Nuclear medicine image generation method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a nuclear medicine image generation method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps that motion information and scanning raw data of a scanned object at all time points in the scanning period are acquired; according to the motion information of each time point and the scanning raw data, determining a distribution centroid coordinate of motion correction of the scanned object at each time point; determining a reference centroid coordinate according to the distribution centroid coordinate of the motion correction at each time point; performing screening processing on the motion information of each time point according to the reference center-of-mass coordinates to obtain processed motion information; and obtaining a nuclear medicine image of the scanned object according to the processed motion information and the scanning raw data. By adopting the method, unreliable motion information can be effectively identified and eliminated, and the accuracy and reliability of the obtained processed motion information are improved, so that the effect of generating an image is ensured.

Description

Technical Field

[0001] This application relates to the field of nuclear medicine imaging technology, and in particular to a method, apparatus, computer device, computer-readable storage medium, and computer program product for generating nuclear medicine images. Background Technology

[0002] Radionuclide emission computed tomography (ECT) includes PET (Positron Emission Computed Tomography) and SPECT (Single Photon Emission Computed Tomography). In nuclear medicine imaging studies based on these methods, the long scanning time of nuclear medicine imaging equipment and the tendency for the scanned object to move during the scan can lead to image artifacts, inaccurate quantification, and other problems, affecting subsequent diagnosis. Therefore, motion information is usually acquired during nuclear medicine scanning for motion correction.

[0003] In related technologies, motion correction is typically achieved by acquiring a set of motion information describing the movement of the scanned object within the acquisition time period, and then combining this motion information to reconstruct nuclear medicine images. However, due to the limitations of the motion information acquisition method, the motion information in some time slices is unreliable, which can affect the quality of the reconstructed image. Summary of the Invention

[0004] Therefore, it is necessary to address the technical problem mentioned above where unreliable motion information in some time slices affects the quality of reconstructed images by providing a method, apparatus, computer device, computer-readable storage medium, and computer program product for generating nuclear medicine images.

[0005] In a first aspect, this application provides a method for generating nuclear medicine images. The method includes:

[0006] Acquire motion information and scanned data of the object at various points in time during the scanning process;

[0007] Based on the motion information at each time point and the scanned data, determine the centroid coordinates of the motion correction distribution of the scanned object at each time point;

[0008] The reference centroid coordinates are determined based on the centroid coordinates of the motion correction distribution at each of the aforementioned time points.

[0009] Based on the reference centroid coordinates, the motion information at each time point is filtered and processed to obtain the processed motion information.

[0010] Based on the processed motion information and the scanned biodata, a nuclear medicine image of the scanned object is obtained.

[0011] In one embodiment, determining the reference centroid coordinates based on the motion-corrected centroid coordinates at each of the time points includes:

[0012] Each of the aforementioned time points is divided into multiple time periods;

[0013] The average value of the centroid coordinates of the motion correction distribution within each time period is calculated to obtain the average coordinate value for each time period.

[0014] The average coordinates for each time period are averaged again, and the resulting average value is used as the reference centroid coordinates.

[0015] In one embodiment, dividing each of the time points into multiple time periods includes:

[0016] Based on the centroid coordinates of the motion correction distribution at each time point, multiple motion-free time periods are determined; wherein, the distance between the centroid coordinates of the motion correction distribution at each pair of time points within each motion-free time period is less than a distance threshold, and the distance between the centroid coordinates of the motion correction distribution between two adjacent motion-free time periods is greater than the distance threshold.

[0017] The multiple periods of inactivity are divided into multiple time periods.

[0018] In one embodiment, the step of filtering the motion information at each time point based on the reference centroid coordinates to obtain processed motion information includes:

[0019] Obtain the deviation of the mean coordinates of each time period relative to the reference centroid coordinates;

[0020] Filter out the abnormal time periods where the deviation does not meet the preset conditions from each of the time periods;

[0021] The abnormal time period contains motion information is removed from the motion information of each time period to obtain the processed motion information.

[0022] In one embodiment, the step of filtering the motion information at each time point based on the reference centroid coordinates to obtain processed motion information further includes:

[0023] For each time point, the deviation of the motion correction distribution centroid coordinates at that time point relative to the reference centroid coordinates is obtained;

[0024] From the motion information at each of the aforementioned time points, motion information whose deviation does not meet the preset conditions is removed to obtain the processed motion information.

[0025] In one embodiment, the motion information at each time point is acquired through a first method; after filtering the motion information at each time point based on the reference centroid coordinates to obtain processed motion information, the method further includes:

[0026] Identify the abnormal time points corresponding to the removed motion information;

[0027] The new motion information corresponding to the abnormal time point is determined by the second method, and the new motion information is used to replace the motion information corresponding to the abnormal time point to obtain the adjusted motion information of the scanned object.

