Multimodality imaging method, apparatus, computer storage medium, and digital pet system

Abstract

The application discloses a kind of multinuclei imaging method, device, computer storage medium and digital PET system.The multinuclei imaging method includes: obtaining several preliminary images by first image reconstruction, obtaining the nuclide activity value corresponding to preliminary image;Provide a time interval, at least one preliminary image is divided into corresponding time interval according to time stamp;At least one preliminary image is cut into slice image, provide a space region, and the slice image is divided into corresponding space region according to spatial position;The slice image located in time interval and located in space region is regarded as a same data set;According to nuclide category and the nuclide activity value, the activity proportion of each nuclide in each data set is obtained by processing the slice image in at least one data set;According to activity proportion, the final image is obtained by second reconstruction to preliminary image.The application can solve the problem that existing global multinuclei imaging method cannot obtain the spatiotemporal distribution of nuclide composition.

Description

Technical Field

[0001] This invention relates to the field of image reconstruction, and in particular to a multi-nucleoside imaging method, apparatus, computer storage medium, and digital PET system. Background Technology

[0002] In multi-nucleoside imaging scenarios such as proton and heavy ion PET dose-guided, biological-guided, clinical PET, and animal PET, multiple positron-emitting nuclides capable of generating gamma photons exist simultaneously.

[0003] For example, in proton and heavy ion therapy (PET) dose-guided scenarios, protons and heavy ions undergo nuclear reactions with the target (such as the human body) to produce... 15 O、 14 O、 13 N、 11 C and 10 Multiple positron-emitting nuclides, including C, are produced. The types and proportions of positron-emitting nuclides produced depend on the energy and type of protons and heavy ions, the elemental composition of the target, and the nuclear reaction pathways between them. Obtaining the spatiotemporal distribution of each nuclide provides richer information for the positron-emitting nuclide activity-proton-heavy ion dose conversion. Furthermore, it can be used for decay correction of multiple nuclides, eliminating the influence of nuclide decay during the Beamoff phase on the quantitative acquisition of activity at the start of the Beamoff phase, thus laying the foundation for the quantitative acquisition of proton-heavy ion doses.

[0004] In proton and heavy ion PET bio-guided scenarios, it is necessary to additionally inject a radioactive tracer (such as FDG) into the organism before proton and heavy ion irradiation to visualize the tumor area; during and after irradiation, the tumor is imaged to guide the proton and heavy ion equipment to precisely irradiate the tumor area. The biggest challenge of proton and heavy ion PET bio-guided irradiation lies in the interaction between protons and heavy ions and the body's own processes. 14 O、 13 N、 11 C and 10 Multiple positron-emitting nuclides, such as C, act as noise, interfering with the imaging of radioactive tracers that serve as signals, leading to unreliable tumor imaging. Obtaining the spatiotemporal distribution of each nuclide can be used to remove noise from other nuclides in radioactive tracer imaging, thereby obtaining accurate tumor imaging results.

[0005] In multi-nuclide imaging scenarios such as clinical PET and animal PET, multiple radiotracers (corresponding to different nuclides) are often used to image the target, comprehensively acquiring information on glucose metabolism, oxygen metabolism, and the immune microenvironment to make a comprehensive diagnosis or study. Because radiotracers can interfere with each other, current techniques often involve injecting one radiotracer and imaging, then waiting for it to completely decay (usually taking about a day) before injecting the next, and so on. However, this method is not only time-consuming and labor-intensive, but also cannot simultaneously observe multiple radiotracers, thus failing to capture the interactions between glucose metabolism, oxygen metabolism, and the immune microenvironment. Obtaining the spatiotemporal distribution of each nuclide can significantly reduce the time and resources required for multi-nuclide imaging and provide a means to understand the interactions between glucose metabolism, oxygen metabolism, and the immune microenvironment.

[0006] In summary, to obtain the spatiotemporal distribution of individual nuclides, multi-nucleoside activity separation is necessary, which is of significant value in preclinical and clinical PET. Currently, multi-nucleoside activity separation can be broadly categorized into three approaches: utilizing transient gamma energy characteristics, utilizing tracer kinetic models (TKM), and utilizing nuclide half-life characteristics. Among these, the method utilizing transient gamma energy characteristics requires the nuclide to possess the property of generating transient gamma rays, while common methods... 18 F, 15 Nuclides such as O do not possess this property, limiting the applicability of this method. Furthermore, this method requires three-photon coincidence of the transient gamma and positron gamma, but the success rate of coincidence is relatively low, resulting in few effective events detected, poor signal-to-noise ratio, and high requirements for system sensitivity and temporal resolution. The TKM method relies on highly complex, uncertain, and insufficiently studied tracer kinetic models, as well as AI training, requiring a large amount of labeled training data (such labeling is currently technically unavailable) and expensive training server time, making it less feasible and costly. Methods utilizing nuclide half-life characteristics require fitting the time spectrum of coincidence counts obtained across all times and spaces, only obtaining a globally spatiotemporally averaged nuclide activity composition, and not the nuclide activity composition specific to a particular space and time. However, in real-world scenarios, due to the spatiotemporal inhomogeneities of proton energy, human elemental composition, and nuclide bio-blowout, the actual nuclide activity composition is also spatiotemporally inhomogeneous.

[0007] Therefore, the existing methods for separating the activities of multiple nuclides all have some problems, and a new method is urgently needed to improve the performance of separating the activities of multiple nuclides. Summary of the Invention

[0008] Therefore, it is necessary to provide a multi-nucleoside imaging method and a digital PET system to address at least one technical problem existing in traditional solutions.

[0009] According to a first aspect of this application, a multi-nucleoside imaging method is provided, comprising: obtaining a plurality of preliminary images through a first image reconstruction, and acquiring nuclide activity values ​​corresponding to the preliminary images; providing... The preliminary images are divided into time intervals according to timestamps, with at least a portion of the preliminary images assigned to corresponding time intervals; the preliminary images are then sliced ​​into slice images to provide... The sliced ​​image is divided into corresponding spatial regions according to its spatial location; the sliced ​​image located in... time interval and located in Sliced ​​images of spatial regions were collected as a single dataset, and aggregated to obtain... A dataset, , Based on the nuclide category and the nuclide activity value, the slice images in at least a portion of the dataset are processed to obtain the activity ratio of several nuclides in each dataset; the preliminary image is reconstructed a second time based on the activity ratio to obtain the final image.

[0010] According to one embodiment of this application, the multi-nucleoside imaging method further includes: acquiring the nuclide activity value of each pixel in a preliminary image; acquiring the sum of nuclide activities of pixels in at least a portion of the dataset; and obtaining the activity of different nuclides on slice images in at least a portion of the dataset based on the activity ratio and the sum of nuclide activities.

[0011] According to one embodiment of this application, the nuclide category is obtained through prior information or mass spectrometry.

[0012] According to one embodiment of this application, before obtaining several preliminary images through first image reconstruction, the multi-nucleoside imaging method further includes: determining the beamoff start time and obtaining coincidence events after the beamoff start time, and performing first image reconstruction using the coincidence events after the beamoff start time to obtain the preliminary images.

[0013] According to one embodiment of this application, dividing at least a portion of preliminary images into corresponding time intervals according to timestamps includes: selecting preliminary images corresponding to a specific time period, or selecting all preliminary images and dividing the selected preliminary images into corresponding time intervals according to timestamps.

[0014] According to one embodiment of this application, in providing Before dividing at least a portion of the preliminary images into corresponding time intervals according to timestamps, the multi-nucleus imaging method further includes: selecting a preset number of nuclides or nuclides with an activity greater than a preset value for subsequent processing based on the activity of all global nuclides.

[0015] According to one embodiment of this application, obtaining the activity of all global nuclides includes: obtaining the activity of all global nuclides through a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0016] According to one embodiment of this application, obtaining the activity of all global nuclides includes: obtaining all coincidence events used to reconstruct several preliminary images; plotting a time spectrum of all coincidence events; and using the following formula to perform multi-exponential fitting on the time spectrum to obtain the activity of all global nuclides:

[0017] , , ,

[0018] in, Indicates the measured time The sum of the activities of all nuclides at that time. , Represents nuclide half-life, Indicates the baseline term. Represents the nuclide obtained from the fitting. The activity level.

[0019] According to one embodiment of this application, The time intervals may be the same or not exactly the same size.

[0020] According to one embodiment of this application, The spatial regions may be the same size or not exactly the same size.

[0021] According to one embodiment of this application, the shape of the spatial region is a cube, cuboid, polygon, or irregular shape.

[0022] According to one embodiment of this application, processing slice images in at least a portion of a dataset to obtain the activity percentage of several nuclides in each dataset based on the nuclide category and the nuclide activity value includes: processing slice images in at least a portion of a dataset using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method to obtain the activity percentage of several nuclides based on the nuclide category and the nuclide activity value.

[0023] According to one embodiment of this application, the activity of each nuclide in each dataset is obtained by multi-exponential fitting using the following formula:

[0024] , , ,

[0025] in, Indicates time When located time interval and located in The sum of the activities of all nuclides within the space region. , Represents nuclide half-life, Indicates the baseline term. Indicates time When located time interval and located in Nuclide obtained by fitting within a spatial region The activity level.

[0026] According to one embodiment of this application, the activity percentage of each nuclide is obtained using the following formula:

[0027] ,

[0028] in, Indicates that nuclide f in time When located time interval and located in The percentage of activity within a spatial region.

