CBCT image truncation artifact correction method, device, equipment and medium

Abstract

The invention provides a CBCT image truncation artifact correction method, device and equipment and a medium, and the method comprises the steps: scanning a target region through dual-energy CBCT equipment, and obtaining high-energy projection data and low-energy projection data of the target region; performing base material decomposition processing on the high-energy projection data and the low-energy projection data to obtain soft tissue projection and bone projection of the target area; positioning a truncation area of the original CBCT image to be corrected, and performing edge fitting on the truncation area based on the edge features of the soft tissue projection to generate an edge fitting curve of the truncation area; and by taking the edge fitting curve as a boundary and combining gray information of soft tissue projection, complementing the truncation region, and fusing with a non-truncation region of the original CBCT image to obtain a corrected CBCT image. According to the method, truncation artifacts can be effectively eliminated, the completeness and accuracy of CBCT images are improved, and the observation requirements of clinical diagnosis on edge details are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and in particular to a CBCT image truncation artifact correction method, device, equipment and medium. BACKGROUND

[0002] In the clinical application of CBCT (Cone Beam Computed Tomography) medical image technology, such as head disease examination, oral diagnosis and treatment, etc., the imaging acquisition range (FOA) of image reconstruction is determined by the physical size of the detector. Once the effective detection area of the detector is fixed, the boundary of the FOA is determined, and the tissue image information beyond the range cannot be effectively captured. Limited by the physical bottleneck of the detector size, the edge of the actual scanned CBCT image often appears as shown in the "truncated image", that is, the tissue image beyond the FOA range is directly truncated, resulting in obvious information loss or artifacts in the edge region (especially the junction region of soft tissue and bone tissue in the head and other parts). Such problems not only destroy the integrity of the image, but also directly interfere with the doctor's judgment of the disease if the truncated region covers the potential lesion; even if the truncated region does not involve the lesion, the incomplete edge information will also reduce the overall image quality, affecting the reliability and accuracy of clinical diagnosis, and causing hidden dangers to subsequent diagnosis and treatment decisions. Figure 1

[0003] To solve the above-mentioned truncated image problem, two types of processing schemes are mainly used in related technologies: one is to directly fit and complete the edge of the truncated region based on the original single-energy projection, however, there are soft tissue and bone tissue in the target region such as the head, and the density difference between the two types of tissue is significant, and the absorption coefficient of X-ray is also different. The traditional fitting method cannot specifically exclude the interference of bone tissue on the edge gray scale, resulting in obvious artifacts or gray scale information deviation in the completed edge, which is difficult to meet the image precision requirements of clinical diagnosis; the other is to rely on iterative reconstruction algorithm for optimization. Although this algorithm has certain effect in handling the scene where the viewing angle is limited (such as insufficient scanning angle caused by local shielding), it has no obvious advantage in completing the truncated image caused by limited projection field of view. This is because the core of this algorithm is to iteratively optimize the projection and back projection process to reduce the reconstruction error, and the essence of the truncated image is data loss, not simply insufficient viewing angle coverage. Therefore, in the iteration process, it is difficult to effectively fill the real tissue information in the truncated region, and instead, it will continuously propagate and amplify the error caused by the initial truncated data, resulting in the problem of truncated image in the final reconstructed image being still prominent, and the precise completion cannot be realized. SUMMARY

[0004] To overcome the problems in the related art, the present application provides a CBCT image truncation artifact correction method, device, equipment and medium. ​

[0005] According to a first aspect of the embodiments of the present application, a method for truncation artifact correction of a CBCT image is provided, and the method comprises: scanning a target region by using a dual-energy CBCT device to obtain high-energy projection data and low-energy projection data of the target region; performing basis material decomposition processing on the high-energy projection data and the low-energy projection data to obtain soft tissue projection and bone projection of the target region; locating a truncation region of an original CBCT image to be corrected, and performing edge fitting on the truncation region based on edge features of the soft tissue projection to generate an edge fitting curve of the truncation region; combining gray scale information of the soft tissue projection to complete the truncation region with the edge fitting curve as a boundary, and fusing the completed truncation region with a non-truncation region of the original CBCT image to obtain a corrected CBCT image.

[0006] According to a second aspect of the embodiments of the present application, a device for truncation artifact correction of a CBCT image is provided, and the device comprises: a dual-energy projection data acquisition module, configured to scan a target region by using a dual-energy CBCT device to obtain high-energy projection data and low-energy projection data of the target region; a basis material projection decomposition module, configured to perform basis material decomposition processing on the high-energy projection data and the low-energy projection data to obtain soft tissue projection and bone projection of the target region; an edge curve fitting module, configured to locate a truncation region of an original CBCT image to be corrected, and perform edge fitting on the truncation region based on edge features of the soft tissue projection to generate an edge fitting curve of the truncation region; an image correction module, configured to combine gray scale information of the soft tissue projection to complete the truncation region with the edge fitting curve as a boundary, and fuse the completed truncation region with a non-truncation region of the original CBCT image to obtain a corrected CBCT image.

[0007] According to a third aspect of the embodiments of the present application, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of the first aspect when executing the computer program.

[0008] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executable on a processor to implement the method of the first aspect.