[0028] Based on the adjusted motion information, the processed motion information, and the scan data, a nuclear medicine image of the scanned object is obtained.

[0029] Secondly, this application also provides a nuclear medicine image generation apparatus. The apparatus includes:

[0030] The data acquisition module is used to acquire motion information and scanned data of the scanned object at various time points during the scanning process.

[0031] The coordinate determination module is used to determine the centroid coordinates of the motion correction distribution of the scanned object at each of the time points based on the motion information at each time point and the scanned data.

[0032] The reference determination module is used to determine the reference centroid coordinates based on the motion correction distribution centroid coordinates at each of the time points.

[0033] The information filtering module is used to filter the motion information at each time point according to the reference centroid coordinates to obtain the processed motion information.

[0034] The image generation module is used to obtain a nuclear medicine image of the scanned object based on the processed motion information and the scanned data.

[0035] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0036] Acquire motion information and scanned data of the object at various points in time during the scanning process;

[0037] Based on the motion information at each time point and the scanned data, determine the centroid coordinates of the motion correction distribution of the scanned object at each time point;

[0038] The reference centroid coordinates are determined based on the centroid coordinates of the motion correction distribution at each of the aforementioned time points.

[0039] Based on the reference centroid coordinates, the motion information at each time point is filtered and processed to obtain the processed motion information.

[0040] Based on the processed motion information and the scanned biodata, a nuclear medicine image of the scanned object is obtained.

[0041] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0042] Acquire motion information and scanned data of the object at various points in time during the scanning process;

[0043] Based on the motion information at each time point and the scanned data, determine the centroid coordinates of the motion correction distribution of the scanned object at each time point;

[0044] The reference centroid coordinates are determined based on the centroid coordinates of the motion correction distribution at each of the aforementioned time points.

[0045] Based on the reference centroid coordinates, the motion information at each time point is filtered and processed to obtain the processed motion information.

[0046] Based on the processed motion information and the scanned biodata, a nuclear medicine image of the scanned object is obtained.

[0047] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0048] Acquire motion information and scanned data of the object at various points in time during the scanning process;

[0049] Based on the motion information at each time point and the scanned data, determine the centroid coordinates of the motion correction distribution of the scanned object at each time point;

[0050] The reference centroid coordinates are determined based on the centroid coordinates of the motion correction distribution at each of the aforementioned time points.

[0051] Based on the reference centroid coordinates, the motion information at each time point is filtered and processed to obtain the processed motion information.

[0052] Based on the processed motion information and the scanned biodata, a nuclear medicine image of the scanned object is obtained.

[0053] The aforementioned nuclear medicine image generation method, apparatus, computer equipment, storage medium, and computer program product acquire motion information and scan data of the scanned object at various time points during the scanning process. Based on the motion information and scan data at each time point, they determine the centroid coordinates of the motion correction distribution of the scanned object at each time point, and determine the reference centroid coordinates based on these coordinates. By filtering the motion information at each time point based on the reference centroid coordinates, unreliable motion information can be effectively identified and eliminated, improving the accuracy and reliability of the processed motion information. This, in turn, improves the image quality of the nuclear medicine image of the scanned object obtained from the scan data and ensures the motion correction effect. Furthermore, by combining the determination of the centroid coordinates of the scanned object at each time point with motion information, the centroid coordinates can be corrected using motion information, resulting in the motion correction distribution centroid coordinates. This ensures the accuracy of the determined centroid coordinates and helps to accurately correct image artifacts caused by motion. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a method for generating nuclear medicine images in one embodiment;

[0055] Figure 2 This is a flowchart illustrating the steps for filtering motion information at various time points in one embodiment.

[0056] Figure 3 This is a schematic diagram of the distribution centroid curve after motion information filtering processing in one embodiment.

[0057] Figure 4 This is a flowchart illustrating a nuclear medicine image generation method in another embodiment;

[0058] Figure 5 This is a structural block diagram of a nuclear medicine image generation device in one embodiment;

[0059] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] To facilitate understanding of this application, the following explanation uses the imaging principle of a PET system. It should be noted that this approach includes, but is not limited to, PET systems and is also applicable to other medical systems, such as SPECT. Before a PET image scan, a radioactively labeled drug / tracer is injected into the patient. During the scan, the drug / tracer decays and emits positrons, which annihilate with surrounding electrons to produce a pair of photons emitted in opposite directions. If both photons are detected simultaneously by the detectors of the PET imaging system, the annihilation event is considered to have occurred on the line connecting the two detectors that captured the photons; this line is called the response line (LOR). The collection of all response lines during the PET scan constitutes the raw data of the PET image. PET imaging systems typically use a continuous scanning mode to acquire raw data and generate PET images.

