A method and apparatus for reconstructing an image, and a storage medium

Abstract

The application discloses a reconstruction method and device of an image and a storage medium. The method comprises the following steps: acquiring a plurality of sub-sector data, the plurality of sub-sector data being data in a same phase range in a plurality of continuous cardiac cycles; acquiring a similarity between each two sub-sector data based on a reference image constructed based on each sub-sector data; acquiring target sub-sector data of a to-be-reconstructed position from the plurality of sub-sector data based on the similarity, and acquiring a reconstructed image of the to-be-reconstructed position based on the target sub-sector data. The application can improve the image quality of the reconstructed image.

Description

Technical Field

[0001] This application relates to the field of medical imaging technology, and in particular to an image reconstruction method, apparatus, and storage medium. Background Technology

[0002] Retrospective ECG gating is the primary method for acquiring coronary artery imaging data in multi-slice spiral CT. Subsequent single-sector or multi-sector reconstruction can be performed based on the acquired projection data to obtain CT images. In multi-sector reconstruction, a small portion of projection data can be selected from a defined phase range across different cardiac cycles. When all the projection data are combined, they constitute sufficient data to complete the CT image reconstruction. For example, selecting half of the projection dataset required for reconstruction from one cardiac cycle and the remaining dataset from another cardiac cycle reduces the temporal resolution to approximately one-quarter of the gantry rotation time. Therefore, selecting projection data from different cardiac cycles for CT image reconstruction can improve temporal resolution.

[0003] However, when performing multi-sector reconstruction, since multi-sector reconstruction uses projection data acquired at different cardiac cycles, if the patient has unexpected respiratory movements or significant heart rate fluctuations during the scanning process, the consistency of projection data at different cardiac cycles will be poor, which will lead to a decrease in the quality of the CT images obtained after reconstruction. Summary of the Invention

[0004] In view of this, this application provides an image reconstruction method, apparatus, storage medium, and computer device. By using the similarity between different sub-sector data, target sub-sector data is determined from multiple sub-sector data. The target sub-sector data used for subsequent image reconstruction have high similarity, that is, high consistency, which can greatly improve the image quality of the reconstructed image while taking into account temporal resolution.

[0005] According to one aspect of this application, an image reconstruction method is provided, comprising:

[0006] Acquire data from multiple sub-sectors, wherein the multiple sub-sectors are data within the same phase target range within multiple consecutive cardiac cycles;

[0007] Based on the reference image constructed from the data of each sub-sector, the similarity between every two data of each sub-sector is obtained;

[0008] Based on the similarity, target sub-sector data of the location to be reconstructed is obtained from multiple sub-sector data, and a reconstructed image of the location to be reconstructed is obtained based on the target sub-sector data.

[0009] According to another aspect of this application, an image reconstruction apparatus is provided, comprising:

[0010] The sub-sector data acquisition module is used to acquire multiple sub-sector data, wherein the multiple sub-sector data are data within the same phase target range within multiple consecutive cardiac cycles;

[0011] A similarity calculation module is used to obtain the similarity between every two sub-sector data based on a reference image constructed from the data of each sub-sector.

[0012] The image reconstruction module is used to obtain target sub-sector data of the location to be reconstructed from multiple sub-sector data based on the similarity, and to obtain a reconstructed image of the location to be reconstructed based on the target sub-sector data.

[0013] According to another aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described image reconstruction method.

[0014] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described image reconstruction method.

[0015] By employing the above technical solutions, this application provides an image reconstruction method, apparatus, storage medium, and computer device. First, the sub-sector data required for image reconstruction can be determined. Here, different sub-sector data correspond to different cardiac cycles. Specifically, a fixed phase can be determined within each cardiac cycle, and the scan projection data within a certain range of that fixed phase within each cardiac cycle is used as the sub-sector data. After acquiring multiple sub-sector data, a corresponding reference image can be constructed based on each sub-sector data. Furthermore, every two reference images can be grouped together, and the image similarity between these two reference images can be determined, using this image similarity as the similarity between the two sub-sector data. Next, target sub-sector data can be determined from multiple sub-sector data based on the similarity between each pair of sub-sector data. Finally, the reconstructed image of the location to be reconstructed can be obtained based on the target sub-sector data. This application's embodiments improve the image quality of the reconstructed image by determining the target sub-sector data from multiple sub-sector data through the similarity between different sub-sector data and thus improving the image quality of the reconstructed image.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A schematic diagram of a reconstruction mode for a CT image is shown;

[0019] Figure 2 A schematic flowchart of an image reconstruction method provided in an embodiment of this application is shown;

[0020] Figure 3 A schematic flowchart of another image reconstruction method provided in an embodiment of this application is shown;

[0021] Figure 4 A flowchart illustrating another image reconstruction method provided in an embodiment of this application is shown;

[0022] Figure 5 This illustration shows a schematic diagram of the relationship between an electrocardiogram curve and a curve at each time point on a scanning bed, according to an embodiment of this application.

