An image reconstruction method, apparatus and device

Abstract

The application provides an image reconstruction method, device and equipment. The method comprises: acquiring a first local projection image at a first scanning angle and a second local projection image at a second scanning angle by a detector, the interval between the second scanning angle and the first scanning angle being a preset interval; determining a projection difference sequence based on the second local projection image and the first local projection image, the projection difference sequence comprising K columns of projection difference values; selecting a first column and a second column from the K columns of the projection difference sequence; determining a lateral offset based on the serial number of the first column, the projection difference value of the first column, the serial number of the second column and the projection difference value of the second column; and determining an actual offset based on the lateral offset, and the actual offset is used for image reconstruction of the local projection image collected by the detector. Through the application, the image quality and image resolution of the reconstructed image of the local scanning can be improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image reconstruction method, apparatus and device. Background Technology

[0002] In computed tomography (CT), taking CBCT (Cone Beam Computed Tomography) as an example, a global scan of the sample is performed. The X-ray source displays all information about the sample on a detector, which then acquires an image of the entire sample area. CBCT is an X-ray-based three-dimensional imaging technique that uses a cone-shaped X-ray beam to rotate and scan the sample, generating high-resolution three-dimensional images. It features low radiation and high efficiency.

[0003] In industrial inspection scenarios, a region of interest (ROI) can be locally scanned on a sample. An X-ray source projects partial information about the sample onto a detector, which then acquires an image of that ROI. Local scanning allows for higher magnification, resulting in higher image resolution, clearer details, and more distinct features, facilitating subsequent defect analysis and feature recognition. It also reduces scanning time and increases inspection speed. However, local scanning can decrease the quality of the reconstructed image, impacting defect identification and assessment. Summary of the Invention

[0004] This application provides an image reconstruction method, the method comprising:

[0005] The detector acquires a first local projection image at a first scanning angle and a second local projection image at a second scanning angle, wherein the interval between the second scanning angle and the first scanning angle is a preset interval.

[0006] A projection difference sequence is determined based on the second local projection image and the first local projection image. The projection difference sequence includes K columns of projection differences. Here, K represents the number of column pixel channels of the detector, and the projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image. The value of j ranges from 1 to K.

[0007] Select a first column and a second column from the K columns of the projection difference sequence; wherein, based on the projection differences of the K columns, the column corresponding to the projection difference closest to 0 is taken as the first column; the difference between the index of the second column and the first column is a fixed value;

[0008] Based on the serial number of the first column, the projection difference of the first column, the serial number of the second column, and the projection difference of the second column, the lateral offset of the detector is determined.

[0009] The actual offset of the detector is determined based on the lateral offset; wherein the actual offset is used to reconstruct the local projection image acquired by the detector.

[0010] This application provides an image reconstruction apparatus, the apparatus comprising:

[0011] The acquisition module is used to acquire a first local projection image at a first scanning angle and a second local projection image at a second scanning angle, wherein the interval between the second scanning angle and the first scanning angle is a preset interval.

[0012] The determination module is used to determine a projection difference sequence based on the second local projection image and the first local projection image. The projection difference sequence includes K columns of projection differences; where K represents the number of column pixel channels of the detector, and the projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image; the value of j ranges from 1 to K.

[0013] The selection module is used to select a first column and a second column from the K columns of the projection difference sequence; wherein, based on the projection differences of the K columns, the column corresponding to the projection difference closest to 0 is selected as the first column; the difference between the index of the second column and the first column is a fixed value;

[0014] The determining module is used to determine the lateral offset of the detector based on the serial number of the first column, the projection difference of the first column, the serial number of the second column, and the projection difference of the second column.

[0015] The determining module is used to determine the actual offset of the detector based on the lateral offset; wherein the actual offset is used to reconstruct the local projection image acquired by the detector.

[0016] This application provides an electronic device, including: a processor and a machine-readable storage medium, the machine-readable storage medium storing machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the image reconstruction method of the example above.

[0017] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the image reconstruction method of the example described above.

[0018] This application provides a machine-readable storage medium storing machine-executable instructions that can be executed by a processor; wherein the processor is configured to execute the machine-executable instructions to implement the image reconstruction method of the example described above when the machine-executable instructions are executed.

[0019] As can be seen from the above technical solutions, in this embodiment, the lateral offset of the detector can be determined based on the first local projection image at the first scanning angle and the second local projection image at the second scanning angle. The actual offset of the detector is then determined based on the lateral offset, and image reconstruction is performed on the local projection image acquired by the detector based on the actual offset. This effectively suppresses artifacts in the reconstructed image, improves the image quality and resolution of the reconstructed image from the local scan, and obtains a clear internal structure and features of the sample to be detected based on the reconstructed image. By fully considering the characteristics of circumferential CT and local CT data, and by analyzing the characteristics of symmetrical projection and local projection, key geometric parameters are directly solved in the projection domain, avoiding the use of geometric phantoms. This reduces the complexity of operation and calculation, effectively suppresses annular artifacts and geometric artifacts, and thus improves the image resolution and image quality of local CT scan imaging. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an image reconstruction method according to one embodiment of this application;

[0021] Figure 2 This is a schematic diagram of the lateral offset of the detector during a partial scan in one embodiment of this application;

[0022] Figure 3 This is a flowchart illustrating an image reconstruction method according to one embodiment of this application;

[0023] Figure 4 This is a schematic diagram of acquiring a local projection image in one embodiment of this application;

[0024] Figure 5 This is a schematic diagram illustrating the acquisition of the actual offset corresponding to the detector in one embodiment of this application;

[0025] Figure 6A The scanning angle in one embodiment of this application is: A two-dimensional projection diagram of the time;

[0026] Figure 6B The scanning angle in one embodiment of this application is: A two-dimensional projection diagram of degrees;

[0027] Figure 7 This is a flowchart illustrating an image reconstruction method according to one embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of an image reconstruction apparatus according to one embodiment of this application;

[0029] Figure 9 This is a hardware structure diagram of an electronic device according to one embodiment of this application. Detailed Implementation

[0030] This application presents an image reconstruction method that can be applied to electronic devices, such as computers. (See [link to relevant documentation]). Figure 1 The diagram shown is a flowchart of the method, which may include:

[0031] Step 101: Obtain a first local projection image at the first scanning angle and a second local projection image at the second scanning angle using the detector. The interval between the second scanning angle and the first scanning angle is a preset interval.

[0032] Step 102: Determine a projection difference sequence based on the second local projection image and the first local projection image. The projection difference sequence includes K columns of projection differences. Here, K can represent the number of column pixel channels of the detector. The projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image. The value of j ranges from 1 to K.