[0029] According to a second aspect of this application, a multi-nucleoside imaging device is provided, comprising: a first reconstruction module configured to reconstruct a plurality of preliminary images; an activity acquisition module configured to acquire nuclide activity values ​​corresponding to the preliminary images; and a time interval division module configured to provide... A time interval is defined, and at least a portion of the preliminary images are divided into corresponding time intervals according to timestamps; a spatial region division module is configured to cut the at least a portion of the preliminary images into slice images, providing... The image slices are divided into spatial regions according to their spatial location; the dataset module is configured to... Time range and in Sliced ​​images of spatial regions were collected as a single dataset, and aggregated to obtain... A dataset, , The nuclide separation module is configured to process slice images in at least a portion of the dataset according to the nuclide category and the nuclide activity value to obtain the activity ratio of several nuclides in each dataset; the second reconstruction module is configured to perform a second reconstruction on the preliminary image according to the activity ratio to obtain the final image.

[0030] According to one embodiment of this application, the activity acquisition module is configured to acquire the nuclide activity value of each pixel in a preliminary image, and to acquire the sum of the nuclide activities of the pixels in the at least part of the dataset; the second reconstruction module is configured to obtain the activity of different nuclides on each slice image in the at least part of the dataset based on the activity ratio and the sum of the nuclide activities.

[0031] According to one embodiment of this application, the multi-nucleoside imaging device further includes a nuclide category acquisition module, which is configured to obtain the nuclide category corresponding to the preliminary image based on prior information or a mass spectrometer.

[0032] According to one embodiment of this application, the multi-nucleoside imaging device further includes a data acquisition module configured to determine the beamoff start time and acquire coincidence events after the beamoff start time, wherein the first reconstruction module is configured to perform image reconstruction using coincidence events after the beamoff start time to obtain the preliminary image.

[0033] According to one embodiment of this application, the time interval division module is configured to select a preliminary image corresponding to a specific time period, or select all preliminary images and divide the selected preliminary images into corresponding time intervals according to the timestamps.

[0034] According to one embodiment of this application, the multi-nucleoside imaging device further includes a screening module configured to select a preset number of nuclides or nuclides with an activity greater than a preset value for subsequent processing based on the activity of all nuclides globally.

[0035] According to one embodiment of this application, the screening module is configured to obtain the activity of all global nuclides by means of a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0036] According to one embodiment of this application, the screening module is configured to: acquire all matching events for reconstructing several preliminary images; plot a time spectrum of all matching events; and perform multi-exponential fitting on the time spectrum using the following formula to obtain the activity of all global nuclides:

[0037] , , ,

[0038] in, Indicates the measured time The sum of the activities of all nuclides at that time. , Represents nuclide half-life, Indicates the baseline term. Represents the nuclide obtained from the fitting. The activity level.

[0039] According to one embodiment of this application, The time intervals may be the same or not exactly the same size.

[0040] According to one embodiment of this application, The spatial regions may be the same size or not exactly the same size.

[0041] According to one embodiment of this application, the shape of the spatial region is a cube, cuboid, polygon, or irregular shape.

[0042] According to one embodiment of this application, the nuclide separation module is configured to: process slice images in at least a portion of the dataset to obtain the activity percentage of several nuclides based on the nuclide category and the nuclide activity value using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0043] According to one embodiment of this application, the nuclide separation module is configured to: perform multi-exponential fitting on slice images in at least a portion of the dataset to obtain the activity of several nuclides in each dataset using the following formula:

[0044] , , ,

[0045] in, Indicates time When located time interval and located in The sum of the activities of all nuclides within the space region. , Represents nuclide half-life, Indicates the baseline term. Indicates time When located time interval and located in Nuclide obtained by fitting within a spatial region The activity level.

[0046] According to one embodiment of this application, the nuclide separation module is configured to obtain the activity percentage of each nuclide using the following formula:

[0047] ,

[0048] in, Indicates that nuclide f in time When located time interval and located in The percentage of activity within a spatial region.

[0049] According to a third aspect of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the steps of the multi-nucleoside imaging method as described above.

[0050] According to a fourth aspect of this application, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the steps of the multi-nucleoside imaging method as described above.

[0051] According to a fifth aspect of this application, a digital PET system is provided, including the multi-nucleoside imaging device as described above.

[0052] The multi-nucleoside imaging method, apparatus, computer storage medium, and digital PET system provided in this application perform nuclide separation after temporal and spatial segmentation of the preliminary image, and then reconstruct the final image based on the results of the nuclide separation. By segmenting the preliminary image temporally and spatially, the problem of not being able to obtain the spatiotemporal distribution of nuclide composition in existing technologies is solved, thus broadening the application scenarios of this type of method. Compared with methods utilizing the characteristics of transient gamma energy, this application does not require the nuclide to have the property of generating transient gamma, therefore it is applicable to… 18 F, 15 Common nuclides such as O are applicable to a wider range of situations. It eliminates the need for three-photon coincidence of the transient gamma and positron gamma, resulting in a high success rate and thus detecting more effective events with a better signal-to-noise ratio. It also lowers the requirements for system sensitivity and temporal resolution. Compared to methods utilizing tracer kinetic models, this application does not rely on highly complex, uncertain, or insufficiently studied tracer kinetic models, nor does it require AI training. Therefore, it eliminates the need for large amounts of labeled training data and expensive training server time, making it more feasible and less costly. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a multi-nucleoside imaging method in one embodiment of this application;

[0055] Figure 2 This is a schematic diagram illustrating a conforming event in one embodiment of this application;

[0056] Figure 3This is a flowchart illustrating the multi-nucleoside imaging method in one embodiment of this application;

[0057] Figure 4 This is a histogram of events conforming to one embodiment of this application;

[0058] Figure 5 This is a schematic diagram of the structure of a multi-nucleoside imaging device in one embodiment of this application;

[0059] Figure 6 This is a schematic diagram of the structure of the multi-nucleoside activity separation system in a multi-nucleoside imaging device according to one embodiment of this application;

[0060] Figure 7 This is an internal structural diagram of a computer device in one embodiment of this application. Detailed Implementation

[0061] To make the above-mentioned objectives, features, and advantages of this application more readily understood, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0062] It should be noted that when an element is said to be "fixed to" another element, it can be directly fixed to the other element or there may be an intervening element. When an element is said to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "substantially equal" or "substantially equal to" as used herein mean that the difference between the two lies within a range of errors considered equivalent in the art. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The terms “and / or” or “and / or” as used herein include any and all combinations of one or more of the associated listed items.

[0064] In proton-related applications, multi-nucleoside activity separation is required to obtain the spatiotemporal distribution of each nuclide, which is of great value for PET clinical and preclinical work. Current technologies for achieving multi-nucleoside activity separation can be broadly categorized into three approaches: utilizing transient gamma energy characteristics, utilizing tracer kinetic models (TKM), and utilizing nuclide half-life characteristics.

[0065] 1) Utilizing the characteristics of transient gamma energy: Some nuclides produce transient gamma photons during positron decay. For example, 44 Sc generates a pair of 511 keV annihilated gamma photons and a 1157 keV transient gamma photon. In principle, for PET, the annihilated gamma photon pair and the transient gamma photon can be paired using time matching, thus determining the nuclide from which the annihilated gamma photon pair originates based on the energy of the transient gamma photon. For multi-nucleus imaging, especially in scenarios where the nuclides possess transient gamma, it is possible to achieve activity separation of multiple nuclides based on the energy of the transient gamma. However, this method requires the nuclide to possess the property of generating transient gamma, which is not commonly found in common nuclides. 18 F, 15 Nuclides such as O do not possess this property, limiting the applicability of this method. On the other hand, this method requires three-photon coincidence of the transient gamma and the positron gamma, which has a low success rate, results in few effective events detected, poor signal-to-noise ratio, and high requirements for system sensitivity and time resolution, making it difficult to implement.

[0066] 2) Using TKM: Using TKM combined with AI to achieve multi-nucleoside activity separation. This method requires not only a highly complex, uncertain, and insufficiently studied tracer kinetic model, but also AI training, which requires a large amount of labeled training data (such labels are currently not technically available) and expensive training server time, making it less feasible and costly.

[0067] 3) Utilizing the half-life characteristics of nuclides: Different nuclides have different half-lives, for example, 18 The half-life of F is 109.8 minutes. 15 The time is 2.04 minutes for O. The activity of a single nuclide as a function of time is:

[0068] ,

[0069] in, The activity at time t is usually expressed in becquerels (Bq) or curies (Ci). Indicates initial activity. Represents the decay constant, in units of s. -1 With half-life The relationship is .

[0070] The activity of multiple nuclides as a function of time is:

[0071] ,

[0072] Where i represents the type of nuclide.

[0073] In PET imaging, the coincidence count time spectrum reflects the change in activity over time. By fitting the coincidence count time spectrum using the model described above, the model parameter A can be obtained. i This allows for the separation of multiple nuclide activities.

[0074] However, the aforementioned method for separating the activities of multiple nuclides using the half-life characteristics of nuclides fits the data using the time spectrum of coincidence counts obtained across all time and space. Therefore, it can only obtain the globally spatiotemporally averaged nuclide activity composition, and cannot obtain the nuclide activity composition at a specific space and time. In real-world scenarios, due to the spatiotemporal inhomogeneities of proton energy, the elemental composition of the measured object, and the biological scouring of nuclides, the actual nuclide activity composition is spatiotemporally non-uniform, and the accuracy of the results obtained by the above method cannot meet higher requirements.