[0023] Figure 6 A schematic diagram of the structure of an image reconstruction apparatus provided in an embodiment of this application is shown. Detailed Implementation

[0024] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0025] Retrospective ECG gating is the primary method for acquiring coronary artery imaging data on multi-slice spiral CT. In this mode, the patient's electrocardiogram (ECG) signal is continuously monitored, and CT scan projection data is acquired continuously (simultaneously) in spiral mode. During the scan, both the projection data and the ECG signal are recorded simultaneously. Then, during image reconstruction, appropriate projection data is selected based on the ECG signal for single-sector or multi-sector image reconstruction, such as... Figure 1 As shown, when obtaining CT images using single-sector reconstruction, sufficient projection data is selected from a specified phase range within a single cardiac cycle for reconstruction. To obtain higher temporal resolution and improve the CT image quality for patients with high heart rates, multi-sector reconstruction techniques can be used. When obtaining CT images using multi-sector reconstruction, the necessary projection data for image reconstruction is selected from consecutive cardiac cycles, rather than from a single cardiac cycle.

[0026] The image reconstruction method provided in this application can identify the inconsistency of projection data required for image reconstruction in different cardiac cycles, and reconstruct the image using projection data with high consistency in different cardiac cycles, which can greatly improve the image quality of the reconstructed image.

[0027] This embodiment provides an image reconstruction method, such as... Figure 2 As shown, the method includes:

[0028] Step 101: Obtain multiple sub-sector data, wherein the multiple sub-sector data are data within the same phase target range within multiple consecutive cardiac cycles;

[0029] The image reconstruction method provided in this application is applied in a multi-sector reconstruction mode, specifically for reconstructing CT images. First, the sub-sector data required for image reconstruction can be determined. Since a multi-sector reconstruction mode is used, multiple sub-sector data can be acquired. For example, if calculations reveal that three sub-sectors at a location to be reconstructed satisfy the stitching conditions for multi-sector reconstruction, then three-sector reconstruction can be performed; if calculations reveal that four sub-sectors at a location to be reconstructed satisfy the stitching conditions for multi-sector reconstruction, then four-sector reconstruction can be performed. Here, different sub-sector data correspond to different cardiac cycles. Specifically, a fixed phase can be determined within each cardiac cycle, and the scan projection data within the target range of that fixed phase within each cardiac cycle is used as the sub-sector data. These sub-sector data collectively reconstruct an image. The target range can be a certain range around the fixed phase that satisfies the reconstruction conditions.

[0030] Step 102: Based on the reference image constructed from the data of each sub-sector, obtain the similarity between every two data of the sub-sectors;

[0031] In this embodiment, after acquiring multiple sub-sector data, a corresponding reference image can be constructed based on each sub-sector data. Each reference image is also a complete image located at the same axial position (Z-axis position) of the scanned subject. Furthermore, every two reference images can be grouped together, and the image similarity between these two reference images can be determined. This image similarity is then used as the similarity between the two sub-sector data.

[0032] Step 103: Based on the similarity, obtain the target sub-sector data of the location to be reconstructed from the multiple sub-sector data, and obtain the reconstructed image of the location to be reconstructed based on the target sub-sector data.

[0033] In this embodiment, the target sub-sector data can then be determined from multiple sub-sector data based on the similarity between every two sub-sector data. Finally, the reconstructed image of the location to be reconstructed can be obtained based on the target sub-sector data. The target sub-sector data is obtained based on similarity. The method for selecting the target sub-sector data differs depending on the similarity. For example, if multiple sub-sector data satisfy multi-sector reconstruction, multi-sector reconstruction is used to improve temporal resolution while ensuring image quality. If multiple sub-sector data only satisfy single-sector reconstruction, single-sector reconstruction is used. This is described in detail below. The embodiments of this application aim to obtain the best image quality based on the actual data acquisition situation. The minimum amount of data required for reconstructing the image is 180° of parallel bundle data. Multi-sector reconstruction uses data from multiple sectors to stitch together 180° of parallel bundle data. For example, when reconstructing n sectors, each target sub-sector data corresponds to at least 180° / n angle range. By using multiple target sub-sector data to construct the reconstructed image of the location to be reconstructed, the temporal resolution can be improved. Here, for CT image reconstruction, the angle refers to the rotation angle of the CT scanning equipment gantry.

