Method, apparatus, electronic device, and storage medium for eliminating artifacts

Abstract

The present disclosure provides a method for eliminating artifacts, including: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on the metal edge feature; and if there is a metal edge in the two-dimensional projection image, eliminating the metal artifacts in the two-dimensional projection image based on the metal edge. The present disclosure also provides a method for volume data reconstruction, performing volume data reconstruction based on multiple two-dimensional projection images after eliminating the metal artifacts, so as to eliminate the metal artifacts after reconstruction. The present disclosure also provides an apparatus, an electronic device, and a readable storage medium for eliminating artifacts.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of medical image processing, and particularly relates to a method, apparatus, electronic device, and storage medium for eliminating artifacts. Background Art

[0002] In the actual CT reconstruction process, if there is a metal object, metal artifacts will be generated.

[0003] The reason for the generation of metal artifacts is that metals have a very strong X-ray absorption ability. If a beam of X-rays passes through a metal, the number of photons reaching the detector is extremely small, and the relative noise is extremely large. As a result, there will also be a large error in the human tissue structure deduced. Therefore, it is necessary to eliminate metal artifacts.

[0004] In the prior art, methods for eliminating artifacts based on hardware, such as increasing the current and using a filter to filter the beam, etc., have limited effects and will seriously reduce the lifespan of the radiation source device (such as an X-ray tube).

[0005] In the prior art, the method for eliminating metal artifacts based on algorithms is to perform threshold segmentation on the metal objects in the reconstructed volume data and project them forward onto the projection image to eliminate. This method believes that the projection data obtained by passing through the metal is distorted, and the distorted data is interpolated and corrected to obtain the correct result. For example, in the reconstructed three-dimensional data, multi-threshold segmentation is used to obtain the metal region. After determining the metal region, it is projected forward to determine the position of the metal in the projection image, and then interpolation processing is performed. The interpolation methods include the simplest linear interpolation, spline interpolation, etc. (row interpolation or column interpolation, taking row interpolation as an example, for any row in the projection image, if the metal region occupies a continuous position in the row, interpolation can be performed according to the values of the first non-metal on the left and right ends of the region, which is equivalent to replacing the incorrect values of the metal region with the values of the non-metal regions at both ends). Finally, reconstruction is performed using the interpolated image. In addition to the direct interpolation method, the prior art also uses prior image correction to improve the accuracy of interpolation. The main idea is to first obtain a prior image without artifacts, generate a projection image based on the information it provides, subtract it from the original projection image containing the metal, and then perform interpolation on the metal region, which can effectively eliminate the data loss caused by direct interpolation. However, the difficulty of this method lies in how to obtain the prior information.

[0006] In the prior art methods for eliminating artifacts, the acquisition of the metal region depends on the volume data, but simple multi-threshold segmentation in the volume data is not accurate enough. For metals outside the reconstructed region, the volume data only contains artifacts and there is no metal present at all, and the position of the metal cannot be segmented at all.

[0007] The size of the actual reconstruction area does not cover all areas from the radiation source to the detector. For metal objects outside the reconstruction area that still have signals received by the detector at certain angles, although the metal is not included in the reconstructed CT image, artifacts generated by this part of the metal will still be included (as Figure 2 shown, the earrings are metals outside the reconstruction area).

[0008] The above methods in the prior art cannot eliminate the artifacts generated by this part of the metal. SUMMARY OF THE INVENTION

[0009] To solve at least one of the above technical problems, the present disclosure provides a method, apparatus, electronic device, and storage medium for eliminating artifacts. The method for eliminating artifacts of the present disclosure is particularly applicable to eliminating artifacts outside the reconstruction area.

[0010] According to one aspect of the present disclosure, a method for eliminating artifacts is provided, including: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features; and if there is a metal edge in the two-dimensional projection image, eliminating the metal artifacts in the two-dimensional projection image based on the metal edge.

[0011] According to the method for eliminating artifacts according to at least one embodiment of the present disclosure, the metal edge features include metal edge gray values.

[0012] According to the method for eliminating artifacts according to at least one embodiment of the present disclosure, determining whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features includes: obtaining the gray value of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is lower than a preset gray threshold; and determining all pixels with gray values lower than the preset gray threshold as metal edges.

[0013] According to the method for eliminating artifacts according to at least one embodiment of the present disclosure, determining whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features includes: obtaining the gray value of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range; and determining all pixels with gray values within the preset gray threshold range as metal edges.

[0014] According to the method for eliminating artifacts according to at least one embodiment of the present disclosure, the metal edge features include metal edge gray gradients.

[0015] An artifact elimination method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray-scale gradient of each pixel is higher than a preset gray-scale gradient threshold; and determining pixels with all gray-scale gradients higher than the preset gray-scale gradient threshold as metal edges.

[0016] An artifact elimination method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray-scale gradient of each pixel is within a preset gray-scale gradient threshold range; and determining pixels with all gray-scale gradients within the preset gray-scale gradient threshold range as metal edges.

[0017] An artifact elimination method according to at least one embodiment of the present disclosure, wherein the metal edge features include metal edge gray-scale values and metal edge gray-scale gradients.

[0018] An artifact elimination method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray-scale value and gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray-scale value of each pixel is lower than a preset gray-scale threshold and the gray-scale gradient is higher than a preset gray-scale gradient threshold; and determining pixels with all gray-scale values lower than the preset gray-scale threshold and gray-scale gradients higher than the preset gray-scale gradient threshold as metal edges.

[0019] An artifact elimination method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray-scale value and gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray-scale value of each pixel is within a preset gray-scale threshold range and the gray-scale gradient is within a preset gray-scale gradient threshold range; and determining pixels with all gray-scale values within the preset gray-scale threshold range and gray-scale gradients within the preset gray-scale gradient threshold range as metal edges.