[0009] The technical scheme provided by the embodiments of the present application can have the following beneficial effects: The embodiment of the present application collects high and low energy projection data of a target region by a dual-energy CBCT device, obtains soft tissue projection without bone tissue interference through base material decomposition, optimizes the edge fitting curve of the original single-energy CBCT image truncation region by the edge feature of the soft tissue projection, and completes the truncation region by combining the gray scale information of the soft tissue projection, so as to effectively eliminate the truncation artifact, improve the integrity and accuracy of the CBCT image, and meet the observation demand of edge details in clinical diagnosis.

[0010] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0011] The drawings incorporated into the specification and forming a part of the present application show embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.

[0012] Figure 1 is a CBCT image truncation artifact schematic diagram according to an exemplary embodiment of the present application.

[0013] Figure 2 is a flowchart of a CBCT image truncation artifact correction method according to an exemplary embodiment of the present application.

[0014] Figure 3 is a projection schematic diagram of a target region before dual-energy decomposition according to an exemplary embodiment of the present application.

[0015] Figure 4 is a soft tissue projection diagram and a bone projection diagram after dual-energy decomposition according to an exemplary embodiment of the present application.

[0016] Figure 5a is a CBCT image correction schematic diagram according to an exemplary embodiment of the present application.

[0017] Figure 5b is a CBCT image correction schematic diagram according to an exemplary embodiment of the present application.

[0018] Figure 6 is a structure schematic diagram of a CBCT image truncation artifact correction device according to an exemplary embodiment of the present application.

[0019] Figure 7 is a structure schematic diagram of a computer device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0020] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description of the exemplary embodiments is intended to apply to any embodiment of the application, unless specifically stated otherwise. It is to be understood that other embodiments can be utilized, and structural or procedural changes can be made without departing from the scope of the present application. Therefore, the following detailed description is not meant to limit the application or the protective scope thereof.

[0021] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0022] It is to be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. It is to be further understood that the terms "comprise", "comprising", "comprises", "including", "includes" or "contain" or "containing" when used in this specification, specify the presence of stated features, integers, steps, operations, elements, or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.

[0023] In the clinical application of CBCT medical imaging technology, such as head disease examination, oral diagnosis and treatment, etc., the imaging acquisition range (i.e. FOA) of image reconstruction is determined by the physical size of the detector. Once the effective detection area of the detector is fixed, the boundary of the FOA is determined, and the tissue image information beyond the range cannot be effectively captured. Limited by the physical bottleneck of the detector size, the edge of the actual scanned CBCT image often appears as Figure 1 the "truncated image" shown by the arrow, that is, the tissue image beyond the FOA range is directly truncated, resulting in obvious information loss or artifacts in the edge region (especially the junction region of soft tissue and bone tissue in the head and other parts). Such problems not only destroy the integrity of the image, but also directly interfere with the doctor's judgment of the disease if the truncated region covers the potential lesion; even if the truncated region does not involve the lesion, the incomplete edge information will also reduce the overall image quality, affecting the reliability and accuracy of clinical diagnosis, and causing hidden dangers to subsequent diagnosis and treatment decisions.

[0024] To solve the above truncation shadow problem, two types of processing schemes are mainly used in the related art: one type is to directly fit the edge of the truncated region based on the original single-energy projection for completion, however, soft tissue and bone tissue exist in the target region such as the head at the same time, the density difference of the two types of tissues is significant, and the absorption coefficient of X-ray is also different, the traditional fitting method cannot specifically exclude the interference of bone tissue on the edge gray value, resulting in that the completed edge still has obvious artifacts or gray value deviation, which is difficult to meet the image accuracy requirement of clinical diagnosis; another type is to rely on iterative reconstruction algorithm for optimization, although this type of algorithm has certain effect in the scene of limited view angle (such as insufficient scanning angle caused by local shielding), but it has no obvious advantage in the truncation shadow completion problem caused by limited projection field, because the core of this algorithm is to iteratively optimize the projection and back projection process to reduce the reconstruction error, and the essence of the truncation shadow is data loss, not simply insufficient view angle coverage, so in the iteration process, it is difficult to effectively fill the real tissue information of the truncated region, but it will continuously propagate and amplify the error caused by the initial truncated data, resulting in that the truncation shadow problem of the final reconstructed image is still prominent, and accurate completion cannot be achieved.

[0025] Based on this, in order to solve the problems of poor truncation shadow completion effect and residual edge artifacts of CBCT images in the related art, an embodiment of the present application provides a CBCT image truncation artifact correction method. The method acquires high-energy projection data and low-energy projection data of a target region through a dual-energy CBCT device, obtains soft tissue projection without bone tissue interference through base material decomposition, optimizes the edge fitting curve of the truncated region of the original single-energy CBCT image based on the edge characteristics of the soft tissue projection, and completes the truncated region combined with the gray value information of the soft tissue projection, thereby effectively eliminating the truncation artifacts and improving the integrity and accuracy of the CBCT image, and meeting the observation requirement of edge details for clinical diagnosis.

[0026] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] Figure 2 is a flowchart of a CBCT image truncation artifact correction method according to an exemplary embodiment of the present application. As shown in Figure 2 , the method includes the following steps S201 to S204.

