Image reconstruction method, device, computer equipment and storage medium

Abstract

The present application relates to an image reconstruction method, apparatus, computer device, and storage medium. The method comprises: sequentially selecting target projection sub-data from the projection data to be reconstructed at preset selection intervals; performing image reconstruction on each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data; and merging each reconstructed sub-image to obtain a reconstructed image of the projection data. This method can improve the real-time performance of the reconstructed image of the projection data to be reconstructed.

Description

Technical Field

[0001] The present application relates to the field of medical imaging technology, and in particular to an image reconstruction method, apparatus, computer device, and storage medium. Background Art

[0002] Continuous reconstruction of computed tomography (CT) images is a method designed to meet clinicians' needs for real-time image viewing. CT image reconstruction can be divided into two processes: scanning and reconstruction. During the scanning process, the CT device acquires projection data from different angles of the scanner. During the reconstruction process, the computer uses a back-projection algorithm to process the projection data from each angle and reconstruct a reconstructed image of the scanned area.

[0003] In conventional technology, continuous reconstruction of CT images is mainly based on a scanning cycle of the CT image. Multiple sets of CT images are sequentially reconstructed using projection data within the scanning cycle as units, thereby obtaining continuous reconstructed CT images.

[0004] However, the traditional CT image continuous reconstruction method has the problem of low reconstruction real-time performance. Summary of the Invention

[0005] Based on this, it is necessary to provide an image reconstruction method, apparatus, computer equipment and storage medium that can improve the real-time performance of image reconstruction in order to address the above technical problems.

[0006] An image reconstruction method, the method comprising:

[0007] Selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval;

[0008] Performing image reconstruction on each of the target projection sub-data to obtain a reconstructed sub-image of each of the target projection sub-data;

[0009] The reconstructed sub-images are merged to obtain a reconstructed image of the projection data.

[0010] In one embodiment, reconstructing each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data includes:

[0011] Splitting each target projection sub-data according to a preset sub-packaging threshold to obtain a projection data packet corresponding to each target projection sub-data;

[0012] Performing image reconstruction on each of the projection data packets to obtain a reconstructed image corresponding to each of the projection data packets;

[0013] The reconstructed images corresponding to the projection data packets are merged to obtain reconstructed sub-images of the projection sub-data.

[0014] In one embodiment, performing image reconstruction on each of the projection data packets to obtain a reconstructed image corresponding to each of the projection data packets includes:

[0015] Determining target projection data in a currently reconstructed projection data packet; wherein the target projection data is the same as the projection data in a previously reconstructed projection data packet;

[0016] Acquiring a first reconstructed image corresponding to the target projection data;

[0017] performing image reconstruction on the projection data except the target projection data in the currently reconstructed projection data packet to obtain a second reconstructed image;

[0018] The first reconstructed image and the second reconstructed image are merged to obtain a reconstructed image corresponding to each of the projection data packets.

[0019] In one embodiment, merging the reconstructed images corresponding to the projection data packets to obtain the reconstructed images of the projection sub-data includes:

[0020] The reconstructed images corresponding to the projection data packets are weighted and merged according to preset weight values ​​to obtain the reconstructed images of the projection sub-data.

[0021] In one embodiment, performing image reconstruction on the projection data other than the target projection data in the currently reconstructed projection data packet to obtain a second reconstructed image includes:

[0022] performing data enhancement on the projection data other than the target projection data according to the subpackaging threshold to obtain enhanced projection data;

[0023] Image reconstruction is performed on the enhanced projection data to obtain the second reconstructed image.

[0024] In one embodiment, performing data enhancement on the projection data other than the target projection data according to the subpackaging threshold to obtain enhanced projection data includes:

[0025] dividing the projection data except the target projection data into a first data sub-interval and a second data sub-interval according to the subpackaging threshold;

[0026] Data enhancement is performed on the second data sub-interval according to the first data sub-interval to obtain the enhanced projection data.

[0027] In one embodiment, reconstructing each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data includes:

[0028] According to a preset image reconstruction algorithm, image reconstruction is performed on each of the target projection sub-data to obtain a reconstructed sub-image of each of the target projection sub-data.

[0029] An image reconstruction device, comprising:

[0030] A selection module, configured to sequentially select target projection sub-data from the projection data to be reconstructed according to a preset selection interval;

[0031] a reconstruction module, configured to perform image reconstruction on each of the target projection sub-data to obtain a reconstructed sub-image of each of the target projection sub-data;

[0032] A merging module is used to merge the reconstructed sub-images to obtain a reconstructed image of the projection data.

[0033] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval;

[0035] Performing image reconstruction on each of the target projection sub-data to obtain a reconstructed sub-image of each of the target projection sub-data;

[0036] The reconstructed sub-images are merged to obtain a reconstructed image of the projection data.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0038] Selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval;

[0039] Performing image reconstruction on each of the target projection sub-data to obtain a reconstructed sub-image of each of the target projection sub-data;

[0040] The reconstructed sub-images are merged to obtain a reconstructed image of the projection data.