[0020] According to the method for artifact elimination according to at least one embodiment of the present disclosure, after determining pixels with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, it further includes a first correction of the metal edges, which is performed based on a preset first correction gray gradient threshold, and the first correction gray gradient threshold is within the preset gray gradient threshold range. The first correction of the metal edges includes: comparing the gray gradient of each pixel with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range with the preset first correction gray gradient threshold. If it is greater than or greater than or equal to the preset first correction gray gradient threshold, then determine this pixel as a strong metal edge; if it is less than or equal to or less than the preset first correction gray gradient threshold, then determine this pixel as a weak metal edge; and determining whether each pixel among all pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge. If it is connected, then determine this pixel determined as a weak metal edge as a true metal edge; if it is not connected, then determine this pixel determined as a weak metal edge as a false metal edge.

[0021] According to the method for artifact elimination according to at least one embodiment of the present disclosure, after determining pixels with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, it further includes a second correction of the metal edges. The second correction of the metal edges includes: obtaining the straight line of the gray gradient direction of each pixel with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range; obtaining two intersection points of the straight line of the gray gradient direction of each pixel with the surrounding eight pixels of this pixel and obtaining the gray gradient values of the two intersection points; and determining whether the gray gradient value of this pixel is greater than the gray gradient values of the two intersection points. If it is greater, then retain this pixel as a metal edge.

[0022] According to the method for artifact elimination according to at least one embodiment of the present disclosure, the gray gradient values of the two intersection points are obtained by interpolation.

[0023] According to the method for artifact elimination according to at least one embodiment of the present disclosure, perform area filling on the metal edges that have undergone the second correction of the metal edges to obtain the metal edges after area filling.

[0024] According to the method for artifact elimination according to at least one embodiment of the present disclosure, before determining whether there are metal edges in at least one of the two-dimensional projection images based on the metal edge features, perform filtering and noise reduction processing on the two-dimensional projection image.

[0025] The method for artifact elimination according to at least one embodiment of the present disclosure further includes: for a certain two-dimensional projection image with a metal edge, i.e., the current two-dimensional projection image, obtaining at least one adjacent two-dimensional projection image adjacent to the current two-dimensional projection image, where the current two-dimensional projection image and the at least one adjacent two-dimensional projection image belong to the same projection image sequence group; comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image to obtain the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image; and identifying the metal edge of the current two-dimensional projection image based on the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image.

[0026] The method for artifact elimination according to at least one embodiment of the present disclosure, comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image and obtaining the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image includes: performing a convolution operation on the current two-dimensional projection image and the at least one adjacent two-dimensional projection image, and obtaining the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image when the convolution is the largest.

[0027] According to another aspect of the present disclosure, a method for volume data reconstruction is provided, including: obtaining a plurality of two-dimensional projection images; determining whether there is a metal edge in each of the plurality of two-dimensional projection images based on the metal edge feature; if there is a metal edge in the two-dimensional projection image, eliminating the metal artifact in the two-dimensional projection image based on the metal edge; and performing volume data reconstruction based on the plurality of two-dimensional projection images after artifact elimination.

[0028] In the method for volume data reconstruction according to at least one embodiment of the present disclosure, the metal edge feature includes a metal edge gray value.

[0029] In the method for volume data reconstruction according to at least one embodiment of the present disclosure, determining whether there is a metal edge in at least one of the plurality of two-dimensional projection images based on the metal edge feature includes: obtaining the gray value of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is lower than a preset gray threshold; and determining all pixels with gray values lower than the preset gray threshold as metal edges.

[0030] A volume data reconstruction method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray value of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range; and determining all pixels whose gray values are within the preset gray threshold range as metal edges.

[0031] A volume data reconstruction method according to at least one embodiment of the present disclosure, wherein the metal edge feature includes a metal edge gray gradient.

[0032] A volume data reconstruction method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray gradient of each pixel in the two-dimensional projection image; determining whether the gray gradient of each pixel is higher than a preset gray gradient threshold; and determining all pixels whose gray gradients are higher than the preset gray gradient threshold as metal edges.

[0033] A volume data reconstruction method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray gradient of each pixel in the two-dimensional projection image; determining whether the gray gradient of each pixel is within a preset gray gradient threshold range; and determining all pixels whose gray gradients are within the preset gray gradient threshold range as metal edges.

[0034] A volume data reconstruction method according to at least one embodiment of the present disclosure, wherein the metal edge feature includes a metal edge gray value and a metal edge gray gradient.

[0035] A volume data reconstruction method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, includes: obtaining the gray value and gray gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is lower than a preset gray threshold and the gray gradient is higher than a preset gray gradient threshold; and determining all pixels whose gray values are lower than the preset gray threshold and gray gradients are higher than the preset gray gradient threshold as metal edges.

[0036] A volume data reconstruction method according to at least one embodiment of the present disclosure, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on the metal edge feature, includes: obtaining the gray value and gray gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range and whether the gray gradient is within a preset gray gradient threshold range; and determining the pixels with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges.

[0037] A volume data reconstruction method according to at least one embodiment of the present disclosure, after determining the pixels with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, further includes a first correction of the metal edge, the first correction of the metal edge being performed based on a preset first correction gray gradient threshold, the first correction gray gradient threshold being within the preset gray gradient threshold range, and the first correction of the metal edge includes: comparing the gray gradient of each pixel with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range with the preset first correction gray gradient threshold, if it is greater than or greater than or equal to the preset first correction gray gradient threshold, then determining the pixel as a strong metal edge, if it is less than or equal to or less than the preset first correction gray gradient threshold, then determining the pixel as a weak metal edge; and determining whether each pixel among all the pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge, if it is connected, then determining the pixel determined as a weak metal edge as a true metal edge, if it is not connected, then determining the pixel determined as a weak metal edge as a false metal edge.