[0028] Step S201: scan the target region through a dual-energy CBCT device to obtain high-energy projection data and low-energy projection data of the target region.

[0029] In this embodiment, the target area can be a site prone to truncation artifacts in clinical practice (such as the head, oral cavity, etc.). This area contains both soft tissue (scalp, muscles, brain tissue, etc.) and bone tissue (skull, jawbone, etc.), and the two types of tissue have significantly different X-ray attenuation. Traditional single-energy completion methods are easily affected by bone tissue interference. Therefore, dual-energy projection data acquisition is necessary to lay the foundation for subsequent accurate correction. During the scanning process, the dual-energy CBCT device can achieve high-energy and low-energy scanning by switching the tube voltage or tube current of the X-ray tube. For example, a tube voltage parameter of 110kVp can be used for high-energy scanning, and a tube voltage parameter of 80kVp can be used for low-energy scanning. During scanning, the device can rotate around the target area to acquire images. For example, the rotation angle range can be set to 360°, and a frame of two-dimensional projection image can be acquired at fixed angle intervals. Then, two-dimensional projection data sequences at corresponding energies are acquired as high-energy projection data and low-energy projection data of the target area, providing a data basis for subsequent soft tissue and bone tissue projection decomposition.

[0030] Step S202: Perform base material decomposition processing on the high-energy projection data and low-energy projection data to obtain the soft tissue projection and bone projection of the target area.

[0031] In this embodiment, the core of the base material decomposition is based on the X-ray attenuation law and the base material equivalence principle, which is the key theoretical basis for achieving projection separation of soft tissue and bone tissue. From a principle perspective, when X-rays of different energies pass through human tissue, their attenuation degree changes with variations in the tissue's atomic number, density, and thickness. The base material equivalence principle states that the attenuation effect of any complex tissue on X-rays can be equivalent to the superposition of attenuations from two "base materials." By acquiring CBCT projection data at both high and low energies, the attenuation information of the mixed tissue within the target area can be decomposed into the attenuation contributions of the two base materials, thereby separating the independent projection information of specific tissues (such as soft tissue and bone tissue), providing an interference-free tissue imaging basis for subsequent edge optimization of the truncated region.

[0032] Specifically, looking at the formula derivation, according to the Lambert-Beer law, the intensity of photons received by the detector after X-rays pass through an object... With incident intensity The relationship satisfies: in, This represents the path of X-rays through the object. For a point on the path Organizations in energy The linear attenuation coefficient is calculated. However, since the detector in a CBCT device actually receives an "energy spectrum integration signal" (non-monochromatic X-rays), the energy spectrum of the X-ray source needs to be introduced. Distribution (unit: number of photons / ( the actual projection gray value at this time (which is proportional to the intensity of the incident X-ray) can be expressed as: Combined with the equivalent principle of base materials, the is substituted into the attenuation formula, and the total attenuation on the path can be decomposed into the superposition of the attenuation of two base materials, that is: wherein is the base material coefficient and the projection value (unit: mm) along the path , which is also the target to be directly solved for dual-energy decomposition.

[0033] If the measured projection data (i.e. the degree of attenuation of the X-ray) is the same, the base material thickness combination and is numerically one-to-one corresponding to the projection value of the base material coefficient , which provides theoretical support for subsequently deducing the base material thickness from the projection data.

[0034] For kV-level X-rays commonly used in diagnostic imaging, the attenuation of monochromatic X-rays with energy after passing through a substance can be linearly represented by the linear attenuation coefficients of two base materials, that is: wherein and are the linear attenuation coefficients (unit: ) of the two base materials at energy . When high-energy and low-energy monochromatic X-rays are used for scanning, two sets of attenuation relationships can be obtained: wherein , are the exit and entrance intensities of the low-energy X-ray, , are the exit and entrance intensities of the high-energy X-ray.

[0035] The core task of dual-energy decomposition is to obtain the high and low energy projection gray values , (corresponding to the gray information of the pixels in the high-energy and low-energy projection data, respectively), solve the above nonlinear equations to obtain the base material projection value , . Wherein, the total low-energy attenuation , the total high-energy attenuation , the solving process needs to establish a base material coefficient projection solving model, that is, to determine the function relationship of , and , . Specifically, it can be realized by an approximate formula: Wherein, the numerical values of the parameters a0, a1, a2, a3, a4, a5, b0, b1, c0, c1, c2, c3, c4, c5, d0, d1 in the formula can be determined by the base material data relationship established by pre-experiment.

[0036] At the actual operation level, in order to efficiently complete the base material decomposition, a "dual-energy projection gray value-base material thickness combination" corresponding relationship lookup table can be constructed in advance: through experiment, collect the dual-energy projection images of soft tissue and bone tissue under different thickness combinations, record the low-energy gray value and the high-energy gray value corresponding to each thickness combination, organize these data into a lookup table and store it, which can be directly called in subsequent decomposition to avoid repeated calculation.

