Multimodality imaging data processing method, image reconstruction method, device and digital pet system

Abstract

The application discloses a kind of multinuclei imaging data processing method, image reconstruction method, device and digital PET system.The multinuclei imaging data processing method includes: providing X time interval, at least a part of coincidence event is divided into corresponding time interval according to time stamp;Providing Y space region, the at least a part of coincidence event is divided into corresponding space region according to the response line corresponding to coincidence event;Coincidence event in x time interval and in y space region is regarded as the same data set, and the data set is obtained,;Combining the activity of all nucleuses and the nucleic category corresponding to coincidence event, the activity proportion of several nucleuses in each data set is obtained by processing the coincidence event in at least a part of data set.The application can obtain the space-time distribution of nucleic composition by time segmentation and space segmentation coincidence event, improve the performance of multinuclei activity separation, and has wide application prospect.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a multi-nucleoside imaging data processing method, image reconstruction method, apparatus, 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 first and imaging it, 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. It requires acquiring a large amount of labeled training data (such labeling is currently technically unavailable) and investing in expensive training servers, 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, but 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 data processing method, image reconstruction method, device, and digital PET system to address at least one technical problem existing in traditional solutions.

[0009] According to a first aspect of this application, a method for processing multi-nucleoside imaging data is provided, comprising: providing x time intervals, and dividing at least a portion of matching events into corresponding time intervals according to timestamps; providing Y spatial regions, and dividing the at least a portion of matching events into corresponding spatial regions according to response lines corresponding to the matching events; and taking the matching events located in the x time intervals and the y spatial region as a single dataset, and summing them to obtain... A dataset, , By combining the nuclide categories corresponding to the matching events and the activities of all nuclides, the matching events in at least a portion of the dataset are processed to obtain the activity percentage of several nuclides in each dataset.

[0010] According to one embodiment of this application, before providing X time intervals, the multi-nucleus imaging data processing method further includes: obtaining coincidence events and nuclide categories corresponding to the coincidence events.

[0011] According to one embodiment of this application, obtaining a coincidence event and the corresponding nuclide category includes: marking two single-pulse events that are located within the same time window, the same energy window, and whose positional line passes through the imaging field of view as a coincidence event. The single-pulse events are obtained by devices or detectors such as clinical PET, animal PET, or proton and heavy ion scalpel PET.

[0012] According to one embodiment of this application, obtaining a coincidence event and the corresponding nuclide category includes: obtaining the nuclide category corresponding to the coincidence event based on prior information or a mass spectrometer.

[0013] According to one embodiment of this application, obtaining a conforming event and the corresponding nuclide category includes: establishing a table including application scenario, particle category and / or nuclide category as prior information, or using a database obtained by simulating the application scenario, particle category and / or nuclide category in a simulation system as prior information, and directly obtaining the nuclide category corresponding to the conforming event based on the prior information.

[0014] According to one embodiment of this application, before obtaining the matching event and the nuclide category corresponding to the matching event, the multi-nuclide data processing method further includes: obtaining matching event data to be processed, and correcting the matching event data to obtain the matching event.

[0015] According to one embodiment of this application, correcting the event data to be processed includes: performing random correction, background correction, or both random correction and background correction on the event data to be processed.

[0016] According to one embodiment of this application, before providing X time intervals and dividing at least a portion of the matching events into the corresponding time intervals according to timestamps, the multi-nucleoside imaging data processing method further includes: filtering out off-beam matching events from the matching events, and performing time and spatial division using the off-beam matching events.

[0017] According to one embodiment of this application, dividing at least a portion of the matching events into corresponding time intervals according to timestamps includes: selecting matching events corresponding to several time periods, or selecting all matching events, and dividing the selected matching events into corresponding time intervals according to timestamps.

[0018] According to one embodiment of this application, before providing X time intervals and dividing at least a portion of the matching events into the corresponding time intervals according to timestamps, the multi-nucleus imaging data processing method further includes: obtaining the activity of all nuclides globally, and selecting a preset number of nuclides or nuclides with an activity greater than a preset value for subsequent processing.

[0019] 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.

[0020] According to one embodiment of this application, obtaining the activity of all global nuclides includes: plotting a first time spectrum for all coincident events; and performing multi-exponential fitting on the first time spectrum using the following formula to obtain the activity of all global nuclides:

[0021] , , ,

[0022] in, This represents the activity of all nuclides at the measured time t. J represents the number of different types of nuclides. Let b represent the half-life of nuclide j, and b represent the baseline term. This represents the activity of nuclide j obtained from the fitting.

[0023] According to one embodiment of this application, X time intervals are provided, and at least a portion of the matching events are divided into the corresponding time intervals according to the timestamps, including: obtaining the acquisition time interval or the off-beam acquisition time interval, and dividing the acquisition time interval or the off-beam acquisition time interval into X time intervals.

[0024] According to one embodiment of this application, the multi-nucleoside imaging data processing method further includes: providing Y spatial regions, and dividing at least a portion of the coincidence events into corresponding spatial regions according to the response lines and flight times corresponding to the coincidence events.

[0025] According to one embodiment of this application, providing Y spatial regions includes: acquiring a detection field of view and dividing the detection field of view into Y spatial regions.

[0026] According to one embodiment of this application, by combining the nuclide category corresponding to the matching event and the activity of all nuclides, the matching events in at least a portion of the dataset are processed to obtain the activity ratio of several nuclides in each dataset, including: processing the matching events 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 ratio of several nuclides in each dataset.

[0027] According to one embodiment of this application, by combining the nuclide category corresponding to the matching event and the activity of all nuclides, the matching events in at least a portion of the dataset are processed to obtain the activity proportion of several nuclides in each dataset, including: plotting a second time spectrum for all matching events in at least a portion of the dataset; and using the following formula to perform multi-exponential fitting on the second time spectrum to obtain the activity of several nuclides in each dataset:

[0028] , , ,

[0029] in, This represents the activity of all nuclides located in the time interval x and the spatial region y at time t. , The term 'b' represents the half-life of nuclide f, and 'b' represents the baseline term. This represents the activity of nuclide f, which is located in the x-time interval and the y-space region, obtained through fitting.

[0030] According to one embodiment of this application, by combining the nuclide category corresponding to the matching event and the activity of all nuclides, processing all matching events in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset includes: obtaining the activity percentage of each nuclide using the following formula:

[0031] ,

[0032] in, This represents the activity percentage of nuclide f located in the x-time interval and the y-space region.

[0033] According to a second aspect of this application, an image reconstruction method is provided, comprising: reconstructing a PET image using the activity percentage obtained by the multi-nucleoside imaging data processing method.

[0034] According to a third aspect of this application, a multi-nucleoside imaging data processing apparatus is provided, comprising: a time interval division module configured to provide X time intervals and divide at least a portion of matching events into corresponding time intervals according to timestamps; a spatial region division module configured to provide Y spatial regions and divide the at least a portion of matching events into corresponding spatial regions according to response lines corresponding to the matching events; and a dataset module configured to treat matching events located in time interval x and spatial region y as a single dataset, and aggregate them to obtain... A dataset, , The nuclide separation module is configured to combine the nuclide category corresponding to the matching event and the activity of all nuclides to process the matching events in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset.

[0035] According to one embodiment of this application, the multi-nucleoside imaging data processing device further includes a data acquisition module configured to acquire coincidence events and nuclide categories corresponding to the coincidence events.

[0036] According to one embodiment of this application, the data acquisition module is configured to mark two single-pulse events that are located within the same time window, the same energy window, and whose positional line passes through the imaging field of view as a single coincidence event. The single-pulse events are acquired by devices or detectors such as clinical PET, animal PET, or proton and heavy ion scalpel PET.

[0037] According to one embodiment of this application, the data acquisition module is configured to obtain the nuclide category corresponding to the event based on prior information or a mass spectrometer.