[0041] The above-mentioned image reconstruction method, apparatus, computer equipment and storage medium can sequentially select target projection sub-data from the projection data to be reconstructed according to a preset selection interval. Since the target projection sub-data are sequentially selected from the projection data to be reconstructed according to the preset selection interval, only image reconstruction is performed on each target projection sub-data. Therefore, the efficiency of obtaining a reconstructed sub-image of each target projection sub-data is improved, and the reconstructed sub-images of each target projection sub-data can be merged to quickly obtain a reconstructed image of the projection data to be reconstructed, thereby improving the real-time performance of obtaining the reconstructed image of the projection data to be reconstructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A diagram showing an application environment of an image reconstruction method in one embodiment;

[0043] Figure 1a is a schematic diagram of the image reconstruction process in one embodiment;

[0044] Figure 2 is a schematic flow chart of an image reconstruction method in one embodiment;

[0045] Figure 2a is a schematic diagram of the image reconstruction process in one embodiment;

[0046] Figure 3 is a schematic flow chart of an image reconstruction method according to another embodiment;

[0047] Figure 4 is a schematic flow chart of an image reconstruction method according to another embodiment;

[0048] Figure 5 is a schematic flow chart of an image reconstruction method according to another embodiment;

[0049] Figure 5a A schematic diagram of the relationship between the projection data to be reconstructed and the acquisition angle provided in one embodiment;

[0050] Figure 5b is a schematic diagram of the image reconstruction process in one embodiment;

[0051] Figure 6 FIG. 4 is a structural block diagram of an image reconstruction device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] The image reconstruction method provided in the embodiment of the present application can be applied to Figure 1 The computer device shown. The computer device includes a processor and a memory connected via a system bus, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the following method embodiment can be executed. Optionally, the computer device may further include a network interface, a display screen, and an input device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory, wherein the non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. Optionally, the computer device can be a server, a personal computer, a personal digital assistant, or other terminal devices, such as a tablet computer, a mobile phone, etc., or a cloud or remote server. The embodiments of the present application do not limit the specific form of the computer device.

[0054] CT image reconstruction can be divided into two processes: scanning and reconstruction. During the scanning process, the CT device obtains projection data from different angles of the scanner. During the reconstruction process, the computer device uses the back projection (BP) algorithm to process the projection data from each angle and reconstruct the reconstructed image of the corresponding scanned part of the scanner. Figure 1a As shown in Figure 1, existing continuous reconstruction methods often use 360-degree projection data as a unit to sequentially reconstruct multiple sets of images. Using this method, image reconstruction can only begin after a circle of data is scanned. If it is assumed that the time it takes for the gantry to rotate one circle (360 degrees) to collect data is T scan , then the time T that the BP algorithm waits for the projection data is wait =T scan , and the time to reconstruct the image through BP is T con , then the time required for the existing method to reconstruct a set of images is T process =T wait +T con =T scan +T con Therefore, to achieve real-time image reconstruction, the gantry rotation speed must be fast enough, and the image reconstruction speed must be fast enough. However, the gantry rotation speed is limited by mechanical properties, and existing data processing capabilities are already saturated, making real-time image reconstruction impossible. Therefore, it is necessary to provide an image reconstruction method, apparatus, computer device, and storage medium that can improve the real-time performance of image reconstruction.

[0055] In one embodiment, Figure 2 As shown, an image reconstruction method is provided, which is applied to Figure 1The computer device in the example is used to illustrate the process, including the following steps:

[0056] S201 : selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval.

[0057] Specifically, the computer device sequentially selects target projection sub-data from the projection data to be reconstructed according to a preset selection interval. Figure 2a As shown in the figure, the horizontal axis represents the pay-off index of the X-ray tube in the CT equipment, and the pay-off index range shown in the figure is 0 to 4500. The vertical axis represents the rotation angle of the CT equipment gantry at the corresponding pay-off index, such as 180°, 200°, 400°, 600°, 800°, 1200°, etc. Figure 2a The middle sinusoidal curve represents the angle change of the gantry rotation, and the 1, 2, 3, 4, 5... below represents the number of image reconstructions. In order to increase the frequency of image reconstruction, the projection data angle range ViewAngleRange is flexibly selected. i To ensure the real-time performance of image reconstruction, in this embodiment, the preset selection interval is θ, that is, an image reconstruction is completed every θ angle interval, then the data range of the i-th image reconstruction is: StartAngle i =θ*(i-1), EndAngle i =StartAngle i +ViewAngleLength, ViewAngleRange i =[StartAngle i ,EndAngle i ], where i is greater than or equal to 1, indicating the sequence number of the image reconstruction; ViewAngleLength is a set value, which can be any value from 0° to 180°, for example, it can be a fixed value such as 90° or 180°; [] represents the data set; θ can be any value from 0° to 180°, which can be determined according to the real-time requirements of the CT scanning system. In this embodiment, [StarAngle i ,EndAngle i ] indicates StarAngle i is the starting point / minimum value, and EndAngle iThe data set of all rotation angles between θ and θ is obtained. For example, if the preset selection interval θ = 10°, then when the projection data angle range used for the first image reconstruction is [0°, 180°], the projection data angle range used for the second image reconstruction is [10°, 190°], and the second image reconstruction only needs to process the data in the range [180°, 190°]. The projection data angle range used for the third image reconstruction is [20°, 200°], and the third image reconstruction only needs to process the data in the range [190°, 200°]. Similarly, an image can be reconstructed when the data of each scan angle θ is scanned. In this way, when the value of θ is small enough, the reconstructed image can be output in a very short time, thereby improving the real-time performance of the reconstructed image.