[0034] By applying the technical solution of this embodiment, firstly, the sub-sector data required for image reconstruction can be determined. Here, different sub-sector data correspond to different cardiac cycles. Specifically, a fixed phase can be determined within each cardiac cycle, and the scan projection data within a certain range of this fixed phase within each cardiac cycle can be used as sub-sector data. After acquiring multiple sub-sector data, a corresponding reference image can be constructed based on each sub-sector data. Furthermore, every two reference images can be grouped together, and the image similarity between these two reference images can be determined, with the image similarity serving as the similarity between these two sub-sector data. Next, target sub-sector data can be determined from multiple sub-sector data based on the similarity between each pair of sub-sector data. Finally, the reconstructed image of the location to be reconstructed can be obtained based on the target sub-sector data. This embodiment of the application determines the reconstructed data selection method for the reconstructed location by using the similarity between different sub-sector data. Based on the reconstructed data selection method, target sub-sector data is determined from multiple sub-sector data, ensuring the consistency of the reconstructed location data and improving the image quality of the reconstructed image.

[0035] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, and to fully illustrate the specific implementation process of this embodiment, another image reconstruction method is provided, such as... Figure 3 As shown, the method includes:

[0036] Step 201: Based on the gantry rotation time, pitch, and patient's heart rate during the CT scanning equipment, acquire multiple sub-sector data, wherein the multiple sub-sector data are data within the same phase target range within multiple consecutive cardiac cycles;

[0037] In this embodiment, taking CT image reconstruction as an example, the acquisition of multiple sub-sector data for CT image reconstruction can be determined based on the gantry rotation time, pitch, and patient's heart rate during the scan. The gantry rotation time can be the time taken for the gantry to rotate one revolution; the pitch is the ratio of the distance the examination table moves during one revolution of the gantry to the collimation aperture; and the patient's heart rate can be the patient's average heart rate during the scan. First, a fixed phase can be selected from each cardiac cycle. Then, based on the gantry rotation time, pitch, and patient's heart rate during the scan, the multiple sub-sector data required for CT image reconstruction are acquired within the target range of the fixed phase.

[0038] Step 202: Based on the reference image constructed from the data of each sub-sector, obtain the pixel value of each pixel in the reference image;

[0039] In this embodiment, after determining multiple sub-sector data, some or all data can be extracted from the sub-sector data to reconstruct a reference image. The reference image includes complete tissue structure information. During reconstruction, the number of pixels in each reconstructed reference image is set to be the same. For example, when reconstructing the reference image, the number of pixels in the reference image can be initially set to m×n, where m can be the number of pixels per row and n can be the number of pixels per column. In this way, the number of pixels in the reconstructed reference image is always m×n. This makes the subsequent calculation of image similarity simpler and more convenient. After reconstructing the reference image corresponding to each sub-sector data, the pixel values ​​of each pixel in each reference image can be further obtained. For example, if the number of pixels in the reference image is m×n, then the pixel values ​​of these m×n pixels can be obtained.

[0040] Step 203: Based on the pixel values ​​at the same pixel position in every two reference images, obtain the image similarity between every two reference images, and use the image similarity as the similarity between every two sub-sector data.

[0041] In this embodiment, after reconstructing the reference images, each pair of reference images can be grouped together, and the image similarity between the two reference images within a group can be calculated. Specifically, the image similarity can be calculated based on the pixel values ​​of pixels at the same pixel location in the two reference images. The calculated image similarity can be used as the similarity between the data of these two sub-sectors.

[0042] Optionally, in this embodiment of the application, the method for "obtaining the image similarity between every two reference images" in step 203 includes at least one of the histogram method, the normalized cross-correlation coefficient method, the structural similarity method, and the mutual information method.

[0043] The histogram method involves collecting histogram data from the source image and the image to be screened. The collected histograms are then normalized, and the Bhattacharyya coefficient algorithm is used to calculate the image similarity value, which ranges from [0,1], where 0 represents extreme dissimilarity and 1 represents extreme similarity (identity). The algorithm can be roughly divided into two steps: First, generate histogram data for the source and candidate images based on their pixel data; second, use the histogram results from the first step and the Bhattacharyya coefficient algorithm to calculate the image similarity.

[0044] The normalized cross-correlation coefficient method determines the image similarity between two images by calculating the normalized cross-correlation coefficient between them. Specifically, the normalized cross-correlation coefficient can be calculated using the pixel values ​​of corresponding pixels in each pair of reference images. The calculation can be performed using the following formula:

[0045]

[0046] Where, ρ kl S represents the normalized cross-correlation coefficient between the k-th reference image and the l-th reference image; m and n represent the number of pixels in each row and column of each reference image, respectively; k (i,j) represents the pixel value of the pixel in the i-th row and j-th column of the k-th reference image; S represents the average pixel value of all pixels in the k-th reference image; l (i,j) represents the pixel value of the pixel in the i-th row and j-th column of the l-th reference image; This represents the average pixel value corresponding to all pixels in the l-th reference image.