[0038] According to one embodiment of this application, the data acquisition module is configured to obtain prior information by establishing a table including application scenarios, particle categories and / or nuclide categories, or by simulating application scenarios, particle categories and / or nuclide categories in a simulation system to obtain a database as prior information, and directly obtain the nuclide category corresponding to the event based on the prior information.

[0039] According to one embodiment of this application, the multi-nucleoside imaging data processing apparatus further includes: a correction module configured to acquire conformance event data to be processed and to correct the conformance event data to obtain the conformance event.

[0040] According to one embodiment of this application, the correction module is configured to perform random correction, background correction, or both random correction and background correction on the event data to be processed.

[0041] According to one embodiment of this application, the multi-nucleoside imaging data processing device further includes: a first screening module configured to screen out-of-beam coincidence events from coincidence events, wherein the time interval division module and the spatial region division module perform time division and spatial division based on the out-of-beam coincidence events.

[0042] According to one embodiment of this application, the time interval division module is configured to: select corresponding matching events within a number of time periods, or select all matching events, and divide the selected matching events into corresponding time intervals according to their timestamps.

[0043] According to one embodiment of this application, the multi-nucleoside imaging data processing device further includes: a second screening module configured to acquire the activity of all nuclides globally and select nuclides with an activity greater than a preset value for subsequent processing.

[0044] According to one embodiment of this application, the second screening module is configured to obtain the activity of all global nuclides through a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0045] According to one embodiment of this application, the second screening module is configured to: plot a first time spectrum for all matching events; and use the following formula to perform multi-exponential fitting on the first time spectrum to obtain the activity of all global nuclides:

[0046] , , ,

[0047] in, This represents the activity of all nuclides at the measured time t. J represents the number of different types of nuclides. Let b represent the half-life of nuclide j, b represent the baseline term, and A represent the half-life of nuclide j. j This represents the activity of nuclide j obtained from the fitting.

[0048] According to one embodiment of this application, the time interval division module is configured to: acquire a collection time interval or an off-beam collection time interval, and divide the collection time interval or the off-beam collection time interval into X time intervals.

[0049] According to one embodiment of this application, the spatial region division module is configured to: provide Y spatial regions, and divide at least a portion of the conforming events into the corresponding spatial regions according to the response lines and flight times corresponding to the conforming events.

[0050] According to one embodiment of this application, the spatial region division module is configured to: acquire a detection field of view and divide the detection field of view into Y spatial regions.

[0051] According to one embodiment of this application, the nuclide separation module is configured to: combine the nuclide category corresponding to the coincident event and the activity of all nuclides, and process the coincident events 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.

[0052] According to one embodiment of this application, the nuclide separation module is configured to: plot a second time spectrum for all coincident events in at least a portion of the dataset; and perform multi-exponential fitting on the second time spectrum using the following formula to obtain the activity of several nuclides in each dataset:

[0053] , , ,

[0054] in, This represents the activity of all nuclides located in the x-interval and the y-space region at time t. , The term 'b' represents the half-life of nuclide f, and 'b' represents the baseline term. This represents the activity of nuclide f, which is located in the x-time interval and the y-space region, obtained through fitting.

[0055] According to one embodiment of this application, the nuclide separation module is configured such that the activity percentage of each nuclide is:

[0056] ,

[0057] in, This represents the percentage of activity of nucleon f located in the x-time interval and the y-space region.

[0058] According to a fourth aspect of this application, an image reconstruction apparatus is provided, configured to reconstruct PET images using the activity percentage obtained by the multi-nucleoside imaging data processing apparatus.

[0059] According to a fifth 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 data processing method or image reconstruction method as described above.

[0060] According to a sixth 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 data processing method or image reconstruction method as described above.

[0061] According to a seventh aspect of this application, a digital PET system is provided, including any multi-nucleoside imaging data processing apparatus or image reconstruction apparatus as described above.

[0062] The multi-nucleoside imaging data processing method, image reconstruction method, apparatus, and digital PET system provided in this application solve the problem of the inability to obtain the spatiotemporal distribution of nuclide composition in existing technologies by using temporal and spatial segmentation of coincidence events. This improves the performance of multi-nucleoside activity separation and broadens the application prospects 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, and therefore 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 acquiring large amounts of labeled training data and investing in expensive training servers, making it more feasible and less costly. Attached Figure Description

[0063] 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.

[0064] Figure 1 This is a flowchart illustrating a multi-nucleoside imaging data processing method in one embodiment of this application.

[0065] Figure 2 This is a flowchart illustrating a multi-nucleoside imaging data processing method in another embodiment of this application;

[0066] Figure 3 This is a schematic diagram illustrating a conforming event in one embodiment of this application;

[0067] Figure 4 This is a histogram of events conforming to one embodiment of this application;

[0068] Figure 5 This is a curve fitted in one embodiment of this application;

[0069] Figure 6 This is a curve fitted in another embodiment of this application;

[0070] Figure 7 This is a schematic diagram of the structure of a multi-nucleoside imaging data processing device in one embodiment of this application;

[0071] Figure 8 This is a schematic diagram of the structure of a multi-nucleoside imaging data processing device in another embodiment of this application;

[0072] Figure 9 This is a partial structural schematic diagram of a multi-nucleoside imaging data processing device in one embodiment of this application;

[0073] Figure 10 This is a schematic diagram of the structure of the multi-nucleoside activity separation system in the multi-nucleoside imaging data processing device in one embodiment of this application;

[0074] Figure 11 This is an internal structural diagram of a computer device in one embodiment of this application. Detailed Implementation

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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. It requires obtaining a large amount of corresponding labeled training data (such labels are currently not technically available) and investing in expensive training servers, making it less feasible and costly.

[0081] 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:

[0082] ,

[0083] 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 .

[0084] The activity of multiple nuclides as a function of time is:

[0085] ,

[0086] Where i represents the type of nuclide.

[0087] 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.

[0088] 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.

[0089] In view of the technical problems existing in the prior art, this application proposes a method, apparatus and supporting applications that can at least separate the activities of multiple nuclides to meet higher application requirements.

[0090] In some embodiments, the multi-nuclide imaging data processing method can be executed by a multi-nuclide imaging data processing device. For example, the multi-nuclide imaging data processing 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 data processing method can be implemented. The multi-nuclide imaging data processing device disclosed in this application for implementing the above-mentioned multi-nuclide imaging data processing 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).

[0091] 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.

[0092] Figure 1This is a flowchart illustrating a multi-nuclide imaging data processing method in one embodiment of the present application. In one embodiment, the multi-nuclide imaging data processing method may include the following steps S200~S500.

[0093] Step S200: Provide X time intervals, and divide at least a portion of the matching events into the corresponding time intervals according to the timestamps.

[0094] Step S300: Provide Y spatial regions, and divide at least a portion of the conforming events into corresponding spatial regions according to the response lines corresponding to the conforming events.

[0095] Step S400: Combine the matching events located in the x-time interval and the y-space region into a single dataset, and sum them to obtain... There are datasets, among which, , x and y should not both be equal to 1.

[0096] Step S500: Combine the nuclide category corresponding to the matching event with the activity of all nuclides, process the matching events in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset.

[0097] Figure 2 This is a flowchart illustrating a multi-nucleus imaging data processing method in another embodiment of this application. In one embodiment, the multi-nucleus imaging data processing method may further include step S100. Thus, steps S100-S500 constitute another new embodiment that can rely on a detector + computer or different computer software for collaborative processing. For example, step S100 can be completed by a separate detector or data processing software, capable of acquiring the matching events and nuclide category information required for further processing in steps S200-S500, and further separating nuclide activity through the software corresponding to steps S200-S500. Since steps S200-S500 in these two embodiments are essentially the same, the following will be combined with... Figure 1 and Figure 2 The related content is combined and described.

[0098] S100: Obtain the matching event and the corresponding nuclide category.