[0038] A volume data reconstruction method according to at least one embodiment of the present disclosure, after determining the pixels with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, further includes a second correction of the metal edge, the second correction of the metal edge includes: obtaining the straight line of the gray gradient direction of each pixel with all gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range; obtaining two intersection points of the straight line of the gray gradient direction of each pixel with the surrounding eight pixels of the pixel and obtaining the gray gradient values of the two intersection points; and determining whether the gray gradient value of the pixel is greater than the gray gradient values of the two intersection points, if it is greater, then retaining the pixel as a metal edge.

[0039] A volume data reconstruction method according to at least one embodiment of the present disclosure, the gray gradient values of the two intersection points are obtained by interpolation.

[0040] According to the volume data reconstruction method of at least one embodiment of the present disclosure, area filling is performed on the metal edge after the second correction of the metal edge, and the metal edge after area filling is obtained.

[0041] According to the volume data reconstruction method of at least one embodiment of the present disclosure, before determining whether there is a metal edge in at least one of the two-dimensional projection images based on the metal edge feature, noise reduction processing is performed on the two-dimensional projection images.

[0042] According to the volume data reconstruction method of at least one embodiment of the present disclosure, it further includes: for a certain two-dimensional projection image with a metal edge, that is, the current two-dimensional projection image, obtaining at least one adjacent two-dimensional projection image adjacent to the current two-dimensional projection image, and the current two-dimensional projection image and the at least one adjacent two-dimensional projection image belong to the same projection image sequence group; comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image to obtain the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image; and based on the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image, identifying the metal edge of the current two-dimensional projection image.

[0043] According to the volume data reconstruction method of at least one embodiment of the present disclosure, comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image and obtaining the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image includes: performing a convolution operation on the current two-dimensional projection image and the at least one adjacent two-dimensional projection image, and obtaining the position on the current two-dimensional projection image corresponding to the metal edge of the at least one adjacent two-dimensional projection image when the convolution is the largest.

[0044] According to another aspect of the present disclosure, there is provided an artifact elimination device, including: an image acquisition module that acquires at least one two-dimensional projection image; a metal edge recognition module that determines whether there is a metal edge in at least one of the two-dimensional projection images based on the metal edge feature; and a processing module that, if there is a metal edge in the two-dimensional projection image, eliminates the metal artifact in the two-dimensional projection image based on the metal edge.

[0045] According to another aspect of the present disclosure, there is provided a volumetric data reconstruction apparatus, including: an image acquisition module configured to acquire a plurality of two-dimensional projection images; a metal edge recognition module configured to determine whether there is a metal edge in each of the plurality of two-dimensional projection images based on metal edge features; a processing module configured to, if there is a metal edge in the two-dimensional projection image, eliminate metal artifacts in the two-dimensional projection image based on the metal edge; and a reconstruction module configured to perform volumetric data reconstruction based on the plurality of two-dimensional projection images after artifact elimination.

[0046] According to another aspect of the present disclosure, there is provided an electronic device, including: a memory configured to store execution instructions; and a processor configured to execute the execution instructions stored in the memory, such that the processor executes the method according to any one of the above.

[0047] According to yet another aspect of the present disclosure, there is provided a readable storage medium storing execution instructions, which are used to implement the method according to any one of the above when executed by a processor. Description of the Drawings

[0048] The drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, are used to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are included in this specification and form a part of this specification.

[0049] Figure 1 is a flowchart of a method for artifact elimination according to an embodiment of the present disclosure.

[0050] Figure 2 is an example of a projection image including earring artifacts outside the reconstruction area.

[0051] Figure 3 is the metal edge recognition result identified by the method for artifact elimination according to an embodiment of the present disclosure.

[0052] Figure 4 is a flowchart of a method for artifact elimination according to another embodiment of the present disclosure.

[0053] Figure 5 is the metal edge recognition result after processing the metal edge in Figure 3 using the first metal edge correction of the method for artifact elimination according to an embodiment of the present disclosure.

[0054] Figure 6 is a flowchart of a method for artifact elimination according to yet another embodiment of the present disclosure.

[0055] Figure 7 Shows the metal edge recognition result after processing the metal edge in Figure 5 using the second correction of the metal edge.

[0056] Figure 8 Shows the gray gradient direction line of the pixel and the two intersection points of the gray gradient direction line and the surrounding eight pixels in one embodiment of the present disclosure.

[0057] Figure 9 Is a flowchart of a method for eliminating artifacts in another embodiment of the present disclosure.

[0058] Figure 10 Is an adjacent two-dimensional projection image in which the metal edge has been accurately identified.

[0059] Figure 11 Is a projection image in which there is interference information so that the metal edge cannot be well identified.

[0060] Figure 12 Is extracted from Figure 11 the metal edge in.

[0061] Figure 13 Is a flowchart of a volume data reconstruction method in one embodiment of the present disclosure.

[0062] Figure 14 Is a cross-sectional view of the reconstruction result without using the volume data reconstruction method of the present disclosure.

[0063] Figure 15 Is a cross-sectional view of the reconstruction result processed using the volume data reconstruction method of the present disclosure.

[0064] Figure 16 Is a schematic structural diagram of an electronic device adopting a processing system in one embodiment of the present disclosure. Detailed Description of the Invention

[0065] The present disclosure will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant content and are not intended to limit the present disclosure. Additionally, it should be noted that for the convenience of description, only the parts related to the present disclosure are shown in the drawings.