[0033] Step 103: Select the first column and the second column from the K columns of the projection difference sequence; wherein, based on the projection difference of the K columns, the column corresponding to the projection difference close to 0 can be used as the first column; in addition, the difference between the index of the second column and the first column is a fixed value (which can be configured according to experience, such as a fixed value of 1).

[0034] For example, the projection difference sequence includes K columns of projection differences. The maximum and minimum projection differences are selected from these K columns. Based on the projection differences of each candidate column, the candidate column corresponding to the projection difference closest to 0 is determined as the first column, meaning the absolute value of the difference between the projection difference of the first column and 0 is the smallest. Each candidate column lies between the column corresponding to the maximum projection difference and the column corresponding to the minimum projection difference. A second column is determined based on the first column, and the difference in index between the second column and the first column is 1.

[0035] Step 104: Based on the serial number of the first column, the projection difference of the first column, the serial number of the second column, and the projection difference of the second column, determine the lateral offset of the detector.

[0036] Step 105: Determine the actual offset of the detector based on the lateral offset; wherein the actual offset is used to reconstruct the local projection image acquired by the detector.

[0037] For example, acquiring a first local projection image at a first scanning angle and a second local projection image at a second scanning angle using a detector may include, but is not limited to: acquiring a first local original image at the first scanning angle and a second local original image at the second scanning angle using a detector; wherein the first local original image and the second local original image may be obtained when scanning the region of interest of the sample to be detected using local scanning parameters. A first local projection image is determined based on the first local original image and the acquired average air image, and a second local projection image is determined based on the second local original image and the average air image; wherein the average air image is obtained by averaging multiple air images, and each air image is obtained when scanning a region where the sample to be detected does not exist.

[0038] For example, determining the lateral offset of the detector based on the serial number of the first column, the projection difference of the first column, the serial number of the second column, and the projection difference of the second column may include, but is not limited to: calculating a reference summation between the projection differences of the first column and the projection differences of the second column; determining a first weighting coefficient based on the projection differences of the first column and the reference summation; determining a second weighting coefficient based on the projection differences of the second column and the reference summation; and determining the lateral offset of the detector based on the first weighting coefficient, the serial number of the first column, the second weighting coefficient, and the serial number of the second column.

[0039] In one possible implementation, the lateral offset of the detector is determined based on the serial number of the first column, the projection difference of the first column, the serial number of the second column, and the projection difference of the second column. This can include, but is not limited to, the following formula: The lateral offset of the detector can be determined using the following formula:

[0040] ;

[0041] in, It can represent the lateral offset. This can represent the projection difference of the first column. This can represent the projection difference in the second column. It can represent the sequence number of the first column. It can represent the serial number of the second column.

[0042] For example, determining the actual offset of the detector based on the lateral offset may include, but is not limited to: determining the actual offset of the detector based on the lateral offset, the number of column pixel channels of the detector, and the pixel size of the column direction supported by the detector.

[0043] For example, the actual offset of the detector is determined based on the lateral offset, the number of column pixel channels of the detector, and the pixel size in the column direction supported by the detector. This may include, but is not limited to: determining the center column position of the detector based on the number of column pixel channels; determining the distance between the projection position of the rotation axis center on the detector and the center column position based on the lateral offset and the center column position; wherein, when the turntable rotates to obtain local projected images at multiple scanning angles, the turntable rotates based on this rotation axis center. The actual offset is determined based on the distance and the pixel size in the column direction.

[0044] In one possible implementation, determining the actual offset of the detector based on the lateral offset may include, but is not limited to, using the following formula to determine the actual offset of the detector: ;in, This represents the actual offset. Indicates the lateral offset. Indicates the pixel size in the column direction supported by the detector. Indicates the position of the center column. This indicates the distance between the projection position of the rotation axis center on the detector and the position of the center column.

[0045] For example, there can be multiple first local projection images, multiple second local projection images, and multiple projection difference sequences. Based on this, selecting the first and second columns from the K columns of the projection difference sequences can include, but is not limited to: determining a target projection difference sequence based on multiple projection difference sequences, where the target projection difference sequence can include the target projection differences in the K columns; wherein the target projection difference in the j-th column of the target projection difference sequence is the sum of the projection differences in the j-th column of the multiple projection difference sequences. Based on this, the maximum and minimum projection differences are selected from the K columns of target projection differences; based on the target projection differences in each candidate column, the candidate column corresponding to the target projection difference closest to 0 is determined as the first column; wherein each candidate column is located between the column corresponding to the maximum projection difference and the column corresponding to the minimum projection difference; the second column is determined based on the first column, where the difference in index between the second column and the first column can be 1.

[0046] As can be seen from the above technical solutions, in this embodiment, the lateral offset of the detector can be determined based on the first local projection image at the first scanning angle and the second local projection image at the second scanning angle. The actual offset of the detector is then determined based on the lateral offset, and image reconstruction is performed on the local projection image acquired by the detector based on the actual offset. This effectively suppresses artifacts in the reconstructed image, improves the image quality and resolution of the reconstructed image from the local scan, and obtains a clear internal structure and features of the sample to be detected based on the reconstructed image. By fully considering the characteristics of circumferential CT and local CT data, and by analyzing the characteristics of symmetrical projection and local projection, key geometric parameters are directly solved in the projection domain, avoiding the use of geometric phantoms. This reduces the complexity of operation and calculation, effectively suppresses annular artifacts and geometric artifacts, and thus improves the image resolution and image quality of local CT scan imaging.

[0047] The technical solutions described above in the embodiments of this application will be explained below in conjunction with specific application scenarios.

[0048] In computed tomography (CT) imaging, a global scan of the sample to be tested is performed. The X-ray source presents all the information of the sample to be tested on the detector. The detector acquires images of the entire area of ​​the sample to be tested. This is called image reconstruction based on the data collected by the detector (such as CT image reconstruction). Image reconstruction is the process of deducing the attenuation coefficient of the internal substances of the sample from the data.

[0049] In industrial inspection scenarios, when inspecting a sample, only certain areas need to be examined. Therefore, only the region of interest (ROI) is scanned; this scanning method is called local scanning (such as a CT scan). Based on this, a local scan of the ROI of the sample is performed. An X-ray source projects partial information about the sample onto a detector, which then acquires an image of the ROI.

[0050] Partial scanning allows for higher magnification imaging, resulting in higher image resolution, clearer image details and features, facilitating subsequent defect analysis and feature recognition. Partial scanning also reduces scanning time and increases inspection speed. Due to its unique advantages, partial scanning is widely used in nondestructive testing. For example, partial scanning refers to a CT inspection technique that focuses imaging on a specific area of ​​the sample to be inspected, rather than a global scan. The core characteristic of partial scanning is that it achieves high-resolution imaging of a designated area by narrowing the scanning range.