[0099] A valid event includes at least two single events. A single event refers to a pulse signal event generated when a high-energy photon is converted into an electrical signal and captured by a detector. This pulse signal includes time, location, and energy information and is typically acquired through PET equipment or detectors. The PET system can be clinical PET, animal PET, or proton / heavy ion PET, originating from scenarios where multiple radionuclides coexist, such as proton therapy, heavy ion therapy, or multi-tracer PET. The event data can be acquired in-beam or post-treatment.

[0100] 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 pulsed single events have energies both within the energy window range, and the line connecting the crystal bars on the detectors capturing these two pulsed single events crosses the field of view (FOV), then these two single events are recorded as a single coincidence event. An example of a coincidence event is shown below. Figure 3 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.

[0101] According to an example of this application, obtaining a conforming event and the corresponding nuclide category includes: establishing a table including application scenario, particle category and / or nuclide category as prior information, or using a database obtained by simulating the application scenario, particle category and / or nuclide category in a simulation system as prior information, and directly obtaining the nuclide category corresponding to the conforming event based on the prior information.

[0102] For example, the nuclide category corresponding to the event can be obtained directly from prior information or by 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.

[0103] 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: 12C 16 O and 14 N.

[0104] 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... , .

[0105] For proton and heavy ion ablation (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 relevant particle physics simulation software, 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]. , .

[0106] 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]. , .

[0107] In one embodiment, the multi-nucleus data processing method provided in this application may further include: before obtaining the matching event and the nuclide category corresponding to the matching event, obtaining matching event data to be processed, and correcting the matching event data to be processed to obtain the matching event.

[0108] Among them, the event data to be processed can be collected by the PET system, and come from scenarios where multiple radionuclides coexist, such as proton therapy, heavy ion therapy, or multi-tracer PET. It can be event data collected in the beam or event data collected after treatment (also known as beam off).

[0109] For example, the correction may include random correction, background correction, or both random correction and background correction to obtain conforming event data. Specific correction methods can be found in the prior art and will not be repeated here.

[0110] It is important to note that while a fully digital PET system can accurately extract the energy, location, and time information of a single event, and the coincidence system uses energy window screening, location exclusion, and time window filtering to determine coincidence events and obtain projection data, the limited width of the time window used to detect true coincidences means that two unrelated single events that are sufficiently close in time may be mistakenly identified as a pair of coincidence events, resulting in random coincidences. In addition to random coincidences, background noise, such as environmental radiation and electronic noise, also exists during actual measurements. This noise forms background events, which also need to be corrected for.

[0111] Specifically, in one example of this application, correcting the event data to be processed includes: performing random correction, background correction, or both random correction and background correction on the event data to be processed.

[0112] The specific operations for random correction and background correction of the data to be processed can be carried out using existing correction methods. Any method that can achieve the purpose of random correction or background correction can be used in this application.

[0113] Specifically, in one example of this application, in the application scenario of a proton and heavy ion knife, the multi-nucleoside imaging data processing method further includes:

[0114] Step S110: Filter out the unbounded coincidence events from the coincidence events, and perform time and space partitioning based on the unbounded coincidence events.

[0115] In this application, "beam off" is a concept relative to "in-beam." Taking proton therapy as an example, during treatment, positron-emitting 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. "Beam off" refers to the proton beam, indicating the end of treatment and the absence of proton beam bombardment of the target. The beam off coincidence event is the coincidence event acquired after the beam off time. PET acquisition can be selected based on different setup configurations, including in-beam acquisition, beam off acquisition, or acquisition covering both in-beam and beam off processes.

[0116] For example, step S110 includes the following steps S111 to S112.

[0117] Step S111: 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.

[0118] Create a list of all N matching events, forming a list pattern set L. Plot a statistical histogram based on the average time for each matching event. The average time for the i-th matching event... The definition is as follows:

[0119] , ,

[0120] 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.

[0121] 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 divided into one bin, then there will be 100 bins.

[0122] Step S112: Count the number of single events in each bin, and identify all bins whose number of single events exceeds a predetermined threshold as bins in the bundle. Use the start time of the first bin after the last bin in the bundle as the start time of the unbundling.

[0123] See Figure 4 The figure shows a histogram of binning statistics for conforming events in a proton therapy scenario.

[0124] Assuming there are n bins, the number of single events is counted for each bin as follows:

[0125] ,

[0126] in, Let j be the start time of the j-th sub-box. Let be the number of single events in the j-th bin. .

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Furthermore, in proton and heavy ion scalpel PET dose-guided scenarios 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-nuclide imaging data processing method also includes: acquiring the activity of all global nuclides, and selecting a preset number of nuclides or nuclides with activities greater than a preset value for subsequent processing.

[0131] 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.

[0132] Furthermore, in other examples, the activities of all global nuclides are obtained, including by using gradient descent or the Levenberg-Marquardt algorithm.

[0133] The main steps of the exponential stripping method include: 1. Processing all coincident 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; 2. At the end of the coordinate system, when the short-lived nuclides have basically decayed, their curves approach 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 that nuclide; 3. Subtracting the contribution of the longest-lived nuclide from the original data to obtain a new residual curve. Repeating steps 1 and 2 on the new residual curve to fit the second longest-lived nuclide until all nuclides are separated, thus completing the acquisition of the activity of all global nuclides.

[0134] The main steps of the matrix solution method include: 1. Dividing all coincident events into several time points or intervals at equal intervals, and obtaining the total activity value of nuclides at each time point or interval; 2. 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 nuclides, and A is a vector representing the measured activity value; 3. Solving the matrix equation using the least squares method to obtain the activity of all nuclides globally.

[0135] Furthermore, a multi-exponential fitting method is used to obtain the nuclide activity. The first time spectrum is plotted on the collected coincidence events, and the activity of all global nuclides is obtained by multi-exponential fitting of the first time spectrum using the following formula:

[0136] , , ,

[0137] in, This represents the activity of all nuclides at the measured time t. J represents the total number of all nuclides. Let A represent the half-life of nuclide j, b represent the baseline term, and b can be 0 or any other arbitrary constant. j This represents the activity of nuclide j obtained from the fitting, where a and c are constants when the half-life of nuclide f is determined.

[0138] After obtaining the activities of all nuclides, the nuclides are sorted from highest to lowest activity. The top F nuclides are selected, or the F nuclides with activity values ​​greater than a preset value are selected, or F nuclides are selected based on the nuclides of interest or application requirements for subsequent processing. These nuclides are numbered 1 to F, with half-lives of [missing information]. , .

[0139] For both proton and heavy ion knife PET dose-guided scenarios and proton and heavy ion knife PET biological-guided scenarios, nuclides can be selected according to actual needs, and the number of nuclides selected for the two scenarios may differ.

[0140] Step S200: Provide X time intervals, and divide at least a portion of the matching events into the corresponding time intervals according to the timestamps.

[0141] In this application, the X time intervals can be the same size, different sizes, or not completely identical. In one example, except for the last time interval, the remaining time intervals are all the same size, with the last time interval being smaller than the others. Those skilled in the art should note that for certain application scenarios, it may only be necessary to reconstruct images of a specific region of interest or a specific time period, or to reconstruct images of a specific number of nuclides. In such cases, selecting a subset of matching events that meet the requirements for segmentation can significantly improve data processing efficiency and shorten detection time while accurately quantifying and qualitatively analyzing data, which is highly valuable for clinical applications. It should also be noted that for certain research needs or special application scenarios, all matching events can be selected for segmentation to obtain the correlation and influence relationships between different types of nuclides. Those skilled in the art can choose the number of matching events to process as needed based on the inspiration of this application, which will not be elaborated further here. Therefore, in this embodiment, step S200 includes: selecting matching events corresponding to several time periods, or selecting all matching events, and dividing the selected matching events into corresponding time intervals according to their timestamps.