[0062] Considering the impact of motion information, current methods involve acquiring patient motion information during nuclear medicine imaging scans and transmitting this information to the imaging equipment of the nuclear medicine imaging system. When generating nuclear medicine images, motion correction is performed on the reconstructed images based on the patient motion information. However, due to the limitations of the motion information acquisition method, the motion information from some time slices is unreliable, thus affecting the correction effectiveness.

[0063] Therefore, based on the above problems, this application provides a nuclear medicine image generation method that can identify discontinuous time segments such as obvious deviations or jumps in the distribution centroid. By processing these time segments, more accurate motion information can be obtained, thereby achieving better motion correction results.

[0064] refer to Figure 1 This is a flowchart illustrating a method for generating nuclear medicine images according to an embodiment of this application. This embodiment uses the application of this method to a terminal as an example for illustration. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. In this embodiment, the method includes the following steps:

[0065] Step S110: Obtain motion information and scan data of the scanned object at various time points during the scanning process.

[0066] The object being scanned refers to a moving living organism or a moving part of a living organism. For example, the object being scanned could be the head or limbs of a human body.

[0067] Among them, motion information refers to rigid motion information such as head movement and limb movement, which can be represented by a rigid body motion matrix.

[0068] In practice, the motion information of the scanned object at various points in time during the scanning process can be acquired by external devices of the nuclear medicine imaging equipment, such as image recording devices (e.g., structured light cameras), scanners, trackers, etc., or it can be obtained through other external signals, or calculated based on the scan data obtained from the nuclear medicine scan.

[0069] Step S120: Based on the motion information and scanned data at each time point, determine the centroid coordinates of the motion correction distribution of the scanned object at each time point.

[0070] Specifically, scan biodata consists of the collection of all response lines during nuclear medicine scanning. Therefore, scan biodata contains the response lines of all events corresponding to each time point. The process of determining the motion-corrected centroid coordinates of the scanned object at each time point, based on the motion information and scan biodata, may include: for each time point, performing motion correction on the response lines corresponding to that time point in the scan biodata, obtaining the corrected response lines. Back-projecting and performing sensitivity correction on the corrected response lines to form a point cloud image for that time point, which is used as an approximation of the tracer distribution image. Then, based on the point cloud image, calculating the centroid of distribution at that time point, obtaining the motion-corrected centroid coordinates of distribution at that time point. If the motion of the scanned object is rigid, then the centroid coordinates of distribution at each time point are the average of the coordinate values ​​of the scanned object in the X, Y, and Z directions at that time point.

[0071] Step S130: Determine the reference centroid coordinates based on the distributed centroid coordinates of motion correction at each time point.

[0072] In practice, the reference centroid coordinates can be determined based on the distribution centroid coordinates of motion correction at each time point. This can be done either from a single time point dimension or from a time period dimension composed of multiple time points.

[0073] When determining the reference centroid coordinates from a single time point, statistical methods can be used, such as calculating the mean or median, as the reference centroid coordinates. Specifically, the mean or median of the motion-corrected distribution centroid coordinates at each time point can be calculated to obtain the reference centroid coordinates. Alternatively, cluster analysis can be used to cluster the motion-corrected distribution centroid coordinates at each time point, and the resulting centroid coordinates can be used as the reference centroid coordinates. Neural networks can also be used to determine the reference centroid coordinates.

[0074] When determining the reference centroid coordinates from a time-period perspective, each time point can be divided into multiple time periods. First, the motion correction distribution centroid coordinates within each time period are determined individually, and these are recorded as the local reference centroid coordinates for each time period. Then, based on the local reference centroid coordinates for each time period, the overall reference centroid coordinates are determined. The methods for determining the reference centroid coordinates for each time period, and for determining the overall reference centroid coordinates based on the reference centroid coordinates for each time period, are based on the same principle as the method described above for determining the reference centroid coordinates from a single time point perspective. Statistical methods, cluster analysis methods, neural networks, etc., can also be used, which will not be elaborated upon here.

[0075] Step S140: Based on the reference centroid coordinates, the motion information at each time point is filtered and processed to obtain the processed motion information.

[0076] In practice, the reference centroid coordinates can be determined from a single time point dimension or from a time period dimension. Correspondingly, the motion information at each time point can be filtered and processed based on the reference centroid coordinates, which can also be determined from both the time point dimension and the time period dimension.

[0077] Specifically, when the reference centroid coordinates are determined from the time point dimension, the filtering and processing of motion information at each time point based on the reference centroid coordinates is also performed from the time point dimension. That is, by judging whether the deviation between the distribution centroid coordinates of motion correction at each time point and the reference centroid coordinates meets the preset conditions, the abnormal time points that need to be removed are determined, thereby realizing the filtering of motion information at each time point and obtaining the processed motion information.