[0051] During partial scanning imaging, inconsistent detector crystal responses can cause data acquisition deviations, resulting in concentric rings centered on the rotation center in the reconstructed image—a phenomenon known as ring artifacts. These ring artifacts reduce image quality and resolution, hindering defect identification and assessment. Furthermore, geometric errors, particularly lateral detector offset, cause the reconstructed image's outline to spread outwards, blurring the structure and internal details of the sample—a phenomenon known as geometric artifacts. These artifacts also negatively impact image quality and resolution, reducing clarity and hindering the observation of internal details and feature recognition.

[0052] See Figure 2 The diagram shows the lateral offset of the detector during partial scanning. A coordinate system (i.e., coordinate system xyz) is established with the intersection of the focal point S of the X-ray source and the rotation axis of the turntable (i.e., the center of the rotation axis of the turntable) as the origin. The straight line between the focal point of the X-ray source (the focal point of the X-ray source is S) and the origin O (i.e., the straight line OS) is the coordinate axis (x-axis).

[0053] When the detector is laterally offset relative to the coordinate system xyz (e.g., the detector is laterally offset from the position corresponding to the center D to the position corresponding to D'), the outline of the reconstructed image of the sample to be detected will spread outwards, and the structure of the sample to be detected will be blurred. As the lateral offset increases, the artifact phenomenon becomes more and more obvious.

[0054] Obviously, during local scanning imaging, the reconstructed image will contain ring artifacts and geometric artifacts, which will reduce the image quality of the reconstructed image and affect the identification and judgment of defects.

[0055] In response to the above findings, this application proposes an image reconstruction method that can improve the image quality of reconstructed images from local scans (CT local scans) in the field of X-ray nondestructive testing. The method considers improving the image quality of the reconstructed image by analyzing the original data and calculating key geometric parameters without using a geometric phantom. It proposes a method that is phantom-free, resource-efficient, and computationally simple, and can suppress ring artifacts and geometric artifacts, improve the resolution of the reconstructed image, and obtain clear internal structures and features.

[0056] See Figure 3 The diagram shows a flowchart of an image reconstruction method. This method may include processes such as acquiring an average air image, acquiring a local projection image, acquiring the actual offset corresponding to the detector, and reconstructing the image based on the actual offset. These processes are described below.

[0057] First, regarding the acquisition of the average air image, the average air image is obtained by averaging multiple air images, each of which is obtained by scanning areas where no sample to be detected exists.

[0058] For example, the average air image can be obtained using the following steps:

[0059] Step S11: Acquire air images (such as multiple air images) through the detector.

[0060] For example, multiple scanning angles can be predetermined. At scanning angle 1, the air (i.e., the area without the sample to be tested) is scanned by the X-ray source, and an air image 1 at scanning angle 1 is obtained by the detector. At scanning angle 2, the air is scanned by the X-ray source, and an air image 2 at scanning angle 2 is obtained by the detector. At scanning angle 3, the air is scanned by the X-ray source, and an air image 3 at scanning angle 3 is obtained by the detector, and so on, multiple air images can be obtained.

[0061] For each air image, the air image can be denoted as , , This refers to the number of row pixel channels of the detector, i.e., the total number of channels present in the air image. Row pixels. , This refers to the number of pixel channels in the detector, i.e., the total number of pixels present in the air image. Column pixels. , This indicates the number of aerial images, i.e., the total number of images present. The number of aerial images. Based on this, Indicates the pixel position of the detector In the The corresponding pixel value in an air image (also known as an air value).

[0062] Step S12: Perform a mean operation on multiple air images to obtain an average air image.

[0063] For example, regarding pixel locations in an average air image The pixel value can be denoted as The pixel value can be determined using the following formula (1). :

[0064] Formula (1)

[0065] As can be seen from formula (1), by adjusting the pixel position... exist By averaging the pixel values ​​in an aerial image, the pixel position can be obtained. The pixel values ​​corresponding to all pixel locations in the mean air image constitute the mean air image.

[0066] At this point, the average air image can be obtained and stored. In subsequent processes, the local original image can be corrected based on the average air image to obtain the local projected image.

[0067] Second, regarding the acquisition of local projection images, the local projection images are obtained by correcting the local original images using the average air image, and the local original images are obtained when scanning the region of interest of the sample to be detected, that is, the local original images of the region of interest of the sample to be detected are acquired by the detector.

[0068] For example, see Figure 4 The diagram shown illustrates how to obtain a local projection image, which may include:

[0069] Step 401: Determine the local scanning parameters (such as cone-beam CT local scanning parameters).

[0070] For example, when testing a sample, a region of interest (ROI) can be locally scanned (e.g., a local CT scan). Based on this, local scanning parameters can be predetermined before performing the local scan. These parameters may include, but are not limited to, at least one of the following: voltage, current, scanning angle, scanning angle interval, and distance from the X-ray source focal point to the center of rotation. SOD, Distance from the focal point of the X-ray source to the detector SDD Of course, the above are just a few examples and are not intended to be restrictive.

[0071] When determining local scanning parameters, they can be determined based on the region of interest of the sample to be detected, or other methods can be used to determine the local scanning parameters; there are no restrictions on this.

[0072] For example, information such as the atomic number, density, and thickness of the region of interest in the sample to be tested can be determined, and local scanning parameters such as voltage and current can be determined based on the atomic number, density, and thickness.

[0073] For example, the scanning angle and scanning angle interval can be pre-configured, such as 360 degrees, 300 degrees, 260 degrees, etc., without any restrictions. The scanning angle interval can also be pre-configured, such as 1 degree, 2 degrees, 3 degrees, etc., without any restrictions.

[0074] Assuming the scanning angle range is 360 degrees and the scanning angle interval is 1 degree, there are a total of 360 scanning angles, such as 1 degree, 2 degrees, ..., 358 degrees, 359 degrees and 360 degrees.

[0075] For example, regarding the distance from the focal point of the radiation source to the center of rotation. SOD The focal point of the X-ray source (the central focal point of the X-ray source) is the focal point of the X-ray source. X-rays are emitted from this focal point. Figure 2 The X-ray source focal point S is shown in the diagram. When testing a sample, the sample can remain stationary while the detector rotates around it. By rotating the detector, images at 360 scanning angles can be obtained. During this rotation, the distance between the detector and the X-ray source remains constant, and the axis of rotation is called the turntable axis, with the center of rotation called the rotation center (or axis center). Alternatively, when testing a sample, the detector can remain stationary while the sample rotates with the turntable. Again, the axis of rotation during this process is called the turntable axis, and the center of rotation is called the rotation center or axis center.