[0058] S202 , performing image reconstruction on each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data.

[0059] Specifically, the computer device performs image reconstruction on the selected target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data. Optionally, after obtaining each target projection sub-data, the computer device may perform preprocessing such as data rearrangement on each target projection sub-data, and then utilize an image reconstruction algorithm on the preprocessed target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data.

[0060] S203: Merge the reconstructed sub-images to obtain a reconstructed image of the projection data.

[0061] Specifically, the computer device merges the reconstructed sub-images of the obtained target projection sub-data to obtain a reconstructed image of the projection data to be reconstructed. Optionally, the computer device may merge the reconstructed sub-images of the target projection sub-data in the order in which the target projection sub-data were extracted to obtain a reconstructed image of the projection data to be reconstructed.

[0062] In the above-mentioned image reconstruction method, the computer device can sequentially select target projection sub-data from the projection data to be reconstructed according to a preset selection interval. Since the target projection sub-data are sequentially selected from the projection data to be reconstructed according to the preset selection interval, only image reconstruction is performed on each target projection sub-data. Therefore, the efficiency of obtaining the reconstructed sub-image of each target projection sub-data is improved, and the reconstructed sub-images of each target projection sub-data can be merged to quickly obtain the reconstructed image of the projection data to be reconstructed, thereby improving the real-time performance of obtaining the reconstructed image of the projection data to be reconstructed.

[0063] The reconstructed sub-images of each target projection sub-data are displayed in real time, allowing the operating physician or the system to automatically assess the quality of the reconstructed sub-images. If the quality does not meet the requirements, feedback is provided to the scanner, generating scan adjustment instructions. This allows for timely detection of erroneous scan data and for adjustments to scan instructions, such as those adjusting the rotation speed of the scanning gantry or the number of times the CT or X-ray tube is deployed.

[0064] In one embodiment, using a spiral CT scan as an example, the tissue or organ corresponding to the current CT scan can be determined based on the reconstructed subimage of the target projection subdata. In actual scanning, patients may have X-ray-sensitive organs or organs with high X-ray sensitivity, which can easily impair the receiving X-ray tube's radiation. The method of the present application can determine the organ or tissue region currently being scanned based on the reconstructed subimage. When the system automatically determines that the organ or tissue region is X-ray-sensitive, it generates a feedback instruction that controls the X-ray tube to stop radiation within a set period of time.

[0065] In the above scenario of performing image reconstruction on each target projection sub-data to obtain a reconstructed image of each target projection sub-data, the computer device may first split each target projection sub-data, and then perform image reconstruction on each split target projection sub-data to obtain a reconstructed image of each target projection sub-data. In one embodiment, Figure 3 As shown, the above S202 includes:

[0066] S301 : splitting each target projection sub-data according to a preset packet splitting threshold to obtain a projection data packet corresponding to each target projection sub-data.

[0067] Specifically, the computer device splits each target projection sub-data according to a preset packet splitting threshold value to obtain a projection data packet corresponding to each target projection sub-data. It can be understood that, assuming that the data size of the projection data packet corresponding to each target projection sub-data is packageViewAngle, the projection data corresponding to each target projection sub-data is split, and the number of projection data packets corresponding to each target projection sub-data obtained by splitting is: packageNum=ViewAngleRange / packageViewAngle. For example, taking the angle range ViewAngleRange1=[0°,180°] of the first target projection sub-data, the angle range ViewAngleRange1=[60°,240°] of the second target projection sub-data, and packageViewAngle of 60° as an example, the projection data of [0°,180°] can be split into projection data packets of [0°,60°], [60°,120°] and [120°,180°]; the projection data of [60°,240°] can be split into projection data packets of [60°,120°], [120°,180°] and [180°,240°].

[0068] S302 , performing image reconstruction on each projection data packet to obtain a reconstructed image corresponding to each projection data packet.

