Self-adaptive ring artifact removal method, device and equipment based on mean projection and medium

Abstract

The invention discloses a self-adaptive ring artifact removal method, device and equipment based on mean projection and a medium, and the method comprises the steps: carrying out reconstruction operation on original mean projection according to a first spatial parameter to obtain pre-reconstructed mean projection, and detecting a non-linear region in the pre-reconstructed mean projection, performing a reconstruction operation on the nonlinear region according to the second spatial parameter to obtain a reconstruction mean projection; and carrying out artifact removal on projection data according to the original mean projection and the reconstructed mean projection. According to the method, the original mean projection is pre-reconstructed by using the first spatial parameter, and the nonlinear region in the pre-reconstructed mean projection is optimized and reconstructed by using the second spatial parameter, so that the accuracy of the nonlinear region is improved on the basis of ensuring the smoothness of the reconstructed mean projection, the removal effect of stripe artifacts is effectively ensured, and the user experience is improved. Therefore, the removal effect of the ring artifacts in the reconstructed CT image is improved.

Description

Technical Field

[0001] This application relates to the field of CT imaging technology, and in particular to an adaptive annular artifact removal method, apparatus, device and medium based on mean projection. Background Technology

[0002] CT imaging technology is widely used in medical diagnosis and industrial inspection. A CT imaging system projects a scan onto the object to be imaged from different angles and uses a detector to acquire the energy data of the X-rays passing through the object. The data is then reconstructed to obtain a CT image of the object. Therefore, the detector is a crucial component of the CT imaging system. However, the development of detector-related physics and hardware is still subject to technological limitations. For example, inconsistent response of detector units can lead to ring artifacts in CT images, thus reducing image quality.

[0003] Currently, methods for removing annular artifacts mainly utilize the linear properties of fringe artifacts on the projected sine wave, processing artifacts that appear as vertical straight lines and using data compensation correction to achieve the effect of removing annular artifacts. However, such methods are prone to causing image structure distortion and are difficult to effectively remove artifacts in areas with large projection abrupt changes, thus affecting the effectiveness of annular artifact removal.

[0004] Therefore, the existing technology still needs to be improved and enhanced. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide an adaptive annular artifact removal method, apparatus, device and medium based on mean projection, which addresses the shortcomings of the prior art.

[0006] To address the aforementioned technical problems, the first aspect of this application provides an adaptive annular artifact removal method based on mean projection, wherein the adaptive annular artifact removal method based on mean projection specifically includes:

[0007] Obtain the raw mean projection of projection data acquired through a CT imaging system;

[0008] A first spatial parameter is selected for the original mean projection, and a reconstruction operation is performed on the original mean projection based on the first spatial parameter to obtain a pre-reconstructed mean projection;

[0009] Detect the nonlinear region in the pre-reconstructed mean projection;

[0010] A second spatial parameter is selected for the nonlinear region based on the first spatial parameter, and a reconstruction operation is performed on the nonlinear region based on the second spatial parameter to obtain the reconstructed mean projection;

[0011] The projection data is corrected for stripe artifacts based on the original mean projection and the reconstructed mean projection.

[0012] The adaptive annular artifact removal method based on mean projection, wherein the reconstruction operation employs a locally intrinsic weighted linear regression algorithm, and the execution process of the reconstruction operation specifically includes:

[0013] Calculate the spatial similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on spatial parameters;

[0014] Calculate the intrinsic similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on the intrinsic parameters;

[0015] The local intrinsic weight of the neighboring projection point is determined based on the spatial similarity weight and the intrinsic similarity weight.

[0016] Based on the local intrinsic weights of the neighboring projection points, a locally intrinsic weighted linear regression is performed on the projection point to be reconstructed to obtain the reconstructed projection point corresponding to the projection point to be reconstructed.

[0017] The adaptive annular artifact removal method based on mean projection, wherein calculating the spatial similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on spatial parameters specifically includes:

[0018] Calculate the spatial distance between the neighboring projection points and the projection point to be reconstructed;

[0019] The spatial similarity weight of the neighboring projection points is calculated based on the spatial distance and the spatial parameters, wherein the spatial distance and the spatial similarity weight increase in opposite directions.

[0020] The adaptive annular artifact removal method based on mean projection, wherein the process of obtaining the intrinsic similarity weight specifically includes:

[0021] Obtain the average difference between the projection values ​​of the neighboring projection point and the projection points in the neighboring region of the neighboring projection point;

[0022] The intrinsic similarity weight of the neighboring projection points is calculated based on the mean difference of the projection values ​​and the intrinsic parameters, wherein the mean difference of the projection values ​​and the intrinsic similarity weight grow in opposite directions.

[0023] The adaptive annular artifact removal method based on mean projection is described in which the first spatial parameter is greater than the second spatial parameter.