[0075] In view of the technical problems existing in the prior art, this application proposes a multi-nuclide imaging method, device and supporting application that can at least separate the activities of multiple nuclides.

[0076] In some embodiments, the multi-nuclide imaging method can be executed by a multi-nuclide imaging device. For example, the multi-nuclide imaging method can be partially or wholly stored in a storage device (such as the built-in storage module of the detection device or an external storage device) in the form of a program or instructions. When the program or instructions are executed, for example, through artificial intelligence, the multi-nuclide imaging method can be implemented. The multi-nuclide imaging device disclosed in this application for implementing the above-described multi-nuclide imaging method can be a device with a large amount of computing resources (e.g., a computer, server, cloud computing, etc.) or a device with limited computing resources (e.g., FPGA (Field Programmable Gate Array) chip board, ASIC (Application-Specific Integrated Circuit) chip board, and other hardware circuits).

[0077] The following description, with reference to the accompanying drawings, illustrates some preferred embodiments of the present application. It should be noted that the following description is for illustrative purposes only and is not intended to limit the scope of protection of this application.

[0078] Figure 1 This is a flowchart illustrating a multi-nuclide imaging method in one embodiment of the present application. In one embodiment, the multi-nuclide imaging method may include the following steps S100~S700.

[0079] S100: Several preliminary images are obtained through the first image reconstruction, and the nuclide activity values ​​corresponding to the preliminary images are obtained.

[0080] The information required for the first image reconstruction is generally the coincidence events. Existing algorithms are used to reconstruct the preliminary image from the coincidence events. Coincidence events can be acquired by a PET system, which can be clinical PET, animal PET, or proton and heavy ion PET. They originate from scenarios where multiple radionuclides coexist, such as proton therapy, heavy ion therapy, or multi-tracer PET. The event data can be acquired in the beam or after treatment (also known as beam off).

[0081] It is important to note that the all-digital PET system can accurately extract the energy, location, and time information of a single event. The coincidence system then uses energy window screening, location exclusion, and time window filtering to determine coincidence events and obtain projection data. Typically, within a time window, if two pulses with energies both within the energy window range are detected, and the line connecting the crystal bars on the detectors capturing these two pulses crosses the field of view (FOV), these two pulses are recorded as a coincidence event. An example of a coincidence event is shown below. Figure 2 As shown, the outer ring is the detector ring, the inner ellipse is the test body, the straight line with a double-headed arrow is the response line, the arrow indicates the direction of photon flight, and the solid dot indicates the location where the nuclide annihilation event occurs.

[0082] Generally, a first image reconstruction is performed on some or all of the collected matching events to obtain... Each preliminary frame contains a frame of images. The nth pixel is recorded as the nth pixel. The timestamp of the initial frame image is pixel The coordinates are Pixel values ​​can be obtained using known methods. Since pixel values ​​are positively correlated with nuclide activity values, nuclide activity values ​​can be derived from pixel values. Step S100 includes obtaining the nuclide activity values ​​of at least a portion of the preliminary images, that is, obtaining the nuclide activity values ​​of at least a portion of the preliminary frames of images. It can be understood that the nuclide activity values ​​of all the pixels in the preliminary images can also be obtained as needed.

[0083] The aforementioned "at least some preliminary images" can be preliminary images corresponding to a specific time period or a specific location, or it can be all preliminary images. The specific time period or specific location can be the time period or location of interest.

[0084] Specifically, in one example of this application, the multi-nucleoside imaging method further includes step S001: determining the beamoff start time and acquiring coincidence events after the beamoff start time. Step S100 includes image reconstruction using the coincidence events after the beamoff start time to obtain the preliminary image.

[0085] Among them, regarding the beamoff start time, beamoff is a concept relative to in-beam. Taking proton therapy as an example, during the treatment process, positron nuclides are generated by bombarding the target with a proton beam, which is called in-beam. The in-beam treatment process covers the time period from the start time (beam on) to the beamoff time (beam off). The "beam" in beamoff refers to the proton beam, indicating that the treatment is over and there is no proton beam bombarding the target.

[0086] There are several ways to obtain the beam start time. One method is to manually input it into the system, but this method obviously increases the workload. To automate the acquisition of the beam start time, in one scenario, when the PET system is connected to the beam system, the PET system can directly obtain the beam start time. However, when the two are not connected, an algorithm is needed to obtain the beam start time. Specifically, to automate the acquisition of the beam start time, in one example of this application, such as... Figure 3 As shown, the multi-nucleoside imaging method also includes the following steps S011 to S012.

[0087] Step S011: Obtain the average time of the matching events, and use the average time as the base time for the corresponding matching events. Then, divide all the matching events into several bins at equal intervals according to the base time.

[0088] For example, a list is created for all N matching events, forming a list pattern set L. A statistical histogram is plotted according to the average time of each matching event, and the average time of the i-th matching event is shown. The definition is as follows:

[0089] ,

[0090] in, Let be the arrival time of the first single event in the i-th matching event. Let be the arrival time of the second single event in the i-th matching event.

[0091] Regarding bins, their size can be selected as needed. For example, if N matching events span a time period of 1 to 100 seconds, and each second is a bin, then there are 100 bins.

[0092] Step S012: Count the number of single events in each sub-box, identify all sub-boxes whose number of single events exceeds a predetermined threshold as in-bundle sub-boxes, and take the start time of the first sub-box after the last in-bundle sub-box as the de-bundle start time.

[0093] See Figure 4 The figure shows a binning statistical histogram for a proton therapy scenario.

[0094] Assuming there are n bins, the number of single events is counted for each bin as follows:

[0095] ,

[0096] in, Let j be the start time of the j-th sub-box. The number of single events in the j-th bin.

[0097] It should be noted that all box sorting times are consecutive. A single event that falls on both the start time of the j-th bin and the end time of the (j-1)-th bin is counted as a single event within the previous bin.

[0098] Assuming that the i-th to k-th sub-bins are bundled sub-bins, then the start time of the (k+1)-th sub-bin is taken as the start time of the unbundling.

[0099] Regarding the predetermined threshold, it can be customized. For example, any number between 20% and 50% of the peak number of single events in all bins can be selected as the predetermined threshold, or a number greater than the average number of single events in all bins can be selected as the predetermined threshold.

[0100] Step S200: Provide Each time interval is used to divide at least a portion of the preliminary images into the corresponding time intervals according to the timestamps.

[0101] in, The sizes of the time intervals can be the same or different, or not exactly the same. In one example, all the time intervals are the same size except for the last time interval, and the last time interval is smaller than the other time intervals.

[0102] Specifically, in one example of this application, step S200 includes:

[0103] Obtain the acquisition time interval of the at least part of the preliminary images, and divide the acquisition time interval into... A time interval.

[0104] The acquisition time interval is generally the time span of all coincidence events used to reconstruct the preliminary image. If the preliminary image is reconstructed from coincidence events acquired by off-beam acquisition, then the acquisition time interval is from the start time of off-beam acquisition to the end time of acquisition.

[0105] It should be noted that each time interval is non-intersecting, and the union of all time intervals constitutes the acquisition time or the off-beam acquisition time.

[0106] Step S300: Cut the at least a portion of the preliminary image into slice images, and provide... The sliced ​​image is divided into corresponding spatial regions according to its spatial location.

[0107] in, The spatial regions may be the same or not exactly the same size, preferably... Each spatial region is the same size. The shape of the spatial region is a cube, cuboid, polygon, or irregular shape, preferably a cube.

[0108] It is worth noting that for multiple preliminary images, each preliminary image may correspond to a timestamp. After dividing the time intervals in step S200, for each preliminary image in each time interval, spatial segmentation can continue as described in step S300. For example, for a preliminary image E that falls into the nth time interval, it corresponds to a specific timestamp. In step S300, the preliminary image E is further cut into multiple, such as 200 slice images. At this time, it can be considered that slice images with the same timestamp constitute a preliminary image. Each slice image corresponds to a spatial region, and the timestamps of these slice images are consistent with the timestamp of the preliminary image E.

[0109] Specifically, in one example of this application, step S300 includes: obtaining the image space of the at least a portion of the preliminary image, and dividing the image space into... A spatial region.

[0110] In this system, each spatial region is non-intersecting, and the union of all spatial regions constitutes the image space.

[0111] For example, the image space is divided into cubic spatial regions with each side length Q. The initial image is first cut into several square slices with side length Q, and then the slices are assigned to each spatial region according to their spatial positions. The spatial positions can be three-dimensional coordinates, and the spatial regions are divided based on three-dimensional coordinate matching.

[0112] Those skilled in the art will understand that the order of steps S200 and S300 can be interchanged. When step S200 comes first, the specific coverage of "at least a portion of the preliminary image" is determined in the time division stage. When step S300 comes first, the specific coverage of "at least a portion of the preliminary image" is determined in the spatial segmentation stage. In this case, after each preliminary image is sliced, slice images with the same timestamp can be simultaneously assigned to the same time interval in the time division stage. Unlike when the time division stage comes first, when the spatial segmentation stage comes first, since the preliminary image has already been divided into multiple slice images, the slice images are assigned to the corresponding time intervals according to their timestamps during time division.