[0076] How to determine the distance from the focal point of the X-ray source to the center of rotation SOD This distance can be configured according to actual needs, or it can be configured based on the region of interest of the sample to be tested; there are no restrictions on this.

[0077] For example, regarding the distance from the focal point of the radiation source to the detector SDD The X-ray source focal point is the center point of the X-ray source, and the detector is used to acquire an image of the region of interest (ROI) of the sample to be tested. This distance can be configured according to actual needs or based on the RPI of the sample; there are no restrictions on this.

[0078] Step 402: Use local scanning parameters to scan the region of interest of the sample to be tested, and obtain the image of the region of interest of the sample to be tested through the detector, which is recorded as the local original image.

[0079] For example, when detecting a sample, a local scan (such as a CT scan) is performed on the region of interest (ROI) of the sample. Local scan parameters are used to perform the local scan of the ROI. Based on this, a local raw image of the ROI of the sample is acquired by a detector, and this local raw image is denoted as... . , This refers to the number of row pixel channels of the detector, i.e., the total number of original image channels in this local area. Row pixels. , This refers to the number of pixel channels in the detector, i.e., the total number of original pixels in this local area. Column pixels. Indicates the scanning angle. , = , Indicates the number of original images in a local area. Indicates the range of scanning angles. This represents the scanning angle interval, assuming a scanning angle range. 360 degrees, scanning angle interval If the degree is 1, then the number of local original images There are 360. Thus, the scanning angle... It can be 1 degree, 2 degrees, ..., 358 degrees, 359 degrees, and 360 degrees. Based on this, the scanning angle... When the degree is 1, acquire a local raw image of the region of interest of the sample to be detected. At the scanning angle At a degree of 2, acquire a local raw image of the region of interest of the sample to be tested. And so on, at the scanning angle When viewed from 360 degrees, acquire local raw images of the region of interest of the sample to be tested. This yields 360 local original images.

[0080] Indicates the pixel position of the detector At scanning angle The corresponding pixel value, that is, the pixel position of the detector. In the The corresponding pixel values ​​in the original partial image.

[0081] Step 403: Determine the local projected image based on the local original image and the average air image.

[0082] For example, a portion of the original image can be processed. Preprocessing is performed to obtain a local projection image, which is denoted as . Local original image This can be understood as an image of X-rays (i.e., X-rays emitted from a radiation source) after signal attenuation, a local projection image. This can be understood as an image of the portion of the sample to be tested from which X-rays are absorbed; in other words, it is obtained by analyzing a localized original image. Preprocessing is performed to obtain a local projection image of the absorption portion of the sample to be tested. .

[0083] According to Lambert-Beer's law, the intensity of X-rays decreases exponentially after penetrating an object (i.e., the sample to be tested). . Indicates the intensity of the incident X-rays. This indicates the intensity of X-rays after penetrating the sample being tested. The attenuation coefficient of the sample to be tested. This refers to the path length of the X-rays through the sample being tested. Based on this principle, a large amount of raw data is collected. The attenuation coefficient can be solved using a reconstruction algorithm. The spatial distribution of the sample was used to obtain the grayscale distribution map of the internal structure of the sample to be tested.

[0084] In image reconstruction, for ease of calculation, X-rays can be set as monoenergetic rays, i.e. Using fixed values ​​ignores the pluripotency and inhomogeneity of X-rays. However, this processing method easily leads to obvious ring artifacts and hardening artifacts in the reconstructed image, reducing image quality. Based on the above principle, this results in concentric rings around the rotation center appearing in the reconstructed image, causing ring artifacts.

[0085] In response to the above findings, this embodiment does not solve for the attenuation coefficient. The spatial distribution is not based on the attenuation coefficient. Spatial distribution of the local original image Preprocessing is performed to obtain a local projection image. Instead, it uses the average air image to analyze the local original image. Preprocessing is performed to obtain a local projection image. For example, based on average air images and local original image The local projection image can be obtained using the following formula (2). .

[0086] Formula (2)

[0087] At scanning angle When the temperature is 1 degree, the average air image is used. For local original images Preprocessing is performed to obtain a local projection image at a scanning angle of 1 degree. And so on. Clearly, by determining the pixel position... Pixel values ​​and pixel locations in the average atmospheric image Substituting the corresponding pixel value in the local original image into formula (2) yields the pixel position. The corresponding pixel value in the local projection image, and the positions of all pixels. This forms a local projection image.

[0088] For example, considering the inherent inhomogeneity of X-rays and data deviations caused by inconsistent detector crystal responses, both will be reflected in the averaged air image. Therefore, instead of using the attenuation coefficient, a local projection image is obtained by preprocessing the original local image using the averaged air image. By preprocessing the local original image using spatial distribution, a local projection image is obtained, which can effectively control data deviation and suppress ring artifacts. Moreover, the local projection image does not lose the detailed information of the local original image.

[0089] At this point, local projected images for each scanning angle can be obtained and stored. In subsequent processes, the corresponding lateral offset of the detector can be determined based on these local projected images.

[0090] Third, regarding obtaining the actual offset corresponding to the detector, a projection difference sequence can be determined based on the local projection image. The first and second columns can be determined based on this projection difference sequence. The lateral offset corresponding to the detector can be determined based on the sequence number of the first column, the projection difference of the first column, the sequence number of the second column, and the projection difference of the second column. The actual offset corresponding to the detector can then be determined based on this lateral offset. For example, see [link to example]. Figure 5 The diagram shown illustrates the acquisition of the actual offset of the detector, which may include:

[0091] Step 501: Obtain the first local projection image under the first scanning angle and the second local projection image under the second scanning angle. The interval between the second scanning angle and the first scanning angle is a preset interval, such as 180 degrees.

[0092] For example, see Figure 4 As shown, local projection images for each scanning angle have been obtained. From these local projection images, a first local projection image at the first scanning angle and a second local projection image at the second scanning angle are selected. The first local projection image can be denoted as... And the second local projection image is denoted as , Represents 180 degrees. (Compared to a partial projected image) compared to, In other words, It can be from 1 to 360, and It can range from 1 to 180.

[0093] For example, at the first scanning angle ( When ) is 1, the second scanning angle ( With a value of 181, the first local projection image at a scanning angle of 1 degree can be obtained. and the second local projection image at a scanning angle of 181° At the first scanning angle ( When ) is 2, the second scanning angle ( With a value of 182, the first local projection image at a scanning angle of 2 degrees can be obtained. and the second local projection image at a scanning angle of 182° By analogy, multiple first local projection images and multiple second local projection images corresponding to the multiple first local projection images can be obtained.