[0142] Specifically, in one example of this application, step S200 includes: obtaining a collection time interval or an off-beam collection time interval, and dividing the collection time interval or the off-beam collection time interval into X time intervals.

[0143] The acquisition time interval is generally the time span of all matching events, and the off-beam acquisition time interval is the time span of the off-beam matching events mentioned above.

[0144] Step S300: Provide Y spatial regions, and divide at least a portion of the conforming events into corresponding spatial regions according to the response lines corresponding to the conforming events.

[0145] The Y spatial regions may be the same size or not completely the same size, preferably all Y spatial regions are the same size. The shape of the spatial regions is a cube, cuboid, polygon, or irregular shape, preferably a cube. Those skilled in the art should note that for some application scenarios, it may only be necessary to reconstruct images of a specific region of interest or a specific time period, or to reconstruct images of a specific number of nuclides. In this case, a subset of matching events that meet the requirements can be selected for segmentation, which can significantly improve data processing efficiency and shorten detection time while accurately quantifying and qualitatively analyzing data, making it extremely valuable for clinical applications. It should also be noted that for certain research needs or special application scenarios, all matching events can be selected for segmentation to obtain the correlation and influence relationships between different types of nuclides. Those skilled in the art can choose the number of matching events to process as needed based on the inspiration of this application, which will not be elaborated further here.

[0146] Specifically, in one example of this application, step S300 includes: acquiring a detection field of view and dividing the detection field of view into Y spatial regions.

[0147] For example, the detection field of view is divided into cubic spatial regions with each side length Q. Coincidence events / off-beam coincidence events are then assigned to these regions according to their response lines. For instance, the three-dimensional coordinates of the coincidence / off-beam coincidence events are obtained from the intersections of a single response line with each spatial region. The spatial region to which these coordinates belong is determined, and the coincidence event is assigned to that region, thus completing the spatial region division. Alternatively, the coordinates of the intersections of two or more response lines corresponding to the same time window are calculated to determine the spatial region to which these coordinates belong. The coincidence event is then assigned to that region, thus completing the spatial region division.

[0148] Specifically, in another example of this application, step S300 may further include: providing Y spatial regions, and dividing at least a portion of the conforming events into the corresponding spatial regions according to the response lines and flight times corresponding to the conforming events.

[0149] In this embodiment, time-of-flight information is added when dividing the spatial region. Accordingly, when dividing the detection field of view into cubic spatial regions with each side length Q, and assigning coincidence events / off-beam coincidence events to each spatial region according to the response line, for example, the distance difference between the occurrence location of the coincidence event / off-beam coincidence event and the two ends of the response line is first calculated using time-of-flight combined with the speed of light. Then, the three-dimensional coordinates of the coincidence event / off-beam coincidence event are obtained using the response line, the spatial region to which the coordinates belong is determined, and the coincidence event is assigned to the corresponding spatial region, thus completing the spatial region division. By incorporating time-of-flight information for spatial region division, the accuracy of spatial region division can be further improved, significantly enhancing the quality of image reconstruction.

[0150] 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 partially compliant events" is determined in the time division stage; when step S300 comes first, the specific coverage of "at least partially compliant events" is determined in the spatial division stage.

[0151] Step S400: Combine the matching events located in the x-time interval and the y-space region into a single dataset, and sum them to obtain... There are datasets, among which, , x and y should not both be equal to 1.

[0152] 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 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.

[0153] Step S500: Combine the nuclide category corresponding to the matching event with the activity of all nuclides, process the matching events in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset.

[0154] Furthermore, in some examples, step S500 includes: combining the nuclide category corresponding to the coincident event and the activity of all nuclides, processing the coincident events 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.

[0155] Furthermore, in some other examples, step S500 includes: combining the nuclide category corresponding to the coincident event and the activity of all nuclides, processing the coincident events 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.

[0156] Furthermore, step S500 includes processing at least a portion of the datasets using an exponential stripping 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 activity of all nuclides. For example, the exponential stripping method includes the following steps: 1. Plot a semi-logarithmic coordinate system for the total activity values ​​of nuclides corresponding to coincidence events / de-bundle coincidence events in at least a portion of the x-th time interval and y-th spatial interval (denoted as (x, y)). The horizontal axis is time, and the vertical axis is the logarithm of activity. 2. At the end of the coordinate system, when the short-lived nuclides have basically decayed, their curves approach a straight line. The slope of the straight line is proportional to the decay constant of the nuclide with the longest half-life. The intercept obtained by extrapolating the straight line to zero time 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 nuclides until all nuclides are separated, thus completing the acquisition of the activity of each nuclide.

[0157] More specifically, step S500 includes using a matrix solving method to process coincidence events in at least a portion of the dataset to obtain the activity of each nuclide in each dataset, and obtaining the activity proportion 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. Establishing a matrix equation Ea=A for the total nuclide activity values ​​corresponding to coincidence events / out-of-bundle coincidence events in at least a portion of the x-th time interval and y-th spatial interval (denoted as (x, y)). E is an exponential matrix whose elements are determined by known decay constants 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. Solving the matrix equation using the least squares method to obtain the activity of each nuclide.

[0158] Specifically, step S500 includes processing coincidence events in at least a portion of the dataset using a multi-exponential fitting method to obtain the activity of each nuclide in each dataset, and obtaining the activity proportion of each nuclide based on the ratio of the activity of each nuclide to the activities of all nuclides. For example, the multi-exponential fitting method includes the following steps: 1. Plotting a second time spectrum for at least a portion of coincidence events / off-bundle coincidence events divided into the x-th time interval and the y-th spatial interval (denoted as (x, y)); 2. Performing multi-exponential fitting on the second time spectrum using the following formula to obtain the activities of several nuclides in each dataset:

[0159] , , ,

[0160] in, This represents the activity of all nuclides located within the x-time interval and the y-space region at the measured time t. , Let f represent the half-life of nuclide f, and b represent the baseline term, which can be 0 or any other arbitrary constant. This represents the activity of nuclide f, which is located in the x-time interval and the y-space region, obtained through fitting. For the result to be fitted, a and c are constants when the half-life of nuclide f is determined.

[0161] Furthermore, for all After normalization, the activity percentage of each nuclide is obtained:

[0162] ,

[0163] in, This represents the percentage of activity of nucleon f located in the x-time interval and the y-space region.

[0164] According to one embodiment, step S500 further includes: combining the nuclide category corresponding to the matching event and the activity of all nuclides, performing multi-exponential fitting on the matching events in all datasets to obtain the activity ratio of several nuclides in each dataset.

[0165] Specifically, for all coincidence events / off-bundle coincidence events divided into the x-th time interval and y-th spatial interval (denoted as (x, y)), the activity of each nuclide in each dataset is obtained by multi-exponential fitting, exponential stripping, or matrix solving in a manner similar to that described in the example above, which will not be elaborated further 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 reconstruct images of a specific region of interest or 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 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 elaborated further here.

[0166] To illustrate the effectiveness of the multi-nucleoside imaging data processing method provided in this application, two specific application examples are given here. In one application example, the prosthetic PMMA is the test subject, and the main nuclide placed inside it is... 11 C 10 C 15 O、 8 B. In another application example, the deceased rat was the test subject. Both application examples were conducted following these steps:

[0167] (1) Place the prosthetic / dead rat in the treatment area of ​​the proton knife;

[0168] (2) Irradiate the prosthetic / dead rat with a proton beam of 110 MeV;

[0169] (3) Simultaneously or after beam irradiation is completed, the PET system is activated to collect coincidence events / beam break coincidence events;

[0170] (4) The collected coincidence events / beam offset coincidence events were processed using the multi-nucleus imaging data processing method provided in this application, and the activity values ​​of each nuclide in the prosthetic PMMA application scenario were obtained by fitting, as shown in Table 1 below. The fitting results were presented as curves, as shown below. Figure 5 and Figure 6 .