[0113] Step S400: Place the located time interval and located in Sliced ​​images of spatial regions were collected as a single dataset, and aggregated to obtain... A dataset, , p and h should not both be equal to 1.

[0114] For example, if the time interval is divided into 10 and the spatial region into 8000, then 10 × 8000 = 80000 datasets can actually be obtained. Those skilled in the art should understand that when dividing time intervals based on timestamps, not every slice image in each time interval necessarily contains matching event information; similarly, when dividing spatial regions based on response lines, not every spatial region is crossed by a response line. Therefore, although 80000 datasets can be obtained, not every dataset contains matching event information.

[0115] Furthermore, the "slice image located in time interval p" mentioned in this step is actually a set of slice images formed after the initial image is cut in step S300. In step S400, these slice images with the same timestamp are further subdivided into different spatial regions to form multiple datasets for reprocessing.

[0116] Step S500: Based on the nuclide category and nuclide activity value, process the slice images in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset.

[0117] For example, the nuclide category corresponding to the preliminary image can be obtained directly based on prior information or mass spectrometry. The prior information is derived from previous experiments or practical applications, and the nuclide category induced by the target can be summarized based on the application scenario or the particles acting on the object.

[0118] For example, the prior information can be a table including the application scenario, particle category, and / or nuclide category, or it can be a database obtained by simulating the application scenario, particle category, and / or nuclide category in a simulation system. For instance, taking a proton therapy scenario, the main component of the positron-emitting nuclide generated by the proton-induced target is... 15 O、 11 C and 10 C. For example, in the context of heavy ion therapy, a heavy ion beam (such as carbon ions) is used... 12 C) The main components of the positron-emitting nuclides induced by the target are: 12 C 16 O and 14 N.

[0119] For multi-nuclide imaging scenarios in clinical and animal PET, the types of radioactive tracers injected are known (e.g., it is known that FDG and... are injected simultaneously). 15 O-H2O). By looking up the molecular formula of a radioactive tracer, one can determine the type of nuclide it corresponds to (e.g., the nuclide corresponding to FDG is...). 18 F, 15 O-H2O corresponds to 15 These nuclides are numbered 1 to M, with half-lives of respectively... , .

[0120] For proton and heavy ion therapy (PET) dose-guided scenarios, the elemental composition of the human body is known, and in order of abundance: O, C, H, N, Ca, P, S, K, Na, Cl, Mg, etc. Therefore, a target with the same elemental composition as the human body can be set in particle physics simulation software such as Geant4, and bombardment can be simulated using proton or heavy ion beams with energies of 70 MeV to 240 MeV and their corresponding clinical energy ranges. The composition of newly generated nuclides can be obtained from the simulation software; these nuclides that emit positrons are numbered 1 to N in order of abundance, with half-lives of [missing information]. .

[0121] For proton and heavy ion therapy (PET) bioguided scenarios, in addition to the proton and heavy ion therapy (PET) dose-guided scenarios, the procedure involves injecting radionuclides corresponding to the radiotracers into the human body before proton and heavy ion irradiation. These radionuclides are numbered 1 to V, with half-lives of [missing information]. .

[0122] Furthermore, in proton and heavy ion scalpel PET dose-guided and proton and heavy ion scalpel PET bio-guided scenarios, it is necessary to first screen the top F nuclides with the highest activity to reduce the degrees of freedom during subsequent spatiotemporal nuclide separation. Specifically, the multi-nucleus imaging method also includes: acquiring the activity of all nuclides globally, and selecting a preset number of nuclides or nuclides with activities greater than a preset value for subsequent processing. In another example, F nuclides can also be selected for subsequent processing based on the nuclides of interest or application requirements.

[0123] Furthermore, in some examples, the activities of all global nuclides are obtained, including by using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0124] Furthermore, in other examples, the activities of all global nuclides are obtained, including by using gradient descent or the Levenberg-Marquardt algorithm.

[0125] The main steps of the exponential stripping method include: 1. Obtaining all coincidence events for reconstructing several preliminary images; 2. Processing all coincidence events to obtain the total activity value of all nuclides, and plotting a semi-logarithmic coordinate system on the total activity value, with time on the horizontal axis and the logarithm of activity on the vertical axis; 3. At the end of the coordinate system, when the short-lived nuclide has basically decayed, its curve approaches a straight line. The slope of the straight line is proportional to the decay constant of the nuclide with the longest half-life. Extrapolating this straight line to zero time yields the intercept, which is the initial activity of the nuclide; 4. Subtracting the contribution of the longest-half-life nuclide from the original data to obtain a new residual curve. Repeating steps 2 and 3 on the new residual curve to fit the second longest-half-life nuclide until all nuclides are separated, thus completing the acquisition of the activity of all global nuclides.

[0126] The main steps of the matrix solution method include: 1. Obtaining all coincidence events used to reconstruct several preliminary images; 2. Dividing all coincidence events into several time points or intervals at equal intervals, and obtaining the total nuclide activity value at each time point or interval; 3. Establishing the matrix equation Ea=A, where E is an exponential matrix whose elements are determined by the known decay constant and time point or interval, a is a vector representing the unknown initial activity of the nuclide, and A is a vector representing the measured activity value; 4. Solving the matrix equation using the least squares method to obtain the activity of all nuclides globally.

[0127] The method employs a multi-exponential fitting approach to obtain nuclide activity. The multi-nuclide imaging method further includes: acquiring all coincidence events used to reconstruct several preliminary images; plotting time spectra for all coincidence events; and using the following formula to perform multi-exponential fitting on the time spectra to obtain the global activity of all nuclides:

[0128] , , ,

[0129] in, Indicates the measured time The activity of all nuclides at that time, , Represents nuclide half-life, Indicates the baseline term. Represents the nuclide obtained from the fitting. The activity of nuclide f is such that a and c are constants when the half-life of nuclide f is determined.

[0130] After obtaining the activities of all nuclides, the nuclides are sorted from highest to lowest activity. The top F nuclides, or the F nuclides with activities greater than a preset value, are selected for further processing. These nuclides are numbered 1 to F, with half-lives of [missing information]. .

[0131] In both proton and heavy ion knife PET dose-guided and proton and heavy ion knife PET bio-guided scenarios, nuclides can be selected according to actual needs, and the number of nuclides selected in the two scenarios may differ.

[0132] Furthermore, in some examples, step S500 includes: processing slice images in at least a portion of the dataset using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method to obtain the activity percentage of several nuclides in each dataset, based on the nuclide category and nuclide activity value.

[0133] Furthermore, in other examples, step S500 includes: processing slice images in at least a portion of the dataset using gradient descent or the Levenberg-Marquardt algorithm to obtain the activity percentage of several nuclides in each dataset, based on the nuclide category and nuclide activity value.

[0134] It should be noted that when the nuclide activity values ​​of each pixel are known, the sum of the nuclide activities can be obtained by summing them. Generally, there is usually only one image within a timestamp. Assume the... The timestamp of the initial frame image is , Located in the Within a certain time interval. In the first... Time interval, the first spatial regions The timestamp is The initial image, actually the first In the spatial region of the frame image A slice image within. By summing, we can obtain the... Within a certain time interval, the first spatial regions Internal time The sum of the nuclide activities of each pixel is , Let j represent a pixel, and let Q be the coordinates of pixel j. The expanded form of Q is... It is understandable. In reality, it is the sum of the nuclide activities of all pixels in a slice of image, in the spatial region. Inside, traversing the first The sum of the nuclide activities of each slice image in a dataset can be obtained by summing all the timestamps within a time interval.

[0135] More specifically, step S500 includes processing at least a portion of the slice images in the dataset using an exponential peeling method to obtain the activity of each nuclide in each dataset, and obtaining the activity percentage of each nuclide based on the ratio of the activity of each nuclide to the activities of all nuclides. Exemplarily, the exponential peeling method includes the following steps: 1. Processing the obtained slice images... Within a certain time interval, the first spatial regions Internal time 1. Plot a semi-logarithmic coordinate system by summing the nuclide activities of each pixel, with time on the horizontal axis and the logarithm of activity on the vertical axis; 2. At the end of the coordinate system, when the shortest half-life nuclide has basically decayed, its curve approaches a straight line. The slope of the straight line is proportional to the decay constant of the longest half-life nuclide. Extrapolate this line to zero time to obtain the intercept, which is the initial activity of the nuclide; 3. Subtract the contribution of the longest half-life nuclide from the original data to obtain a new residual curve. Repeat steps 1 and 2 on the new residual curve to fit the second longest half-life nuclide until all nuclides are separated, thus completing the acquisition of the activity of each nuclide.

[0136] More specifically, step S500 includes processing the slice images in at least a portion of the dataset using a matrix solving method to obtain the activity of each nuclide in each dataset, and obtaining the activity percentage of each nuclide based on the ratio of the activity of each nuclide to the activities of all nuclides. For example, the matrix solving method includes the following steps: 1. Processing the obtained slice images... Within a certain time interval, the first spatial regions Internal time 1. Establish a matrix equation Ea=A based on the sum of the nuclide activities of each pixel, where E is an exponential matrix whose elements are determined by the known decay constant and time points or intervals, a is a vector representing the unknown initial activity of the nuclide, and A is a vector representing the measured activity value; 2. Solve the matrix equation using the least squares method to obtain the activity of each nuclide.