[0094] For example, during a circular CT scan, while the sample is being examined, the sample can remain stationary while the detector rotates around it. By rotating the detector, images from 360 scanning angles can be obtained. For instance, a turntable can be used to rotate the detector around its center axis to obtain images from 360 scanning angles. Alternatively, the detector can remain stationary while the sample rotates around it. Again, this rotation of the sample can yield images from 360 scanning angles, for example, by rotating the sample around its center axis.

[0095] For example, see the two-dimensional fan bundle. Figure 6A As shown, the scanning angle is... A two-dimensional projection diagram of the time, see [link / reference]. Figure 6B As shown, the scanning angle is... A two-dimensional projection diagram of degrees. From Figure 6A and Figure 6B It can be seen that the X-rays emitted from the X-ray source and passing through the center of rotation follow the same path at two scanning angles that differ by 180 degrees, meaning that the local projected images at these two scanning angles exhibit a symmetrical relationship. Under ideal conditions where the X-ray energy is uniform and there is no noise interference, the values ​​received by the detector are the same, meaning that the local projected images at these two scanning angles are identical.

[0096] However, during actual data acquisition by the detector, external factors such as X-ray energy spectrum, noise, and current can affect its consistency, but this inherent characteristic remains constant. Utilizing this characteristic, two local projection images spaced 180 degrees apart can be analyzed to determine the detector's lateral offset.

[0097] In summary, the lateral offset of the detector can be calculated based on the first local projection image at the first scanning angle and the second local projection image at the second scanning angle. This will be explained below.

[0098] Step 502: Determine a projection difference sequence based on the second local projection image and the first local projection image. This projection difference sequence includes K columns of projection differences. Here, K can represent the number of column pixel channels of the detector, and the projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image; the value of j ranges from 1 to K.

[0099] For example, the first projection difference is the difference between the pixel value in the first column and i-th row of the second local projection image and the pixel value in the first column and i-th row of the first local projection image; the second projection difference is the difference between the pixel value in the second column and i-th row of the second local projection image and the pixel value in the second column and i-th row of the first local projection image, and so on. The Kth projection difference is the difference between the pixel value in the Kth column and i-th row of the second local projection image and the pixel value in the Kth column and i-th row of the first local projection image. Thus, we can obtain K columns of projection differences (i.e., K projection differences). These K columns of projection differences are then combined to form a projection difference sequence. The value of i can be from 1 to M. Let i be the number of row pixel channels of the detector. For example, when i is 2, the first projection difference is the difference between the pixel value of the second row of the first column in the second local projection image and the pixel value of the second row of the first local projection image, and so on.

[0100] For example, the first local projection image can be denoted as The second local projection image can be denoted as Take the first local projection image A certain line (e.g.) Row, that is, the j-th column and the i-th row. The pixel values ​​of the rows are used to obtain the first projected sine curve. ,and That is, the first projected sine curve includes the first local projected image in the first local projected image. The first projected sine wave contains K pixel values ​​from each row, and the second local projected image contains K pixel values ​​from each column. The The pixel values ​​of the row are used to obtain the second projected sine curve. The second projected sine curve may include the second local projected image from the first... The K pixel values ​​of the row.

[0101] Based on this, the following formula (3) can be used to determine the projection difference sequence, which can be the difference between the second local projection image and the first local projection image with a 180-degree interval, so as to find the difference between the projection pairs with a 180-degree interval on the two-dimensional sine graph (i.e. the projection sine graph).

[0102] Formula (3)

[0103] In formula (3), This represents the projection difference sequence, where column j is column 1. This represents the pixel value of the first column of the first projected sine wave. This represents the pixel value of the first column of the second projected sine wave. This represents the projection difference of the first column of the projection difference sequence. Similarly, the value of j ranges from 1 to K, resulting in K projection differences.

[0104] Step 503: Determine the target projection difference sequence based on multiple projection difference sequences. The target projection difference sequence may include K columns of target projection differences. For example, the j-th column of the target projection difference sequence is the sum of the projection differences in the j-th column of multiple projection difference sequences.

[0105] For example, the first scanning angle ( () can be multiple, such as The second scanning angle can be from 1 to 180 degrees. There can be multiple ) such as ( The angle can be from 181 to 360, therefore, there are multiple first local projection images and multiple second local projection images. In this way, multiple projection difference sequences can be obtained, such as the first scanning angle (…). The projection difference sequence when ) is 1, the first scanning angle ( The projection difference sequence is obtained when the projection difference is 2, and so on, resulting in a total of 180 projection difference sequences.

[0106] Based on this, the projection differences in these projection difference sequences can be summed to obtain the target projection difference sequence. For example, summing the first projection difference in all projection difference sequences yields the first target projection difference in the target projection difference sequence, summing the second projection difference in all projection difference sequences yields the second target projection difference in the target projection difference sequence, and so on.

[0107] For example, the target projection difference sequence can be determined using the following formula (4):

[0108] Formula (4)

[0109] In formula (4), Let j represent the target projection difference sequence, where j takes values ​​from 1 to K, meaning the target projection difference sequence includes K target projection differences. This represents the first scanning angle, and the range of the first scanning angle is from 1 to... . Indicates the first scanning angle The above formula represents the summation of all projection difference sequences.

[0110] Step 504: Select the first column from the K columns of the target projection difference sequence. Based on the target projection differences in the K columns of the target projection difference sequence, select the column corresponding to the target projection difference closest to 0 as the first column.

[0111] For example, the target projection difference sequence may include K columns of target projection differences, from which the maximum and minimum projection differences can be selected; based on the target projection difference of each candidate column, the candidate column corresponding to the target projection difference close to 0 can be determined as the first column; wherein, each candidate column is located between the column corresponding to the maximum projection difference and the column corresponding to the minimum projection difference.

[0112] For example, suppose the target projection difference in column 5 is the maximum projection difference, and the target projection difference in column 20 is the minimum projection difference. Then, the candidate columns could be columns 5, 6, ..., 20; these columns are considered as candidate columns. Then, based on the target projection difference in each candidate column, we find the target projection difference closest to 0, i.e., the one with the smallest absolute value of the difference between the target projection difference and 0. Thus, the candidate column corresponding to the target projection difference closest to 0 can be determined as the first column. For example, suppose the target projection difference in column 8 has the smallest absolute value of the difference between the target projection difference and 0; then, column 8 is determined as the first column, and its index can be 8.

[0113] For example, based on the projection characteristics of local circular CT, data consistency is highest near the center of the rotation axis, with the difference fluctuating around 0, and the difference increases with distance from the central axis. Based on this, in this embodiment, the target projection difference sequence... Find the columns corresponding to the extreme values ​​(i.e., the columns corresponding to the maximum projection difference and the minimum projection difference), and then find the columns close to 0 between these two extreme values. For example, the columns corresponding to the maximum projection difference and the minimum projection difference are candidate columns. Based on the target projection difference corresponding to each candidate column, the column close to 0 is taken as the first column.