[0069] Specifically, the computer device performs image reconstruction on the projection data packets corresponding to the respective target projection sub-data obtained above, to obtain a reconstructed image corresponding to each projection data packet. Optionally, the computer device may utilize a preset image reconstruction algorithm to perform image reconstruction on the projection data packets corresponding to the respective target projection sub-data, to obtain a reconstructed image corresponding to each projection data packet. Optionally, the computer device may perform image reconstruction on the projection data packets corresponding to the respective target projection sub-data according to the order in which the projection data packets corresponding to the respective target projection sub-data were split, to obtain a reconstructed image corresponding to each projection data packet.

[0070] S303: Merge the reconstructed images corresponding to the projection data packets to obtain reconstructed sub-images of the projection sub-data.

[0071] Specifically, the computer device merges the reconstructed images corresponding to the above-obtained projection data packets to obtain the reconstructed images of the projection sub-data. Optionally, the computer device may perform weighted merging of the reconstructed images corresponding to the projection data packets according to a preset weight value to obtain the reconstructed images of the projection sub-data. Optionally, the computer device may also merge the reconstructed images corresponding to the projection data packets in the order of the obtained reconstructed images corresponding to the projection data packets to obtain the reconstructed images of the projection sub-data. It should be noted that the overall idea of ​​merging the reconstructed images corresponding to the projection data packets is to reduce repeated calculations. The data is first segmented and calculated, and then the corresponding output results are merged as needed. For example, assuming that the projection data range of the i-th set of images is: ViewAngleRange i =[θ*i,θ*i+ViewAngleLength], the projection data range of the i+1th set of images is: ViewAngleRange i+1 =[θ*i+θ,θ*i+ViewAngleLength+θ]. Since the images are reconstructed sequentially, when reconstructing the i+1th set of images, the data of [θ*i+θ,θ*i+ViewAngleLength] has been calculated when reconstructing the i-th set of images. Therefore, only the newly added θ projection data needs to be calculated, that is, [θ*i+ViewAngleLength,θ*i+ViewAngleLength+θ]. For example, the first set of images requires projection data of a gantry rotation angle of 1°-1200°, and the second set of images requires projection data of a gantry rotation angle of 600°-1800°. The projection data can be divided into three packages for processing. The projection data of [1-600] generates sub-packages. Figure 1 , the projection data generator of [600-1200] Figure 2 , the projection data generator of [1200-1800] Figure 3 , so that the child Figure 1 Kazuko Figure 2 Merge, that is, get the result of the first set of images, and Figure 2 Kazuko Figure 3 Merge and you get the result of the second set of images.

[0072] In this embodiment, the computer device can split each target projection sub-data through a preset packetization threshold to obtain a projection data packet corresponding to each target projection sub-data. In this way, the amount of data required to process for image reconstruction of each projection data packet is relatively small, thereby improving the efficiency of image reconstruction, and further improving the efficiency of obtaining the reconstructed image corresponding to each projection data packet. The reconstructed sub-image of each projection sub-data is obtained by merging the reconstructed images corresponding to each projection data packet, thereby improving the efficiency of obtaining the reconstructed sub-image of each projection sub-data. It is further mentioned that the efficiency of merging the reconstructed sub-images of each projection sub-data to obtain the reconstructed image of the projection data is improved, that is, the real-time performance of obtaining the reconstructed image of the projection data is improved.

[0073] In the above scenario of performing image reconstruction on the projection data packets corresponding to each target projection sub-data to obtain the reconstructed image corresponding to each projection data packet, the computer device can determine the same projection data in each projection data packet, perform image reconstruction on the projection data, and then, when subsequently performing image reconstruction on each projection data packet, only perform image reconstruction on the projection data in the projection data packet except for the same projection data. In one embodiment, Figure 4 As shown, the above S302 includes:

[0074] S401 , determining target projection data in a currently reconstructed projection data packet; wherein the target projection data is the same as the projection data in a previously reconstructed projection data packet.

[0075] Specifically, the computer device determines target projection data in a currently reconstructed projection data packet, wherein the target projection data is the same as projection data in a previously reconstructed projection data packet. For example, continuing with the above S301, the angle range of the first target projection sub-data ViewAngleRange1 = [0°, 180°], the angle range of the second target projection sub-data ViewAngleRange1 = [60°, 240°], the projection data packets corresponding to the first target projection sub-data are [0°, 60°], [60°, 120°] and [120°, 180°]; the projection data packets corresponding to the second target projection sub-data are [60°, 120°], [120°, 180°] and [180°, 240°] as an example, it can be seen that the two projection data packets [60°, 120°] and [120°, 180°] have been calculated when reconstructing the image of the first target projection sub-data, and the data in the two projection data packets [60°, 120°] and [120°, 180°] can be determined as the target projection data.

[0076] S402: Acquire a first reconstructed image corresponding to the target projection data.

[0077] Specifically, the computer device obtains a first reconstructed image corresponding to the target projection data. Optionally, the computer device may obtain the first reconstructed image corresponding to the target projection data from an image reconstruction result corresponding to a previously reconstructed projection data packet.