[0171] Table 1 Activity values ​​of various nuclides in PMMA application scenarios

[0172] Types of nuclides Activity value (AU) <![CDATA[ 11 C]]> 2349.7±28.4 <![CDATA[ 10 C]]> 8002.0±57.9 <![CDATA[ 15 O]]> 10492.6±62.8 <![CDATA[ 8 B]]> 61513.9±975.4

[0173] pass Figure 5 and Figure 6 It can be observed that the horizontal axis represents the time after beam separation, with 0 representing the start time of beam separation, and the vertical axis represents the event count rate, the count-time spectrum R. 2 A value of 0.999 indicates that the fitting result almost perfectly matches the radioactive index decay theory, the data reliability is very high, and the fitting effect is very good.

[0174] It should be noted that, for cost reasons, this application only illustrates the two application examples described above, and these examples should not be considered as limiting the scope of protection of this application. Based on the above examples, those skilled in the art can foresee that the methods provided in this application can achieve the same or similar effects in other application examples.

[0175] The multi-nucleoside imaging data processing method provided in this application solves the problem of the inability to obtain the spatiotemporal distribution of nuclide composition in existing technologies by using temporal and spatial segmentation of coincidence events. This improves the performance of multi-nucleoside activity separation and broadens the application prospects 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, and therefore 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 higher success rate and thus more effective events detected. This leads to 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 acquiring large amounts of labeled training data and investing in expensive training servers, making it more feasible and less costly.

[0176] Corresponding to the above-described multi-nucleoside imaging data processing method, embodiments of this application also provide an image reconstruction method. This image reconstruction method can first use existing methods to perform image reconstruction, merging all nuclides into one image, and then obtaining the coordinates at time t in the image (…). activity value The nuclide activity ratio obtained based on the above multi-nucleoside imaging data processing method Then at time t, at coordinates ( The activity of nuclide f in ) is , Where t belongs to the x-th time interval, and the coordinates are ( () belongs to the y-th spatial region. After obtaining the activity of each nuclide, the image is reconstructed according to the activity of each nuclide, which will result in an image with the same number of nuclide species. Compared with existing image reconstruction, the image obtained in this application is directly related to the nuclide, making it easier to clearly understand the contribution of each nuclide.

[0177] Based on the description of the above embodiments of the multi-nuclide imaging data processing method, this application also provides a multi-nuclide imaging data processing apparatus. The apparatus may include devices (including distributed systems), software (applications), modules, components, servers, clients, etc., using the methods described in the embodiments of this specification, combined with necessary hardware implementations. Based on the same innovative concept, the apparatuses 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 specific apparatuses in the embodiments of this specification can refer to the implementation of the foregoing methods, and repeated details will not be elaborated further. 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 apparatuses described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible.

[0178] Figure 7 This is a schematic diagram of the structure of a multi-nucleoside imaging data processing device in one embodiment of this application. Figure 7 As shown, the multi-nucleoside imaging data processing device 700 may include: a time interval division module 720, a spatial region division module 730, a dataset module 740, and a nuclide separation module 750. Further, Figure 8 This is a schematic diagram of the structure of a multi-nucleoside imaging data processing device in another embodiment of this application. Figure 8 As shown, the multi-nucleoside imaging data processing device 700 may further include a data acquisition module 710. The data acquisition module 710 is configured to acquire coincidence events and the corresponding nuclide categories. Coincidence events can be acquired through a PET system, which may be clinical PET, animal PET, or proton / heavy ion PET, originating from scenarios involving the coexistence of multiple nuclides such as proton therapy, heavy ion therapy, or multi-tracer PET. The data can be in-beam acquired event data or event data acquired after treatment. The acquisition of coincidence events can be described in the method section and will not be repeated here.

[0179] According to an example of this application, the data acquisition module 710 is configured to obtain a database as prior information by establishing a table including application scenarios, particle categories and / or nuclide categories, or by simulating application scenarios, particle categories and / or nuclide categories in a simulation system, and directly obtain the nuclide category corresponding to the event based on the prior information.

[0180] For example, the nuclide category corresponding to the event can be obtained directly from prior information or by 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.

[0181] 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. Specific examples can be found in the methods section, and will not be repeated here.

[0182] In one embodiment, the multi-nucleoside imaging data processing apparatus 700 further includes: a correction module configured to acquire conformance event data to be processed and to correct the conformance event data to obtain the conformance event.

[0183] The coincidence event data to be processed can be acquired through a PET system, originating from scenarios involving the coexistence of multiple radionuclides, such as proton therapy, heavy ion therapy, or multi-tracer PET. This data can be acquired in-beam or after treatment (beam off). For example, correction can include random correction, background correction, or simultaneous random and background correction to obtain the coincidence event data. Specific correction methods can be found in existing technologies and will not be elaborated upon here.

[0184] It is important to note that while a fully digital PET system can accurately extract the energy, location, and time information of a single event, and the coincidence system uses energy window screening, location exclusion, and time window filtering to determine coincidence events and obtain projection data, the limited width of the time window used to detect true coincidences means that two unrelated single events that are sufficiently close in time may be mistakenly identified as a pair of coincidence events, resulting in random coincidences. In addition to random coincidences, background noise, such as environmental radiation and electronic noise, also exists during actual measurements. This noise forms background events, which also need to be corrected for.

[0185] Specifically, in one example of this application, the correction is configured to perform random correction, background correction, or both random correction and background correction on the event data to be processed.

[0186] The specific operations for random correction and background correction of the data to be processed can be carried out using existing correction methods. Any method that can achieve the purpose of random correction or background correction can be used in this application.

[0187] Specifically, see Figure 9 The data acquisition module 710 further includes a first filtering module 711, which is configured to filter out off-beam coincidence events from the coincidence events. The time interval division module 720 and the spatial region division module 730 perform time division and spatial division based on the off-beam coincidence events.

[0188] For example, the first filtering module 711 is 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 according to the reference time, count the number of single events in each bin, identify all bins with a number of single events exceeding a predetermined threshold as bins in the bundle, and use the start time of the first bin after the last bin in the bundle as the start time of the unbundling.

[0189] For example, create a list of all N matching events, forming a list pattern set L. Then, plot a statistical histogram based on the average time for each matching event. The average time for the i-th matching event... The definition is as follows:

[0190] , ,

[0191] 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.

[0192] 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 divided into one bin, then there will be 100 bins.

[0193] Assuming there are n bins, the number of single events is counted for each bin as follows:

[0194] ,

[0195] in, Let j be the start time of the j-th sub-box. Let be the number of single events in the j-th bin. .

[0196] 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.

[0197] 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.

[0198] 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.

[0199] Furthermore, in both proton and heavy ion knife PET dose-guided 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 degrees of freedom during subsequent spatiotemporal nuclide separation. Specifically, the data acquisition module 710 further includes a second screening module 712, configured to acquire the activity of all global nuclides and select nuclides with activities greater than a preset value for subsequent processing.

[0200] Furthermore, in some examples, the second screening module 712 is configured to obtain the activity of all global nuclides through a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

[0201] Furthermore, in other examples, the second screening module 712 is configured to obtain the activity of all global nuclides using gradient descent or the Levenberg-Marquardt algorithm.

[0202] Furthermore, the second screening module 712 is configured to obtain the activity of all global nuclides through an exponential stripping method. The main steps include: 1. Processing all coincident 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; 2. 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, the intercept obtained is the initial activity of the nuclide; 3. Subtracting the contribution of the longest half-life nuclide from the original data to obtain a new residual curve. Repeating 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 all global nuclides.