[0114] For example, targeting The column corresponding to the extreme values ​​is denoted as the column corresponding to the maximum projection difference. The column corresponding to the minimum projection difference is denoted as .so, , , The column containing the minimum value among all target projection differences can be... , The column containing the maximum value among all target projection differences can be... .

[0115] from and Find the column that is closest to 0, for example, using the following formula: Thus, from the maximum projection difference and minimum projection difference Among the candidate columns, find the column closest to 0, denoted as . .

[0116] Step 505: Select a second column from the K columns of the target projection difference sequence based on the first column. The difference between the index of the second column and the first column is a fixed value, such as 1. For example, if the index of the first column is 8, then the index of the second column is 9; if the index of the first column is 11, then the index of the second column is 12.

[0117] Step 506: Based on the serial number of the first column, the target projection difference of the first column, the serial number of the second column, and the target projection difference of the second column, determine the lateral offset of the detector.

[0118] For example, in order to ensure the accuracy of the solution and make the solution more accurate, instead of directly using the serial number of the first column as the lateral offset of the detector, a weighted operation is performed on the values ​​near 0 (i.e. the difference between the target projection in the first column and the difference between the target projection in the second column) to obtain the lateral offset of the detector.

[0119] For example, a reference summation can be calculated between the target projection differences in the first column and the target projection differences in the second column; that is, the reference summation is the sum of the target projection differences in the first and second columns. Based on this, a first weighting coefficient (i.e., the weighting coefficient of the index in the first column) is determined based on the target projection differences in the first column and the reference summation, and a second weighting coefficient (i.e., the weighting coefficient of the index in the second column) is determined based on the target projection differences in the second column and the reference summation. Then, based on the first weighting coefficient, the index in the first column, the second weighting coefficient, and the index in the second column, the lateral offset corresponding to the detector is determined.

[0120] For example, the lateral offset of the detector can be determined using the following formula (5):

[0121] Formula (5)

[0122] In formula (5), Indicates the lateral offset. This represents the target projection difference in the first column. This indicates the target projection difference in the second column. This indicates the sequence number of the first column. This indicates the serial number of the second column.

[0123] Indicates a reference summation value. This represents the first weighting coefficient. This represents the second weighting coefficient.

[0124] In one possible implementation, the lateral offset obtained by the above process is based on the first local projection image. The first row and the second local projection image To improve the accuracy of the solution, the i-th row of the first local projection image and the i-th row of the second local projection image can be taken, with i ranging from 1 to M, where M is the number of row pixel channels of the detector. This process can be repeated to obtain M lateral offsets. Then, the average of the M lateral offsets is calculated to obtain the corresponding lateral offset of the detector. For example, the lateral offset can be determined using the following formula (6):

[0125] Formula (6)

[0126] In formula (6), This represents the lateral offset (i.e., the lateral offset sequence) corresponding to the detector. Let represent the i-th lateral offset, which is obtained by taking the i-th row of the first local projection image and the i-th row of the second local projection image and using the above process. Obviously, the above process can be repeated to obtain M lateral offsets, where M is the number of row pixel channels of the detector.

[0127] Step 507: Determine the actual offset of the detector based on the lateral offset. For example, the lateral offset can be the number of pixels, while the actual offset can be a physical quantity.

[0128] For example, after obtaining the lateral offset corresponding to the detector, this lateral offset can be converted into the actual offset corresponding to the detector. This actual offset represents the positional shift of the detector. For instance, if the detector should be at position A, but is actually at position A', the offset of position A' from position A is the actual offset corresponding to the detector. See also... Figure 2 As shown, the detector shifts laterally from position D to position D', and the offset between these two positions is the actual offset. When this actual offset exists, it causes the outline of the reconstructed image of the sample to spread outwards, resulting in a blurred structure of the sample, i.e., artifacts in the reconstructed image. Furthermore, the lateral offset is an intermediate parameter used to determine the actual offset, and the actual offset can be determined based on the lateral offset.

[0129] For example, the actual offset of the detector can be determined based on the lateral offset, the number of column pixel channels of the detector, and the pixel size in the column direction supported by the detector. For instance, the center column position of the detector is determined based on the number of column pixel channels; the distance between the projection position of the rotation axis center on the detector and the center column position is determined based on the lateral offset and the center column position; and the actual offset is determined based on this distance and the pixel size in the column direction. Regarding the rotation axis center, when the turntable rotates to obtain local projected images at multiple scanning angles, the turntable rotates based on this rotation axis center. Referring to the above embodiments, the turntable can rotate the detector to obtain local projected images at multiple scanning angles, and the turntable can also rotate the sample to be detected to obtain local projected images at multiple scanning angles. Whether rotating the detector or the sample to be detected, the center of this rotation is the rotation axis center.

[0130] For example, when determining the actual offset of the detector, the following formula (7) can be used to determine the actual offset of the detector. Formula (7) is just an example.

[0131] Formula (7)

[0132] In formula (7), This represents the actual offset of the detector. This indicates the number of pixels with lateral offset corresponding to the detector. Indicates the number of column pixel channels of the detector. This indicates the pixel size in the column direction supported by the detector. This pixel size is an inherent property of the detector and is a known value.

[0133] In formula (7), This can indicate the position of the center column of the detector. It can represent the distance between the projection position of the rotation axis center on the detector and the position of the center column.

[0134] Fourth, image reconstruction based on actual offsets.

[0135] For example, after obtaining the actual offset corresponding to the detector, this actual offset can be stored. In subsequent detection processes, after each local projection image is acquired by the detector at the same location, this actual offset can be used to reconstruct the image from the local projection image acquired by the detector.

[0136] For example, when testing a sample, a local scan of the region of interest (ROI) is performed, and the detector acquires a local raw image of the ROI. Then, the local original image can be analyzed based on the average air image. Preprocessing is performed to obtain a local projection image. Based on this, the actual offset can be used. For local projection images Image reconstruction is performed to obtain a clear local slice image. Because of the actual offset of the detector, artifacts exist in the local projected image. Therefore, image reconstruction can be performed on the local projected image based on this actual offset to eliminate the artifacts. This embodiment does not impose restrictions on how to perform image reconstruction based on the actual offset.