[0066] It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The technical solutions of the present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

[0067] Unless otherwise stated, the illustrated exemplary embodiments will be understood to provide exemplary features of various details of some ways in which the technical concept of the present disclosure can be implemented in practice. Thus, unless otherwise stated, the features of the various embodiments can be additionally combined, separated, interchanged, and / or rearranged without departing from the technical concept of the present disclosure.

[0068] In the drawings, the use of cross-hatching and / or shading is generally used to clarify the boundaries between adjacent components. Thus, unless stated otherwise, the presence or absence of cross-hatching or shading does not convey or imply any preference or requirement for the specific material, material properties, dimensions, proportions, commonality between the components shown, and / or any other characteristics, attributes, properties, etc. of the components. Additionally, in the drawings, for clarity and / or descriptive purposes, the dimensions and relative dimensions of components may be exaggerated. When the exemplary embodiments can be implemented differently, the specific process sequences may be performed in an order different from that described. For example, two consecutively described processes may be performed substantially simultaneously or in an order opposite to that described. Further, the same reference numerals denote the same components.

[0069] When a component is referred to as being "on" or "above" another component, "connected to" or "coupled to" another component, the component may be directly on the other component, directly connected to or directly coupled to the other component, or there may be intervening components. However, when a component is referred to as being "directly on" another component, "directly connected to" or "directly coupled to" another component, there are no intervening components. For this reason, the term "connected" may refer to a physical connection, an electrical connection, etc., and may or may not have intervening components.

[0070] The terms used herein are for the purpose of describing particular embodiments and are not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are also intended to include the plural forms. Additionally, when the terms "comprises" and / or "comprising" and their variants are used in this specification, it is stated that there are the stated features, integers, steps, operations, components, assemblies, and / or groups thereof, but does not preclude the presence or addition of one or more other features, integers, steps, operations, components, assemblies, and / or groups thereof. It should also be noted that, as used herein, the terms "substantially", "about", and other similar terms are used as approximate terms and not as terms of degree, and thus they are used to interpret the inherent deviations of measured, calculated, and / or provided values that would be recognized by a person of ordinary skill in the art.

[0071] Figure 1It is a schematic flowchart of an artifact elimination method according to an embodiment of the present disclosure. The artifact elimination method of the present disclosure is particularly applicable to the elimination of artifacts outside the reconstruction region.

[0072] As Figure 1 shown, according to an embodiment of the present disclosure, the artifact elimination method 100 includes: 102, obtaining at least one two-dimensional projection image; 104, determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature; and 106, if there is a metal edge in the two-dimensional projection image, eliminating the metal artifact in the two-dimensional projection image based on the metal edge.

[0073] Those skilled in the art should understand that the metal edge can be a closed edge or a non-closed edge, and the reconstruction region is the target region / region of interest (Region of interest) in ray transmission imaging (such as CT imaging), such as a partial human tissue region.

[0074] Those skilled in the art should understand that the artifacts described above can be metal artifacts or other high atomic number object artifacts.

[0075] Figure 2 Exemplarily shows an earring artifact outside the reconstruction region.

[0076] In the artifact elimination method of the above embodiment, preferably, the metal edge feature includes the metal edge gray value.

[0077] Among them, the metal edge gray value can be a preset gray threshold. For example, when the gray value of a certain pixel in the two-dimensional projection image is lower than the preset gray threshold, this pixel is determined as a metal edge.

[0078] The metal edge gray value can also be a preset gray threshold range. For example, when the gray value of a certain pixel in the two-dimensional projection image is within the preset gray threshold range, this pixel is determined as a metal edge.

[0079] According to another embodiment of the present disclosure, the artifact elimination method includes: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature; if there is a metal edge in the two-dimensional projection image, eliminating the metal artifact in the two-dimensional projection image based on the metal edge.

[0080] Among them, determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature includes: obtaining the gray value of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is lower than the preset gray threshold; and determining all pixels with gray values lower than the preset gray threshold as metal edges.

[0081] An artifact elimination method according to another embodiment of the present disclosure includes: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on metal edge features; and if there is a metal edge in the two-dimensional projection image, eliminating metal artifacts in the two-dimensional projection image based on the metal edge.

[0082] Among them, determining whether there is a metal edge in the at least one two-dimensional projection image based on metal edge features includes: obtaining the gray value of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range; and determining all pixels with gray values within the preset gray threshold range as metal edges.

[0083] Figure 1 In the illustrated artifact elimination method 100, the metal edge features include a metal edge gray gradient.

[0084] Among them, the metal edge gray gradient can be a preset gray gradient (i.e., gray change rate). For example, when the gray gradient of a certain pixel in the two-dimensional projection image is lower than the preset gray gradient, this pixel is determined as a metal edge.

[0085] The metal edge gray gradient can also be a preset gray gradient threshold range. For example, when the gray gradient of a certain pixel in the two-dimensional projection image is within the preset gray gradient threshold range, this pixel is determined as a metal edge.

[0086] An artifact elimination method according to another embodiment of the present disclosure includes: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on metal edge features; and if there is a metal edge in the two-dimensional projection image, eliminating metal artifacts in the two-dimensional projection image based on the metal edge.

[0087] Among them, determining whether there is a metal edge in the at least one two-dimensional projection image based on metal edge features includes: obtaining the gray gradient of each pixel in the two-dimensional projection image; determining whether the gray gradient of each pixel is higher than a preset gray gradient threshold; and determining all pixels with gray gradients higher than the preset gray gradient threshold as metal edges.

[0088] An artifact elimination method according to another embodiment of the present disclosure includes: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on metal edge features; and if there is a metal edge in the two-dimensional projection image, eliminating metal artifacts in the two-dimensional projection image based on the metal edge.