[0024] The adaptive annular artifact removal method based on mean projection, wherein detecting the nonlinear region in the pre-reconstructed mean projection specifically includes:

[0025] For each pre-reconstructed mean projection point in the pre-reconstructed mean projection, the absolute difference between the pre-reconstructed mean projection point and its neighboring region is obtained to obtain the nonlinearity of the pre-reconstructed mean projection point.

[0026] The nonlinear threshold is calculated based on the maximum and minimum nonlinear values ​​in the pre-reconstructed mean projection.

[0027] Pre-reconstructed mean projection points with nonlinear quantities greater than the nonlinear threshold are selected to obtain the nonlinear region.

[0028] The adaptive annular artifact removal method based on mean projection, wherein the step of correcting stripe artifacts in the projection data according to the original mean projection and the reconstructed mean projection specifically includes:

[0029] Calculate the mean projection difference between the reconstructed mean projection and the original mean projection;

[0030] Based on the mean projection difference, artifact removal is performed on the projection data, and a CT image is generated based on the projection data after the ring artifact removal.

[0031] A second aspect of this application provides an adaptive annular artifact removal device based on mean projection, wherein the adaptive annular artifact removal device based on mean projection specifically includes:

[0032] The acquisition module is used to acquire the original mean projection of the projection data collected by the CT imaging system, and to select a first spatial parameter for the original mean projection;

[0033] The pre-reconstruction module is used to perform a reconstruction operation on the original mean projection based on the first spatial parameters to obtain a pre-reconstructed mean projection.

[0034] The detection module is used to detect nonlinear regions in the pre-reconstructed mean projection and select second spatial parameters for the nonlinear regions based on the first spatial parameters.

[0035] The reconstruction module is used to perform a reconstruction operation on the nonlinear region based on the second spatial parameters to obtain the reconstructed mean projection.

[0036] The correction module is used to correct stripe artifacts on the projection data based on the original mean projection and the reconstructed mean projection.

[0037] A third aspect of this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the adaptive annular artifact removal method based on mean projection as described above.

[0038] A fourth aspect of this application provides a terminal device, which includes: a processor and a memory;

[0039] The memory stores a computer-readable program that can be executed by the processor;

[0040] When the processor executes the computer-readable program, it implements the steps in any of the above-described adaptive annular artifact removal methods based on mean projection.

[0041] Beneficial Effects: Compared with existing technologies, this application provides an adaptive annular artifact removal method, apparatus, device, and medium based on mean projection. The method includes acquiring the original mean projection of projection data collected by a CT imaging system and selecting a first spatial parameter for the original mean projection; performing a reconstruction operation on the original mean projection according to the first spatial parameter to obtain a pre-reconstructed mean projection; detecting nonlinear regions in the pre-reconstructed mean projection and selecting a second spatial parameter for the nonlinear region according to the first spatial parameter; performing a reconstruction operation on the nonlinear region according to the second spatial parameter to obtain a reconstructed mean projection; and correcting stripe artifacts in the projection data based on the original mean projection and the reconstructed mean projection. This application first uses the first spatial parameter to pre-reconstruct the original mean projection, and then uses the second spatial parameter to optimize the reconstruction of the nonlinear region in the pre-reconstructed mean projection. This improves the accuracy of the nonlinear region while ensuring the smoothness of the reconstructed mean projection, effectively ensuring the smoothness and accuracy of the reconstructed mean projection, thereby effectively ensuring the removal effect of stripe artifacts and improving the removal effect of annular artifacts in reconstructed CT images. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an example image of projection data containing stripe artifacts.

[0044] Figure 2 for Figure 1The image shown is an example of a reconstructed CT image obtained from the projection data.

[0045] Figure 3 Example diagram of ideal projection data.

[0046] Figure 4 A flowchart of an adaptive annular artifact removal method based on mean projection provided in an embodiment of this application.

[0047] Figure 5 This is a schematic diagram illustrating the execution process of the adaptive annular artifact removal method based on mean projection provided in an embodiment of this application.

[0048] Figure 6 A comparison chart of the reconstructed mean projections for different spatial parameters.

[0049] Figure 7 Example diagrams for linear and nonlinear regions with several different properties.

[0050] Figure 8 This is a schematic diagram of the adaptive annular artifact removal device based on mean projection provided in the embodiments of this application.

[0051] Figure 9 A schematic block diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0052] This application provides an adaptive annular artifact removal method, apparatus, device, and medium based on mean projection. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0053] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0054] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0055] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0056] This embodiment provides an adaptive annular artifact removal method based on mean projection applied to a scenario where annular artifacts in reconstructed CT images are removed. In this scenario, when a CT imaging system scans an object using a detector, inconsistent responses among the detector units can lead to stripe artifacts in the projected data acquired by the detector, resulting in annular artifacts in the reconstructed CT image and affecting its image quality. For example, ... Figure 1 As shown, the projection data acquired by the detector in a CT imaging system contains striated artifacts. Therefore, if... Figure 2 As shown, the reconstructed CT image obtained from this projection data contains ring artifacts.