[0078] S403 , performing image reconstruction on the projection data except the target projection data in the currently reconstructed projection data packet to obtain a second reconstructed image.

[0079] Specifically, the computer device performs image reconstruction on the projection data except the target projection data in the currently reconstructed projection data packet to obtain a second reconstructed image. For example, continuing to take the projection data packets corresponding to the first target projection sub-data as [0°, 60°], [60°, 120°], and [120°, 180°]; and the projection data packets corresponding to the second target projection sub-data as [60°, 120°], [120°, 180°], and [180°, 240°] as an example, it can be seen that the two projection data packets [60°, 120°] and [120°, 180°] have been calculated when reconstructing the image of the first target projection sub-data, and [60°, 120°] and [180°, 240°] have been calculated. The data in the two projection data packets [60°, 120°] and [120°, 180°] are determined as the target projection data. Then, when the computer device performs image reconstruction on the projection data packet corresponding to the second target projection sub-data, it will only reconstruct the projection data corresponding to the projection data packet [180°, 240°]. That is, the reconstructed images corresponding to the two projection data packets [60°, 120°] and [120°, 180°] are the first reconstructed images, and the reconstructed image corresponding to the projection data packet [180°, 240°] is the second reconstructed image.

[0080] S404 : Merge the first reconstructed image and the second reconstructed image to obtain reconstructed images corresponding to the projection data packets.

[0081] Specifically, the computer device merges the first reconstructed image and the second reconstructed image corresponding to each of the projection data packets obtained above to obtain a reconstructed image corresponding to each of the projection data packets. Optionally, the computer device may merge the first reconstructed image and the second reconstructed image in the order in which the first reconstructed image and the second reconstructed image correspond in the projection data packet. For example, taking the projection data packets [60°, 120°], [120°, 180°], and [180°, 240°] corresponding to the second target projection sub-data as the currently reconstructed projection data packet as an example, it can be understood that the reconstructed images corresponding to the two projection data packets [60°, 120°] and [120°, 180°] are the first reconstructed images, and the reconstructed image corresponding to the projection data packet [180°, 240°] is the second reconstructed image. The computer device may then merge the first reconstructed image and the second reconstructed image in this order to obtain a reconstructed image corresponding to the projection data packet of the second target projection sub-data.

[0082] In this embodiment, the computer device first determines the target projection data in the currently reconstructed projection data packet that is identical to the projection data in the previously reconstructed projection data packet, and then can quickly obtain a first reconstructed image corresponding to the target projection data based on the target projection data. In addition, when image reconstruction is performed on the projection data other than the target projection data in the currently reconstructed projection data packet, the computational complexity of image reconstruction is reduced, and the efficiency of obtaining the second reconstructed image is improved. The second reconstructed image can be quickly obtained, and then the first reconstructed image and the second reconstructed image can be quickly merged, thereby improving the efficiency of obtaining the reconstructed images corresponding to each projection data packet.

[0083] In the above scenario where the projection data other than the target projection data in the currently reconstructed projection data packet are reconstructed to obtain the second reconstructed image, the computer device may perform data enhancement on the projection data other than the target projection data, and then perform image reconstruction on the enhanced projection data to obtain the second reconstructed image. In one embodiment, Figure 5 As shown, the above S403 includes:

[0084] S501 : performing data enhancement on the projection data except the target projection data according to the subpackaging threshold to obtain enhanced projection data.

[0085] Specifically, the computer device performs data enhancement on the projection data other than the target projection data according to the above-mentioned subpackaging threshold to obtain the enhanced projection data. Optionally, the computer device can divide the projection data other than the target projection data into a first data sub-interval and a second data sub-interval according to the above-mentioned subpackaging threshold. The subpackaging threshold can be determined manually or according to the weight curve of the projection data, wherein the difference between the endpoints of the first data sub-interval and the difference between the endpoints of the second data sub-interval are both the above-mentioned subpackaging thresholds, and then the second data sub-interval is data enhanced according to the first data sub-interval to obtain the above-mentioned enhanced projection data. In this embodiment, the subpackaging threshold is determined based on the non-overlapping data of the first target projection sub-data and the second target projection sub-data. For example, Figure 5a As shown, when the projection data of [0°, 240°] is enhanced, the projection data of [0°, 240°] can be divided into four sub-intervals of [0°, 60°], [60°, 120°], [120°, 180°], and [180°, 240°] according to the subpackaging threshold of 60°. Figure 5a It can be seen that [0°, 60°] can be determined as the above-mentioned first data sub-interval, and [180°, 240°] can be determined as the above-mentioned second data sub-interval. The computer device can enhance the projection data within [0°, 60°] based on the projection data within the interval [180°, 240°] to obtain the enhanced projection data within [0°, 60°].

[0086] S502: Perform image reconstruction on the enhanced projection data to obtain a second reconstructed image.