[0089] Among them, determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature includes: obtaining the gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray-scale gradient of each pixel is within a preset gray-scale gradient threshold range; and determining the pixels with gray-scale gradients within the preset gray-scale gradient threshold range as metal edges.

[0090] Figure 1 In the artifact elimination method 100 shown, preferably, the metal edge feature includes the metal edge gray value and the metal edge gray-scale gradient.

[0091] An artifact elimination method according to another embodiment of the present disclosure includes: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on the metal edge feature; and if there is a metal edge in the two-dimensional projection image, eliminating the metal artifact in the two-dimensional projection image based on the metal edge.

[0092] Among them, determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature includes: obtaining the gray value and the gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is lower than a preset gray threshold and whether the gray-scale gradient is higher than a preset gray-scale gradient threshold; and determining the pixels with gray values lower than the preset gray threshold and gray-scale gradients higher than the preset gray-scale gradient threshold as metal edges.

[0093] An artifact elimination method according to another embodiment of the present disclosure includes: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on the metal edge feature; and if there is a metal edge in the two-dimensional projection image, eliminating the metal artifact in the two-dimensional projection image based on the metal edge.

[0094] Among them, determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature includes: obtaining the gray value and the gray-scale gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range and whether the gray-scale gradient is within a preset gray-scale gradient threshold range; and determining the pixels with gray values within the preset gray threshold range and gray-scale gradients within the preset gray-scale gradient threshold range as metal edges.

[0095] Figure 3 Exemplarily shows the metal edge recognition result of this embodiment. As Figure 3 shown, the circular area in the image is the metal edge.

[0096] The artifact elimination method 100 according to another embodiment of the present disclosure, based on the artifact elimination method in the above embodiment, after determining all pixels with gray values within a preset gray threshold range and gray gradients within a preset gray gradient threshold range as metal edges, further includes a first metal edge correction 1051. The first metal edge correction 1051 is performed based on a preset first corrected gray gradient threshold, and the first corrected gray gradient threshold is within the preset gray gradient threshold range. The first metal edge correction 1051 includes:

[0097] Compare the gray gradient of each pixel with a gray value within the preset gray threshold range and a gray gradient within the preset gray gradient threshold range with the preset first corrected gray gradient threshold. If it is greater than or greater than or equal to the preset first corrected gray gradient threshold, determine the pixel as a strong metal edge. If it is less than or equal to or less than the preset first corrected gray gradient threshold, determine the pixel as a weak metal edge; and determine whether each pixel among all the pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge. If it is connected, determine the pixel determined as a weak metal edge as a true metal edge. If it is not connected, determine the pixel determined as a weak metal edge as a false metal edge.

[0098] Figure 4 The flowchart of the artifact elimination method 100 of this embodiment is shown.

[0099] Among them, all the pixels determined as strong metal edges are considered as the true metal edges required by the artifact elimination method of the present disclosure, and the pixels determined as false metal edges are discarded.

[0100] Figure 5 Exemplarily shows the metal edge recognition result after processing the metal edges in Figure 3 using the first metal edge correction 1051.

[0101] From Figure 5 it can be seen that after the processing of the first metal edge correction 1051, most of the interfering edges in Figure 3 are eliminated.

[0102] The artifact elimination method 100 according to another embodiment of the present disclosure, based on the artifact elimination method in the above embodiment, after determining all pixels with gray values within a preset gray threshold range and gray gradients within a preset gray gradient threshold range as metal edges, further includes a second metal edge correction 1052. The second metal edge correction 1052 includes:

[0103] Obtain the gray gradient direction lines of the pixels whose each gray value is within a preset gray threshold range and the gray gradient is within a preset gray gradient threshold range; obtain the two intersection points of the gray gradient direction line of each pixel with the surrounding eight pixels of the pixel and obtain the gray gradient values of the two intersection points; and determine whether the gray gradient value of the pixel is greater than the gray gradient values of the two intersection points. If it is greater, retain the pixel as a metal edge.

[0104] Figure 6 FIG. 4 shows a schematic flowchart of the method 100 for eliminating artifacts according to this embodiment.

[0105] Figure 7 Exemplarily shows the metal edge recognition result after processing the metal edge in Figure 5 using the second metal edge correction 1052.

[0106] From Figure 7 it can be seen that after the processing of the second metal edge correction 1052, the metal edge recognition result in Figure 5 is further optimized.

[0107] Through the processing of the second metal edge correction 1052, the problem of thick metal edges is solved.

[0108] Figure 8 FIG. 5 shows the gray gradient direction line D of pixel C and the two intersection points dTmp1, dTmp2 of the gray gradient direction line D with the surrounding eight pixels in an embodiment of the present disclosure.

[0109] Figure 8 In , the eight hollow circles are the surrounding eight pixels of the current pixel C (the pixel to be determined).

[0110] Regarding the determination of the gray gradient direction line D, it can be carried out by the following method. For each pixel, the gray gradient formula is:

[0111]

[0112] Further, record e1 = v1 - v, e2 = v2 - v. The direction of e1 is the horizontal direction, and the direction of e2 is the vertical direction. The gray gradient direction line D can be determined according to the magnitudes of e1 and e2. Those skilled in the art can adopt the existing gray gradient direction determination methods in the prior art.

[0113] In the method for eliminating artifacts in the above embodiment, preferably, the gray gradient values of the two intersection points (dTmp1, dTmp2) can be obtained by the interpolation method.

[0114] The interpolation methods for two-dimensional images include row interpolation, column interpolation, etc. The interpolation methods for two-dimensional images belong to the prior art and will not be elaborated in the present disclosure.