[0057] In stable CT imaging systems, striate artifacts in the projection domain typically have approximately constant values. Based on this property of striate artifacts, the performance of individual detector units can be extracted and expressed using mean projection, and annular artifacts can be removed using mean projection. Simultaneously, by... Figure 3 The ideal mean projection curve shown and as Figure 1 The mean projection curves showing the inclusion of stripe artifacts demonstrate that an ideal mean projection curve exhibits good smoothness, while stripe artifacts cause abnormal spikes in the mean projection. Therefore, accurate reconstruction of the ideal mean projection, combined with the removal of outliers (stripe artifacts) using the constant numerical properties of the stripe artifacts, can correct for ring artifacts. In the process of reconstructing an ideal mean projection, the accuracy of the reconstructed values ​​and the smoothness of the reconstructed mean projection curve are two crucial criteria.

[0058] Based on this, in this embodiment, the original mean projection of the projection data acquired by the CT imaging system is first obtained, and a first spatial parameter is selected for the original mean projection; a reconstruction operation is performed on the original mean projection according to the first spatial parameter to obtain a pre-reconstructed mean projection; nonlinear regions in the pre-reconstructed mean projection are detected, and a second spatial parameter is selected for the nonlinear region according to the first spatial parameter; a reconstruction operation is performed on the nonlinear region according to the second spatial parameter to obtain a reconstructed mean projection; and stripe artifact correction is performed on the projection data based on the original mean projection and the reconstructed mean projection. This application first uses the first spatial parameter to pre-reconstruct the original mean projection, and then uses the second spatial parameter to optimize the reconstruction of the nonlinear region in the pre-reconstructed mean projection. This improves the accuracy of the nonlinear region while ensuring the smoothness of the reconstructed mean projection, effectively guaranteeing the smoothness and accuracy of the reconstructed mean projection, thereby effectively ensuring the stripe artifact removal effect and improving the image quality of the reconstructed CT image.

[0059] The application content will be further explained below with reference to the accompanying drawings and the description of the embodiments.

[0060] This embodiment provides an adaptive ring artifact removal method based on mean projection, such as... Figure 4 and Figure 5 As shown, the adaptive annular artifact removal method based on mean projection specifically includes:

[0061] S10. Obtain the original mean projection of the projection data acquired through the CT imaging system.

[0062] Specifically, projection data includes projected image data of the same scanned object at multiple scanning angles. The scanned object can be a patient, a sick small animal, a workpiece, etc. The projection data can be represented as:

[0063] f = {F(i,j)}, i = 0, ...,N r j = 0, ..., N d ,

[0064] Where F represents the projection data, N r N represents the scanning angle (typically set to 360°) during projection by the CT imaging system. d This indicates the number of detector units.

[0065] Furthermore, when the j-th d When a detector unit has a defect, the projection data received by that detector unit will have abnormal values ​​at all projection angles. Therefore, the j-th detector unit... d The projection data collected by each detector unit can be represented as:

[0066]

[0067] in, In ideal condition, detector element j represents d The received projection values, ε(i,j) d () indicates an abnormal value.

[0068] In a stable CT imaging system, the abnormal value ε(i,j) d The values ​​of ε(i) are approximately the same in the scanning angle direction, i.e. m ,j d )=ε(i n ,j d ),m,n∈{0,…,N r This leads to stripe artifacts in the projected data.

[0069] The original mean projection is the average of the data collected by the detector unit at all projection angles. Specifically, the original mean projection can be expressed as:

[0070] MP = {MP(j)}, j = 0, ..., N d ,

[0071]

[0072] Where MP represents the original mean projection, and MP(j) represents the original mean projection of detector element j.

[0073] S20. Select a first spatial parameter for the original mean projection, and perform a reconstruction operation on the original mean projection based on the first spatial parameter to obtain a pre-reconstructed mean projection.

[0074] Specifically, when projection data is affected by fringe artifacts, the mean projection contains anomalous abrupt changes in value. These anomalous abrupt changes disrupt the smoothness of the original mean projection, making it impossible to accurately obtain the geometric properties of the ideal mean projection. Therefore, after obtaining the original mean projection, a first spatial parameter is selected for it. Then, a reconstruction operation is performed on the original mean projection based on the first spatial parameter to obtain a pre-reconstructed mean projection. This effectively ensures the smoothness of the mean projection and removes the influence of anomalous values ​​on the analysis of the geometric properties of the mean projection.