[0047] Structural similarity is a full-reference image quality assessment method that measures image similarity from three aspects: brightness, contrast, and structure. Image similarity values ​​range from [0,1], with larger values ​​indicating smaller differences between images. In practical applications, a sliding window can be used to divide the image into blocks, with a total of N blocks. Considering the influence of window shape on the blocks, Gaussian weighting is used to calculate the mean, variance, and covariance of each window. Then, the structural similarity of the corresponding blocks is calculated, and finally, the average value is used as the measure of structural similarity between the two images, i.e., the average structural similarity, thus obtaining the image similarity. The implementation process involved in this application is not described in detail; existing related technologies can be used.

[0048] Mutual information (MI) is an important concept in information theory, describing the correlation or the amount of information contained between two systems. In image registration, the mutual information of two images reflects the degree of mutual information inclusion between them through their entropy and joint entropy. For images R and F, their mutual information is expressed as: MI(R, F) = H(R) + H(F) - H(R, F), where H(R) and H(F) are the entropies of images R and F, respectively, and H(R, F) is the joint entropy between images R and F. The higher the similarity or the greater the overlap between two images, the greater their correlation and the smaller the joint entropy, i.e., the greater the mutual information. The implementation process involved in this application is not described in detail; existing related technologies can be used.

[0049] Step 204: If the similarity between any two sub-sector data is less than or equal to a preset threshold, then select one sub-sector data from the multiple sub-sector data as the target sub-sector data.

[0050] In this embodiment, the similarity between any two sub-sector data points and a preset threshold can then be determined. If, after evaluation, it is found that the similarity between any two sub-sector data points is less than or equal to the preset threshold, then it indicates that the consistency between any two sub-sector data points is poor. In this case, only one of these sub-sector data points can be selected as the target sub-sector data point. For example, if there are a total of 6 sub-sector data points, and the similarity between any two of these 6 sub-sector data points is less than or equal to the preset threshold, then only one of these 6 sub-sector data points can be selected as the target sub-sector data point. The sub-sector data point closest to the reconstructed location can be selected.

[0051] Step 205: If there are at least two sub-sectors with a similarity greater than a preset threshold between any two sub-sectors, then the target sub-sector data is obtained from the at least two sub-sectors.

[0052] In this embodiment, if it is determined that there is a similarity greater than a preset threshold between any two sub-sector data, then the target sub-sector data for reconstructing the image of the location to be reconstructed can be obtained from the sub-sector data corresponding to the similarity greater than the preset threshold.

[0053] Step 206: Obtain the reconstructed image of the location to be reconstructed based on the target sub-sector data.

[0054] In this embodiment, a reconstructed image of the location to be reconstructed can be obtained based on the target sub-sector data.

[0055] Optionally, in this embodiment of the application, step 205 includes:

[0056] Step 205-1: If there are at least two sub-sectors with a similarity greater than a preset threshold between any two sub-sectors, set the similarity greater than the preset threshold as the first similarity, and set the similarity less than or equal to the preset threshold as the second similarity.

[0057] Step 205-2: Based on the first similarity and the second similarity, obtain the similarity and value between each sub-sector data and the other sub-sector data;

[0058] Step 205-3: Obtain the target sub-sector data from at least one group of sub-sector data with the highest similarity and value.

[0059] In this embodiment, the similarity can be set to a first similarity or a second similarity based on the relationship between the similarity between two sub-sectors and a preset threshold. Specifically, if the similarity between two sub-sectors is greater than the preset threshold, it indicates that the consistency between the two sub-sectors is good, and the similarity can be set to the first similarity. If the similarity between two sub-sectors is less than or equal to the preset threshold, it indicates that the consistency between the two sub-sectors is poor, and the similarity can be set to the second similarity. Then, based on the first or second similarity, the sum of similarities between each sub-sector and the other sub-sectors can be calculated. For example, if there are six sub-sectors, sub-sectors 1 to 6, the similarity sum of sub-sector 1 can be calculated by comparing it with the other five sub-sectors, specifically using either the first or second similarity. Then, the sum of these five similarities is used as the sum of the similarities for sub-sector 1. The similarity and values ​​of sub-sector data 2 to sub-sector data 6 can be calculated using the same method.

[0060] Here, the first similarity can be 1, and the second similarity can be 0. For example, with a preset threshold of T, the following formula is used to further determine whether the similarity is the first or second similarity:

[0061]

[0062] Where, r kl This represents the similarity between the data in the k-th sub-sector and the data in the l-th sub-sector; when r kl When r = 1, it means that the first similarity is set to 1; when r klWhen the value is 0, it means that the second similarity is set to 0. According to the above formula, each normalized cross-correlation coefficient can be converted into either the first similarity or the second similarity to represent the similarity between two sub-sectors.