[0115] In the above embodiments, preferably, the metal edge that has undergone the second correction 1052 of the metal edge is subjected to area filling to obtain the metal edge after area filling.

[0116] For the method of artifact elimination in each of the above embodiments, preferably, before determining whether there is a metal edge in at least one two-dimensional projection image based on the metal edge feature, the two-dimensional projection image is subjected to filtering and noise reduction processing.

[0117] In the case of low radiation dose, the noise in the projection image will be very serious. In order to obtain a better metal edge recognition effect, preferably, the projection image is subjected to filtering and noise reduction processing.

[0118] The filtering and noise reduction processing can adopt median filtering algorithm, Gaussian filtering algorithm, Pwls filtering algorithm, etc. The present disclosure does not make special limitations on the filtering and noise reduction processing algorithms used in the method of artifact elimination in each of the above embodiments.

[0119] According to the method 100 of artifact elimination according to another embodiment of the present disclosure, as Figure 9 shown, on the basis of the method of artifact elimination shown in Figure 6 it further includes: correcting 1053 the metal edge of the current two-dimensional projection image based on the metal edges of adjacent two-dimensional projection images.

[0120] Correcting 1053 the metal edge of the current two-dimensional projection image based on the metal edges of adjacent two-dimensional projection images includes: for a certain two-dimensional projection image with a metal edge, that is, the current two-dimensional projection image, obtaining at least one adjacent two-dimensional projection image adjacent to the current two-dimensional projection image, and the current two-dimensional projection image and the at least one adjacent two-dimensional projection image belong to the same projection image sequence group; comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image to obtain the position on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image; and identifying the metal edge of the current two-dimensional projection image based on the position on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image.

[0121] Preferably, comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image and obtaining the position on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image includes:

[0122] Performing a convolution operation on the current two-dimensional projection image and the at least one adjacent two-dimensional projection image to obtain the position on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image when the convolution is maximum.

[0123] For example, after most of the projection images are processed by the artifact removal method of the present disclosure described above, the metal edge regions (such as metal-metal edge regions) can already be accurately identified. However, there are still a small number of projection images in which it is difficult to identify because the metal artifact regions overlap with hard object regions such as bones. The above-mentioned correction 1053 of the metal edge of the current two-dimensional projection image based on the metal edges of adjacent two-dimensional projection images is particularly applicable to the processing of such situations. Based on the recognition results of the previous adjacent projection image or the next adjacent projection image, the artifact regions of the current difficult-to-recognize projection image can be accurately identified.

[0124] Since there is only a very small angle difference between two adjacent images in the projection image group generated by CT, for a certain metal object, the size and shape of the metal object change little in the projection images.

[0125] Therefore, further correction is performed by comparing the already identified metal edges with the poorly recognized metal edges.

[0126] For example, convert the current two-dimensional projection image into a binary image, and also convert the adjacent two-dimensional projection image into a binary image. The adjacent two-dimensional projection image has accurately identified the metal edge, as Figure 10 shown. Figure 11 is the current two-dimensional projection image, which still has interference information, making the metal edge not well recognized.

[0127] Perform a convolution operation on the two pictures shown in Figure 10 and Figure 11 to find the position where the convolution is the largest, that is, the metal edge of the picture shown in Figure 10 contains the position of the metal edge of the picture shown in Figure 11 , which is the position where the two metal edges overlap the most. Based on this result, for the projection picture shown in Figure 10 , the metal edge can be accurately extracted from numerous interference information, so that the difficult-to-recognize metal edge can be determined. Figure 12 is the metal edge extracted from Figure 11 .

[0128] Figure 13 FIG. 200 is a schematic flow chart of a volume data reconstruction method 200 according to an embodiment of the present disclosure.

[0129] As shown in Figure 13As shown, the volumetric data reconstruction method 200 includes: 202, obtaining a plurality of two-dimensional projection images; 204, determining whether there is a metal edge in each of the plurality of two-dimensional projection images based on the metal edge feature; 206, if there is a metal edge in the two-dimensional projection image, eliminating the metal artifact in the two-dimensional projection image based on the metal edge; and 208, performing volumetric data reconstruction based on the plurality of two-dimensional projection images after artifact elimination.

[0130] Figure 14 FIG. is a cross-sectional view of a reconstruction result processed by the volumetric data reconstruction method not using the present disclosure. Figure 15 FIG. is a cross-sectional view of a reconstruction result processed by the volumetric data reconstruction method using the present disclosure.

[0131] As Figure 14 and 15 shown, Figure 14 the artifacts in Figure 15 have been mostly eliminated in

[0132] Figure 16 FIG. is a schematic structural diagram of an electronic device 1000 employing a processing system.

[0133] The artifact elimination device / volumetric data reconstruction device of the present disclosure may be included within the electronic device 1000.

[0134] An artifact elimination device according to an embodiment of the present disclosure includes: an image acquisition module 1002 that acquires at least one two-dimensional projection image; a metal edge recognition module 1004 that determines whether there is a metal edge in the at least one two-dimensional projection image based on the metal edge feature; and a processing module 1006 that, if there is a metal edge in the two-dimensional projection image, eliminates the metal artifact in the two-dimensional projection image based on the metal edge.

[0135] Among them, the image acquisition module 1002 may be obtained from an image acquisition device such as a radiation detector.

[0136] A volumetric data reconstruction device according to an embodiment of the present disclosure includes: an image acquisition module 1002 that acquires a plurality of two-dimensional projection images; a metal edge recognition module 1004 that determines whether there is a metal edge in each of the plurality of two-dimensional projection images based on the metal edge feature; a processing module 1006 that, if there is a metal edge in the two-dimensional projection image, eliminates the metal artifact in the two-dimensional projection image based on the metal edge; and a reconstruction module 1008 that performs volumetric data reconstruction based on the plurality of two-dimensional projection images after artifact elimination.