[0137] More specifically, step S500 further includes obtaining the activity of each nuclide in each dataset by performing a multi-exponential fitting using the following formula:

[0138] , , ,

[0139] in, Indicates time When located time interval and located in The sum of nuclide activities of all pixels within the spatial region. , Represents nuclide half-life, Indicates the baseline term. It can be 0 or any other arbitrary constant. Indicates time When located time interval and located in Nuclide obtained by fitting within a spatial region activity, For the result to be fitted, a and c are constants when the half-life of nuclide f is determined.

[0140] Furthermore, for all After normalization, the activity percentage of each nuclide is obtained:

[0141] ,

[0142] in, Indicates that nuclide f in time When located time interval and located in The percentage of activity within a spatial region.

[0143] According to one embodiment, step S500 further includes: performing multi-exponential fitting, exponential stripping, or matrix solving on the slice images in all datasets according to the nuclide category and nuclide activity value to obtain the activity ratio of several nuclides in each dataset.

[0144] Specifically, the activity of each nuclide in each dataset is obtained by performing multi-exponential fitting on all preliminary images divided into the p-th time interval and the h-th spatial interval. The methods of multi-exponential fitting, exponential stripping, or matrix solving are the same as those in the above examples and will not be repeated here. It should be noted that the only difference between this embodiment and the above embodiments is the number of datasets selected. Since digital PET acquires a huge amount of data, this may have different significant effects in practical applications. For example, for some application scenarios, it may only be necessary to perform secondary reconstruction on preliminary images of a specific region of interest, or on preliminary images of a specific number of nuclides. Therefore, selecting several datasets that meet the requirements can achieve the relevant objectives, which can significantly improve data processing efficiency and shorten detection time while accurately quantifying and qualitatively analyzing data, which is of great value for clinical applications. It should also be noted that for some research needs or special application scenarios, it may be necessary to select all datasets and all types of nuclides for secondary image reconstruction to obtain the correlation and influence relationship between different types of nuclides. Those skilled in the art can select the number of datasets and types of nuclides as needed based on the inspiration of this application, which will not be repeated here.

[0145] Step S600: Perform a second reconstruction on the preliminary image based on the activity ratio to obtain the final image.

[0146] In step S600, the coordinates of each element on the preliminary image i are first obtained. The activity value of the nuclide Based on the nuclide activity percentage obtained in step S500 ,but At any time at coordinates ( The activity of nuclide f in ) is , ,in Belonging to the p-th time interval, coordinates ( If the image belongs to the h-th spatial region, perform the same operation on other coordinate points of the preliminary image i, and perform the same operation on other preliminary images as on the preliminary image i. After obtaining the activity of each nuclide, reconstruct all preliminary images according to the activity of each nuclide to obtain the final image.

[0147] The multi-nucleus imaging method provided in this application, by performing temporal and spatial segmentation of the image to distinguish the contributions of various nuclides in each time interval and spatial region, and performing secondary reconstruction of the image, solves the problem that current global multi-nucleus imaging methods based on nuclide half-life characteristics cannot obtain the spatiotemporal distribution of nuclide composition. This improves the performance of multi-nucleus activity separation and broadens the application prospects of this type of method. Compared with methods utilizing transient gamma energy characteristics, this application does not require nuclides to have the property of generating transient gamma, therefore it is applicable to… 18 F,15 Common nuclides such as O are applicable to a wider range of situations. It eliminates the need for three-photon coincidence of the transient gamma and positron gamma, resulting in a high success rate and thus more effective events detected. It also offers a better signal-to-noise ratio and lower requirements for system sensitivity and temporal resolution. Compared to methods utilizing tracer kinetic models, this application does not rely on highly complex, uncertain, or insufficiently studied tracer kinetic models, nor does it require AI training. Therefore, it eliminates the need for large amounts of labeled training data and expensive training server time, making it more feasible and less costly.

[0148] Based on the description of the above embodiments of the multi-nuclide imaging method, this application also provides a multi-nuclide imaging device. The device may include an apparatus (including a distributed system), software (application), module, component, server, client, etc., using the method described in the embodiments of this specification, combined with necessary hardware implementation. Based on the same innovative concept, the apparatus in one or more embodiments provided in this application are as described in the following embodiments. Since the implementation schemes and methods for solving the problem by the apparatus are similar, the implementation of the specific apparatus in the embodiments of this specification can refer to the implementation of the foregoing method, and repeated details will not be repeated. As used below, the terms "module" or "module group" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible.

[0149] Figure 5 This is a schematic diagram of the structure of a multi-nucleoside imaging device in one embodiment of the present application. In one embodiment, the multi-nucleoside imaging device 500 may include a first reconstruction module 510, an activity acquisition module 520, a time interval division module 530, a spatial region division module 540, a dataset module 550, a nuclide separation module 560, and a second reconstruction module 570.

[0150] Specifically, the first reconstruction module 510 is configured to reconstruct several preliminary images.

[0151] The information required for the first image reconstruction is generally the coincidence events. Existing algorithms are used to reconstruct the preliminary image from the coincidence events. Coincidence events can be acquired by a PET system, which can be clinical PET, animal PET, or proton and heavy ion PET. They originate from scenarios where multiple radionuclides coexist, such as proton therapy, heavy ion therapy, or multi-tracer PET. The event data can be acquired in the beam or after treatment (also known as beam off).

[0152] It is important to note that the all-digital PET system can accurately extract the energy, location, and time information of a single event. The coincidence system then uses energy window screening, location exclusion, and time window filtering to determine coincidence events and obtain projection data. Typically, within a time window, if two pulses with energies both within the energy window range are detected, and the line connecting the crystal bars on the detectors capturing these two pulses crosses the field of view (FOV), these two pulses are recorded as a coincidence event. An example of a coincidence event is shown below. Figure 2 As shown, the outer ring is the detector ring, the inner ellipse is the test body, the straight line with a double-headed arrow is the response line, the arrow indicates the direction of photon flight, and the solid dot indicates the location where the nuclide annihilation event occurs.

[0153] Generally, a first image reconstruction is performed on all collected matching events to obtain... Each preliminary frame contains a frame of images. The nth pixel is denoted as the nth pixel. The timestamp of the initial image frame is pixel The coordinates are , Pixel values ​​can be obtained using known methods. Since pixel values ​​are positively correlated with the activity values ​​of nuclides, the activity values ​​of nuclides can be obtained from the pixel values. .

[0154] The activity acquisition module 520 is configured to acquire the nuclide activity values ​​of at least a portion of the preliminary image pixels, that is, to acquire the nuclide activity values ​​of all pixels of at least a portion of the preliminary image frames. It can be understood that the nuclide activity values ​​of all pixels of the preliminary image can also be acquired as needed.

[0155] Specifically, the activity acquisition module 520 can also be configured to acquire the activity of all global nuclides. Further, in some examples, the activity acquisition module 520 is configured to acquire the activity of all global nuclides using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0156] Furthermore, in other examples, the activity acquisition module 520 is configured to acquire the activity of all global nuclides using either gradient descent or the Levenberg-Marquardt algorithm.

[0157] Furthermore, the screening module is configured to obtain the activity of all global nuclides using an exponential stripping method. The main steps include: 1. Obtaining all coincidence events used to reconstruct several preliminary images; 2. Processing all coincidence events to obtain the total activity value of all nuclides, and plotting a semi-logarithmic coordinate system on the total activity value, with time on the horizontal axis and the logarithm of activity on the vertical axis; 3. At the end of the coordinate system, when the short-lived nuclide has basically decayed, its curve approaches a straight line. The slope of the straight line is proportional to the decay constant of the nuclide with the longest half-life. Extrapolating this straight line to zero time yields the intercept, which is the initial activity of the nuclide; 4. Subtracting the contribution of the longest-half-life nuclide from the original data to obtain a new residual curve. Repeating steps 2 and 3 on the new residual curve to fit the second longest-half-life nuclide until all nuclides are separated, thus completing the acquisition of the activity of all global nuclides.

[0158] Furthermore, the activity acquisition module 520 is configured to acquire the activity of all global nuclides using a matrix solution method. The main steps include: 1. Acquiring all coincidence events used to reconstruct several preliminary images; 2. Dividing all coincidence events into several time points or intervals at equal intervals, and acquiring the total nuclide activity value at each time point or interval; 3. Establishing a matrix equation Ea=A, where E is an exponential matrix whose elements are determined by the known decay constant and time point or interval, a is a vector representing the unknown initial activity of the nuclide, and A is a vector representing the measured activity value; 4. Solving the matrix equation using the least squares method to obtain the activity of all global nuclides.

[0159] More specifically, the activity acquisition module 520 is configured to: perform nuclide activity using a multi-exponential fitting method, the main steps of which include: 1. acquiring all coincidence events used to reconstruct several preliminary images, and plotting time spectra for all coincidence events; 2. using the following formula to perform multi-exponential fitting on the time spectra to obtain the activity of all global nuclides:

[0160] , , ,

[0161] in, Indicates the measured time The activity of all nuclides at that time, , Represents nuclide half-life, Indicates the baseline term. Represents the nuclide obtained from the fitting. The activity of nuclide f is such that a and c are constants when the half-life of nuclide f is determined.

[0162] The aforementioned "at least some preliminary images" can be preliminary images corresponding to a specific time period or a specific location, or it can be all preliminary images. The specific time period or specific location can be the time period or location of interest.