[0037] The specific decomposition steps are as follows: first, extract the high-energy gray value of each pixel point from the high-energy projection data obtained in step S201, and extract the low-energy gray value of the corresponding pixel point from the low-energy projection data. Here, "corresponding pixel point" refers to the same coordinate point in high and low energy projections at the same scanning position, ensuring that the gray value corresponds to the actual tissue position one by one; then, in the pre-constructed lookup table, take the and of the pixel point as the retrieval conditions, find the matching soft tissue thickness (i.e. the base material projection value in the above formula) and bone thickness (i.e. the base material projection value ); finally, fit the soft tissue thickness of all pixel points according to their coordinate positions to generate a soft tissue projection containing only soft tissue attenuation information, and similarly, based on the bone thickness of all pixel points, fit to generate a bone projection containing only bone tissue attenuation information. The soft tissue / bone projection before and after dual-energy decomposition can be referred to Figure 3 , 4 , which can directly and intuitively observe the clear separation effect of the two tissue projections.

[0038] It should be particularly pointed out that the selection of the base material in the embodiment is not random, but follows the core principles of "equivalent coverage + energy spectrum differentiation": "equivalent coverage" requires that the combination of the linear attenuation coefficients of the two base materials , , can approximately cover the attenuation characteristics of all tissues in the target imaging region (such as the head), that is, the linear attenuation coefficients of any tissue in the region , can be obtained by superimposing and with specific weights; "energy spectrum differentiation" requires that the attenuation difference of the two base materials in the high and low energy intervals be significant, ensuring that the projection information of the two types of tissues after decomposition does not overlap. Considering that the present application is aimed at CBCT truncation artifact correction in regions such as the head, and these regions mainly contain soft tissue (such as scalp, brain tissue) and bone (such as skull, jaw), and the attenuation difference of these two types of tissues to X-rays is significant, fully meeting the "equivalent coverage" and "energy spectrum differentiation" principles, so soft tissue and bone tissue can be finally selected as the base material, providing accurate tissue image basis for subsequent truncation region edge optimization.

[0039] Step S203: positioning the truncation region of the original CBCT image to be corrected, and fitting the edge of the truncation region based on the edge features of the soft tissue projection to generate an edge fitting curve of the truncation region.

[0040] In the embodiment, the original CBCT image to be corrected is a single-energy CBCT image directly obtained in clinical diagnosis, and the positioning of the truncation region thereof can be performed in combination with the dual logic of "geometric constraint" and "gray level mutation detection" to ensure the positioning accuracy, avoiding both the omission of the actual truncation region due to pure geometric judgment and the false positive gray level mutation region caused by interference such as metal artifacts.

[0041] Specifically, the CBCT device can preset the geometric parameters of the imaging field of view (FOV) before scanning, such as the preset circular FOV scanning radius R, and the image center as the scanning isocenter , that is, the center reference point of the target region during scanning.

[0042] First, the candidate truncation region is screened by geometric constraint: the distance d of each pixel point in the original CBCT image to the scanning isocenter is calculated, and the distance formula can be ; considering the possible slight positioning deviation during scanning, an edge error such as 2 pixels can also be reserved, and when the distance When the pixel point is determined to be beyond the effective imaging range of the FOV, it is marked as a geometrically-constrained candidate truncation region. Subsequently, another set of candidate truncation regions is screened through gray level mutation detection: the core feature of a truncation region is "sudden loss of image information", which is reflected in the gray level as a significant jump in the gray level values of adjacent pixel points. Therefore, the embodiment can also use gray level mutation detection such as the Sobel gradient algorithm to detect the gray level mutation of the original CBCT image. The gray level gradient value (i.e., the gray level mutation value) of each pixel point in the horizontal and vertical directions is calculated to quantify the gray level difference with the surrounding pixels. A preset gray level mutation threshold value (which is determined based on the common gray level range of the target region CBCT image) is set. When the gray level gradient value of a certain pixel point exceeds the threshold value, it is determined that it is in a gray level mutation region, and it is marked as a gray level mutation candidate truncation region. Finally, to further improve the accuracy of the positioning of the truncation region, the intersection of the geometrically-constrained candidate truncation region and the gray level mutation candidate truncation region is taken as the final truncation region: geometric constraints ensure that the region is spatially consistent with the physical limitations of the FOV, and gray level mutation detection ensures that the region is consistent with the appearance of the truncation shadow in image features. The combination of the two can effectively exclude false positive gray level mutation regions caused by metal implants and the like (such regions have gray level jumps, but do not exceed the geometric range of the FOV and are not part of the truncation region), while avoiding geometric misjudgment regions caused by minor deviations in the FOV parameters, and ultimately obtaining an accurate truncation region range.

[0043] After the truncation region is positioned, the soft tissue projection obtained in step S202 can be used to fit the edge of the truncation region, and an edge fitting curve is generated.

[0044] The traditional method directly fits the edge points of the truncation region of the original single-energy CBCT image (such as linear fitting and quadratic polynomial fitting), but the edge of the truncation region of the original single-energy image is disturbed by bone tissue (such as the overlap of the skull edge and the truncation boundary), resulting in large fluctuations in the gray level of the edge points and "sawtooth" deviations in the fitting curve. In the embodiment, the soft tissue projection obtained in step S202 has excluded the disturbance of bone tissue through dual-energy decomposition (bone tissue has been separated into a separate bone projection), and the edge of the truncation region is only composed of soft tissue (such as the scalp and muscles), with continuous gray level distribution and clear boundary profile, which can provide a "pure edge reference". By mapping the edge features of the soft tissue projection to the original single-energy CBCT image, the edge fitting deviation caused by bone tissue in the single-energy image can be corrected.