[0203] Furthermore, the second screening module 712 is configured to obtain the activity of all global nuclides through a matrix solution method. The main steps include: 1. Dividing all matching events into several time points or intervals at equal intervals, and obtaining the total activity value of nuclides at each time point or interval; 2. Establishing a matrix equation Ea=A, where E is an exponential matrix whose elements are determined by the known decay constant and the 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; 3. Solving the matrix equation using the least squares method to obtain the activity of all global nuclides.

[0204] More specifically, the second screening module 712 is configured to obtain nuclide activity using a multi-exponential fitting method. The main steps include: 1. plotting a first time spectrum for the collected coincidence events; 2. using the following formula to perform multi-exponential fitting on the first time spectrum to obtain the activity of all global nuclides:

[0205] , , ,

[0206] in, This represents the activity of all nuclides at the measured time t. J represents the number of all nuclides. Let A represent the half-life of nuclide j, b represent the baseline term, and b can be 0 or any other arbitrary constant. j This represents the activity of nuclide j obtained from the fitting, where a and c are constants when the half-life of nuclide f is determined.

[0207] After obtaining the activities of all nuclides, the nuclides are sorted from highest to lowest activity. The top F nuclides are selected, or the F nuclides with activity values ​​greater than a preset value are selected for further processing, or F nuclides are selected based on the nuclide of interest or application requirements. These nuclides are numbered 1 to F, with half-lives of [missing information]. , .

[0208] For both proton and heavy ion knife PET dose-guided scenarios and proton and heavy ion knife PET biological-guided scenarios, nuclides can be selected according to actual needs, and the number of nuclides selected for the two scenarios may differ.

[0209] Specifically, the time interval division module 720 is configured to provide X time intervals, and to divide at least a portion of the matching events into the corresponding time intervals according to their timestamps. The X time intervals can be the same size, different sizes, or not entirely the same. In one example, except for the last time interval, the remaining time intervals are all the same size, with the last time interval being smaller than the others. Those skilled in the art should note that for some application scenarios, it may only be necessary to reconstruct images of a specific region of interest or a specific time period, or to reconstruct images of a specific number of nuclides. In this case, selecting a subset of matching events that meet the requirements for division can significantly improve data processing efficiency and shorten detection time while accurately quantifying and qualitatively analyzing data, which is highly valuable for clinical applications. It should also be noted that for certain research needs or special application scenarios, all matching events can be selected for division to obtain the correlation and influence relationships between different types of nuclides. Those skilled in the art can choose the number of matching events to process as needed based on the inspiration of this application, which will not be elaborated further here. Therefore, in this embodiment, the time interval division module 720 is configured to select matching events corresponding to several time periods, or to select all matching events, and to divide the selected matching events into the corresponding time intervals according to their timestamps.

[0210] More specifically, the time interval division module 720 is configured to: acquire a collection time interval or an off-beam collection time interval, and divide the collection time interval or the off-beam collection time interval into X time intervals. The collection time interval is generally the time span of all matching events, and the off-beam collection time interval is the time span of the aforementioned off-beam matching events.

[0211] Specifically, the spatial region division module 730 is configured to provide Y spatial regions, and divides at least a portion of the matching events into corresponding spatial regions according to the response lines corresponding to the matching events. The Y spatial regions may be the same size or not completely the same size, preferably the Y spatial regions are the same size. The shape of the spatial regions is a cube, cuboid, polygon, or irregular shape, preferably a cube. Those skilled in the art should note that for some application scenarios, it may only be necessary to reconstruct images of a specific region of interest or a specific time period, or to reconstruct images of a specific number of nuclides. In this case, selecting a portion of the matching events that meet the requirements for division 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 certain research needs or special application scenarios, all matching events can be selected for division to obtain the correlation and influence relationships between different types of nuclides. Those skilled in the art can select the number of matching events to process as needed based on the inspiration of this application, which will not be elaborated further here.

[0212] More specifically, the spatial region division module 730 is configured to: acquire the detection field of view for coincidence event acquisition, and divide the detection field of view into Y spatial regions. For example, the detection field of view is divided into cubic spatial regions with a side length of Q, and coincidence events / off-beam coincidence events are assigned to each spatial region according to the response lines. For instance, the three-dimensional coordinates of the coincidence event / off-beam coincidence event are obtained based on the intersection points of a single response line with each spatial region, the spatial region to which the coordinates belong is determined, and the coincidence event is assigned to that spatial region after finding the corresponding spatial region, thus completing the spatial region division. Alternatively, the coordinates of the intersection points of two or more response lines corresponding to the same time window are calculated, the spatial region to which the coordinates belong is determined, and the coincidence event is assigned to that spatial region after finding the corresponding spatial region, thus completing the spatial region division.

[0213] Specifically, in another example of this application, the spatial region division module 730 is configured to: provide Y spatial regions, and divide at least a portion of the conforming events into the corresponding spatial regions according to the response lines and flight times corresponding to the conforming events.

[0214] The detection field of view is divided into cubic spatial regions with each side length Q. Coincidence events / off-beam coincidence events are then assigned to these regions according to the response line. For example, the distance difference between the occurrence location of a coincidence event / off-beam coincidence event and the two ends of the response line is first calculated using time-of-flight and the speed of light. Then, the three-dimensional coordinates of the coincidence event / off-beam coincidence event are obtained using the response line, and the spatial region to which the coordinates belong is determined. Once the corresponding spatial region is found, the coincidence event is assigned to that region, thus completing the spatial region division. By incorporating time-of-flight information into spatial region division, the accuracy of spatial region division can be further improved, significantly enhancing the quality of image reconstruction.

[0215] Those skilled in the art will understand that the order of time interval division and spatial region segmentation can be reversed. The selection of "at least partially conforming events" is based on actual application needs or regions of interest. Under the same conditions, different choices of the order of time interval and spatial region division will not result in different specific coverage of "at least partially conforming events".

[0216] Specifically, the dataset module 740 is configured to treat matching events within the x-time interval and the y-space region as a single dataset, and to obtain a total of [dataset details missing]. A dataset, , .

[0217] Specifically, the nuclide separation module 750 is configured to combine the nuclide category corresponding to the matching event and the activity of all nuclides to process the matching events in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset.

[0218] Furthermore, in some examples, the nuclide separation module 750 is configured to combine the nuclide category corresponding to the coincident event and the activity of all nuclides, and process the coincident events 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.

[0219] Furthermore, in other examples, the nuclide separation module 750 is configured to combine the nuclide category corresponding to the coincidence event with the activity of all nuclides, and process the coincidence events 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.

[0220] Furthermore, the nuclide separation module 750 is configured to use an exponential stripping method to process the coincidence events in at least a portion of the dataset to obtain the activity of each nuclide in each dataset, and to obtain the activity percentage of each nuclide based on the ratio of the activity of each nuclide to the activity of all nuclides. For example, the exponential stripping method includes the following steps: 1. Plot a semi-logarithmic coordinate system for the total activity values ​​of nuclides corresponding to coincidence events / de-bundle coincidence events in at least a portion of the x-th time interval and y-th spatial interval (denoted as (x, y)). The horizontal axis is time, and the vertical axis is the logarithm of activity. 2. At the end of the coordinate system, when the short-lived nuclides have basically decayed, their curves approach a straight line. The slope of the straight line is proportional to the decay constant of the nuclide with the longest half-life. The intercept obtained by extrapolating the straight line to zero time 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 nuclides until all nuclides are separated, thus completing the acquisition of the activity of each nuclide.

[0221] More specifically, the nuclide separation module 750 is configured to use a matrix solving method to process coincidence events in at least a portion of the dataset to obtain the activity of each nuclide in each dataset, and to obtain the activity proportion 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. Establishing a matrix equation Ea=A for the total nuclide activity values ​​corresponding to coincidence events / out-of-bundle coincidence events in at least a portion of the x-th time interval and y-th spatial interval (denoted as (x, y)). E is an exponential matrix whose elements are determined by known decay constants 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. Solving the matrix equation using the least squares method to obtain the activity of each nuclide.