[0163] Specifically, in one example of this application, the multi-nucleoside imaging device further includes a data acquisition module configured to determine the beamoff start time and acquire coincidence events after the beamoff start time, and the first reconstruction module 510 is configured to perform image reconstruction using coincidence events after the beamoff start time to obtain the preliminary image.

[0164] Among them, regarding the beam off start time, beam off is a concept relative to in beam. Taking proton therapy as an example, during the treatment process, positron nuclides are generated by bombarding the target with a proton beam, which is called in beam. The in beam treatment process covers the time period from the start time (beam on) to the beam off time. The "beam" in beam off refers to the proton beam, indicating that the treatment is over and there is no proton beam bombarding the target.

[0165] There are various methods for obtaining the beam departure start time. One method involves manual input into the system, but this method significantly increases the workload. To automate the acquisition of the beam departure start time, in one scenario, when the PET system is connected to the beam system, the PET system can directly obtain the beam departure start time. However, when the two are not connected, an algorithm is needed to obtain the beam departure start time. Specifically, to automate the acquisition of the beam departure start time, in one example of this application, the data acquisition module is further configured to: obtain the average time of the matching events; use the average time as the reference time for the corresponding matching events; divide all matching events into several bins at equal intervals after arranging them according to the reference time; count the number of single events in each bin; identify bins with a number of single events exceeding a predetermined threshold as in-beam bins; and use the start time of the first bin after the last in-beam bin as the beam departure start time.

[0166] For example, a list is created for all N matching events, forming a list pattern set L. A statistical histogram is plotted according to the average time of each matching event, and the average time of the i-th matching event is shown. The definition is as follows:

[0167] ,

[0168] in, Let be the arrival time of the first single event in the i-th matching event. Let be the arrival time of the second single event in the i-th matching event.

[0169] Regarding bins, their size can be selected as needed. For example, if N matching events span a time period of 1 to 100 seconds, and each second is a bin, then there are 100 bins.

[0170] See Figure 4 The figure shows a binning statistical histogram for a proton therapy scenario.

[0171] Assuming there are n bins, the number of single events is counted for each bin as follows:

[0172] ,

[0173] in, Let j be the start time of the j-th sub-box. The number of single events in the j-th bin.

[0174] It should be noted that all box sorting times are consecutive. A single event that falls on both the start time of the j-th bin and the end time of the (j-1)-th bin is counted as a single event within the previous bin.

[0175] Assuming that the i-th to k-th sub-bins are bundled sub-bins, then the start time of the (k+1)-th sub-bin is taken as the start time of the unbundling.

[0176] Regarding the predetermined threshold, it can be customized. For example, any number between 20% and 50% of the peak number of single events in all bins can be selected as the predetermined threshold, or a number greater than the average number of single events in all bins can be selected as the predetermined threshold.

[0177] Specifically, the multi-nucleoside imaging device may further include: a screening module configured to select a preset number of nuclides or nuclides with activities greater than a preset value for subsequent processing. After obtaining the activities of all nuclides, the nuclides are sorted from largest to smallest activity, and the top F nuclides, or the F nuclides with activities greater than a preset value, are selected for subsequent processing. These nuclides are numbered 1 to F, with half-lives of respectively... .

[0178] In both proton and heavy ion knife PET dose-guided and proton and heavy ion knife PET bio-guided scenarios, nuclides can be selected according to actual needs, and the number of nuclides selected may differ between the two scenarios. Specifically, the time interval division module 530 is configured to provide... Each time interval is used to divide at least a portion of the preliminary images into the corresponding time intervals according to the timestamps.

[0179] in, The sizes of the time intervals can be the same or different, or not exactly the same. In one example, all the time intervals are the same size except for the last time interval, and the last time interval is smaller than the other time intervals.

[0180] More specifically, in one example of this application, the time interval division module 530 is configured to acquire the acquisition time interval of the at least a portion of the preliminary images, and divide the acquisition time interval into... A time interval.

[0181] The acquisition time interval is generally the time span of all coincidence events used to reconstruct the preliminary image. If the preliminary image is reconstructed from coincidence events acquired by off-beam acquisition, then the acquisition time interval is from the start time of off-beam acquisition to the end time of acquisition.

[0182] It should be noted that each time interval is non-intersecting, and the union of all time intervals constitutes the acquisition time or the off-beam acquisition time.

[0183] Specifically, the spatial region segmentation module 540 is configured to cut the at least a portion of the preliminary image into slice images, providing... The sliced ​​image is divided into corresponding spatial regions according to its spatial location.

[0184] in, The spatial regions may be the same or not exactly the same size, preferably... Each spatial region is the same size. The shape of the spatial region is a cube, cuboid, polygon, or irregular shape, preferably a cube.

[0185] More specifically, in one example of this application, the spatial region segmentation module 540 is configured to acquire the image space of the at least a portion of the preliminary image and segment the image space into... A spatial region.

[0186] In this system, each spatial region is non-intersecting, and the union of all spatial regions constitutes the image space.

[0187] For example, the image space is divided into cubic spatial regions with each side length Q. The initial image is first cut into several square slices with side length Q, and then the slices are assigned to each spatial region according to their spatial positions. The spatial positions can be three-dimensional coordinates, and the spatial regions are divided based on three-dimensional coordinate matching.

[0188] Those skilled in the art will understand that the division of time intervals and spatial regions is not in any particular order, and the selection of "at least a portion of preliminary images" is based on actual application needs or regions of interest. Under the same conditions, different choices of the order of dividing time intervals and spatial regions will not result in different images specifically covered by "at least a portion of preliminary images".

[0189] Specifically, the dataset module 550 is configured to store data located in... time interval and located in Sliced ​​images of spatial regions were collected as a single dataset, and aggregated to obtain... Data sets, , .

[0190] According to a further embodiment, the multi-nucleoside imaging device provided in this application further includes a nuclide category acquisition module, which is configured to acquire the nuclide category corresponding to the preliminary image.

[0191] For example, the nuclide category corresponding to the preliminary image can be obtained directly based on prior information or mass spectrometry. The prior information is derived from previous experiments or practical applications, and the nuclide category induced by the target can be summarized based on the application scenario or the particles acting on the object.

[0192] For example, the prior information can be a table including the application scenario, particle category, and / or nuclide category, or it can be a database obtained by simulating the application scenario, particle category, and / or nuclide category in a simulation system. For instance, taking a proton therapy scenario, the main component of the positron-emitting nuclide generated by the proton-induced target is... 15 O、 11 C and 10 C. For example, in the context of heavy ion therapy, a heavy ion beam (such as carbon ions) is used... 12 C) The main components of the positron-emitting nuclides induced by the target are: 12 C 16 O and 14 N.

[0193] For multi-nuclide imaging scenarios in clinical and animal PET, the types of radioactive tracers injected are known (e.g., it is known that FDG and... are injected simultaneously). 15 O-H2O). By looking up the molecular formula of a radioactive tracer, one can determine the type of nuclide it corresponds to (e.g., the nuclide corresponding to FDG is...). 18 F, 15 O-H2O corresponds to 15 These nuclides are numbered 1 to M, with half-lives of respectively... .

[0194] For proton and heavy ion therapy (PET) dose-guided scenarios, the elemental composition of the human body is known, and in order of abundance: O, C, H, N, Ca, P, S, K, Na, Cl, Mg, etc. Therefore, a target with the same elemental composition as the human body can be set in particle physics simulation software such as Geant4, and bombardment can be simulated using proton or heavy ion beams with energies of 70 MeV to 240 MeV and their corresponding clinical energy ranges. The composition of newly generated nuclides can be obtained from the simulation software; these nuclides that emit positrons are numbered 1 to N in order of abundance, with half-lives of [missing information]. , .

[0195] For proton and heavy ion therapy (PET) bioguided scenarios, in addition to the proton and heavy ion therapy (PET) dose-guided scenarios, the procedure involves injecting radionuclides corresponding to the radiotracers into the human body before proton and heavy ion irradiation. These radionuclides are numbered 1 to V, with half-lives of [missing information]. , .

[0196] Furthermore, in proton and heavy ion knife PET dose-guided scenarios and proton and heavy ion knife PET bio-guided scenarios, it is necessary to first screen the top F nuclides with the highest activity to reduce the degree of freedom in subsequent spatiotemporal nuclide separation.

[0197] Specifically, the nuclide separation module 560 is configured to process slice images in at least a portion of the dataset according to the nuclide category and nuclide activity value to obtain the activity percentage of several nuclides in each dataset.

[0198] Furthermore, in some examples, the nuclide separation module 560 is configured to process slice images in at least a portion of the dataset to obtain the activity percentage of several nuclides based on the nuclide category and nuclide activity value using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0199] Furthermore, in other examples, the nuclide separation module 560 is configured to process slice images in at least a portion of the dataset using gradient descent or the Levenberg-Marquardt algorithm to obtain the activity percentage of several nuclides based on the nuclide category and nuclide activity value.

[0200] It is understandable that, as mentioned above, when the nuclide activity values ​​of each pixel are known, the sum of nuclide activities can be obtained by summing them. Generally, there is usually only one image within a timestamp. Assume the... The timestamp of the initial frame image is , Located in the Within a certain time interval. In the first... Time interval, the first spatial regions The timestamp is The initial image, actually the first In the spatial region of the frame image A slice image within. By summing, we can obtain the... Within a certain time interval, the first spatial regions Internal time The sum of the nuclide activities of each pixel is Let j represent a pixel, and let Q be the coordinates of pixel j. The expanded form of Q is... It is understandable. In reality, it is the sum of the nuclide activities of all pixels in a slice of image, in the spatial region. Inside, traversing the first The sum of the nuclide activities of each slice image in a dataset can be obtained by summing all the timestamps within a time interval.