[0063] After determining the similarity score and value corresponding to each sub-sector data, the maximum similarity score and value can be found. Here, the maximum similarity score and value can be one or more. When there is only one maximum similarity score and value, the target sub-sector data can be obtained from the set of sub-sector data corresponding to that maximum similarity score and value. When there are multiple maximum similarity scores and values, meaning multiple similarity scores and values ​​are equal and maximum, the target sub-sector data can be obtained from the multiple sets of sub-sector data corresponding to these maximum similarity scores and values.

[0064] Optionally, in this embodiment of the application, step 205-2 includes: determining a similarity matrix based on the first similarity and the second similarity, wherein each row element and each column element in the similarity matrix respectively indicate the similarity between any of the sub-sector data and other sub-sector data; and summing the similarities corresponding to each column element in the similarity matrix to obtain multiple similarity sum values.

[0065] In this embodiment, after determining the similarity as a first similarity or a second similarity, a similarity matrix can be determined based on the first and second similarities. For example, the similarity matrix can be represented as follows:

[0066]

[0067] Where R represents the similarity matrix; r in the similarity matrix 11 ~r KK These represent the similarity between each pair of sub-sectors, specifically either the first similarity or the second similarity.

[0068] After determining the similarity matrix, the similarity sum for each column element can be calculated. For example, for the similarity matrix R, the similarity sum for the first column element is r. 11 +r 21 +……+r k1 That is, the sum of similarities between sub-sector data 1 and the other sub-sector data; the sum of similarities for the elements in the second column is r. 12 +r 22 +……+r k2That is, the similarity sum between sub-sector data 2 and the data of the other sub-sectors; and so on, the similarity sum for each column element can be obtained. This embodiment calculates the similarity sum between each sub-sector data and the data of other sub-sectors using a similarity matrix, making the determination of the similarity sum clearer and simpler.

[0069] Optionally, in this embodiment, step 205-3 includes: if the similarity in the at least one set of sub-sector data is all of the first similarity, then the at least one set of sub-sector data is used as target sub-sector data; if the similarity in the at least one set of sub-sector data includes the second similarity, then from the at least one set of sub-sector data, the sub-sector data closest to the location to be reconstructed is determined as the first sub-sector data, the sub-sector data with the first similarity to the first sub-sector data is used as the second sub-sector data, and the first sub-sector data and the second sub-sector data are used as target sub-sector data.

[0070] In this embodiment, after obtaining the similarity and value corresponding to each sub-sector data, one or more groups of sub-sector data with the highest similarity and value can be determined. For example, when there is only one with the highest similarity and value, then the corresponding sub-sector data with the highest similarity and value is also a group; if there are multiple groups with the highest similarity and value and the values ​​are equal, then the corresponding sub-sector data with the highest similarity and value is also multiple groups.

[0071] If, among the similarity scores of at least one set of sub-sector data, each score is the first similarity score, it indicates that the consistency between these sub-sector data is good. In this case, these sets of sub-sector data can be directly used as target sub-sector data for subsequent image reconstruction. If, among the similarity scores of at least one set of sub-sector data, there is a second similarity score, it indicates that the consistency between some sub-sector data is poor. In this case, the sub-sector data closest to the location to be reconstructed can be found from these sets of sub-sector data and used as the first sub-sector data. Then, the sub-sector data with the first similarity score from all sub-sector data can be found as the second sub-sector data. Finally, the first and second sub-sector data are used together as the target sub-sector data.

[0072] In one specific embodiment, the similarity between the data in each sub-sector can be expressed as a similarity matrix. For example, the similarity matrix can be as follows:

[0073]

[0074] As can be seen from the similarity matrix above, the similarity sum for each column element is 2, and the maximum similarity sum is also 2. This indicates that these 4 columns of elements can be considered as the aforementioned "at least one set of sub-sector data," that is, four sets of sub-sector data. Specifically, the element in the first row and first column (r...) 11 ), first row, second column element (r) 12 ), the element in the second row and first column (r) 21 ), the element in the second row and second column (r) 22 The similarity scores of the elements in the third row and third column are all at the first similarity level, indicating that the corresponding two sub-sector data (sub-sector data 1 and sub-sector data 2) have a good similarity; 33 ), the element in the third row and fourth column (r) 34 ), the element in the fourth row and third column (r) 43 ), the element in the fourth row and fourth column (r) 44 The similarity scores of the two sub-sectors (sub-sector data 3 and sub-sector data 4) are all at the first similarity level, which indicates that the similarity between the two sub-sectors is good.