[0137] AsFigure 16 As shown, the electronic device 1000 may include modules corresponding to the respective steps of the method for artifact elimination / volume data reconstruction method of the above various embodiments.

[0138] Therefore, each step or several steps of the above method may be executed by the corresponding module, and the electronic device 1000 may include one or more of these modules. The module may be one or more hardware modules specifically configured to execute the corresponding steps, or implemented by a processor configured to execute the corresponding steps, or stored in a computer-readable medium for implementation by a processor.

[0139] The electronic device 1000 may be implemented using a bus architecture. The bus architecture may include any number of interconnecting buses and bridges, depending on the specific application of the hardware and overall design constraints. The bus 1100 connects various circuits including one or more processors 1200, a memory 1300, and / or hardware modules together. The bus 1100 may also connect various other circuits 1400 such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0140] The bus 1100 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one connecting line is shown in this figure, but it does not mean that there is only one bus or one type of bus.

[0141] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed in a manner other than shown or discussed, including in a substantially simultaneous manner according to the relevant functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present disclosure pertain. A processor executes the various methods and processes described above. For example, a method embodiment in the present disclosure can be implemented as a software program tangibly embodied in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via the memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the processor can be configured to execute one of the above methods by any other suitable means (such as by means of firmware).

[0142] The logic and / or steps represented in a flowchart or otherwise described herein can be embodied in any readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch instructions from and execute instructions of the instruction execution system, apparatus, or device.

[0143] As used in this specification, a "readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the readable storage medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the readable storage medium can even be paper or other suitable medium on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a memory.

[0144] It should be understood that various parts of the present disclosure can be implemented by hardware, software, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0145] Those of ordinary skill in the art of this technology can understand that all or part of the steps of implementing the above embodiments of the method can be completed by instructing relevant hardware through a program. The program can be stored in a readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0146] In addition, in each of the various embodiments of the present disclosure, the functional units can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. The storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0147] The present disclosure also provides an electronic device, including: a memory that stores execution instructions; and a processor or other hardware module that executes the execution instructions stored in the memory, such that the processor or other hardware module executes the above method.

[0148] The present disclosure also provides a readable storage medium that stores execution instructions, and the execution instructions are used to implement the above method when executed by a processor.

[0149] In the description of this specification, the descriptions with reference to terms such as "one embodiment / way", "some embodiments / ways", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment / way or example are included in at least one embodiment / way or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment / way or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments / ways or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments / ways or examples described in this specification and the features of different embodiments / ways or examples.

[0150] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0151] Those skilled in the art should understand that the above embodiments are only for clearly explaining the present disclosure and are not intended to limit the scope of the present disclosure. For those skilled in the art, other changes or variations can be made based on the above disclosure, and these changes or variations are still within the scope of the present disclosure.

Claims

1. A method for eliminating artifacts, characterized in that Including: Obtaining at least one two-dimensional projection image; Judging whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, where the metal edge features include metal edge gray values and metal edge gray gradients, including: obtaining the gray value and gray gradient of each pixel in the two-dimensional projection image; judging whether the gray value of each pixel is within a preset gray threshold range and whether the gray gradient is within a preset gray gradient threshold range; determining all pixels with gray values within the gray threshold range and gray gradients within the gray gradient threshold range as metal edges, and first correction of the metal edges based on a preset first corrected gray gradient threshold, where the first corrected gray gradient threshold is within the gray gradient threshold range, and the first correction of the metal edges includes: comparing the gray gradient of each pixel with gray value within the gray threshold range and gray gradient within the gray gradient threshold range with the first corrected gray gradient threshold, if it is greater than or greater than or equal to the first corrected gray gradient threshold, then determining this pixel as a strong metal edge and determining the pixel determined as a strong metal edge as a true metal edge, if it is less than or equal to or less than the first corrected gray gradient threshold, then determining this pixel as a weak metal edge; and judging whether each pixel among all pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge, if it is connected, then determining this pixel determined as a weak metal edge as a true metal edge, if it is not connected, then determining this pixel determined as a weak metal edge as a false metal edge; and If there is a true metal edge in the two-dimensional projection image, eliminating metal artifacts in the two-dimensional projection image based on the true metal edge.

2. The method for eliminating artifacts according to claim 1, characterized in that, After determining all pixels with gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, it further includes second correction of the metal edges, and the second correction of the metal edges includes: Obtaining the straight line of the gray gradient direction of each pixel with gray value within the preset gray threshold range and gray gradient within the preset gray gradient threshold range; Obtaining two intersection points of the straight line of the gray gradient direction of each pixel and the surrounding eight pixels of this pixel and obtaining the gray gradient values of the two intersection points; and Judging whether the gray gradient value of this pixel is greater than the gray gradient values of the two intersection points, if it is greater, then retaining this pixel as a metal edge.

3. The method for eliminating artifacts according to claim 2, wherein The gray gradient values of the two intersection points are obtained by interpolation method.

4. The method for eliminating artifacts according to claim 2, wherein Performing region filling on the metal edges after the second correction of the metal edges to obtain the metal edges after region filling.

5. The method for artifact elimination according to any one of claims 1 to 4, characterized in that, Before judging whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features, performing filtering and noise reduction processing on the two-dimensional projection image.