[0201] More specifically, the nuclide separation module 560 is configured to process at least a portion of the slice images in the dataset using an exponential stripping method to obtain the activity of each nuclide, and to obtain the activity percentage of each nuclide based on the ratio of the activity of each nuclide to the activities of all nuclides. Exemplarily, the exponential stripping method includes the following steps: 1. Processing the obtained slice images... Within a certain time interval, the first spatial regions Internal time 1. Plot a semi-logarithmic coordinate system by summing the nuclide activities of each pixel, with time on the horizontal axis and the logarithm of activity on the vertical axis; 2. At the end of the coordinate system, when the shortest half-life nuclide has basically decayed, its curve approaches a straight line. The slope of the straight line is proportional to the decay constant of the longest half-life nuclide. Extrapolate this line to zero time to obtain the intercept, which is the initial activity of the nuclide; 3. Subtract the contribution of the longest half-life nuclide from the original data to obtain a new residual curve. Repeat steps 1 and 2 on the new residual curve to fit the second longest half-life nuclide until all nuclides are separated, thus completing the acquisition of the activity of each nuclide.

[0202] More specifically, the nuclide separation module 560 is configured to process the slice images in at least a portion of the dataset using a matrix solving method to obtain the activity of each nuclide, and to obtain the activity percentage of each nuclide based on the ratio of the activity of each nuclide to the activities of all nuclides. Exemplarily, the matrix solving method includes the following steps: 1. Processing the obtained slice images... Within a certain time interval, the first spatial regions Internal time 1. Establish a matrix equation Ea=A based on the sum of the nuclide activities of each pixel, where E is an exponential matrix whose elements are determined by the known decay constant and time points or intervals, a is a vector representing the unknown initial activity of the nuclide, and A is a vector representing the measured activity value; 2. Solve the matrix equation using the least squares method to obtain the activity of each nuclide.

[0203] More specifically, the nuclide separation module 560 is configured to perform multi-exponential fitting using the following formula to obtain the activity of each nuclide in each dataset:

[0204] , , ,

[0205] in, Indicates time When located time interval and located in The sum of nuclide activities of all pixels within the spatial region. , Represents nuclide half-life, Indicates the baseline term. It can be 0 or any other arbitrary constant. Indicates time When located time interval and located in Nuclide obtained by fitting within a spatial region activity level For the result to be fitted, a and c are constants when the half-life of nuclide f is determined.

[0206] More specifically, the nuclide separation module 560 is configured to separate all nuclides. After normalization, the activity percentage of each nuclide is obtained:

[0207] ,

[0208] in, Indicates that nuclide f in time When located time interval and located in The percentage of activity within a spatial region.

[0209] According to one embodiment, the nuclide separation module 560 is configured to perform multi-exponential fitting on slice images in all datasets based on nuclide category and nuclide activity value to obtain the activity percentage of several nuclides in each dataset.

[0210] Specifically, the activity of each nuclide in each dataset is obtained by performing multi-exponential fitting, exponential stripping, or matrix solving on all preliminary images divided into the p-th time interval and h-th spatial interval. The methods of multi-exponential fitting, exponential stripping, or matrix solving are the same as those in the above examples and will not be repeated here. It should be noted that the only difference between this embodiment and the above embodiments is the number of datasets selected. Due to the massive amount of data acquired by digital PET, this may have different significant effects in practical applications. For example, for some application scenarios, it may only be necessary to perform secondary reconstruction on preliminary images of a specific region of interest, or on preliminary images of a specific number of nuclides. Therefore, selecting several datasets that meet the requirements can achieve the relevant objectives, significantly improving data processing efficiency and shortening detection time while accurately quantifying and qualitatively analyzing data, which is of great value for clinical applications. It should also be noted that for certain research needs or special application scenarios, it may be necessary to select all datasets and all types of nuclides for secondary image reconstruction to obtain the correlation and influence relationships between different types of nuclides. Those skilled in the art can select the number of datasets and types of nuclides as needed based on the inspiration of this application, which will not be repeated here.

[0211] Specifically, the second reconstruction module 570 is configured to perform a second reconstruction on the preliminary image based on the activity ratio to obtain the final image. More specifically, the second reconstruction module 570 is configured to first acquire the coordinates of each element on the preliminary image i. The activity value of the nuclide The proportion of nuclide activity obtained from the nuclide separation module 560 ,but At any time at coordinates ( The activity of nuclide f in ) is , ,in Belonging to the p-th time interval, coordinates ( If the image belongs to the h-th spatial region, perform the same operation on other coordinate points of the preliminary image i, and perform the same operation on other preliminary images as on the preliminary image i. After obtaining the activity of each nuclide, reconstruct all preliminary images according to the activity of each nuclide to obtain the final image.

[0212] The multi-nucleus imaging device provided in this application, by performing temporal and spatial segmentation of the image, distinguishes the contributions of various nuclides in each time interval and spatial region, and performs secondary reconstruction of the image, solves the problem that current global multi-nucleus imaging methods based on nuclide half-life characteristics cannot obtain the spatiotemporal distribution of nuclide composition. This improves the performance of multi-nucleus activity separation and broadens the application prospects of this type of method. Compared with methods utilizing transient gamma energy characteristics, this application does not require nuclides to have the property of generating transient gamma, therefore it is suitable for… 18F, 15 Common nuclides such as O are applicable to a wider range of situations. It eliminates the need for three-photon coincidence of the transient gamma and positron gamma, resulting in a high success rate and thus more effective events detected. It also offers a better signal-to-noise ratio and lower requirements for system sensitivity and temporal resolution. Compared to methods utilizing tracer kinetic models, this application does not rely on highly complex, uncertain, or insufficiently studied tracer kinetic models, nor does it require AI training. Therefore, it eliminates the need for large amounts of labeled training data and expensive training server time, making it more feasible and less costly.

[0213] It should be understood that Figure 5 The apparatus and modules shown can be implemented in various ways. For example, in some embodiments, the apparatus and modules can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution device, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and apparatus described above can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The apparatus and modules described in this application can be implemented not only with hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also with software, for example, executed by various types of processors, or with a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0214] It should be noted that the above description of the modules is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principle of the device, may arbitrarily combine the modules or construct subsystems connected to other modules without departing from this principle. For example, the modules may share a single storage module, or each module may have its own separate storage module. Such modifications are all within the scope of this specification.

[0215] Figure 6 This is a schematic diagram of a multi-nuclide activity separation system used to implement a multi-nuclide imaging method in one embodiment of this application. (Refer to...) Figure 6The multi-nucleoside activity separation system S00 may include a processing component S20, which further includes one or more processors, and memory resources represented by a memory S22 for storing instructions, such as application programs, that can be executed by the processors of the processing component S20. The application programs stored in the memory S22 may include one or more instructions, with each module corresponding to a set of instructions. Furthermore, the processing component S20 is configured to execute instructions to perform the aforementioned multi-nucleoside imaging method.

[0216] The operations and / or methods described in the embodiments of this specification, implemented by a single processor, may also be implemented jointly or independently by multiple processors. For example, if, in this application specification, the processor of the processing device executes steps S100-S700, it should be understood that steps S100-S700 may also be executed jointly or independently by two different processors of the processing device (e.g., the first processor executes steps S100-S500, the second processor executes steps S600-S700, or the first and second processors jointly execute steps S100-S700).

[0217] The multi-nuclide activity separation system S00 may further include: a power supply component S24 configured to perform power management of the multi-nuclide activity separation system S00; a wired or wireless network interface S26 configured to connect the multi-nuclide activity separation system S00 to a network; and an input / output (I / O) interface S28. The multi-nuclide activity separation system S00 can operate on an operating system stored in memory S22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or similar.

[0218] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory S22 including instructions, which can be executed by the processor of the multi-nucleoside imaging system S00 to perform the above method. The storage medium may be a computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0219] In an exemplary embodiment, a computer program product is also provided, the computer program product including instructions that can be executed by a processor of a multi-nucleoside activity separation system S00 to perform the above method.

[0220] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, Figure 7This is an internal structural diagram of a computer device according to one embodiment of this application. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores user- and task-related data used in the aforementioned multi-nuclide imaging method. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-nuclide imaging method.

[0221] Those skilled in the art will understand that Figure 7 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.

[0222] 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, and when executed, it 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.

[0223] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0224] It should be noted that the devices, electronic devices, servers, etc., described above according to the method embodiments may also include other implementation methods, and specific implementation methods can be referred to the description of the relevant method embodiments. Furthermore, new embodiments formed by the combination of features between various methods, devices, and server embodiments still fall within the scope of this application, and will not be elaborated upon here.

[0225] In the description of this specification, the references to "one embodiment," "an embodiment," and / or "some embodiments," "some embodiments," "other embodiments," "ideal embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiment or example, and certain features, structures, or characteristics in one or more embodiments of this specification may be appropriately combined.

[0226] 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.

[0227] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

[0228] The basic concepts have been described herein. It is obvious that the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0229] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “module,” “component,” or “system.” Furthermore, various aspects of this specification may be represented as a computer product located on one or more computer-readable media, including computer-readable program code.