[0078] When the reference centroid coordinates are determined from the time period dimension, the filtering and processing of motion information at each time point based on the reference centroid coordinates can be performed from either the time period dimension or the time point dimension. The method for performing the process from the time point dimension is the same as described above and will not be repeated here. When performing the process from the time period dimension, each time period is used as a unit. The deviation between the local reference centroid coordinates and the overall reference centroid coordinates for that time period is checked against preset conditions to determine the abnormal time periods that need to be removed. This process filters the motion information at each time point, resulting in processed motion information.

[0079] Step S150: Based on the processed motion information and scan data, obtain the nuclear medicine image of the scanned object.

[0080] In practice, after filtering and processing the motion information at each time point, the scanned biodata can be corrected using the processed motion information to compensate for the effects of motion. Specifically, motion correction algorithms can be used to correct the scanned biodata. Based on the processed scanned biodata, nuclear medicine image reconstruction algorithms are used to generate images.

[0081] Furthermore, after generating the nuclear medicine image of the scanned object, post-processing operations such as noise reduction and filtering can be performed on the nuclear medicine image to further improve the image quality and clarity.

[0082] In the aforementioned nuclear medicine image generation method, after acquiring the motion information and scan data of the scanned object at various time points during the scanning process, the centroid coordinates of the motion correction distribution of the scanned object at each time point are determined based on the motion information and scan data. A reference centroid coordinate is then determined based on these coordinates. By filtering the motion information at each time point using the reference centroid coordinates, unreliable motion information can be effectively identified and eliminated, improving the accuracy and reliability of the processed motion information. This, in turn, enhances the image quality of the nuclear medicine image of the scanned object generated from the scan data and ensures the motion correction effect. Furthermore, by combining the determination of the centroid coordinates of the scanned object at each time point with the motion information, centroid coordinate correction can be achieved through motion information, obtaining the motion correction distribution centroid coordinates and ensuring the accuracy of the determined centroid coordinates. This helps to accurately correct image artifacts caused by motion.

[0083] In an exemplary embodiment, step S130 above, determining the reference centroid coordinates based on the motion-corrected centroid coordinates at each of the time points, includes:

[0084] Step S131: Divide each time point into multiple time periods;

[0085] Step S132: Calculate the mean value of the centroid coordinates of the motion correction distribution in each time period to obtain the mean coordinate value for each time period.

[0086] Step S133: Calculate the mean of the coordinates for each time period again, and use the resulting average value as the reference centroid coordinates.

[0087] In practice, each time point is divided into multiple time periods. The time points within each time period are consecutive, meaning the time periods are divided according to the chronological order of the time points. For example, multiple time periods are obtained by dividing the time periods into segments of 5 time points each, without overlap. The duration of each time period can be the same or different; there is no specific limitation on the length of each time period.

[0088] More specifically, based on the division of time periods according to the chronological order of each time point, random division can be performed, meaning each time period can have a random duration, or it can be divided according to a fixed preset duration. Furthermore, it can be set that the motion state represented by the motion information within each time period is consistent, meaning that the distance between the centroid coordinates of the motion correction distribution at any pair of time points within each time period is very close.

[0089] After dividing the data into multiple time periods, the average value of the centroid coordinates of the motion correction distribution within each time period can be calculated to obtain the average coordinate value for each time period. Then, the average coordinate value for each time period can be calculated again, and the resulting average value can be used as the reference centroid coordinates.

[0090] In this embodiment, dividing each time point into multiple time periods can smooth out data fluctuations at each time point to a certain extent, helping to remove some noise and outliers, and making the obtained reference centroid coordinates more stable and reliable. Furthermore, multiple averaging processes in stages can eliminate instantaneous fluctuations within certain time periods and reduce the possibility of the reference centroid coordinates deviating from the true value due to error accumulation, further improving the accuracy and reliability of the reference centroid coordinates.

[0091] In an exemplary embodiment, the step of dividing each time point into multiple time periods includes: determining multiple non-motion time periods based on the motion-corrected centroid coordinates of each time point; and using the multiple non-motion time periods as the multiple time periods to be divided.

[0092] Specifically, the distance between the centroid coordinates of the motion correction distribution at any pair of time points within a motion-free frame (MFF) is less than a distance threshold, while the distance between the centroid coordinates of the motion correction distribution between any two adjacent motion-free frames is greater than the distance threshold.