[0137] In summary, this embodiment considers data deviations caused by the multi-energy nature of the X-ray source and inconsistencies in the detector crystal response, and uses an averaged air image to analyze local original images. Correction is performed to obtain a local projection image. Taking full account of the data characteristics of circular CT and local CT, by analyzing the characteristics of symmetrical projection and local projection, key geometric parameters (i.e., solving for the actual offset of the detector) are solved in the projection domain. This avoids the use of geometric phantoms, reduces the complexity of operation and calculation, effectively suppresses annular artifacts and geometric artifacts, and improves the image resolution and image quality of local CT scan imaging.

[0138] In one possible implementation, see Figure 7 The diagram illustrates the flowchart of an image reconstruction method, which may include: acquiring multiple air images; averaging the multiple air images to obtain an average air image; determining local scanning parameters based on the region of interest (ROI); scanning the ROI of the sample to be detected using the local scanning parameters; acquiring a local original image of the ROI of the sample to be detected using a detector; performing non-uniformity correction on the local original image based on the average air image to obtain a local projected image; determining the actual offset of the detector based on the local projected image; and reconstructing the image based on the actual offset of the local projected image acquired by the detector to obtain a local slice image of the ROI.

[0139] As can be seen from the above technical solutions, in this embodiment, to address the multi-energy nature of X-rays and the data deviation caused by inconsistent detector crystal responses, ring artifacts can be effectively suppressed by correcting the local original image using an averaged air image. Regarding geometric artifacts caused by geometric errors, by fully considering the data characteristics of circular CT and local CT, and analyzing the characteristics of symmetrical projection and local projection, key geometric parameters can be directly solved in the projection domain, effectively suppressing geometric artifacts. By suppressing ring artifacts and geometric artifacts, the image resolution and image quality of local CT scans can be improved.

[0140] Based on the same concept as the methods described above, this application proposes an image reconstruction apparatus, see [link to relevant documentation]. Figure 8 The diagram shown is a structural schematic of the image reconstruction device, which may include:

[0141] The acquisition module 81 is used to acquire a first local projection image under a first scanning angle and a second local projection image under a second scanning angle, wherein the interval between the second scanning angle and the first scanning angle is a preset interval.

[0142] The determining module 82 is used to determine a projection difference sequence based on the second local projection image and the first local projection image. The projection difference sequence includes K columns of projection differences; where K represents the number of column pixel channels of the detector, and the projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image; the value of j ranges from 1 to K.

[0143] The selection module 83 is used to select a first column and a second column from the K columns of the projection difference sequence; wherein, based on the projection differences of the K columns, the column corresponding to the projection difference closest to 0 is selected as the first column; the difference between the index of the second column and the first column is a fixed value.

[0144] The determining module 82 is used to determine the lateral offset corresponding to the detector based on the serial number of the first column, the projection difference of the first column, the serial number of the second column, and the projection difference of the second column; the determining module 82 is used to determine the actual offset corresponding to the detector based on the lateral offset; wherein the actual offset is used to perform image reconstruction on the local projection image acquired by the detector.

[0145] For example, when the acquisition module 81 acquires the first local projection image at the first scanning angle and the second local projection image at the second scanning angle, it is specifically used to: acquire the first local original image at the first scanning angle and the second local original image at the second scanning angle through the detector; the first local original image and the second local original image are obtained by scanning the region of interest of the sample to be detected using local scanning parameters; the first local projection image is determined based on the first local original image and the acquired average air image, and the second local projection image is determined based on the second local original image and the average air image; wherein, the average air image is obtained by averaging multiple air images, and each air image is obtained by scanning a region where there is no sample to be detected.

[0146] For example, when determining the lateral offset of the detector based on the sequence number of the first column, the projection difference of the first column, the sequence number of the second column, and the projection difference of the second column, the determining module 82 is specifically used to: calculate a reference summation between the projection difference of the first column and the projection difference of the second column; determine a first weighting coefficient based on the projection difference of the first column and the reference summation; determine a second weighting coefficient based on the projection difference of the second column and the reference summation; and determine the lateral offset of the detector based on the first weighting coefficient, the sequence number of the first column, the second weighting coefficient, and the sequence number of the second column.

[0147] For example, when determining the lateral offset corresponding to the detector based on the sequence number of the first column, the projection difference of the first column, the sequence number of the second column, and the projection difference of the second column, the determining module 82 is specifically used to determine the lateral offset corresponding to the detector using the following formula:

[0148] ;

[0149] in, This indicates the lateral offset. This represents the projection difference of the first column. This represents the projection difference in the second column. This indicates the sequence number of the first column. This indicates the sequence number of the second column.

[0150] For example, when determining the actual offset of the detector based on the lateral offset, the determining module 82 is specifically used to: determine the actual offset of the detector based on the lateral offset, the number of column pixel channels of the detector, and the pixel size of the column direction supported by the detector.

[0151] For example, when determining the actual offset of the detector based on the lateral offset, the number of column pixel channels of the detector, and the pixel size of the column direction supported by the detector, the determining module 82 is specifically used to: determine the center column position of the detector based on the number of column pixel channels of the detector; determine the distance between the projection position of the rotation axis center on the detector and the center column position based on the lateral offset and the center column position; wherein, when the turntable rotates to obtain local projection images at multiple scanning angles, the turntable rotates based on the rotation axis center; and the actual offset is determined based on the distance and the pixel size of the column direction.

[0152] For example, when determining the actual offset of the detector based on the lateral offset, the determining module 82 is specifically used to: determine the actual offset of the detector using the following formula: ;in, This represents the actual offset. This indicates the lateral offset. This indicates the pixel size in the column direction supported by the detector;

[0153] in, Indicates the position of the center column. This indicates the spacing.

[0154] For example, there are multiple first local projection images, multiple second local projection images, and multiple projection difference sequences; when the selection module 83 selects the first column and the second column from the K columns of the projection difference sequence, it is specifically used to: determine a target projection difference sequence based on multiple projection difference sequences, the target projection difference sequence including the target projection difference of the K columns; the target projection difference of the j-th column in the target projection difference sequence is the sum of the projection differences of the j-th column in the multiple projection difference sequences; select the maximum projection difference and the minimum projection difference from the target projection difference of the K columns; based on the target projection difference of each candidate column, determine the candidate column corresponding to the target projection difference close to 0 as the first column; wherein, each candidate column is located between the column corresponding to the maximum projection difference and the column corresponding to the minimum projection difference; determine the second column based on the first column, the difference between the index of the second column and the first column is 1.

[0155] Based on the same concept as the above method, this application proposes an electronic device, see [link to previous application]. Figure 9 As shown, the electronic device includes a processor 91 and a machine-readable storage medium 92, the machine-readable storage medium 92 storing machine-executable instructions that can be executed by the processor 91; the processor 91 is used to execute the machine-executable instructions to implement the image reconstruction method disclosed in the above example of this application.