[0075] The first spatial parameter is selected with the goal of ensuring smoothness. When performing a reconstruction operation on the original projected mean, this first spatial parameter is used as the reconstruction parameter to reconstruct the original mean projection to obtain pre-reconstructed mean projection data with better smoothness. The first spatial parameter can be preset; it can also be randomly generated based on a preset spatial parameter range, such as a preset range of 6-16. When selecting the first spatial parameter for the original mean projection, a value can be randomly selected from 6-16; alternatively, the first spatial parameter can be selected based on the number of abnormal values ​​in the projection data, such as the correspondence between the number of abnormal values ​​and the first spatial parameter. When selecting the first spatial parameter for the original mean projection, the number of abnormal values ​​in the projection data is detected, and the first spatial parameter is selected based on the detected number of abnormal values ​​and this correspondence. The larger the number of abnormal values, the larger the selected first spatial parameter.

[0076] Furthermore, the reconstruction operation performed on the original mean projection and the reconstruction operation performed on the nonlinear region in the pre-reconstructed mean projection are performed in the same process. The difference lies in that the original mean projection uses a first spatial parameter, while the nonlinear region in the pre-reconstructed mean projection uses a second spatial parameter. Therefore, the reconstruction operation process will be explained here using spatial parameters as an example.

[0077] For example, the reconstruction operation employs a locally intrinsically weighted linear regression algorithm, which is an efficient regression model that assumes the linear properties of local regions of a function. Therefore, a linear regression model / function can be used to reconstruct / regress the relationships between local data. Simultaneously, the intrinsic property weights of the sampled data (mean projection) are used to distinguish the reasonableness of the sampled data, thereby improving the accuracy of the regression / reconstruction results. The regression / reconstruction process of the locally intrinsically weighted linear regression algorithm can be represented as follows:

[0078]

[0079] in, Indicates sampling point Linear regression model / function Represents the regression coefficient. express The local neighborhood, Indicates the size of the neighborhood. Represents local neighborhood The sampled data in the middle.

[0080] As can be seen from the regression / reconstruction process of the Local Intrinsic Weighted Linear Regression (LIWR) algorithm, it constructs a linear regression model within a local neighborhood and uses only local neighborhood data to regress and reconstruct the linear relationship, thus ensuring the efficiency of the reconstruction operation.

[0081] Furthermore, due to the presence of outliers caused by fringe artifacts in the original mean projection, this application, when reconstructing the original mean projection using the locally intrinsically weighted linear regression algorithm, will incorporate the locally intrinsic weights w. i The weights are decomposed into the product of spatial similarity weights and intrinsic similarity weights (i.e., local intrinsic weight = spatial similarity weight * intrinsic similarity weight). By using spatial similarity weights and intrinsic similarity weights, the contributions of different data to the regression model can be distinguished, thereby effectively differentiating the effects of reasonable mean projection and abnormal mean projection on the regression model and improving the reconstruction effect.

[0082] Based on this, the specific execution process of the reconstruction operation includes:

[0083] Calculate the spatial similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on spatial parameters;

[0084] Calculate the intrinsic similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on the intrinsic parameters;

[0085] The local intrinsic weight of the neighboring projection point is determined based on the spatial similarity weight and the intrinsic similarity weight.

[0086] Based on the local intrinsic weights of the neighboring projection points, a locally intrinsic weighted linear regression is performed on the projection point to be reconstructed to obtain the reconstructed projection point corresponding to the projection point to be reconstructed.

[0087] Specifically, spatial similarity weights reflect the spatial distance between the projected point to be reconstructed and its neighboring projected points, thus mitigating the impact of outlier mean projections on the regression process. Intrinsic similarity weights reflect the reasonableness of using local differences to measure mean projections, thereby increasing the contribution of reasonable projections to the regression model. This embodiment determines local intrinsic weights using spatial and intrinsic similarity weights, and then reconstructs the regression using a locally weighted linear regression algorithm based on these local intrinsic weights. This not only weakens the impact of outlier mean projections on the regression process but also increases the contribution of reasonable projections to the regression model, thereby improving the accuracy of the regression results.

[0088] In one embodiment, calculating the spatial similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on spatial parameters specifically includes:

[0089] Calculate the spatial distance between the neighboring projection points and the projection point to be reconstructed;

[0090] The spatial similarity weights of the neighboring projection points are calculated based on the spatial distance and the spatial parameters.

[0091] Specifically, spatial distance can be used to measure the numerical similarity between neighboring projected points and the projected point to be reconstructed. A smaller spatial distance indicates a higher numerical similarity between the neighboring projected points and the projected point to be reconstructed, and vice versa. Neighboring projected points with higher numerical similarity should have a larger spatial similarity weight, and vice versa; that is, the spatial distance and the spatial similarity weight increase in opposite directions. Based on this, the formula for calculating the spatial similarity weight can be:

[0092]

[0093] in, The weights for spatial similarity are represented by x. i Represents the neighborhood projection point. σ represents the projection point to be reconstructed. s Indicates spatial parameters.