[0075] After determining the elements with a first similarity score from the sub-sector data corresponding to the maximum similarity sum, the sub-sector data closest to the pre-determined location to be reconstructed can be further identified from the sub-sector data indicated by these elements with the first similarity score as the first sub-sector data. Next, one or more second sub-sector data with a first similarity score can be found from the entire similarity matrix. Finally, the first and second sub-sector data are combined as the target sub-sector data. This embodiment can effectively find highly consistent target sub-sector data from multiple sub-sector data sets. Subsequent image reconstruction based on these target sub-sector data can significantly improve image quality while maintaining temporal resolution.

[0076] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, and to fully illustrate the specific implementation process of this embodiment, another image reconstruction method is provided, such as... Figure 4 As shown, the method includes:

[0077] Step 301: Obtain multiple sub-sector data, wherein the multiple sub-sector data are data within the same phase target range within multiple consecutive cardiac cycles;

[0078] Step 302: Obtain the midpoint time of the acquisition time of the first and last sub-sector data in the multiple sub-sector data; obtain the reconstruction position of the reference image based on the Z-axis position of the scanning bed corresponding to the midpoint time; obtain the reference image corresponding to each sub-sector data based on the reconstruction position of the reference image;

[0079] In this embodiment, sub-sector data is obtained using a retrospective ECG-gated spiral scan. After acquiring multiple sub-sector data, the first sub-sector data (i.e., the first sub-sector data within multiple consecutive cardiac cycles) and the last sub-sector data (i.e., the last sub-sector data within multiple consecutive cardiac cycles) can be identified. Then, the acquisition time corresponding to the first sub-sector data (i.e., the time of the phase) and the acquisition time corresponding to the last sub-sector data can be determined, and the midpoint time can be determined based on these two acquisition times. Since the scanning bed moves along the Z-axis, i.e., it feeds and returns along the Z-direction, the Z-direction position of the scanning bed at the midpoint time can be determined, and based on this, the reconstruction position of the reference image can be determined. Here, the position corresponding to the scanned area at the Z-direction position is the reconstruction position of the reference image. After determining the reconstruction position of the reference image, the reference image corresponding to each sub-sector data can be obtained. Figure 5 As shown, when obtaining sub-sector data using retrospective ECG-gated spiral scanning, the ECG curve and the curve at each time point of the scanning bed correspond to each other. In this embodiment, the reconstruction position of the reference image is determined by the midpoint time, and a reference image for each sub-sector is obtained based on this reconstruction position, which can significantly reduce the computational workload for similarity calculations between sub-sector data.

[0080] Step 303: Based on the reference image constructed from the data of each sub-sector, obtain the similarity between every two data of the sub-sectors;

[0081] Step 304: Determine the third sub-sector data with a similarity less than or equal to a preset threshold and the fourth sub-sector data with a similarity greater than a preset threshold; obtain reference images of other phases located in the same sector as the third sub-sector data; obtain the target phase whose similarity to the reference image of the fourth sub-sector data meets a preset threshold; use the sub-sector data of the target phase and the fourth sub-sector data as the target sub-sector data.

[0082] In this embodiment, after determining the similarity between any two sub-sector data, similarity scores less than or equal to a preset threshold are found. Sub-sector data with similarity scores less than or equal to the preset threshold are designated as the third sub-sector data, and sub-sector data with similarity scores greater than the preset threshold are designated as the fourth sub-sector data. The third sub-sector data may include at least one sub-sector data, and the fourth sub-sector data may also include at least one sub-sector data. Next, reference images located in the same sector as the third sub-sector data in other phases are acquired. The image similarity between the reference images of other phases and the reference image of the fourth sub-sector is calculated. Image similarity scores that satisfy a preset threshold are found from the image similarity scores. The phase corresponding to the image similarity scores that satisfy the preset threshold is determined as the target phase. Finally, the sub-sector data corresponding to the target phase, along with the fourth sub-sector data, are used together as the target sub-sector data. For example, taking dual-sector reconstruction as an example, sector 1 selects the 75% phase. However, due to the irregularity of the patient's cardiac motion, the similarity between the position of the 80% phase in sector 2 and the position of the 75% phase in sector 1 meets the preset threshold. Therefore, in order to avoid this situation from affecting the image quality of the reconstructed image, the target phase is first identified, and then the target sub-sector data is determined using the target phase, which can greatly improve the image quality of the reconstructed image.

[0083] Step 305: Obtain the reconstructed image of the location to be reconstructed based on the target sub-sector data.

[0084] Furthermore, as Figure 2 To specifically implement the method, this application provides an image reconstruction apparatus, such as... Figure 6 As shown, the device includes:

[0085] The sub-sector data determination module is used to acquire multiple sub-sector data, wherein the multiple sub-sector data are data within the same phase target range within multiple consecutive cardiac cycles;

[0086] A similarity calculation module is used to obtain the similarity between every two sub-sector data based on a reference image constructed from the data of each sub-sector.