[0093] In practical implementation, a motion-free period can be understood as a relatively static period without significant motion, where time points have relatively consistent centroid coordinates for motion correction distribution. Therefore, to determine motion-free periods based on the centroid coordinates for motion correction distribution at each time point, the distance between the centroid coordinates for motion correction distribution at each pair of time points can be calculated first. Based on a preset distance threshold, time points with distances less than the threshold are grouped into the same time period, thus obtaining multiple time periods. Furthermore, since the time points within each time period have temporal continuity, after obtaining multiple time periods, quality control can be performed on each time period to check for discontinuous or isolated time points, which are then removed from the corresponding time periods, thus obtaining multiple motion-free periods.

[0094] Multiple periods of inactivity are treated as separate time segments. The centroid coordinates of the motion correction distribution within each of these inactivity periods are averaged to obtain the mean coordinates for each inactivity period. Finally, the mean coordinates for each inactivity period are averaged again, and the resulting average value is used as the baseline centroid coordinates.

[0095] It is understood that other methods can also be used to determine multiple non-motion time periods based on the centroid coordinates of the motion correction distribution at each time point, such as clustering methods or other methods that can identify similar data. This application does not make any specific limitations on this.

[0096] In this embodiment, by dividing each time point into multiple non-motion time periods, interference caused by data noise or outliers can be reduced, and data volatility can be reduced, thereby making the reference centroid coordinates determined based on multiple non-motion time periods more stable and representative.

[0097] It is understandable that, based on the distribution centroid coordinates of motion corrections at various time points, the reference centroid coordinates can be determined from a single time point dimension, or from a time period composed of multiple time points, thus determining them from a time period dimension. Correspondingly, based on the reference centroid coordinates, the motion information at each time point can be filtered either from a single time point dimension or from a time period dimension.

[0098] When filtering by time period, correspondingly, in an exemplary embodiment, such as Figure 2 As shown, in step S140 above, the motion information at each time point is filtered and processed based on the reference centroid coordinates to obtain the processed motion information, including:

[0099] Step S210: Obtain the deviation of the mean coordinates of each time period relative to the reference centroid coordinates;

[0100] Step S220: Filter out the abnormal time periods from each time period where the deviation does not meet the preset conditions;

[0101] Step S230: Remove motion information from the motion information of abnormal time periods from the motion information of each time period to obtain the processed motion information.

[0102] In practice, when filtering motion information from a time-period perspective, the scanned object can be divided into multiple time periods during the scanning process. The mean coordinates of the centroid coordinates for motion correction at each time point within each time period are calculated. Then, the distance between the mean coordinates of each time period and the baseline centroid coordinates is calculated to obtain the deviation. Based on the deviation, abnormal time periods requiring motion information removal are identified. The motion information from each time period within these abnormal time periods is then removed from the motion information at each time point, resulting in processed motion information.

[0103] More specifically, the method for filtering out abnormal time periods based on deviation can be to define time periods with deviations greater than a preset deviation threshold (e.g., 5mm) as abnormal time periods, or to sort the deviations of each time period from largest to smallest and define the time periods with deviations in the top N (N is an integer) or a certain proportion as abnormal time periods. Correspondingly, the preset conditions for filtering out abnormal time periods based on deviation can be that the deviation is less than the deviation threshold or the deviation does not reach the top N or a certain proportion.

[0104] refer to Figure 3 This is a schematic diagram of the motion-corrected centroid distribution curve after motion information filtering processing, as shown in one embodiment. The motion-corrected centroid distribution curve represents the curve of the coordinates of the motion-corrected centroid of the scanned object changing over time. The wavy curve 30 in the figure represents the original curve formed by the motion-corrected centroid coordinates of the scanned object at various time points. Each segment 31 represents the identified abnormal time period, and the longer line segment 32 represents the time period that is retained.

[0105] In this embodiment, motion information is filtered and processed from the time period dimension, which simplifies the data processing process and improves the processing efficiency of motion information at each time point. By obtaining the deviation of the mean coordinate of each time period from the reference centroid coordinate, the relative offset of data points within each time period can be accurately measured, which helps to identify abnormal time periods and reduce their impact on data processing, thereby improving the accuracy and stability of data processing.

[0106] When filtering from the time point dimension, in an exemplary embodiment, step S140 above filters the motion information at each time point based on the reference centroid coordinates to obtain processed motion information. It also includes: for each time point, obtaining the deviation of the motion correction distribution centroid coordinates of the time point relative to the reference centroid coordinates; and removing motion information from the motion information at each time point whose deviation does not meet the preset conditions to obtain processed motion information.