[0094] In one embodiment, the process of obtaining the intrinsic similarity weight specifically includes:

[0095] Obtain the average difference between the projection values ​​of the neighboring projection point and the projection points in the neighboring region of the neighboring projection point;

[0096] The intrinsic similarity weight of the neighboring projection points is calculated based on the mean difference of the projection values ​​and the intrinsic parameters, wherein the mean difference of the projection values ​​and the intrinsic similarity weight grow in opposite directions.

[0097] Specifically, the mean difference in projection values ​​is used to reflect the difference between a neighboring projection point and projection points in its neighboring region. The expression for the mean difference in projection values ​​can be:

[0098]

[0099] Among them, Idis(x i ) represents the neighborhood projection point x i The mean difference of the projected values, Represents the neighborhood projection point x i The local neighborhood, Represents the neighborhood projection point x i Sampling data in the local neighborhood.

[0100] Furthermore, the mean difference in projection values ​​can be used to measure the reasonableness of neighboring projection points, when x i When projecting the anomalous mean of a mutation, Idis(x)i If the value of ) is large, it indicates that the rationality of the neighboring projection point is low. Therefore, it is necessary to adjust the value of the neighboring projection point x. i Configure small intrinsic similarity weights, and conversely, when x i When the mean projection is reasonable, Idis(x) i If the value of ) is small, it indicates that the rationality of the neighboring projection point is high. Therefore, it is necessary to define the neighboring projection point x. i Configure a large intrinsic similarity weight, meaning the mean difference between the projected values ​​increases in the opposite direction to the intrinsic similarity weight. Based on this, the expression for the intrinsic similarity weight can be:

[0101]

[0102] in, σ represents the intrinsic similarity weight. is This represents the intrinsic parameter.

[0103] Furthermore, in the locally intrinsically weighted linear regression model, the spatial parameter σ s and intrinsic parameter σ is These are two important parameters of the model. The intrinsic parameter σ... is With the spatial parameter σ remaining unchanged s The larger the value of the spatial parameter σ, the smoother the regression curve, but the worse the regression performance in the nonlinear region; conversely, the smaller the value of the spatial parameter σ, the better the smoothness of the regression curve. s The smaller the value, the worse the smoothness of the regression curve, and the better the regression effect in the nonlinear region. For example, in... Figure 6 As shown in σ is With the spatial parameter σ remaining constant, s =15 and spatial parameter σ s In the regression results with σ = 5, s The regression curve corresponding to 15 has good smoothness, but its regression effect in nonlinear regions is not ideal (e.g., Figure 6 (as shown in image (a)) thus leading to a large numerical error; σ s The regression curve corresponding to 5 can reconstruct the nonlinear region more accurately, but the smoothness of the regression curve cannot be well guaranteed (e.g., Figure 6 (as shown in image (b)).

[0104] Therefore, in this embodiment, a larger spatial parameter is used when performing a reconstruction operation on the original mean projection to obtain a pre-reconstructed mean projection that can describe the geometric properties of the ideal mean curve and has good smoothness. Then, a smaller spatial parameter is used to perform a reconstruction operation again on the nonlinear regions in the pre-reconstructed mean projection to improve the accuracy of the nonlinear regions, resulting in a reconstructed mean projection with high accuracy. In other words, the second spatial parameter used when subsequently performing a reconstruction operation on the nonlinear regions in the pre-reconstructed mean projection is smaller than the first spatial parameter to improve the accuracy of the nonlinear regions, so that the reconstructed mean projection obtained simultaneously maintains smoothness and accuracy.

[0105] S30. Detect the nonlinear region in the pre-reconstructed mean projection.

[0106] Specifically, since curves can generally be divided into linear and nonlinear regions (including convex and concave regions, etc.), for example, Figure 7 The diagram shows several different types of linear and nonlinear regions with varying properties. Figure 7 (a) and (b) in the diagram represent linear regions. Figure 7 (c), (d), and (e) in the diagram represent nonlinear regions.

[0107] For a continuous function f(x), if there exists an interval [a, b] such that any two points x1 and x2 within the interval [a, b] satisfy the following condition: Then the continuous function f(x) is concave on the interval [a,b].

[0108] like Then the continuous function f(x) is a convex function on the interval [a,b].

[0109] like Then the continuous function f(x) is a linear function.

[0110] Unlike continuous functions f(x), pre-reconstructed mean projection {(x... j ,MP pre (x j ))},x j ∈{0,…,N d},in, The data consists of a series of discrete pre-reconstructed mean projection numerical points. Therefore, the concavity / convexity definition of a continuous function cannot be directly used for judgment. To address this, based on the discrete properties of the mean projection, embodiments of this application utilize neighborhood features of the data to determine the degree of nonlinearity in each region, thereby detecting concave / convex regions to obtain nonlinear regions.