[0156] Based on the same concept as the above method, this application also provides a machine-readable storage medium storing a plurality of computer instructions, which, when executed by a processor, can implement the image reconstruction method disclosed in the above examples of this application.

[0157] The aforementioned machine-readable storage medium can be any electronic, magnetic, optical, or other physical storage device that can contain or store information, such as executable instructions, data, etc. For example, machine-readable storage media can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), solid-state drives, any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or combinations thereof.

[0158] Based on the same concept as the methods described above, this application also provides a computer program product, which may include a computer program. When executed by a processor, the computer program implements the image reconstruction method disclosed in the examples above.

[0159] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An image reconstruction method, characterized in that, The method includes: The detector acquires a first local projection image at a first scanning angle and a second local projection image at a second scanning angle, wherein the interval between the second scanning angle and the first scanning angle is a preset interval. A projection difference sequence is determined based on the second local projection image and the first local projection image. The projection difference sequence includes K columns of projection differences. Here, K represents the number of column pixel channels of the detector, and the projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image. The value of j ranges from 1 to K. Select a first column and a second column from the K columns of the projection difference sequence; wherein, based on the projection differences of the K columns, the column corresponding to the projection difference closest to 0 is taken as the first column; the difference between the index of the second column and the first column is a fixed value; Based on the sequence number of the first column, the projection difference of the first column, the sequence number of the second column, and the projection difference of the second column, the lateral offset corresponding to the detector is determined; wherein, a reference summation value is calculated between the projection difference of the first column and the projection difference of the second column; a first weighting coefficient is determined based on the projection difference of the first column and the reference summation value; a second weighting coefficient is determined based on the projection difference of the second column and the reference summation value; and the lateral offset is determined based on the first weighting coefficient, the sequence number of the first column, the second weighting coefficient, and the sequence number of the second column. The actual offset of the detector is determined based on the lateral offset; wherein the actual offset is used to reconstruct the local projection image acquired by the detector.

2. The method according to claim 1, characterized in that, The step of acquiring a first local projection image at a first scanning angle and a second local projection image at a second scanning angle using a detector includes: The detector acquires a first local original image at a first scanning angle and a second local original image at a second scanning angle; wherein the first local original image and the second local original image are obtained by scanning the region of interest of the sample to be detected using local scanning parameters; The first local projection image is determined based on the first local original image and the acquired average air image, and the second local projection image is determined based on the second local original image and the average air image; wherein, the average air image is obtained by averaging multiple air images, and each air image is obtained by scanning a region where there is no sample to be detected.

3. The method according to claim 1, characterized in that, The step of determining the lateral offset of the detector based on the sequence number of the first column, the projection difference of the first column, the sequence number of the second column, and the projection difference of the second column includes: The lateral offset of the detector is determined using the following formula: ； in, This indicates the lateral offset. This represents the projection difference of the first column. This represents the projection difference in the second column. This indicates the sequence number of the first column. This indicates the sequence number of the second column.

4. The method according to claim 1, characterized in that, Determining the actual offset of the detector based on the lateral offset includes: The actual offset of the detector is determined based on the lateral offset, the number of column pixel channels of the detector, and the pixel size of the column direction supported by the detector.

5. The method according to claim 4, characterized in that, The determination of the actual offset of the detector based on the lateral offset, the number of column pixel channels of the detector, and the pixel size of the column direction supported by the detector includes: The center column position of the detector is determined based on the number of column pixel channels of the detector; Based on the lateral offset and the center column position, the distance between the projection position of the rotation axis center on the detector and the center column position is determined; wherein, when the turntable rotates to obtain local projection images at multiple scanning angles, the turntable rotates based on the rotation axis center; The actual offset is determined based on the spacing and the pixel size in the column direction.

6. The method according to claim 5, characterized in that, Determining the actual offset of the detector based on the lateral offset includes: The actual offset of the detector is determined using the following formula: ； Among them, the This represents the actual offset, the Indicates the lateral offset, the This indicates the pixel size in the column direction supported by the detector; in, Indicates the position of the center column. This indicates the spacing.

7. The method according to claim 1, characterized in that, There are multiple first local projection images, multiple second local projection images, and multiple projection difference sequences; The step of selecting the first and second columns from the K columns of the projection difference sequence includes: A target projection difference sequence is determined based on multiple projection difference sequences, wherein the target projection difference sequence includes K columns of target projection differences; wherein the j-th column of the target projection difference sequence is the sum of the j-th column projection differences in the multiple projection difference sequences; Select the maximum and minimum projection differences from the target projection differences in column K; Based on the target projection difference of each candidate column, the candidate column corresponding to the target projection difference closest to 0 is determined as the first column; wherein, each candidate column is located between the column corresponding to the maximum projection difference and the column corresponding to the minimum projection difference; The second column is determined based on the first column, and the difference between the serial number of the second column and the first column is 1.

8. An image reconstruction apparatus, characterized in that, The device includes: The acquisition module is used to acquire a first local projection image at a first scanning angle and a second local projection image at a second scanning angle, wherein the interval between the second scanning angle and the first scanning angle is a preset interval. The determination module is used to determine a projection difference sequence based on the second local projection image and the first local projection image. The projection difference sequence includes K columns of projection differences; where K represents the number of column pixel channels of the detector, and the projection difference in the j-th column is the difference between the pixel value in the j-th column and i-th row of the second local projection image and the pixel value in the j-th column and i-th row of the first local projection image; the value of j ranges from 1 to K. The selection module is used to select a first column and a second column from the K columns of the projection difference sequence; wherein, based on the projection differences of the K columns, the column corresponding to the projection difference closest to 0 is taken as the first column; the difference between the index of the second column and the first column is a fixed value; The determining module is configured to determine the lateral offset corresponding to the detector based on the sequence number of the first column, the projection difference of the first column, the sequence number of the second column, and the projection difference of the second column; wherein, when determining the lateral offset corresponding to the detector, the determining module is specifically configured to: calculate a reference summation value between the projection difference of the first column and the projection difference of the second column; determine a first weighting coefficient based on the projection difference of the first column and the reference summation value; determine a second weighting coefficient based on the projection difference of the second column and the reference summation value; and determine the lateral offset based on the first weighting coefficient, the sequence number of the first column, the second weighting coefficient, and the sequence number of the second column. The determining module is used to determine the actual offset of the detector based on the lateral offset; wherein the actual offset is used to reconstruct the local projection image acquired by the detector.

9. An electronic device, characterized in that, include: A processor and a machine-readable storage medium, the machine-readable storage medium storing machine-executable instructions that can be executed by the processor; The processor is configured to execute machine-executable instructions to implement the method of any one of claims 1-7.

Images