[0087] Specifically, the computer device performs image reconstruction on the enhanced projection data to obtain the second reconstructed image. Optionally, the computer device may use a preset image reconstruction algorithm to obtain the second reconstructed image. For example, the preset image reconstruction algorithm may be a filtered back projection algorithm or a local reconstruction algorithm. In this embodiment, Figure 5a The figure shows the weight curve of the projection data collected in a scan reconstruction process of this application. The horizontal axis represents the gantry rotation angle (ViewAngle) corresponding to the projection data, in degrees (Degree); the vertical axis represents the weight (ViewWeight) of the corresponding projection data. Figure 5aIt can be seen that the projection data in the interval [0°, 60°] is assigned an increasing weight value, and all weight values ​​in this range gradually change from 0 to 1. The projection data in the intervals [60°, 120°] and [120°, 180°] are assigned a weight value of 1. The projection data in the interval [180°, 240°] is assigned a decreasing weight value, and all weight values ​​in this range gradually change from 1 to 0. In this embodiment, the projection data in the central area is assigned a larger weight value, and the projection data in the edge area is assigned a smaller weight value, which helps to reduce the impact of patient movement on subsequent image reconstruction and ensure image continuity and accuracy. Data reconstruction is performed on the sub-interval data based on the weight curve of the projection data. In this embodiment, each sub-interval data can be directly reconstructed to obtain a sub-interval image; and the sub-interval data is weighted and reconstructed according to the weight values ​​in the weight curve of the projection data to obtain a sub-interval weighted image.

[0088] Based on the obtained multiple sub-interval images and sub-interval weighted images, an enhanced image is obtained. The merging of enhanced images can be performed using the following formula: Where i is a natural number greater than or equal to 1, representing the sequence number of image reconstruction; packageNum represents the number of sub-packages of projection data, packageNum is a natural number, and packageNum≤i; I sub Represents the subinterval image; I wgt Represents the subinterval weighted image, n is a natural number less than or equal to i; from the above merging formula, it can be seen that except for the first set of data that requires reconstruction of all sub-images, subsequent data only need to recalculate the new I sub and I wgt The new image can be calculated.

[0089] Please refer to Figure 5b FIG. 1 is a schematic diagram showing the merging of the second reconstructed image of the present application. sub represents the subinterval image, I wgt Represents the sub-interval weighted image. Label ① corresponds to the projection data in the interval [0°, 60°], label ② corresponds to the projection data in the interval [60°, 120°], label ③ corresponds to the projection data in the interval [120°, 180°], and labels ④ and ⑤ both correspond to the projection data in the interval [180°, 240°]. This application uses i = 4 and packageNum = 4 as an example. According to the above formula, the expression of the second reconstructed image is:

[0090] I out (4)=I wgt (1)-I wgt (4)+I sub (2)+I sub (3)+I sub (4)

[0091] Among them, I out (4) is the enhanced image obtained by this reconstruction; I wgt (1) corresponds to label ① and is the sub-interval weighted image of the projection data in the interval [60°, 120°]; I sub (2) corresponds to label ② and is a sub-interval image of the projection data in the interval [60°, 120°]; I sub (3) corresponds to label ③ and is the sub-interval image of the projection data in the interval [120°, 180°]; I sub (4) corresponds to label ④ and is a sub-interval image of the projection data in the interval [180°, 240°]; I wgt (4) corresponds to label ⑤ and is the sub-interval weighted image of the projection data in the interval [180°, 240°].

[0092] In one embodiment, to accelerate image processing, reconstruction of sub-interval images and sub-interval weighted images can be achieved using a multi-texture approach or texture interpolation. In this embodiment, the CT scanning system's underlying graphics processing unit supports multi-texturing and renders each of the two adjacent slices being interpolated as a texture. The GPU hardware is then instructed to perform the necessary calculations to generate the interpolated slices in the frame buffer. The multi-texturing approach overcomes the problem of large memory usage. Optionally, processing the CT data may include texturing the dataset for each projection data interval and then transferring it to the graphics memory in its original slice form. Alternatively, two adjacent slices can be dynamically constructed by extracting two adjacent scan lines from each axial slice in the scanner. These two adjacent slices can be processed using a multi-texture interpolation graphics system. Optionally, the weight curves of the acquired projection data can be cached in the graphics card's shared memory to accelerate data readout. The sub-interval images and sub-interval weighted images can be stored using a circular cache structure to improve cache utilization. Data reconstruction for different intervals can be performed concurrently, improving data processing speed.

[0093] In this embodiment, the computer device can perform data enhancement on the projection data other than the target projection data based on the subpackaging threshold, thereby improving the accuracy of the enhanced projection data obtained, and then can perform image reconstruction based on the enhanced projection data with higher accuracy, thereby improving the accuracy of the second reconstructed image.

[0094] In the above scenario of performing image reconstruction on each projection sub-data to obtain a reconstructed sub-image corresponding to each projection sub-data, in one embodiment, the above S202 includes: performing image reconstruction on each projection sub-data according to a preset image reconstruction algorithm to obtain a reconstructed sub-image of each projection sub-data.