[0045] The specific fitting steps are as follows: first, soft tissue projection edge extraction is performed: the same gray level mutation detection method (such as Sobel gradient + threshold screening) as the original CBCT image can be used to detect the gray level mutation of the soft tissue projection image, so as to ensure the consistency of the edge extraction logic; the pixel points meeting the preset gray level mutation characteristics in the soft tissue projection are extracted to form a soft tissue projection truncated edge point set P, which directly reflects the true edge profile of the soft tissue in the truncated region. Then, edge point screening and segmented curve fitting are performed: to further remove possible noise points (such as isolated gray level mutation points caused by scanning noise), the least square method can be used to calculate the local slope of the edge point set P, for example, taking 5 adjacent edge points as a calculation window, calculating the gray level gradient slope in each window, and screening the edge points with a slope change meeting the preset continuity condition (such as a slope change amplitude less than a preset change threshold), the slope of these points is continuous, which meets the natural form of the soft tissue edge, and then the abnormal points with a sudden change in slope (such as noise interference, not the true soft tissue edge) can be removed; then, based on the slope change trend of the effective edge points, the segmented curve fitting is performed to generate the edge fitting curve of the truncated region: for example, when the slope of a certain segment of effective edge points remains stable, linear fitting is adopted; when the slope changes slowly, quadratic polynomial fitting is adopted, and through segmented processing, the edge curve can meet the natural contour of the soft tissue, and also has local continuity and overall smoothness. Finally, the edge fitting curve obtained by fitting can be further smoothed and optimized: for example, the edge curve obtained by segmented fitting is subjected to Gaussian filtering, and the standard deviation of the Gaussian filtering is set (for example, the standard deviation is set to 0.5) (this parameter can effectively eliminate the curve fluctuation caused by slight noise while preserving the edge contour), and the residual noise is further smoothed by filtering to obtain the optimized edge fitting curve of the truncated region, which can accurately reflect the true edge form of the soft tissue in the truncated region, and provide accurate boundary reference for subsequent truncated image completion. =0.5) (this parameter can effectively eliminate the curve fluctuation caused by slight noise while preserving the edge contour), and the residual noise is further smoothed by filtering to obtain the optimized edge fitting curve of the truncated region, which can accurately reflect the true edge form of the soft tissue in the truncated region, and provide accurate boundary reference for subsequent truncated image completion.

[0046] Step S204: The edge fitting curve is taken as the boundary, the gray level information of the soft tissue projection is combined, the truncated region is completed, and is fused with the non-truncated region of the original CBCT image to obtain the corrected CBCT image.

[0047] In this embodiment, the original CBCT image can be fused and completed by using the "layered processing, gray level matching" method, that is, taking the optimized edge fitting curve obtained in step S203 as the boundary reference, combining the gray level distribution law of the original CBCT image and the true tissue gray level characteristics of the soft tissue projection, dividing the image into different regions for targeted processing, so as to ensure the authenticity of the tissue information in the completed region, avoid the gray level discontinuity between the completed region and the normal region, and finally realize the seamless fusion of the image.

[0048] Specifically, the original CBCT image can be divided into regions first: based on the positional relationship between the pixels and the edge fitting curve in the original CBCT image, the image is divided into three regions: the first is the normal region (R1), which is the region inside the edge fitting curve and close to the preset scan center. This region is within the effective FOV imaging range, and the tissue grayscale information is complete, so no supplementation is needed and the original grayscale can be directly retained later; the second is the transition region (R2), which is the region with a preset width on both sides of the edge fitting curve (such as a range of 5 pixels on both sides of the edge fitting curve). This region serves as the "grayscale connection zone" between the normal region and the supplemented region. Since the grayscale sources of the normal region and the supplemented region are different (the former is the original scan data, and the latter is the soft tissue projection mapping data), direct stitching is prone to obvious grayscale jumps. Therefore, it is necessary to set a transition region to achieve smooth grayscale transition by grayscale fusion; the third is the supplemented region (R3), which is the truncated region outside the edge fitting curve and beyond the FOV range. This region has no effective tissue scan data and needs to be supplemented based on the grayscale information of soft tissue projection.

[0049] After the regions are divided, targeted grayscale processing is performed on the three regions respectively. The completion region (R3) and the transition region (R2) are the core of the processing, while the normal region (R1) can simply retain the original grayscale.

