corresponding to the u-th pixel point in the Gaussian filtering window, h u represents the value corresponding to the u-th pixel point in the Gaussian filtering window, and n represents the number of pixel points in the Gaussian filtering window; Calculating a filtered pixel value of the projection image data according to the Gaussian filtering weight using the following formula: where m v represents the filtered pixel value of the center point v in the projection image data corresponding to the size of the Gaussian filtering window, w u represents the Gaussian filtering weight corresponding to the u-th pixel point in the Gaussian filtering window, n represents the number of pixel points in the Gaussian filtering window, H u represents the value of the pixel point in the projection image data corresponding to the u-th pixel point in the Gaussian filtering window; Determining the projection filtered image according to the filtered pixel value.

3. The method according to claim 1, characterized in that, The extracting edge features from the projection filtered image to obtain projection edge features includes: Configuring a horizontal operator and a vertical operator of the projection filtered image using the following formula: Among them, s x represents the horizontal operator, and s y represents the vertical operator; Performing horizontal pixel conversion and vertical pixel conversion on the projection filtered image according to the horizontal operator and the vertical operator using the following formula to obtain a horizontally converted pixel and a vertically converted pixel; K x = (a2 + 2a3 + a4) - (a0 + 2a7 + a6) K y = (a0 + 2a1 + a2) - (a6 + 2a5 + a4) Among them, K x represents the horizontally converted pixel, and K y represents the vertically converted pixel. [i, j] represents the value of the pixel center point in the pixel box with a 3×3 ratio size in the projection filtering image. a0, a1, a2, a7, a3, a4, a5, and a6 represent the values of the neighboring pixel points of [i, j]. Calculating a pixel approximation value between the horizontally converted pixel and the vertically converted pixel using the following formula: Among them, G[i, j] represents the pixel approximation value, and K x represents the horizontally converted pixel, and K y represents the vertically converted pixel; Determining the projection edge features according to the pixel approximation value.

4. The method according to claim 1, wherein The adjusting the coefficient of the feature correlation coefficient according to the motion vector to obtain an adjustment coefficient includes: Obtaining a current projection transformation matrix to be adjusted corresponding to the feature correlation coefficient and a current motion-to-be-compensated reconstructed image, and calculating a motion transformation matrix of the motion vector using the following formula: wherein, R represents the motion transformation matrix, and r = {α, β, γ, t x , t y , t z} represents the motion vector, including motion parameters of six degrees of freedom for rotation and translation along the x, y, and z axes of the scanning object space coordinate system; According to the motion transformation matrix, the current projection transformation matrix to be adjusted is vector-adjusted using the following formula to obtain the current adjusted projection transformation matrix: A k = A k-1 ·R k Among them, A k represents the current adjusted projection transformation matrix, A k-1 represents the current projection transformation matrix to be adjusted, R k represents the current motion transformation matrix, and k represents the current number of corresponding iterations performed; According to the current adjusted projection transformation matrix, a forward projection operation is performed on the current motion to be compensated reconstructed image using the following formula to obtain an adjusted forward projection image: g = F(A k , I k-1 ) where g represents the forward projection image before the adjustment, A k represents the current adjustment projection transformation matrix, I k-1 represents the current motion to be compensated reconstruction image, and k represents the current number of corresponding iterations performed; According to the adjusted forward projection image, the feature correlation coefficient is coefficient-adjusted using the following formula to obtain an adjusted coefficient: S = S(I k-1 , g) where S represents the adjustment coefficient, g represents the pre-adjustment forward projection image, and I k-1 represents the current motion compensation reconstruction image to be processed, and k represents the current corresponding iteration number.

5. The method according to claim 1, wherein The optimizing the motion vector according to the adjusted coefficient to obtain an optimized motion vector includes: Performing iterative adjustment on the motion vector to obtain an iteratively adjusted vector; According to the adjusted coefficient and the iteratively adjusted vector, the optimized motion vector is calculated using the following formula: where r' represents the optimized motion vector, and S represents the adjustment coefficient. represents adjusting the iterative adjustment vector such that the value is minimized.

6. The method according to claim 1, characterized in that, The performing linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector includes: Sorting the optimized motion vector in the scanning direction to obtain a sorted vector; Combining and pairing the sorted vectors to obtain paired vectors; According to the paired vectors, the linear interpolation vector is calculated using the following formula: where (x, r) represents the linear interpolation vector, and (x0, r0) and (x1, r1) represent the paired vectors.

7. A motion compensation reconstruction method and device for oral cone beam CBCT, characterized in that, The device includes: A projection image acquisition module, configured to scan a scanning object with motion using an oral cone beam CBCT to collect projection image data of the oral cone beam CBCT, and perform image reconstruction on the projection image data using a projection transformation matrix to obtain a motion to be compensated reconstructed image; A correlation coefficient calculation module, configured to perform a forward projection operation on the motion to be compensated reconstructed image to obtain a forward projection image, perform Gaussian filtering on the projection image data to obtain a projection filtered image, perform Gaussian filtering on the forward projection image to obtain a forward projection filtered image, extract edge features from the projection filtered image to obtain projection edge features, extract edge features from the forward projection filtered image to obtain forward projection edge features, and calculate a feature correlation coefficient between the projection edge features and the forward projection edge features; A vector mean filtering module, configured to configure a motion vector of the feature correlation coefficient, coefficient-adjust the feature correlation coefficient according to the motion vector to obtain an adjusted coefficient, optimize the motion vector according to the adjusted coefficient to obtain an optimized motion vector, perform linear interpolation processing on the optimized motion vector to obtain a linear interpolation vector, and perform mean filtering on the linear interpolation vector to obtain a mean filtered vector; A compensation projection calculation module, configured to perform vector correction on the projection transformation matrix according to the mean filtered vector to obtain a vector correction matrix, perform image compensation reconstruction on the projection image data according to the vector correction matrix to obtain a compensated reconstructed image, and calculate a compensated forward projection of the compensated reconstructed image; A reconstructed image determination module, configured to calculate a projection deviation of the compensated forward projection, and determine a motion compensation reconstructed image of the oral cone beam CBCT according to the projection deviation, wherein calculating the projection deviation of the compensated forward projection includes: Calculating the projection deviation of the compensated forward projection by using the following formula: Wherein, d represents the projection deviation of the pre-compensation forward projection, X represents the number of pixels in the region of interest of the pre-compensation forward projection, α represents the serial number of the pixel position in the projection region of interest, Y represents the number of projection scanning angles, β represents the serial number of the projection scanning angle, g αβk represents the pre-compensation forward projection of the k-th iteration, g αβk-1 represents the pre-compensation forward projection of the (k-1)-th iteration.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the motion compensation reconstruction method for oral cone beam CBCT according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the motion compensation reconstruction method for oral cone beam CBCT according to any one of claims 1 to 6.

Images