[0107] In practical implementation, when filtering motion information from a time-point perspective, the deviation of the motion-corrected distribution centroid coordinates relative to the reference centroid coordinates at each time point can be directly obtained; that is, the distance between the motion-corrected distribution centroid coordinates and the reference centroid coordinates at each time point is calculated. For the deviation at each time point, it is determined whether the deviation meets the preset conditions. If the deviation meets the conditions, the motion information at that time point is retained; if it does not meet the conditions, the motion information at that time point is discarded. The motion information from time points that meet the preset conditions is integrated to obtain the processed motion information.

[0108] The preset conditions are the same as those for filtering motion information from a time period perspective, which can be that the deviation is less than a deviation threshold, the deviation does not reach the Top N, or a certain percentage. Specifically, when the deviation at a given time point is greater than the deviation threshold, the deviation at that time point is determined to be ineligible. Alternatively, the deviations at each time point can be sorted from largest to smallest, and time points whose deviations fall within the Top N or the percentage of the top preset number are determined to be ineligible.

[0109] In this embodiment, motion information is filtered from a time point perspective, which can refine the filtering of abnormal data, better retain reliable data, and improve the accuracy of the data processing process.

[0110] In an exemplary embodiment, motion information at each time point is acquired through a first method; after step S140 filters the motion information at each time point based on the reference centroid coordinates to obtain processed motion information, it further includes:

[0111] Step S141: Determine the abnormal time point corresponding to the removed motion information;

[0112] Step S142: Determine the new motion information corresponding to the abnormal time point through the second method, replace the motion information corresponding to the abnormal time point with the new motion information, and obtain the adjusted motion information of the scanned object.

[0113] Step S143: Based on the adjusted motion information, the processed motion information, and the scan data, obtain the nuclear medicine image of the scanned object.

[0114] The second method differs from the first. Both methods involve acquiring motion information through external devices of nuclear medicine imaging equipment (such as structured light cameras) or calculating motion information from biodata from nuclear medicine scans. Acquiring motion information through external devices like structured light cameras is generally faster and more accurate, but it can fail in a few scenarios, such as when the patient's facial expressions change drastically or the patient's movements are minimal; in these cases, the structured light camera cannot capture the motion information. Acquiring motion information from biodata from nuclear medicine scans (such as PET biodata) is unaffected by facial expressions, the camera's field of view, or changes in ambient light, and generally does not fail, but requires a larger computational load.

[0115] It is understandable that when step S140 filters and processes the motion information at each time point, it is necessary to identify abnormal time points. After identifying abnormal time points, the motion information at abnormal time points can be removed from the motion information at each time point to obtain the processed motion information. Based on the processed motion information and the scan data, the nuclear medicine image of the scanned object can be obtained.

[0116] In another implementation, after determining the abnormal time point, quality control can be performed on the motion information at the abnormal time point. This is done by using a second method, different from the first method used to initially determine the motion information, to determine the motion information at the abnormal time point. Steps S120-S140 are then executed again to identify whether the new motion information at the abnormal time point is still abnormal. If so, the processed motion information and scan data are used together to obtain the nuclear medicine image of the scanned object. If not, the new motion information at the abnormal time point, along with the processed motion information and scan data, are used together to obtain the nuclear medicine image of the scanned object.

[0117] More specifically, after identifying the abnormal time points, new motion information corresponding to the abnormal time points is determined using a second method. For example, if step S110 uses an external structured light camera as the first method to obtain motion information at each time point, then after identifying the abnormal time points through steps S110-S140, motion information obtained through scanned raw data can be used as the second method to re-determine the motion information at the abnormal time points, which is recorded as new motion information. Then, the motion information corresponding to the abnormal time points in the motion information at each time point is replaced with the new motion information to obtain the adjusted motion information of the scanned object. Steps S110-S140 are then executed again on the motion information at each time point in the adjusted motion information to obtain new processed motion information. If the new processed motion information contains new motion information corresponding to the abnormal time points, it indicates that the new motion information at the abnormal time points has not been identified as abnormal and discarded. Therefore, the new motion information at the abnormal time points can be retained and used in the motion correction of the nuclear medicine image. That is, the nuclear medicine image of the scanned object is obtained by combining the new motion information at the abnormal time points, the processed motion information, and the scanned raw data.

[0118] It is understandable that when filtering motion information from the perspective of time period, step S141 determines the abnormal time period corresponding to the motion information to be removed. Correspondingly, the motion information in the abnormal time period is determined by the second method and secondary quality control is performed. The principle is the same as the principle of handling abnormal time points, and will not be elaborated here.

[0119] In this embodiment, new motion information corresponding to the abnormal time point of the removed motion information is determined by using different methods, and the original motion information of the abnormal time point is replaced with the new motion information. Quality control of the motion information at the abnormal time point is performed to identify whether it is still abnormal. If it is not abnormal, the nuclear medicine image of the scanned object is obtained by combining the new motion information at the abnormal time point, the processed motion information, and the scan data. This improves the data volume and completeness of motion information while ensuring the reliability and accuracy of motion information, and avoids affecting the motion correction effect due to insufficient motion information data.