[0095] Specifically, the computer device reconstructs each projection sub-data according to a preset image reconstruction algorithm to obtain a reconstructed sub-image corresponding to each projection sub-data. Optionally, the preset image reconstruction algorithm can be a filtered back projection algorithm or a local reconstruction algorithm.

[0096] In this embodiment, the computer device can accurately reconstruct the image of each target projection sub-data according to a preset image reconstruction algorithm, thereby improving the accuracy of the reconstructed sub-image of each target projection sub-data; in addition, according to the preset image reconstruction algorithm, the image of each target projection sub-data can be quickly reconstructed, thereby improving the efficiency of obtaining the reconstructed sub-image of each target projection sub-data.

[0097] It should be noted that, for the image reconstruction method described in the above embodiment, in order to accelerate the image reconstruction process of the method, a data processing flow based on a graphics processing unit (GPU) is proposed: 1) Pixel calculation method design: Threads are organized according to pixel size, and each thread calculates the corresponding pixel using the BP formula, i.e., iThread=ipexel, I sub (iThread) = BP (projectionData), the weighted calculation of the image domain is I wgt (iThread)=I sub (iThread)*fWeight(viewIdx); 2) Design of a circular cache image fusion method: Design a packageNum circular cache ImageBuff for collaboration between the CPU (Central Processing Unit) and GPU. When all package data within a buff is calculated on the GPU side, all generated sub-images are weightedly fused and copied to the CPU side. The image buff location is indexed by PackageIdx. This method increases the parallelism of the calculation process and the image data fusion process, fully utilizing the computing resources of the CPU and GPU, allowing the above method to still achieve good performance in environments with low-end hardware resources.

[0098] It should be understood that although Figure 2-5 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2-5At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The order of execution of these steps or stages is not necessarily one by one, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0099] In one embodiment, Figure 6 As shown, an image reconstruction device is provided, comprising: a selection module, a reconstruction module and a merging module, wherein:

[0100] The selection module is used to sequentially select target projection sub-data from the projection data to be reconstructed according to a preset selection interval.

[0101] The reconstruction module is used to perform image reconstruction on each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data.

[0102] The merging module is used to merge the reconstructed sub-images to obtain a reconstructed image of the projection data.

[0103] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0104] Based on the above embodiment, optionally, the above reconstruction module includes: a splitting unit, a reconstruction unit and a merging unit, wherein:

[0105] The splitting unit is used to split each target projection sub-data according to a preset packet sub-threshold value to obtain a projection data packet corresponding to each target projection sub-data.

[0106] The reconstruction unit is used to perform image reconstruction on each projection data packet to obtain a reconstructed image corresponding to each projection data packet.

[0107] The merging unit is used to merge the reconstructed images corresponding to the projection data packets to obtain the reconstructed sub-images of the projection sub-data.

[0108] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0109] Based on the above embodiment, optionally, the above reconstruction unit is specifically configured to determine target projection data in a currently reconstructed projection data packet; wherein the target projection data is the same as the projection data in a previously reconstructed projection data packet; obtain a first reconstructed image corresponding to the target projection data; perform image reconstruction on the projection data other than the target projection data in the currently reconstructed projection data packet to obtain a second reconstructed image; and perform image merging on the first reconstructed image and the second reconstructed image to obtain a reconstructed image corresponding to each projection data packet.

[0110] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0111] Based on the above embodiment, optionally, the merging unit is specifically configured to perform weighted merging on the reconstructed images corresponding to the projection data packets according to a preset weight value to obtain the reconstructed images of the projection sub-data.

[0112] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0113] Based on the above embodiment, optionally, the above reconstruction unit is specifically used to perform data enhancement on the projection data except the target projection data according to the subpackaging threshold to obtain enhanced projection data; and perform image reconstruction on the enhanced projection data to obtain a second reconstructed image.

[0114] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0115] Based on the above embodiment, optionally, the above reconstruction unit is specifically used to divide the projection data except the target projection data into a first data sub-interval and a second data sub-interval according to a subpackaging threshold; wherein the difference between the endpoints of the first data sub-interval and the difference between the endpoints of the second data sub-interval are both subpackaging thresholds; and data enhancement is performed on the second data sub-interval according to the first data sub-interval to obtain enhanced projection data.

[0116] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0117] Based on the above embodiment, optionally, the reconstruction module includes a reconstruction unit, wherein:

[0118] The reconstruction unit is used to perform image reconstruction on each target projection sub-data according to a preset image reconstruction algorithm to obtain a reconstructed sub-image of each target projection sub-data.

[0119] The image reconstruction device provided in this embodiment can execute the above method embodiment. Its implementation principles and technical effects are similar and will not be described in detail here.