[0087] The image reconstruction module is used to obtain target sub-sector data of the location to be reconstructed from multiple sub-sector data based on the similarity, and to obtain a reconstructed image of the location to be reconstructed based on the target sub-sector data.

[0088] Optionally, the similarity calculation module includes:

[0089] A pixel value acquisition unit is used to acquire the pixel value of each pixel in a reference image constructed based on the data of each sub-sector.

[0090] The similarity calculation unit is used to obtain the image similarity between each two reference images based on the pixel value of the same pixel position in each two reference images, and use the image similarity as the similarity between each two sub-sector data.

[0091] Optionally, the method for obtaining the image similarity between every two reference images includes at least one of the following: histogram method, normalized cross-correlation coefficient method, structural similarity method, and mutual information method.

[0092] Optionally, the image reconstruction module includes:

[0093] The first selection unit is used to select one of the sub-sector data as the target sub-sector data from a plurality of sub-sector data if the similarity between any two sub-sector data is less than or equal to a preset threshold.

[0094] The second selection unit is used to obtain target sub-sector data from the at least two sub-sector data if the similarity between any two sub-sector data is greater than a preset threshold.

[0095] Optionally, the second selection unit is further configured to: if there are at least two sub-sectors with a similarity greater than a preset threshold between any two sub-sectors, set the similarity greater than the preset threshold as a first similarity and the similarity less than or equal to the preset threshold as a second similarity; based on the first similarity and the second similarity, obtain the similarity sum value between each sub-sector and other sub-sectors; and obtain the target sub-sector data from at least one group of sub-sectors with the largest similarity sum value.

[0096] Optionally, the second selection unit is further configured to: if the similarity in the at least one set of sub-sector data is all of the first similarity, then use the at least one set of sub-sector data as target sub-sector data; if the similarity in the at least one set of sub-sector data includes the second similarity, then determine the sub-sector data closest to the location to be reconstructed from the at least one set of sub-sector data as the first sub-sector data, use the sub-sector data with the first similarity to the first sub-sector data as the second sub-sector data, and use the first sub-sector data and the second sub-sector data as target sub-sector data.

[0097] Optionally, the image reconstruction module further includes:

[0098] The sub-sector data determination unit is used to determine the third sub-sector data whose similarity is less than or equal to a preset threshold and the fourth sub-sector data whose similarity is greater than the preset threshold.

[0099] The reference image acquisition unit is used to acquire reference images of other phases located in the same sector as the data of the third sub-sector;

[0100] The target phase acquisition unit is used to acquire the target phase in which the similarity between the reference image of other phases and the reference image of the fourth sub-sector data meets a preset threshold.

[0101] The target sub-sector data determination unit is used to take the sub-sector data of the target period and the fourth sub-sector number as the target sub-sector data.

[0102] Optionally, the sub-sector data is obtained using a retrospective ECG-gated spiral scan; the device further includes:

[0103] The midpoint time acquisition module is used to acquire the midpoint time of the first and last sub-sector data acquisition times in the multiple sub-sector data before acquiring the similarity between every two sub-sector data in the reference image constructed based on each sub-sector data;

[0104] The reconstruction position acquisition module is used to acquire the reconstruction position of the reference image based on the Z-axis position of the scanning bed corresponding to the midpoint time.

[0105] The image acquisition module is used to acquire a reference image corresponding to each sub-sector data based on the reconstruction position of the reference image.

[0106] It should be noted that other corresponding descriptions of the functional units involved in the image reconstruction apparatus provided in this application embodiment can be found in the following references. Figures 2 to 5 The corresponding descriptions in the method will not be repeated here.

[0107] Based on the above, Figures 2 to 5 Accordingly, this application also provides a storage medium storing a computer program, which, when executed by a processor, implements the above-described method. Figures 2 to 5 The image reconstruction method shown.

[0108] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0109] Based on the above, Figures 2 to 5 The method shown, and Figure 6To achieve the above objectives, the present application also provides a computer device, specifically a personal computer, server, network device, etc., as shown in the virtual device embodiment. This computer device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figures 2 to 5 The image reconstruction method shown.

[0110] Optionally, the computer device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Bluetooth interfaces, Wi-Fi interfaces), etc.

[0111] Those skilled in the art will understand that the computer device structure provided in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0112] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and stores the hardware and software resources of a computer device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software within the physical device.