[0120] In one embodiment, to facilitate understanding of the embodiments of this application by those skilled in the art, the following description will use a patient's head as the scanned object. (Reference) Figure 4 This is a flowchart illustrating a method for generating nuclear medicine images, using PET images as an example. The method includes the following steps:

[0121] Step S410: Obtain the patient's head movement information and scan data during the PET scan using a first method; the head movement information includes movement information at multiple time points.

[0122] Step S420: Apply the head motion information to the corresponding time period of PET to obtain the centroid coordinates (MCCOD) of motion correction at each time point.

[0123] Step S430: Based on the centroid coordinates of the motion correction distribution at each time point, determine multiple time periods without motion.

[0124] Step S440: Calculate the mean value of the centroid coordinates of the motion correction distribution in each motion-free time period to obtain the mean coordinate value for each motion-free time period.

[0125] Step S450: The mean coordinates of each time period without motion are averaged again, and the average value is used as the reference centroid coordinates.

[0126] Step S460: Calculate the deviation between the mean coordinates and the reference centroid coordinates for each time period without motion, and filter out the abnormal time periods whose deviations do not meet the preset conditions from each time period without motion.

[0127] Step S470: Remove the motion information of each time point included in the abnormal time period from the patient's head motion information to obtain the processed motion information.

[0128] Step S480: Obtain the patient's new motion information during the abnormal time period through the second method, replace the motion information of the abnormal time period in the patient's head motion information with the new motion information to obtain the adjusted head motion information, and return to execute steps S420-S470 to obtain the new processed motion information.

[0129] Step S490: If the new processed motion information includes new motion information corresponding to the abnormal time period, the PET image of the patient's head is obtained by combining the new motion information at the abnormal time point, the processed motion information, and the scan data.

[0130] It should be noted that, in one implementation, signals other than the centroid of distribution (COD) can also be used as quality control tools for motion information to filter it. For example, a count rate curve can be used for motion information filtering. The count rate is the number of gamma photons detected by the detector when positrons annihilate. The count rate curve is a curve showing the change in count rate over time, reflecting the intensity of the radiation within the object being detected. The range of nuclear medicine reconstruction can be determined based on the change in count rate, and the count rate curve is updated in real time following the scan data.

[0131] In this embodiment, the motion information acquired during the scan is applied to the centroid of distribution (COD) based on nuclear medicine acquisition data to obtain the motion-corrected centroid of distribution (MCCOD). Abnormal or erroneous motion information will cause significant deviations, jumps, or other discontinuous time segments on the MCCOD. These time segments are identified through certain signal processing methods and either discarded or recalculated, ultimately resulting in a better HMC (Head Motion Correction) result.

[0132] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0133] Based on the same inventive concept, this application also provides a nuclear medicine image generation apparatus for implementing the nuclear medicine image generation method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the nuclear medicine image generation apparatus provided below can be found in the limitations of the nuclear medicine image generation method described above, and will not be repeated here.

[0134] In one embodiment, such as Figure 5 As shown, a nuclear medicine image generation apparatus is provided, comprising:

[0135] The data acquisition module 510 is used to acquire motion information and scanned data of the scanned object at various time points during the scanning process.

[0136] The coordinate determination module 520 is used to determine the centroid coordinates of the motion correction distribution of the scanned object at each time point based on the motion information and scanned data at each time point.

[0137] The reference determination module 530 is used to determine the reference centroid coordinates based on the distribution centroid coordinates of motion correction at each time point;

[0138] The information filtering module 540 is used to filter and process the motion information at each time point based on the reference centroid coordinates to obtain the processed motion information.

[0139] The image generation module 550 is used to obtain a nuclear medicine image of the scanned object based on the processed motion information and scan data.

[0140] In one embodiment, the reference determination module 530 is further configured to divide each time point into multiple time periods; to calculate the average value of the distribution centroid coordinates of motion correction within each time period to obtain the average value of coordinates for each time period; to calculate the average value of coordinates for each time period again, and to use the obtained average value as the reference centroid coordinates.

[0141] In one embodiment, the reference determination module 530 is further configured to determine multiple motion-free time periods based on the motion-corrected centroid coordinates of each time point; and to divide the multiple motion-free time periods into multiple time periods; wherein the distance between the motion-corrected centroid coordinates of any two time points within a motion-free time period is less than a distance threshold, and the distance between the motion-corrected centroid coordinates of any two adjacent motion-free time periods is greater than the distance threshold.