[0222] Specifically, the nuclide separation module 750 is configured to use a multi-exponential fitting method to process coincidence events in at least a portion of the dataset to obtain the activity of each nuclide in each dataset, and to obtain the activity proportion of each nuclide based on the ratio of the activity of each nuclide to the activities of all nuclides. For example, the multi-exponential fitting method includes the following steps: 1. Plotting a second time spectrum for at least a portion of coincidence events / decoupling coincidence events divided into the x-th time interval and the y-th spatial interval (denoted as (x, y)); 2. Using the following formula to perform multi-exponential fitting on the second time spectrum to obtain the activities of several nuclides in each dataset:

[0223] , , ,

[0224] in, This represents the activity of all nuclides located within the x-time interval and the y-space region at the measured time t. , Let f represent the half-life of nuclide f, and b represent the baseline term, which can be 0 or any other arbitrary constant. This represents the activity of nuclide f, which is located in the x-time interval and the y-space region, obtained through fitting. For the result to be fitted, a and c are constants when the half-life of nuclide f is determined.

[0225] Furthermore, the nuclide separation module 750 is configured to separate all nuclides. After normalization, the activity percentage of each nuclide is obtained:

[0226] ,

[0227] in, This represents the percentage of activity of nucleon f located in the x-time interval and the y-space region.

[0228] According to one embodiment, the nuclide separation module 750 is configured to combine the nuclide category corresponding to the coincident event and the activity of all nuclides to perform multi-exponential fitting on the coincident events in all datasets to obtain the activity ratio of several nuclides in each dataset.

[0229] Specifically, for all coincidence events / off-bundle coincidence events divided into the x-th time interval and y-th spatial interval (denoted as (x, y)), the activity of each nuclide in each dataset is obtained by multi-exponential fitting, exponential stripping, or matrix solving in a manner similar to that described in the example above, which will not be elaborated further 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 reconstruct images of a specific region of interest or 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 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 elaborated further here.

[0230] The multi-nucleoside imaging data processing apparatus provided in this application solves the problem of the inability to obtain the spatiotemporal distribution of nuclide composition in the prior art by using time segmentation and spatial segmentation of coincidence events, thereby improving the performance of multi-nucleoside activity separation and broadening the application prospects 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, and therefore 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 higher success rate and thus more effective events detected. This leads to 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 acquiring large amounts of labeled training data and investing in expensive training servers, making it more feasible and less costly.

[0231] Corresponding to the above-mentioned multi-nucleoside imaging data processing device, embodiments of this application also provide an image reconstruction device. This image reconstruction device can first perform image reconstruction using existing methods, merging all nuclides into one image, and then obtain the coordinates at time t in the image (…). activity value The nuclide activity ratio obtained from the aforementioned multi-nucleoside imaging data processing device Then at time t, at coordinates ( The activity of nuclide f in ) is , Where t belongs to the x-th time interval, and the coordinates are ( () belongs to the y-th spatial region. After obtaining the activity of each nuclide, the image is reconstructed according to the activity of each nuclide, which will result in the same number of images as the number of nuclide types. Compared with existing image reconstruction, the images obtained in this application are directly related to the nuclides, making it easier to clearly understand the contribution of each nuclide.

[0232] It should be understood that Figure 7 and Figure 8 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).

[0233] 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.

[0234] Figure 10 This is a schematic diagram of a multi-nuclide imaging data processing system for implementing a multi-nuclide imaging data processing method in one embodiment of this application. (Refer to...) Figure 10The multi-nuclide imaging data processing 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-nuclide imaging data processing method.

[0235] 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 specification, the processor of the processing device executes steps S100 to S500, it should be understood that steps S100 to S500 may also be executed jointly or independently by two different processors of the processing device (e.g., the first processor executes step S100, the second processor executes steps S200 to S500, or the first and second processors jointly execute steps S100 to S500).

[0236] The multi-nuclide imaging data processing system S00 may further include: a power supply component S24 configured to perform power management of the multi-nuclide imaging data processing system S00; a wired or wireless network interface S26 configured to connect the multi-nuclide imaging data processing system S00 to a network; and an input / output (I / O) interface S28. The multi-nuclide imaging data processing system S00 can operate on an operating system stored in memory S22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or similar.

[0237] 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 data processing 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.

[0238] 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 imaging data processing system S00 to perform the above-described method.

[0239] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, Figure 11This 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 data processing 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 data processing method.

[0240] Those skilled in the art will understand that Figure 11 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.

[0241] 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 resistive 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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 method for processing multi-nucleoside imaging data, characterized in that, include: Given X time intervals, assign at least a portion of the matching events to the corresponding time intervals based on their timestamps; Y spatial regions are provided, and at least a portion of the conforming events are divided into corresponding spatial regions according to the response lines corresponding to the conforming events; Combine the matching events located in the x-time interval and the y-space region into a single dataset to obtain the total dataset. A dataset, , ; By combining the nuclide categories corresponding to the matching events and the activities of all nuclides, the matching events in at least a portion of the datasets are processed to obtain the activity percentages of several nuclides in each dataset.

2. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, Before providing X time intervals, the multi-nucleoside imaging data processing method further includes: Retrieve the matching event and the corresponding nuclide category.

3. The multi-nucleoside imaging data processing method according to claim 2, characterized in that, Retrieve the matching event and the corresponding nuclide category, including: Two single-pulse events that are located within the same time window, the same energy window, and whose position lines cross the imaging field of view are marked as a single coincidence event.

4. The multi-nucleoside imaging data processing method according to claim 2, characterized in that, Retrieve the matching event and the corresponding nuclide category, including: The nuclide category corresponding to the event is obtained based on prior information or mass spectrometry.

5. The multi-nucleoside imaging data processing method according to claim 2, characterized in that, Retrieve the matching event and the corresponding nuclide category, including: By establishing a table including application scenarios, particle categories, and / or nuclide categories as prior information, or by using a database obtained through simulation in a simulation system based on application scenarios, particle categories, and / or nuclide categories as prior information, the nuclide category corresponding to the matching event can be directly obtained based on the prior information.

6. The multi-nucleoside imaging data processing method according to claim 2, characterized in that, Before obtaining the matching event and the corresponding nuclide category, the multi-nuclide data processing method further includes: Obtain the data of the matching events to be processed, and correct the data of the matching events to obtain the matching events.

7. The multi-nucleoside imaging data processing method according to claim 6, characterized in that, Correcting the event data to be processed includes: The event data to be processed is subject to random correction, background correction, or both random correction and background correction.

8. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, Before providing X time intervals, the multi-nucleoside imaging data processing method further includes: Select off-beam coincidence events from the coincidence events, and use the off-beam coincidence events for temporal and spatial partitioning.

9. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, At least a portion of the matching events are assigned to corresponding time intervals based on their timestamps, including: Select matching events within a certain time period, or select all matching events, and divide the selected matching events into the corresponding time intervals according to their timestamps.

10. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, Before providing X time intervals, the multi-nucleoside imaging data processing method further includes: Obtain the activity of all nuclides globally, and select a preset number of nuclides or nuclides with an activity greater than a preset value for subsequent processing.

11. The multi-nucleoside imaging data processing method according to claim 10, characterized in that, Obtain the activity of all nuclides globally, including: The activity of all nuclides can be obtained by using the multi-exponential fitting method, the exponential stripping method, or the matrix solving method.

12. The multi-nucleoside imaging data processing method according to claim 11, characterized in that, Obtain the activity of all nuclides globally, including: Draw the first time spectrum for all matching events; The activity of all nuclides worldwide is obtained by performing multi-exponential fitting on the first time spectrum using the following formula: ， ， ， in, This represents the activity of all nuclides at the measured time t. J represents the number of different types of nuclides. Let b represent the half-life of nuclide j, and b represent the baseline term. This represents the activity of nuclide j obtained from the fitting.

13. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, Given X time intervals, assign at least a portion of matching events to the corresponding time intervals based on their timestamps, including: Obtain the acquisition time interval or the off-beam acquisition time interval, and divide the acquisition time interval or the off-beam acquisition time interval into X time intervals.

14. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, The multi-nucleoside imaging data processing method further includes: Y spatial regions are provided, and at least a portion of the coincident events are divided into corresponding spatial regions according to the response lines and flight times corresponding to the coincident events.

15. The multi-nucleoside imaging data processing method according to claim 1 or 14, characterized in that, Provide Y spatial regions, including: Obtain the detection field of view and divide the detection field of view into Y spatial regions.

16. The multi-nucleoside imaging data processing method according to claim 1, characterized in that, By combining the nuclide categories corresponding to the matching events and the activities of all nuclides, the matching events in at least a portion of the dataset are processed to obtain the activity percentages of several nuclides in each dataset, including: The activity percentages of several nuclides in each dataset are obtained by processing the coincident events in at least a portion of the dataset using the multi-exponential fitting method, the exponential stripping method, or the matrix solving method.

17. The multi-nucleoside imaging data processing method according to claim 16, characterized in that, By combining the nuclide categories corresponding to the matching events and the activities of all nuclides, the matching events in at least a portion of the dataset are processed to obtain the activity percentages of several nuclides in each dataset, including: Draw a second temporal spectrum for all matching events in at least a portion of the dataset; The activities of several nuclides in each dataset are obtained by performing multi-exponential fitting on the second time spectrum using the following formula: ， ， ， in, This represents the activity of all nuclides located in the time interval x and the spatial region y at time t. , The term 'b' represents the half-life of nuclide f, and 'b' represents the baseline term. This represents the activity of nuclide f, which is located in the x-time interval and the y-space region, obtained through fitting.

18. The multi-nucleoside imaging data processing method according to claim 17, characterized in that, By combining the nuclide categories corresponding to the matching events and the activities of all nuclides, the matching events in at least a portion of the dataset are processed to obtain the activity percentages of several nuclides in each dataset, including: The activity percentage of each nuclide can be obtained using the following formula: ， in, This represents the activity percentage of nuclide f located in the x-time interval and the y-space region.

19. An image reconstruction method, characterized in that, include: PET image reconstruction is performed using the activity percentage obtained by the multi-nucleoside imaging data processing method as described in any one of claims 1 to 18.

20. A multi-nucleoside imaging data processing device, characterized in that, include: The time interval division module is configured to provide X time intervals and divide at least a portion of the matching events into the corresponding time intervals according to their timestamps; The spatial region division module is configured to provide Y spatial regions and divide at least a portion of the conforming events into corresponding spatial regions according to the response lines corresponding to the conforming events. The dataset module is configured to treat matching events located in the x-time interval and the y-space region as a single dataset, and aggregate them to obtain... A dataset, , ; The nuclide separation module is configured to combine the nuclide category corresponding to the matching event and the activity of all nuclides to process the matching events in at least a portion of the dataset to obtain the activity percentage of several nuclides in each dataset.

21. The multi-nucleoside imaging data processing apparatus according to claim 20, characterized in that, The multi-nucleoside imaging data processing device further includes: The data acquisition module is configured to acquire matching events and the corresponding nuclide categories.

22. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The data acquisition module is configured to mark two single-pulse events that are located within the same time window, the same energy window, and whose position lines cross the imaging field of view as a single coincident event.

23. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The data acquisition module is configured to obtain the nuclide category corresponding to the event based on prior information or mass spectrometry.

24. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The data acquisition module is configured to obtain prior information by establishing a table including application scenarios, particle categories and / or nuclide categories, or by simulating application scenarios, particle categories and / or nuclide categories in a simulation system to obtain a database as prior information, and directly obtain the nuclide category corresponding to the event based on the prior information.

25. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The multi-nucleoside imaging data processing device further includes: The correction module is configured to acquire data of conforming events to be processed and to correct the data of conforming events to obtain the conforming events.

26. The multi-nucleoside imaging data processing apparatus according to claim 25, characterized in that, The correction module is configured to perform random correction, background correction, or both random correction and background correction on the event data to be processed.

27. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The data acquisition module includes: a first filtering module configured to filter out off-beam coincidence events from coincidence events; and the time interval division module and the spatial region division module configured to perform time division and spatial division based on off-beam coincidence events.

28. The multi-nucleoside imaging data processing apparatus according to claim 20, characterized in that, The time interval division module is configured to: select corresponding matching events within a number of time periods, or select all matching events, and divide the selected matching events into corresponding time intervals according to their timestamps.

29. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The data acquisition module further includes: The second screening module is configured to obtain the activity of all nuclides globally and select nuclides with an activity greater than a preset value for subsequent processing.

30. The multi-nucleoside imaging data processing apparatus according to claim 29, characterized in that, The second screening module is configured to obtain the activity of all global nuclides through a multi-exponential fitting method, an exponential stripping method, or a matrix solving method.

31. The multi-nucleoside imaging data processing apparatus according to claim 30, characterized in that, The second filtering module is configured as follows: Draw the first time spectrum for all matching events; The activity of all nuclides worldwide is obtained by performing multi-exponential fitting on the first time spectrum using the following formula: ， ， ， in, This represents the activity of all nuclides at the measured time t. J represents the number of different types of nuclides. Let b represent the half-life of nuclide j, b represent the baseline term, and A represent the half-life of nuclide j. j This represents the activity of nuclide j obtained from the fitting.

32. The multi-nucleoside imaging data processing apparatus according to claim 21, characterized in that, The time interval division module is configured to: acquire the acquisition time interval or the off-beam acquisition time interval, and divide the acquisition time interval or the off-beam acquisition time interval into X time intervals.

33. The multi-nucleoside imaging data processing apparatus according to claim 20, characterized in that, The spatial region division module is configured to provide Y spatial regions and divide at least a portion of the matching events into the corresponding spatial regions according to the response lines and flight times of the matching events.

34. The multi-nucleoside imaging data processing apparatus according to claim 20 or 33, characterized in that, The spatial region division module is configured to: acquire the detection field of view and divide the detection field of view into Y spatial regions.

35. The multi-nucleoside imaging data processing apparatus according to claim 20, characterized in that, The nuclide separation module is configured to: combine the nuclide category corresponding to the coincident event and the activity of all nuclides, and process the coincident events 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.

36. The multi-nucleoside imaging data processing apparatus according to claim 35, characterized in that, The nuclide separation module is configured as follows: Draw a second temporal spectrum for all matching events in at least a portion of the dataset; The activities of several nuclides in each dataset are obtained by performing multi-exponential fitting on the second time spectrum using the following formula: ， ， ， in, This represents the activity of all nuclides located in the time interval x and the spatial region y at time t. , The term 'b' represents the half-life of nuclide f, and 'b' represents the baseline term. This represents the activity of nuclide f, which is located in the x-time interval and the y-space region, obtained through fitting.

37. The multi-nucleoside imaging data processing apparatus according to claim 36, characterized in that, The nuclide separation module is configured to calculate the activity percentage of each nuclide according to the following formula: ， in, This represents the percentage of activity of nucleon f located in the x-time interval and the y-space region.

38. An image reconstruction apparatus, characterized in that, The device is configured to reconstruct PET images using the activity percentage obtained from the multi-nucleoside imaging data processing apparatus as described in any one of claims 20 to 37.

39. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-nucleoside imaging data processing method as described in any one of claims 1 to 18 or the image reconstruction method as described in claim 19.

40. 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 data processing method as described in any one of claims 1 to 18 or the image reconstruction method as described in claim 19.

41. A digital PET system, characterized in that, It includes the multi-nucleoside imaging data processing apparatus as described in any one of claims 20 to 37 or the image reconstruction apparatus as described in claim 38.

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