[0111] For example, detecting the nonlinear region in the pre-reconstructed mean projection specifically includes:

[0112] For each pre-reconstructed mean projection point in the pre-reconstructed mean projection, the absolute difference between the pre-reconstructed mean projection point and its neighboring region is obtained to obtain the nonlinearity of the pre-reconstructed mean projection point.

[0113] The nonlinear threshold is calculated based on the maximum and minimum nonlinear values ​​in the pre-reconstructed mean projection.

[0114] Pre-reconstructed mean projection points with nonlinear quantities greater than the nonlinear threshold are selected to obtain the nonlinear region.

[0115] Specifically, the nonlinearity quantity reflects the degree of nonlinearity of the pre-reconstructed mean projection point. It is the absolute difference between the intermediate data and endpoint values ​​in the neighborhood of the pre-reconstructed mean projection point. The nonlinearity quantity can be used to determine whether the pre-reconstructed mean projection point belongs to a nonlinear region. The nonlinearity threshold is the criterion for determining the nonlinear region. That is, after obtaining the nonlinearity quantity of the pre-reconstructed mean projection point, the nonlinearity quantity is compared with the nonlinearity threshold. If the nonlinearity quantity is greater than the nonlinearity threshold, the pre-reconstructed mean projection point belongs to a nonlinear region; otherwise, if the nonlinearity quantity is less than or equal to the nonlinearity threshold, the pre-reconstructed mean projection point does not belong to a nonlinear region.

[0116] In one embodiment, the expression for the nonlinearity of the pre-reconstructed mean projection points can be:

[0117]

[0118] Where, diff(x) j ) represents x j Nonlinear quantities, x represents j The radius of the neighborhood region, x j This represents the sampling point of the j-th detector unit.

[0119] Accordingly, the mask diagram of the nonlinear region can be represented as:

[0120]

[0121] M_diff = max(diff(x) j ))-min(diff(x j )),

[0122] Among them, mask(x) j ) represents x j The mask value, mask(x) j ) = 1 means x j It belongs to the non-linear region, mask(x) j If ) = 0, then xj It belongs to the linear region; S represents the proportional value (such as 0.2), and M_diff represents the numerical range of the non-linear metric.

[0123] S40. Select a second spatial parameter for the nonlinear region based on the first spatial parameter, and perform a reconstruction operation on the nonlinear region based on the second spatial parameter to obtain the reconstructed mean projection.

[0124] Specifically, the second spatial parameter is the spatial parameter used when performing a reconstruction operation on the nonlinear region again. It is determined based on the first spatial parameter and is smaller than the first spatial parameter. That is, when performing a reconstruction operation on the nonlinear region again, the first spatial parameter used when reconstructing the original mean projection can be read first, and then a second spatial parameter smaller than the first spatial parameter can be selected. For example, a second spatial parameter smaller than the first spatial parameter can be randomly selected, or the second spatial parameter can be selected according to a preset ratio between the second spatial parameter and the first spatial parameter. For example, if the ratio between the second spatial parameter and the first spatial parameter is 1 / 3, then one-third of the first spatial parameter can be used as the second spatial parameter. Furthermore, when the second spatial parameter is determined to be non-integer based on the ratio, the calculated second spatial parameter can be rounded down to update the second spatial parameter.

[0125] Furthermore, after obtaining the second spatial parameters, the nonlinear region can be reconstructed according to the aforementioned reconstruction operation process to improve the accuracy of the nonlinear region. That is, after detecting a nonlinear region, the values ​​in the nonlinear region are reconstructed using the second spatial parameters, which are smaller than the first spatial parameters. This improves the accuracy of the reconstructed mean projection and ensures its smoothness, thereby guaranteeing both the accuracy and smoothness of the reconstructed mean projection. Specifically, when reconstructing the nonlinear region, only the projected mean of the nonlinear region is reconstructed, while the values ​​in the linear region remain unchanged.

[0126] Based on this, the embodiments of this application first use the first spatial parameters to perform a reconstruction operation on the original mean projection to obtain a pre-reconstructed mean projection, and then use the second spatial parameters to perform a reconstruction operation on the nonlinear region in the pre-reconstructed mean projection. The resulting reconstructed mean projection can be expressed as:

[0127]

[0128] Among them, MP rec (x j ) represents x j The reconstructed mean projection, This indicates that based on the pre-reconstructed mean projection, in The reconstruction result is obtained by using the local intrinsic weighted linear regression algorithm to reconstruct the nonlinear region. This represents the reconstruction result in the pre-reconstructed mean projection.

[0129] S50. Perform stripe artifact correction on the projection data based on the original mean projection and the reconstructed mean projection.