[0142] In one embodiment, the information filtering module 540 is further configured to obtain the deviation of the mean coordinate of each time period relative to the reference centroid coordinate; filter out the abnormal time periods whose deviation does not meet the preset conditions from each time period; and remove the motion information contained in the abnormal time periods from the motion information of each time period to obtain the processed motion information.

[0143] In one embodiment, the information filtering module 540 is further configured to, for each time point, obtain the deviation of the distribution centroid coordinates of the motion correction at that time point relative to the reference centroid coordinates; and remove motion information from the motion information at each time point whose deviation does not meet the preset conditions to obtain the processed motion information.

[0144] In one embodiment, motion information at each time point is acquired through a first method; the device further includes a secondary screening module for determining abnormal time points corresponding to the rejected motion information; new motion information corresponding to the abnormal time points is determined through a second method, and the motion information corresponding to the abnormal time points is replaced with the new motion information to obtain the adjusted motion information of the scanned object; and a nuclear medicine image of the scanned object is obtained based on the adjusted motion information, the processed motion information, and the scanned biodata.

[0145] Each module in the aforementioned nuclear medicine image generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0146] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for generating nuclear medicine images. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0147] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0148] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0149] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0150] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0152] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0154] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating nuclear medicine images, characterized in that, The method includes: Acquire motion information and scanned data of the object at various time points during the scanning process; Based on the motion information at each time point and the scanned data, determine the centroid coordinates of the motion correction distribution of the scanned object at each time point; The reference centroid coordinates are determined based on the centroid coordinates of the motion correction distribution at each of the aforementioned time points. Based on the reference centroid coordinates, the motion information at each time point is filtered and processed to obtain the processed motion information. Based on the processed motion information and the scanned biodata, a nuclear medicine image of the scanned object is obtained.

2. The method according to claim 1, characterized in that, The step of determining the reference centroid coordinates based on the motion-corrected centroid coordinates at each of the aforementioned time points includes: Each of the aforementioned time points is divided into multiple time periods; The average value of the centroid coordinates of the motion correction distribution within each time period is calculated to obtain the average coordinate value for each time period. The average coordinates for each time period are averaged again, and the resulting average value is used as the reference centroid coordinates.

3. The method according to claim 2, characterized in that, The process of dividing each of the aforementioned time points into multiple time periods includes: Based on the centroid coordinates of the motion correction distribution at each time point, multiple motion-free time periods are determined; wherein, the distance between the centroid coordinates of the motion correction distribution at each pair of time points within each motion-free time period is less than a distance threshold, and the distance between the centroid coordinates of the motion correction distribution between two adjacent motion-free time periods is greater than the distance threshold. The multiple periods of inactivity are divided into multiple time periods.

4. The method according to claim 2, characterized in that, The step of filtering and processing the motion information at each time point based on the reference centroid coordinates to obtain processed motion information includes: Obtain the deviation of the mean coordinates of each time period relative to the reference centroid coordinates; Filter out the abnormal time periods where the deviation does not meet the preset conditions from each of the time periods; The abnormal time period contains motion information is removed from the motion information of each of the time periods to obtain the processed motion information.

5. The method according to claim 1, characterized in that, The step of filtering and processing the motion information at each time point based on the reference centroid coordinates to obtain processed motion information further includes: For each time point, the deviation of the motion correction distribution centroid coordinates at that time point relative to the reference centroid coordinates is obtained; From the motion information at each of the aforementioned time points, motion information whose deviation does not meet the preset conditions is removed to obtain the processed motion information.

6. The method according to claim 5, characterized in that, The motion information at each time point is acquired through a first method; after filtering the motion information at each time point based on the reference centroid coordinates to obtain processed motion information, the process further includes: Identify the abnormal time points corresponding to the removed motion information; The new motion information corresponding to the abnormal time point is determined by the second method, and the new motion information is used to replace the motion information corresponding to the abnormal time point to obtain the adjusted motion information of the scanned object. Based on the adjusted motion information, the processed motion information, and the scan data, a nuclear medicine image of the scanned object is obtained.

7. A nuclear medicine image generation device, characterized in that, The device includes: The data acquisition module is used to acquire motion information and scanned data of the scanned object at various time points during the scanning process. The coordinate determination module is used to determine the centroid coordinates of the motion correction distribution of the scanned object at each of the time points based on the motion information at each time point and the scanned data. The reference determination module is used to determine the reference centroid coordinates based on the motion correction distribution centroid coordinates at each of the time points. The information filtering module is used to filter the motion information at each time point according to the reference centroid coordinates to obtain the processed motion information. The image generation module is used to obtain a nuclear medicine image of the scanned object based on the processed motion information and the scanned data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the nuclear medicine image generation method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the nuclear medicine image generation method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the nuclear medicine image generation method according to any one of claims 1 to 6.

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