[0050] For the completed region (R3), it is necessary to generate a completed grayscale value that matches the original image based on the grayscale information of the soft tissue projection. The core of this is to achieve grayscale matching through "coordinate mapping + grayscale transformation": First, coordinate mapping is performed to transform any pixel within the completed region R3. Directly mapped to the same coordinates of the soft tissue projection Since the soft tissue projection has the same scanning field of view and spatial position as the original CBCT image, and the same coordinates correspond to the same physical location, the gray value of that location in the soft tissue projection can be directly obtained. Next, grayscale conversion is performed to avoid grayscale discrepancies in the completed area caused by differences in grayscale measurement standards between the soft tissue projection and the original image. Specifically, grayscale conversion can employ a mapping logic of "background grayscale alignment + edge grayscale matching," first obtaining two sets of key grayscale averages: one being the grayscale average of the background region in the original monoenergetic CBCT image. (Typically close to -1000 HU), and the mean edge gray value at the R2 boundary in the original image. Secondly, the average grayscale value of the background region (air region without any tissue) in the soft tissue projection. And the mean gray value of the soft tissue in the soft tissue projection of the transition region R2. Furthermore, based on the gray-scale mean of the soft tissue in the transition region of the original CBCT image... Compared with the average gray level of the background The difference, and the mean gray value of the soft tissue in the transition region of the soft tissue projection. Compared with the average gray level of the background The difference is used to determine the grayscale mapping relationship between the original CBCT image and the soft tissue projection. Based on this grayscale mapping relationship, the grayscale value of the corresponding position of the pixel in the soft tissue projection is determined. The grayscale value is converted to a padded grayscale that matches the original CBCT image, and this padded grayscale value is used as the target grayscale value for that pixel. That is, the grayscale value in the soft tissue projection is mapped using the following formula. Converted to a padded grayscale that matches the original image : The above formula uses background grayscale and Alignment is performed to ensure that the background grayscale of the filled area matches that of the original image; grayscale transitions are used to ensure the grayscale of the transition area matches that of the original image. and The proportional mapping ensures a seamless connection between the grayscale at the edge of the completed area and the transition area, completely avoiding grayscale discontinuities.

[0051] For the transition region (R2), a distance-weighted fusion method can be used to achieve a smooth grayscale transition: First, obtain any pixel within the transition region R2. Original grayscale values ​​in the original CBCT image And the completed grayscale value of the corresponding position of the pixel in the soft tissue projection, obtained based on the above grayscale mapping relationship. Then, calculate the pixel point. The vertical distance *d* to the edge-fitted curve is calculated, where *d* > 0 indicates the pixel is inside the curve (closer to the normal region R1), *d* < 0 indicates it is outside the curve (closer to the padded region R3), and *d* = 0 represents the curve itself. Then, based on the vertical distance *d*, the fusion weights of the original grayscale value and the padded grayscale value are determined. These weights can change linearly with distance to ensure a smooth transition of grayscale from R1 to R3. When *d* > 0, the original image grayscale fusion weights can be set to... The grayscale blending weights can be set to... (That is, the closer a pixel is to the normal region R1, the higher the fusion weight of the original grayscale value, so as to retain more original scan information); when d<0, the original image grayscale fusion weight can be set to The grayscale blending weights can be set to... (That is, the closer a pixel is to the padding region R3, the higher the fusion weight of the padding grayscale, to ensure a natural transition with the padding region.) The specific form of the fusion weight between the original grayscale value and the padding grayscale can be set according to the user's actual needs; this embodiment does not impose any restrictions. Finally, the original grayscale value and the padding grayscale are weighted based on the fusion weight to obtain the fused grayscale of the pixel, and this fused grayscale is used as the target grayscale of the pixel. The weighting formula can be... .

[0052] After the grayscale processing of the three regions is completed, the target grayscale of all pixels in the normal region, transition region, and padding region can be stitched together (i.e., the original grayscale of the normal region R1 and the blended grayscale of the transition region R2). The completed grayscale of region R3. This process yields a complete, corrected CBCT image. This image eliminates edge truncation artifacts while preserving the original image's tissue detail and authenticity, fully meeting the clinical diagnostic requirements for image integrity and accuracy.

[0053] Furthermore, to achieve further edge smoothing and noise suppression in CBCT images, the overall image can be optimized: for example, a 3×3 Gaussian filter can be applied to the transition region R2 (e.g., by setting...). =0.8) is used for smoothing to further eliminate subtle gray-level fluctuations after weighted fusion and ensure a more natural gray-level transition near the edge curve; and non-local mean filtering is used on the completed region R3 (such as setting a 7×7 search window and a 3×3 similarity window) to suppress scanning noise while preserving the soft tissue edge features of the completed area to the greatest extent (non-local mean filtering can reduce noise by matching the gray-level distribution of similar pixel blocks, avoiding the blurring of edges caused by traditional filtering).

[0054] To more intuitively demonstrate the effect of the proposed solution on correcting truncation artifacts in CBCT images, Figure 5a , 5b This image shows a comparison of head CBCT images before and after correction. Figure 5a The original CBCT image to be corrected. Figure 5b The image shown is a corrected CBCT image obtained using the method described in the embodiments of this application. From... Figure 5a It is evident that there are obvious truncation artifacts in the edge regions of the original CBCT images, while Figure 5b The truncation artifacts in the corrected images have been effectively eliminated. Therefore, it is clear that the solution of this application embodiment can completely solve the problems of artifact residue and information deviation after completion using traditional methods, significantly improving the integrity and accuracy of CBCT images, ensuring that doctors can clearly observe tissue details in the edge areas, fully meeting the clinical diagnostic needs for observing image edge details, and providing reliable image support for subsequent disease assessment and treatment plan formulation.