[0130] Specifically, after obtaining the reconstructed mean projection, the reconstructed mean projection is used to remove stripe artifacts from the projection data to achieve the goal of correcting annular artifacts in the reconstructed CT image. In order to improve the accuracy of annular artifact removal when using the reconstructed mean projection to remove artifacts from the projection data, this embodiment utilizes the difference between the original mean projection and the reconstructed mean projection to remove stripe artifacts from the projection data, thereby correcting annular artifacts in the reconstructed CT image.

[0131] In one embodiment, the step of correcting the stripe artifacts in the projection data based on the original mean projection and the reconstructed mean projection specifically includes:

[0132] Calculate the mean projection difference between the reconstructed mean projection and the original mean projection;

[0133] Based on the mean projection difference, artifact removal is performed on the projection data, and a CT image is generated based on the projection data after the ring artifact removal.

[0134] Specifically, the stripe artifact removal process of the projection data can be represented as follows:

[0135]

[0136] in, This represents the projection data after artifact removal.

[0137] It can be seen that all the projection data received by detector unit j at projection angle i are corrected by using the difference between the original mean projection and the reconstructed ideal mean projection. This fully utilizes the numerical similarity property of the stripe artifact along the scanning angle direction, and achieves the goal of removing the stripe artifact to remove the ring artifact in the reconstructed CT image.

[0138] In summary, this embodiment provides an adaptive annular artifact removal method based on mean projection. The method utilizes a local adaptive intrinsic weighted linear regression algorithm based on the geometric properties of the mean projection to remove artifacts from the projection data. Specifically, firstly, a first spatial parameter is used to perform local linear weighted regression / reconstruction on the original mean projection to obtain a pre-reconstructed mean projection that describes the geometric properties of the ideal mean curve and has good smoothness. Then, using the geometric properties of the pre-reconstructed mean projection, nonlinear regions in the mean projection are detected, and the values ​​of the nonlinear regions are reconstructed again using a second spatial parameter determined according to the first spatial parameter, thereby improving the accuracy of the reconstruction results. Finally, based on the constant numerical property of stripe artifacts, the difference between the reconstructed mean projection and the original mean projection is used to remove stripe artifacts from the projection data, thus repairing the stripe artifacts in the projection data and completing the correction of annular artifacts in the reconstructed CT image. This application reconstructs the original mean projection using a first spatial parameter to obtain a pre-reconstructed mean projection with good smoothness. Then, it reconstructs the nonlinear region in the pre-reconstructed mean projection using a second spatial parameter smaller than the first spatial parameter to improve the accuracy of the reconstruction result in the nonlinear region. This effectively ensures the smoothness and accuracy of the reconstructed mean projection, thereby effectively ensuring the removal of stripe artifacts and the removal of annular artifacts in the reconstructed CT image, thus improving the image quality of the reconstructed CT image.

[0139] Based on the above-described adaptive annular artifact removal method based on mean projection, this embodiment provides an adaptive annular artifact removal device based on mean projection, such as... Figure 8 As shown, the adaptive annular artifact removal device based on mean projection specifically includes:

[0140] The acquisition module 100 is used to acquire the original mean projection of the projection data collected by the CT imaging system, and to select a first spatial parameter for the original mean projection;

[0141] The pre-reconstruction module 200 is used to perform a reconstruction operation on the original mean projection based on the first spatial parameters to obtain a pre-reconstructed mean projection.

[0142] The detection module 300 is used to detect nonlinear regions in the pre-reconstructed mean projection and select second spatial parameters for the nonlinear regions based on the first spatial parameters.

[0143] The reconstruction module 400 is used to perform a reconstruction operation on the nonlinear region based on the second spatial parameters to obtain the reconstructed mean projection.

[0144] The correction module 500 is used to correct stripe artifacts on the projection data based on the original mean projection and the reconstructed mean projection.

[0145] Based on the above-described adaptive annular artifact removal method based on mean projection, this embodiment provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the adaptive annular artifact removal method based on mean projection as described in the above embodiment.

[0146] Based on the above-described adaptive annular artifact removal method based on mean projection, this application also provides a terminal device, such as... Figure 9 As shown, it includes at least one processor 20; a display screen 21; and a memory 22, and may also include a communications interface 23 and a bus 24. The processor 20, display screen 21, memory 22, and communications interface 23 can communicate with each other via the bus 24. The display screen 21 is configured to display a preset user guide interface in the initial setup mode. The communications interface 23 can transmit information. The processor 20 can invoke logical instructions in the memory 22 to execute the methods described in the above embodiments.

[0147] Furthermore, the logical instructions in the aforementioned memory 22 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0148] The memory 22, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of this disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions, or modules stored in the memory 22, thereby implementing the methods in the above embodiments.