6. The method for eliminating artifacts according to any one of claims 1 to 4, characterized in that, Also including: For a certain two-dimensional projection image with a metal edge, i.e., the current two-dimensional projection image, obtain at least one adjacent two-dimensional projection image adjacent to the current two-dimensional projection image, and the current two-dimensional projection image and the at least one adjacent two-dimensional projection image belong to the same projection image sequence group; Compare the current two-dimensional projection image with the at least one adjacent two-dimensional projection image, and obtain the positions on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image; And Based on the positions on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image, identify the metal edges of the current two-dimensional projection image.

7. The method for eliminating artifacts according to claim 6, characterized in that, Comparing the current two-dimensional projection image with the at least one adjacent two-dimensional projection image and obtaining the positions on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image include: Perform a convolution operation on the current two-dimensional projection image and the at least one adjacent two-dimensional projection image, and obtain the positions on the current two-dimensional projection image corresponding to the metal edges of the at least one adjacent two-dimensional projection image when the convolution is the largest.

8. A method for reconstructing volume data, characterized in that, Include: Obtain multiple two-dimensional projection images; Based on the metal edge features, determine whether there is a metal edge in each of the multiple two-dimensional projection images. The metal edge features include the metal edge gray value and the metal edge gray gradient, including obtaining the gray value and gray gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range and whether the gray gradient is within a preset gray gradient threshold range; determining all pixels with gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, and the first correction of the metal edge based on a preset first corrected gray gradient threshold, where the first corrected gray gradient threshold is within the preset gray gradient threshold range. The first correction of the metal edge includes: comparing the gray gradient of each pixel with a gray value within the preset gray threshold range and a gray gradient within the preset gray gradient threshold range with the preset first corrected gray gradient threshold. If it is greater than or greater than or equal to the preset first corrected gray gradient threshold, then determine the pixel as a strong metal edge and determine the pixel determined as a strong metal edge as a true metal edge. If it is less than or equal to or less than the preset first corrected gray gradient threshold, then determine the pixel as a weak metal edge; and determining whether each pixel among all pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge. If it is connected, then determine the pixel determined as a weak metal edge as a true metal edge. If it is not connected, then determine the pixel determined as a weak metal edge as a false metal edge; If there is a true metal edge in the two-dimensional projection image, then eliminate the metal artifacts in the two-dimensional projection image based on the true metal edge; and Perform volume data reconstruction based on the multiple two-dimensional projection images after eliminating the artifacts.

9. An artifact elimination device, characterized in that, Include: An image acquisition module, which acquires at least one two-dimensional projection image; A metal edge recognition module, which determines whether there is a metal edge in at least one of the two-dimensional projection images based on metal edge features. The metal edge features include metal edge gray values and metal edge gray gradients, including obtaining the gray value and gray gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range and whether the gray gradient is within a preset gray gradient threshold range; determining all pixels with gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, and the first correction of metal edges based on a preset first corrected gray gradient threshold, where the first corrected gray gradient threshold is within the preset gray gradient threshold range. The first correction of metal edges includes: comparing the gray gradient of each pixel with a gray value within the preset gray threshold range and a gray gradient within the preset gray gradient threshold range with the preset first corrected gray gradient threshold. If it is greater than or greater than or equal to the preset first corrected gray gradient threshold, then determine the pixel as a strong metal edge and determine the pixel determined as a strong metal edge as a true metal edge. If it is less than or equal to or less than the preset first corrected gray gradient threshold, then determine the pixel as a weak metal edge; and determining whether each pixel among all pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge. If it is connected, then determine the pixel determined as a weak metal edge as a true metal edge. If it is not connected, then determine the pixel determined as a weak metal edge as a false metal edge; And A processing module, if there is a true metal edge in the two-dimensional projection image, then the processing module eliminates metal artifacts in the two-dimensional projection image based on the true metal edge.

10. A volume data reconstruction device, characterized in that, Including: An image acquisition module, which acquires multiple two-dimensional projection images; A metal edge recognition module, which determines whether there is a metal edge in each of the multiple two-dimensional projection images based on metal edge features. The metal edge features include metal edge gray values and metal edge gray gradients, including obtaining the gray value and gray gradient of each pixel in the two-dimensional projection image; determining whether the gray value of each pixel is within a preset gray threshold range and whether the gray gradient is within a preset gray gradient threshold range; determining all pixels with gray values within the preset gray threshold range and gray gradients within the preset gray gradient threshold range as metal edges, and a first correction of the metal edges based on a preset first corrected gray gradient threshold, where the first corrected gray gradient threshold is within the preset gray gradient threshold range. The first correction of the metal edges includes: comparing the gray gradient of each pixel with a gray value within the preset gray threshold range and a gray gradient within the preset gray gradient threshold range with the preset first corrected gray gradient threshold. If it is greater than or greater than or equal to the preset first corrected gray gradient threshold, then determine the pixel as a strong metal edge and determine the pixel determined as a strong metal edge as a true metal edge. If it is less than or equal to or less than the preset first corrected gray gradient threshold, then determine the pixel as a weak metal edge; and determining whether each pixel among all the pixels determined as weak metal edges is connected to a certain pixel determined as a strong metal edge. If it is connected, then determine the pixel determined as a weak metal edge as a true metal edge. If it is not connected, then determine the pixel determined as a weak metal edge as a false metal edge; A processing module, if there is a true metal edge in the two-dimensional projection image, then the processing module eliminates metal artifacts in the two-dimensional projection image based on the true metal edge; and A reconstruction module, which performs volume data reconstruction based on the multiple two-dimensional projection images after artifact elimination.

11. An electronic device, characterized in that, Including: A memory that stores execution instructions; And A processor that executes the execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1 to 8.

12. A readable storage medium, characterized in that, Execution instructions are stored in the readable storage medium, and when the execution instructions are executed by the processor, they are used to implement the method according to any one of claims 1 to 8.

Images