[0113] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. First, the sub-sector data required for image reconstruction can be determined. Here, different sub-sector data correspond to different cardiac cycles. Specifically, a fixed phase can be determined within each cardiac cycle, and the scan projection data within a certain range of the fixed phase within each cardiac cycle can be used as sub-sector data. After acquiring multiple sub-sector data, a corresponding reference image can be constructed based on each sub-sector data. Furthermore, every two reference images can be grouped together, the image similarity between the two reference images can be determined, and the image similarity can be used as the similarity between the two sub-sector data. Next, the target sub-sector data can be determined from the multiple sub-sector data according to the similarity corresponding to every two sub-sector data. Finally, the reconstructed image of the location to be reconstructed can be obtained based on the target sub-sector data. The embodiments of this application determine the target sub-sector data from multiple sub-sector data by using the similarity between different sub-sector data. The target sub-sector data used for subsequent image reconstruction have a high degree of similarity, that is, a high degree of consistency. This can greatly improve the image quality of the reconstructed image while taking into account temporal resolution.

[0114] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0115] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. An image reconstruction method, characterized in that, include: Acquire data from multiple sub-sectors, wherein the multiple sub-sectors are data within the same phase target range within multiple consecutive cardiac cycles; Based on the reference image constructed from the data of each sub-sector, the similarity between every two data of each sub-sector is obtained; Based on the similarity, target sub-sector data of the location to be reconstructed is obtained from multiple sub-sector data, and a reconstructed image of the location to be reconstructed is obtained based on the target sub-sector data; The step of obtaining target sub-sector data for the location to be reconstructed from multiple sub-sector data based on the similarity includes: If at least two sub-sectors have a similarity greater than a preset threshold between any two sub-sectors, the sub-sectors with a similarity greater than the preset threshold are set as first similarity, and the sub-sectors with a similarity less than or equal to the preset threshold are set as second similarity. Based on the first similarity and the second similarity, the similarity sum between each sub-sector and other sub-sectors is obtained. The target sub-sector is obtained from at least one group of sub-sectors with the largest similarity sum.

2. The method according to claim 1, characterized in that, The reference image constructed based on the data of each sub-sector, obtaining the similarity between every two sub-sector data, includes: A reference image is constructed based on the data of each sub-sector, and the pixel value of each pixel in the reference image is obtained; Based on the pixel value at the same pixel position in every two reference images, the image similarity between every two reference images is obtained, and the image similarity is used as the similarity between every two sub-sector data.

3. The method according to claim 2, characterized in that, The method for obtaining the image similarity between any two reference images includes at least one of the following: histogram method, normalized cross-correlation coefficient method, structural similarity method, and mutual information method.

4. The method according to claim 1, characterized in that, Also includes: If the similarity between any two sub-sector data is less than or equal to a preset threshold, then one sub-sector data is selected from the multiple sub-sector data as the target sub-sector data, and the reconstructed image of the location to be reconstructed is obtained based on the target sub-sector data.

5. The method according to claim 1, characterized in that, Obtaining target sub-sector data from at least one group of sub-sector data with the highest similarity and sum includes: If the similarity of the at least one set of sub-sector data is all of the first similarity, then the at least one set of sub-sector data is taken as the target sub-sector data; If the similarity in the at least one set of sub-sector data includes a second similarity, then from the at least one set of sub-sector data, the sub-sector data that is closest to the location to be reconstructed is determined as the first sub-sector data, the sub-sector data that has a first similarity with the first sub-sector data is determined as the second sub-sector data, and the first sub-sector data and the second sub-sector data are determined as the target sub-sector data.

6. The method according to claim 1, characterized in that, The sub-sector data is obtained using retrospective ECG-gated spiral scanning; before obtaining the similarity between every two sub-sector data points using the reference image constructed based on each sub-sector data point, the method further includes: Obtain the midpoint time of the acquisition time of the first and last sub-sector data in the multiple sub-sector data; Based on the Z-axis position of the scanning bed corresponding to the midpoint time, the reconstructed position of the reference image is obtained; Based on the reconstructed position of the reference image, obtain the reference image corresponding to each sub-sector data.

7. An image reconstruction apparatus, characterized in that, include: The sub-sector data acquisition module is used to acquire multiple sub-sector data, wherein the multiple sub-sector data are data within the same phase target range within multiple consecutive cardiac cycles; A similarity calculation module is used to obtain the similarity between every two sub-sector data based on a reference image constructed from the data of each sub-sector. An image reconstruction module is used to obtain target sub-sector data of the location to be reconstructed from multiple sub-sector data based on the similarity, and to obtain a reconstructed image of the location to be reconstructed based on the target sub-sector data; The image reconstruction module includes a second selection unit; the second selection unit is used for: If at least two sub-sectors have a similarity greater than a preset threshold between any two sub-sectors, the sub-sectors with a similarity greater than the preset threshold are set as first similarity, and the sub-sectors with a similarity less than or equal to the preset threshold are set as second similarity. Based on the first similarity and the second similarity, the similarity sum between each sub-sector and other sub-sectors is obtained. The target sub-sector is obtained from at least one group of sub-sectors with the largest similarity sum.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

Images