[0055] Corresponding to the embodiments of the foregoing method, the embodiments of the present application also provide a CBCT image truncation artifact correction device. Figure 6 is a structural schematic diagram of a CBCT image truncation artifact correction device according to an exemplary embodiment of the present application. As shown in the figure, the device comprises: Figure 6 A dual-energy projection data acquisition module 601 is configured to scan a target region by a dual-energy CBCT device, and acquire high-energy projection data and low-energy projection data of the target region. A basis material projection decomposition module 602 is configured to perform basis material decomposition processing on the high-energy projection data and the low-energy projection data, and obtain soft tissue projection and bone projection of the target region. An edge curve fitting module 603 is configured to locate a truncation region of an original CBCT image to be corrected, and perform edge fitting on the truncation region based on edge features of the soft tissue projection, to generate an edge fitting curve of the truncation region. An image correction module 604 is configured to complete the truncation region based on the edge fitting curve as a boundary and in combination with gray scale information of the soft tissue projection, and fuse the non-truncation region of the original CBCT image, to obtain a corrected CBCT image.

[0056] The implementation process of the functions and roles of each module in the above device is specifically described in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0057] For the device embodiments, since they basically correspond to the method embodiments, the related parts can be referred to the part of the method embodiments. The above described device embodiments are only illustrative, and the modules described as separate components can be or can not be physically separated, and the components displayed as modules can be or can not be physical modules, that is, they can be located in one place, or can be distributed on multiple network modules. According to actual needs, part or all of the modules can be selected to achieve the purpose of the present application scheme. Those skilled in the art can understand and implement without creative labor.

[0058] Corresponding to the embodiments of the foregoing method, the embodiments of the present application also provide a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor; wherein the processor executes the computer program to implement the steps of the CBCT image truncation artifact correction method described in any of the embodiments.

[0059] ​Exemplarily, the processor includes, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), or a field-programmable gate array (FPGA), etc.

[0060] Exemplarily, the memory can include at least one type of storage medium, including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc.

[0061] Figure 7 is a structural block diagram of a computer device according to an exemplary embodiment of the present application. As shown in Figure 7 the hardware level, the computer device includes a processor 701, an internal bus 702, a network interface 703, a memory 704, and a non-volatile memory 705, and can also include other hardware required by a business. One or more embodiments of the present application can be implemented in a software manner, such as reading a corresponding computer program from the non-volatile memory 705 into the memory 704 by the processor 701 and then running. Of course, in addition to the software implementation, one or more embodiments of the present application do not exclude other implementation manners, such as a logic device or a combination of software and hardware, etc., that is, the execution subject of the above processing flow is not limited to each logic unit, but can also be hardware or a logic device.

[0062] Corresponding to the embodiments of the foregoing method, the embodiments of the present application also provide a computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement the steps of the CBCT image truncation artifact correction method described in any of the embodiments.

[0063] Corresponding to the embodiments of the foregoing method, the embodiments of the present application also provide a computer program product including a computer program, and the computer program is executed by a processor to implement the steps of the CBCT image truncation artifact correction method described in any of the embodiments.

[0064] The above described specific embodiments of the application. Other embodiments are within the scope of the following claims. In some cases, an act or step can be performed in a different order from the order described in an embodiment, and still achieve the desired outcome. Also, the processes depicted in the accompanying figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0065] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0066] It is to be understood that the application is not limited to the precise details of construction and the above-described and shown exact construction, and that various modifications and changes can be effected therein by those skilled in the art without departing from the scope of the application. The scope of the application should be determined by the claims appended hereto.

[0067] The above description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the application should, therefore, be determined not with reference to the above description, but instead with reference to the appended claims, along with their full scope of equivalents.

Claims

1. A method for correcting truncation artifacts in CBCT images, characterized in that, include: The target area is scanned using a dual-energy CBCT device to obtain high-energy projection data and low-energy projection data of the target area; The high-energy projection data and low-energy projection data are subjected to matrix material decomposition processing to obtain the soft tissue projection and bone projection of the target region. Locate the truncated region of the original CBCT image to be corrected, and perform edge fitting on the truncated region based on the edge features of the soft tissue projection to generate the edge fitting curve of the truncated region. Using the edge fitting curve as the boundary and combining the grayscale information of the soft tissue projection, the truncated region is completed and fused with the non-truncated region of the original CBCT image to obtain the corrected CBCT image.

2. The method according to claim 1, characterized in that, The high-energy projection data and low-energy projection data are subjected to matrix material decomposition processing to obtain the soft tissue projection and bone projection of the target region, including: Extract the high-energy grayscale value of each pixel in the high-energy projection data and the low-energy grayscale value of the corresponding pixel in the low-energy projection data respectively. In a pre-built lookup table of correspondence between dual-energy projection gray values ​​and base material thickness, the soft tissue thickness and bone thickness that match the low-energy gray values ​​and high-energy gray values ​​of each pixel are searched. The soft tissue projection is generated by fitting the soft tissue thickness of each pixel, and the bone projection is generated by fitting the bone thickness of each pixel.