[0120] The specific definition of the image reconstruction device can be found in the definition of the image reconstruction method above and will not be repeated here. Each module in the above-mentioned image reconstruction device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0121] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0122] Selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval;

[0123] Performing image reconstruction on each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data;

[0124] The reconstructed sub-images are merged to obtain a reconstructed image of the projection data.

[0125] The implementation principle and technical effects of the computer device provided in the above embodiment are similar to those of the above method embodiment and will not be repeated here.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0127] Selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval;

[0128] Performing image reconstruction on each target projection sub-data to obtain a reconstructed sub-image of each target projection sub-data;

[0129] The reconstructed sub-images are merged to obtain a reconstructed image of the projection data.

[0130] The computer-readable storage medium provided in the above embodiment has similar implementation principles and technical effects to those of the above method embodiment, and will not be described in detail here.

[0131] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0132] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. An image reconstruction method, characterized in that: The method comprises: Selecting target projection sub-data in sequence from the projection data to be reconstructed according to a preset selection interval; Splitting each target projection sub-data according to a preset sub-packaging threshold to obtain a projection data packet corresponding to each target projection sub-data; Determining target projection data in a currently reconstructed projection data packet; wherein the target projection data is the same as the projection data in a previously reconstructed projection data packet; Acquiring a first reconstructed image corresponding to the target projection data; dividing the projection data except the target projection data into a first data sub-interval and a second data sub-interval according to the subpackaging threshold; performing data enhancement on the second data subinterval according to the first data subinterval to obtain the enhanced projection data; performing image reconstruction on the enhanced projection data to obtain a second reconstructed image; Merging the first reconstructed image and the second reconstructed image to obtain a reconstructed image corresponding to each projection data packet; Merging the reconstructed images corresponding to the projection data packets to obtain reconstructed sub-images of the projection sub-data; The reconstructed sub-images are merged to obtain a reconstructed image of the projection data.

2. The method according to claim 1, characterized in that The merging of the reconstructed images corresponding to the projection data packets to obtain the reconstructed images of the projection sub-data includes: The reconstructed images corresponding to the projection data packets are weighted and merged according to preset weight values ​​to obtain the reconstructed images of the projection sub-data.

3. The method according to claim 2, characterized in that The weight value of the projection data packet in the central area is greater than the weight value of the projection data packet in the edge area.

4. The method according to any one of claims 1 to 3, characterized in that The performing image reconstruction on the enhanced projection data to obtain the second reconstructed image includes: based on Determine a second reconstructed image corresponding to the i-th projection data packet; Where i is a natural number greater than or equal to 1; packageNum represents the number of projected data packages, packageNum≤i; I sub Represents the subinterval image; I wgt represents a subinterval weighted image, where n is a natural number less than or equal to i. The subinterval image is an image directly reconstructed from the corresponding subinterval data, and the subinterval data is an image obtained by weighted reconstruction according to the weight values ​​in the weight curve of the corresponding projection data.

5. The method according to claim 4, characterized in that The method further comprises: The sub-interval image and the sub-interval weighted image are reconstructed using a multi-texture method or a texture interpolation method.

6. The method according to claim 4, characterized in that The method further comprises: A circular buffer structure is used to store the sub-interval image and the sub-interval weighted image.

7. An image reconstruction device, characterized in that: The device comprises: A selection module, configured to sequentially select target projection sub-data from the projection data to be reconstructed according to a preset selection interval; a reconstruction module, configured to perform image reconstruction on each of the target projection sub-data to obtain a reconstructed sub-image of each of the target projection sub-data; a merging module, configured to merge the reconstructed sub-images to obtain a reconstructed image of the projection data; The reconstruction module comprises: a splitting unit, configured to split each target projection sub-data according to a preset sub-packaging threshold value to obtain a projection data packet corresponding to each target projection sub-data; a reconstruction unit, configured to perform image reconstruction on each of the projection data packets to obtain a reconstructed image corresponding to each of the projection data packets; a merging unit, configured to merge the reconstructed images corresponding to the projection data packets to obtain reconstructed sub-images of the projection sub-data; The reconstruction unit is further configured to determine target projection data in a currently reconstructed projection data packet; wherein the target projection data is the same as the projection data in a previously reconstructed projection data packet; obtain a first reconstructed image corresponding to the target projection data; perform image reconstruction on the projection data in the currently reconstructed projection data packet except for the target projection data to obtain a second reconstructed image; and perform image merging on the first reconstructed image and the second reconstructed image to obtain a reconstructed image corresponding to each of the projection data packets; The reconstruction unit is further used to divide the projection data other than the target projection data into a first data sub-interval and a second data sub-interval according to the subpackaging threshold; perform data enhancement on the second data sub-interval according to the first data sub-interval to obtain the enhanced projection data; and perform image reconstruction on the enhanced projection data to obtain the second reconstructed image.

8. The device according to claim 7, characterized in that The merging unit is configured to perform weighted merging on the reconstructed images corresponding to the projection data packets according to a preset weight value to obtain the reconstructed images of the projection sub-data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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