[0149] The memory 22 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 22 may include high-speed random access memory (RAM) and non-volatile memory. Examples include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, as well as transient storage media.

[0150] Furthermore, the specific process of loading and executing multiple instruction processors in the aforementioned storage medium and terminal device has been described in detail in the above method, and will not be repeated here.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An adaptive ring artifact removal method based on mean projection, characterized in that, The adaptive annular artifact removal method based on mean projection specifically includes: Obtain the raw mean projection of projection data acquired through a CT imaging system; A first spatial parameter is selected for the original mean projection, and a reconstruction operation is performed on the original mean projection based on the first spatial parameter to obtain a pre-reconstructed mean projection; Detect the nonlinear region in the pre-reconstructed mean projection; A second spatial parameter is selected for the nonlinear region based on the first spatial parameter, and a reconstruction operation is performed on the nonlinear region based on the second spatial parameter to obtain the reconstructed mean projection; The projection data is corrected for stripe artifacts based on the original mean projection and the reconstructed mean projection.

2. The adaptive annular artifact removal method based on mean projection according to claim 1, characterized in that, The reconstruction operation employs a locally intrinsic weighted linear regression algorithm, and the specific execution process of the reconstruction operation includes: Calculate the spatial similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on spatial parameters; Calculate the intrinsic similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on the intrinsic parameters; The local intrinsic weight of the neighboring projection point is determined based on the spatial similarity weight and the intrinsic similarity weight. Based on the local intrinsic weights of the neighboring projection points, a locally intrinsic weighted linear regression is performed on the projection point to be reconstructed to obtain the reconstructed projection point corresponding to the projection point to be reconstructed.

3. The adaptive annular artifact removal method based on mean projection according to claim 2, characterized in that, The calculation of the spatial similarity weight of each neighboring projection point in the local neighborhood of the projection point to be reconstructed based on spatial parameters specifically includes: Calculate the spatial distance between the neighboring projection points and the projection point to be reconstructed; The spatial similarity weight of the neighboring projection points is calculated based on the spatial distance and the spatial parameters, wherein the spatial distance and the spatial similarity weight increase in opposite directions.

4. The adaptive annular artifact removal method based on mean projection according to claim 2, characterized in that, The process of obtaining the intrinsic similarity weights specifically includes: Obtain the average difference between the projection values ​​of the neighboring projection point and the projection points in the neighboring region of the neighboring projection point; The intrinsic similarity weight of the neighboring projection points is calculated based on the mean difference of the projection values ​​and the intrinsic parameters, wherein the mean difference of the projection values ​​and the intrinsic similarity weight grow in opposite directions.

5. The adaptive annular artifact removal method based on mean projection according to any one of claims 1-4, characterized in that, The first spatial parameter is greater than the second spatial parameter.

6. The adaptive annular artifact removal method based on mean projection according to claim 1, characterized in that, The detection of nonlinear regions in the pre-reconstructed mean projection specifically includes: For each pre-reconstructed mean projection point in the pre-reconstructed mean projection, the absolute difference between the pre-reconstructed mean projection point and its neighboring region is obtained to obtain the nonlinearity of the pre-reconstructed mean projection point. The nonlinear threshold is calculated based on the maximum and minimum nonlinear values ​​in the pre-reconstructed mean projection. Pre-reconstructed mean projection points with nonlinear quantities greater than the nonlinear threshold are selected to obtain the nonlinear region.

7. The adaptive annular artifact removal method based on mean projection according to claim 1, characterized in that, The step of correcting stripe artifacts in the projection data based on the original mean projection and the reconstructed mean projection specifically includes: Calculate the mean projection difference between the reconstructed mean projection and the original mean projection; Based on the mean projection difference, artifact removal is performed on the projection data, and a CT image is generated based on the projection data after the ring artifact removal.

8. An adaptive annular artifact removal device based on mean projection, characterized in that, The adaptive annular artifact removal device based on mean projection specifically includes: The acquisition module is used to acquire the original mean projection of the projection data collected by the CT imaging system, and to select a first spatial parameter for the original mean projection; The pre-reconstruction module is used to perform a reconstruction operation on the original mean projection based on the first spatial parameters to obtain a pre-reconstructed mean projection. The detection module is used to detect nonlinear regions in the pre-reconstructed mean projection and select second spatial parameters for the nonlinear regions based on the first spatial parameters. The reconstruction module is used to perform a reconstruction operation on the nonlinear region based on the second spatial parameters to obtain the reconstructed mean projection. The correction module is used to correct stripe artifacts on the projection data based on the original mean projection and the reconstructed mean projection.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the steps in the adaptive annular artifact removal method based on mean projection as described in any one of claims 1-7.

10. A terminal device, characterized in that, include: Processor and memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps of the adaptive annular artifact removal method based on mean projection as described in any one of claims 1-7.

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