[0230] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0231] The computer program code required for the operation of each part of this manual can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc.; conventional procedural programming languages ​​such as C, Visual Basic, Fortran 3003, Perl, COBOL 3002, PHP, ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0232] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0233] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0234] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0235] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0236] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A multi-nucleoside imaging method, characterized in that, include: Several preliminary images are obtained through the first image reconstruction, and the nuclide activity values ​​corresponding to the preliminary images are obtained. supply Each time interval is used to divide at least a portion of the preliminary images into the corresponding time intervals according to the timestamps; Cut the at least a portion of the preliminary image into slice images, and provide The sliced ​​image is divided into corresponding spatial regions according to its spatial location; Will be located time interval and located in Sliced ​​images of spatial regions were collected as a single dataset, and aggregated to obtain... A dataset, , ; Based on the nuclide category and the nuclide activity value, the slice images in at least a portion of the dataset are processed to obtain the activity percentage of several nuclides in each dataset. The final image is obtained by performing a second reconstruction on the preliminary image based on the activity ratio.

2. The multi-nucleoside imaging method according to claim 1, characterized in that, The multi-nucleoside imaging method further includes: Obtain the nuclide activity value of each pixel in the preliminary image; Obtain the sum of nuclide activities of pixels in at least a portion of the dataset; The activities of different nuclides on slice images in at least a portion of the dataset are obtained based on the sum of the activity percentage and the nuclide activity.

3. The multi-nucleoside imaging method according to claim 1, characterized in that, Before obtaining several preliminary images through the first image reconstruction, the multi-nucleoside imaging method further includes: Determine the beamout start time and obtain the coincidence events after the beamout start time; The preliminary image is obtained by performing a first image reconstruction based on the coincidence event after the said beamout start time.

4. The multi-nucleoside imaging method according to claim 1, characterized in that, At least a portion of the preliminary images are divided into corresponding time intervals according to their timestamps, including: Select preliminary images corresponding to a specific time period or a specific location, or select all preliminary images, and divide the selected preliminary images into corresponding time intervals according to the timestamps.

5. The multi-nucleoside imaging method according to claim 2, characterized in that, In providing Before dividing at least a portion of the preliminary images into corresponding time intervals according to timestamps, the multi-nucleoside imaging method further includes: Based on the activity of all nuclides globally, a preset number of nuclides or nuclides with an activity greater than a preset value are selected for subsequent processing.

6. The multi-nucleoside imaging method according to claim 5, characterized in that, Obtain the activity of all global nuclides, including by using the multi-exponential fitting method, the exponential stripping method, or the matrix solving method.

7. The multi-nucleoside imaging method according to claim 6, characterized in that, Obtain the activity of all nuclides globally, including: Obtain all coincidence events used to reconstruct several preliminary images, and plot the temporal spectrum of all coincidence events; The activity of all nuclides worldwide is obtained by performing multi-exponential fitting on the time spectrum using the following formula: ， ， ， in, Indicates time The sum of the activities of all nuclides at that time. , Indicates nuclide half-life, Represents the baseline term, A f Represents the nuclide obtained from the fitting. The activity level.

8. The multi-nucleoside imaging method according to claim 1, characterized in that, The time intervals may be the same or not exactly the same size.

9. The multi-nucleoside imaging method according to claim 1, characterized in that, The spatial regions may be the same size or not exactly the same size.

10. The multi-nucleoside imaging method according to claim 1, characterized in that, The shape of the spatial region is a cube, cuboid, polygon, or irregular shape.

11. The multi-nucleoside imaging method according to claim 1, characterized in that, The nuclide categories are obtained through prior information or mass spectrometry.

12. The multi-nucleoside imaging method according to claim 1, characterized in that, Based on the nuclide category and the nuclide activity value, at least a portion of the dataset's slice images are processed to obtain the activity percentage of several nuclides in each dataset, including: Based on the nuclide category and the nuclide activity value, the slice images in at least a portion of the dataset are processed using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method to obtain the activity percentage of several nuclides.

13. The multi-nucleoside imaging method according to claim 12, characterized in that, The activities of several nuclides in each dataset are obtained by multi-exponential fitting using the following formula: ， ， ， in, Indicates time When located time interval and located in The sum of the activities of all nuclides within the space region. , Indicates nuclide half-life, Indicates the baseline term. Indicates time When located time interval and located in Nuclide obtained by fitting within a spatial region The activity level.

14. The multi-nucleoside imaging method according to claim 13, characterized in that, The activity percentage of each nuclide can be obtained using the following formula: ， in, Indicates that nuclide f in time When located time interval and located in The percentage of activity within a spatial region.

15. A multi-nucleoside imaging device, characterized in that, include: The first reconstruction module is configured to reconstruct several preliminary images; The activity acquisition module is configured to acquire the nuclide activity value corresponding to the preliminary image; The time interval division module is configured to provide... Each time interval is used to divide at least a portion of the preliminary images into the corresponding time intervals according to the timestamps; The spatial region segmentation module is configured to cut the at least a portion of the preliminary image into slice images, providing... The sliced ​​image is divided into corresponding spatial regions according to its spatial location; The dataset module is configured to be located in time interval and located in Sliced ​​images of spatial regions were collected as a single dataset, and aggregated to obtain... A dataset, , ; The nuclide separation module is configured to process slice images in at least a portion of the dataset according to the nuclide category and the nuclide activity value to obtain the activity ratio of several nuclides in each dataset. The second reconstruction module is configured to perform a second reconstruction on the preliminary image based on the activity ratio to obtain the final image.

16. The multi-nucleoside imaging device according to claim 15, characterized in that, The activity acquisition module is configured to acquire the nuclide activity value of each pixel in each preliminary image, and to acquire the sum of the nuclide activities of pixels in the at least part of the dataset; The second reconstruction module is configured to obtain the activity of different nuclides on each slice image in the at least part of the dataset based on the sum of the activity ratio and the nuclide activity.

17. The multi-nucleoside imaging device according to claim 15, characterized in that, It also includes a data acquisition module configured to determine the beamout start time and acquire coincidence events after the beamout start time, and the first reconstruction module configured to perform image reconstruction using coincidence events after the beamout start time to obtain the preliminary image.

18. The multi-nucleoside imaging device according to claim 15, characterized in that, The time interval division module is configured to select preliminary images corresponding to a specific time period or a specific location, or to select all preliminary images and divide the selected preliminary images into corresponding time intervals according to the timestamps.

19. The multi-nucleoside imaging device according to claim 16, characterized in that, Also includes: The screening module is configured to select a preset number of nuclides or nuclides with an activity greater than a preset value for subsequent processing based on the activity of all nuclides globally.

20. The multi-nucleoside imaging device according to claim 19, characterized in that, The screening module is configured to obtain the activity of all nuclides globally through a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

21. The multi-nucleoside imaging device according to claim 20, characterized in that, The activity acquisition module is configured to: acquire all coincidence events used to reconstruct several preliminary images, and draw a time spectrum for all coincidence events; The activity of all nuclides worldwide is obtained by performing multi-exponential fitting on the time spectrum using the following formula: ， ， ， in, Indicates the measured time The sum of the activities of all nuclides at that time. , Indicates nuclide half-life, Indicates the baseline term. Represents the nuclide obtained from the fitting. The activity level.

22. The multi-nucleoside imaging device according to claim 15, characterized in that, The time intervals may be the same or not exactly the same size.

23. The multi-nucleoside imaging device according to claim 15, characterized in that, The spatial regions may be the same size or not exactly the same size.

24. The multi-nucleoside imaging device according to claim 15, characterized in that, The shape of the spatial region is a cube, cuboid, polygon, or irregular shape.

25. The multi-nucleoside imaging device according to claim 15, characterized in that, The multi-nucleoside imaging device further includes a nuclide category acquisition module, which is configured to obtain the nuclide category corresponding to the preliminary image based on prior information or a mass spectrometer.

26. The multi-nucleoside imaging device according to claim 15, characterized in that, The nuclide separation module is configured to: process slice images in at least a portion of the dataset using a multi-exponential fitting method, an exponential stripping method, or a matrix solving method, based on the nuclide category and the nuclide activity value, to obtain the activity percentage of several nuclides.

27. The multi-nucleoside imaging device according to claim 26, characterized in that, The nuclide separation module is configured to: perform multi-exponential fitting on slice images from at least a portion of the dataset to obtain the activity of several nuclides in each dataset using the following formula: ， ， ， in, Indicates time When located time interval and located in The sum of the activities of all nuclides within the space region. , Indicates nuclide half-life, Indicates the baseline term. Indicates time When located time interval and located in Nuclide obtained by fitting within a spatial region The activity level.

28. The multi-nucleoside imaging device according to claim 27, characterized in that, The nuclide separation module is configured to obtain the activity percentage of each nuclide using the following formula: ， in, Indicates that nuclide f in time When located time interval and located in The percentage of activity within a spatial region.

29. A computer 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 multi-nucleoside imaging method as described in any one of claims 1 to 14.

30. A computer program product, characterized in that, It includes a computer program or instructions, characterized in that, when the computer program or instructions are executed by a processor, they implement the steps of the multi-nucleoside imaging method as described in any one of claims 1 to 14.

31. A digital PET system, characterized in that, Includes the multi-nucleoside imaging apparatus as described in any one of claims 15 to 28.

Images