3. The method according to claim 1, characterized in that, Locate the truncated region of the original CBCT image to be corrected, including: Calculate the distance from each pixel in the original CBCT image to the preset scan isocenter, and mark the pixels whose distance exceeds the sum of the preset scan radius and the edge error as geometric constraint candidate truncation regions; The original CBCT image is subjected to gray-level abrupt change detection, and pixels with gray-level abrupt change values ​​exceeding a preset threshold are marked as gray-level abrupt change candidate truncation regions. The intersection of the geometric constraint candidate truncation region and the gray-scale abrupt change candidate truncation region is taken as the truncation region.

4. The method according to claim 1, characterized in that, Based on the edge features of the soft tissue projection, edge fitting is performed on the truncated region to generate an edge fitting curve for the truncated region, including: Perform grayscale abrupt change detection on the soft tissue projection and extract the edge point set of the truncated region in the soft tissue projection; Local slope calculations are performed on the set of edge points to filter out valid edge points whose slope changes meet preset continuity conditions; Based on the slope change trend of the effective edge points, piecewise curve fitting is performed to generate the edge fitting curve of the truncated region.

5. The method according to claim 1, characterized in that, Using the edge fitting curve as the boundary and combining the grayscale information of the soft tissue projection, the truncated region is completed and fused with the non-truncated region of the original CBCT image to obtain the corrected CBCT image, including: Based on the positional relationship between the pixels in the original CBCT image and the edge fitting curve, the original CBCT image is divided into a normal region, a transition region, and a complete region; wherein, the normal region is the non-truncated region inside the edge fitting curve and close to the preset scan isocenter, the transition region is the region with a preset width on both sides of the edge fitting curve, and the complete region is the truncated region outside the edge fitting curve. For any pixel in the normal region, the original gray value of that pixel in the original CBCT image is taken as the target gray value of that pixel. For any pixel within the completed area, a target grayscale value for that pixel is generated based on the grayscale value of the corresponding position in the soft tissue projection. For any pixel within the transition region, the target grayscale of the pixel is generated by fusing the original grayscale value of the pixel in the original CBCT image and the grayscale value of the corresponding position of the pixel in the soft tissue projection. By stitching together the target grayscale values ​​of all pixels in the normal region, transition region, and complete region, the corrected CBCT image is obtained.

6. The method according to claim 5, characterized in that, For any pixel within the completed area, based on the grayscale value of the pixel at its corresponding position in the soft tissue projection, a target grayscale value for that pixel is generated, including: For any pixel within the completed area, extract the grayscale value of the corresponding position of that pixel in the soft tissue projection; The average background grayscale value of the original CBCT image, the average grayscale value of the soft tissue in the transition area of ​​the original CBCT image, the average background grayscale value of the soft tissue projection, and the average grayscale value of the soft tissue in the transition area of ​​the soft tissue projection are obtained respectively. Based on the difference between the mean gray value of the soft tissue in the transition region and the mean gray value of the background in the original CBCT image, and the difference between the mean gray value of the soft tissue in the transition region and the mean gray value of the background in the soft tissue projection, the gray-level mapping relationship between the original CBCT image and the soft tissue projection is determined. According to the grayscale mapping relationship, the grayscale value of the pixel at the corresponding position in the soft tissue projection is converted into a completed grayscale that matches the original CBCT image, and the completed grayscale is used as the target grayscale of the pixel.

7. The method according to claim 6, characterized in that, For any pixel within the transition region, based on the original grayscale value of that pixel in the original CBCT image and the grayscale value of the corresponding position of that pixel in the soft tissue projection, a target grayscale value for that pixel is generated by fusing the grayscale values, including: For any pixel in the transition region, obtain the original grayscale value of the pixel in the original CBCT image, and the completed grayscale value of the pixel at the corresponding position in the soft tissue projection based on the grayscale mapping relationship. Calculate the vertical distance from the pixel to the edge fitting curve; Based on the vertical distance, the fusion weight of the original grayscale value and the padded grayscale value is determined, wherein the closer the pixel is to the normal area, the higher the fusion weight of the original grayscale value, and the closer the pixel is to the padded area, the higher the fusion weight of the padded grayscale value. The original grayscale value and the padded grayscale value are weighted and calculated based on the fusion weight to obtain the fused grayscale value of the pixel, and the fused grayscale value is used as the target grayscale value of the pixel.

8. A device for correcting truncation artifacts in CBCT images, characterized in that, include: The dual-energy projection data acquisition module is used to scan a target area using a dual-energy CBCT device to acquire high-energy projection data and low-energy projection data of the target area. The base material projection decomposition module is used to perform base material decomposition processing on the high-energy projection data and low-energy projection data to obtain the soft tissue projection and bone projection of the target area. The edge curve fitting module is used to locate the truncated region of the original CBCT image to be corrected, and to perform edge fitting on the truncated region based on the edge features of the soft tissue projection, thereby generating the edge fitting curve of the truncated region. The image correction module is used to complete the truncated region by using the edge fitting curve as the boundary and combining the grayscale information of the soft tissue projection, and then merge it with the non-truncated region of the original CBCT image to obtain the corrected CBCT image.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7.

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