A method and system for calibration of a scanning device

Abstract

The embodiment of the specification provides a correction method and system of a scanning device, the method comprising: acquiring first background coincidence event data; determining a reference normalization correction factor corresponding to the background coincidence event based on the first background coincidence event data; and determining a normalization correction factor of the scanning device based on the reference normalization correction factor and a mapping relationship.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of medical technology, and in particular, to a correction method and system of a scanning device. BACKGROUND

[0002] In the field of nuclear medicine today, Positron Emission Tomography (PET) is a rapidly developing imaging technology that is widely used in clinical detection. Among them, the PET-CT system combining PET and X-ray Computer Tomography (CT) is often used, and the PET-CT system with a long axial field of view has gained rapid attention and development due to its extremely high sensitivity. In the PET-CT system, the radiation generated by CT scanning is the main source of radiation. In order to reduce the radiation dose of the entire system, a PET-CT system without CT needs to be designed.

[0003] In the PET-CT system, PET data needs to be corrected for attenuation and scattering, and the PET part needs to be corrected for normalization. All of these require the use of CT scan data. Among them, the CT image can obtain an attenuation image of the scanning object for gamma photons with an energy of 511 keV after conversion, which can be used for attenuation correction and scattering correction of PET data after registration, so as to reconstruct the image. At the same time, in the normalization correction of the system, a uniform phantom is used for scanning including CT scanning, and after attenuation, scattering and other physical corrections of the scanning data, true coincidence event data is obtained, and after processing, a normalization correction factor is obtained. After removing the CT, the problems of attenuation correction, scattering correction and normalization correction of PET under this condition need to be solved.

[0004] Therefore, it is desirable to provide a correction method and system of a scanning device. SUMMARY

[0005] One of the embodiments of the present specification provides a correction method of a scanning device. The method comprises: obtaining first background coincidence event data; determining a reference normalization correction factor corresponding to the background coincidence event based on the first background coincidence event data; and determining a normalization correction factor of the scanning device based on the reference normalization correction factor and a mapping relationship.

[0006] One of the embodiments of the present specification provides a correction system of a scanning device, comprising a data acquisition module, an event correction factor acquisition module and a device correction factor determination module; the data acquisition module is used to acquire first background coincidence event data; the event correction factor acquisition module is used to determine a reference normalization correction factor corresponding to a background coincidence event based on the first background coincidence event data; and the device correction factor determination module is used to determine a normalization correction factor of the scanning device based on the reference normalization correction factor and a mapping relationship.

[0007] One of the embodiments of the present specification provides a correction device of a scanning device, comprising a processor, which is used to execute a correction method of the scanning device.

[0008] One of the embodiments of the present specification provides a computer readable storage medium, which stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes a correction method of the scanning device.

[0009] One of the embodiments of the present specification provides an image reconstruction method. The method comprises acquiring background coincidence event data and target coincidence event data of a target object; acquiring a normalization correction factor of a scanning device by using a correction method of the scanning device, and correcting the target coincidence event data according to the normalization correction factor; and performing image reconstruction based on the corrected target coincidence event data to obtain a target image.

[0010] One of the embodiments of the present specification provides another image reconstruction method, comprising: acquiring background coincidence event data and target coincidence event data of a target object; estimating an initial attenuation chord map based on the background coincidence event data; and performing image reconstruction based on the initial attenuation chord map and the target coincidence event data to obtain a target image.

[0011] One of the embodiments of the present specification provides an image reconstruction system, comprising a data acquisition module, a chord map generation module and an image reconstruction module: the data acquisition module is used to acquire background coincidence event data and target coincidence event data of a target object; the chord map generation module is used to estimate an initial attenuation chord map based on the background coincidence event data; and the image reconstruction module is used to perform image reconstruction based on the initial attenuation chord map and the target coincidence event data to obtain a target image.

[0012] One of the embodiments of the present specification provides a computer readable storage medium, which stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the image reconstruction method. BRIEF DESCRIPTION OF DRAWINGS

[0013] The present specification will be further explained in the way of example embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, in which the same numbers refer to the same structures, and wherein:

[0014] Figure 1 is a schematic diagram of an application scenario of a system for reconstructing an image according to some embodiments of the present specification;

[0015] Figure 2 is a schematic diagram of a system for reconstructing an image according to some embodiments of the present specification;

[0016] Figure 3 is a schematic diagram of a correction system of a scanning device according to some embodiments of the present specification;

[0017] Figure 4 is an exemplary flowchart of a method for reconstructing an image according to some embodiments of the present specification;

[0018] Figure 5 is an exemplary flowchart of a method for correcting a scanning device according to some embodiments of the present specification;

[0019] Figure 6 is a schematic diagram of decay energy levels of Lu-176 according to some embodiments of the present specification;

[0020] Figure 7 is a schematic diagram of a discrimination process of a background coincidence event according to some embodiments of the present specification;

[0021] Figure 8 is a schematic diagram of a discrimination process of a target coincidence event according to some embodiments of the present specification;

[0022] Figure 9 is a schematic diagram of a calibration process of a normalization correction factor corresponding to 511 keV gamma photons according to some embodiments of the present specification;

[0023] Figure 10 is a schematic diagram of an acquisition process of a normalization correction factor corresponding to 511 keV gamma photons according to some embodiments of the present specification;

[0024] Figure 11 is a schematic diagram of an acquisition of a normalization correction factor using data of a coincidence event according to some embodiments of the present specification;

[0025] Figure 12 is a schematic diagram of an acquisition of a normalization correction factor using data of a background coincidence event according to some embodiments of the present specification;

[0026] Figure 13is a schematic diagram of a method of reconstruction of an image according to some embodiments of the present specification;

[0027] Figure 14 is a schematic diagram of a method of reconstruction of an image according to some embodiments of the present specification;

[0028] Figure 15 is a schematic diagram of a method of reconstruction of an image according to some embodiments of the present specification. DETAILED DESCRIPTION

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor on the basis of these drawings. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structure or operation.

[0030] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0031] As shown in the specification and claims, unless the context clearly indicates otherwise, the words "one", "a", "an", and / or "the" do not mean singular, but can also include plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0032] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps of the operation can be removed from these processes.

[0033] In some application scenarios, the image reconstruction system can include a processing device, a medical imaging device, the image reconstruction system can acquire the background radiation signal through the medical imaging device, and by using the background radiation signal, the method and / or process disclosed in the present specification are implemented through the processing device to determine the normalization correction factor of the medical imaging device (for example, the PET device) and the attenuation correction factor of the scanned object, thereby performing normalization correction on the medical imaging device, performing attenuation correction and / or scatter correction on the PET scan data of the scanned object, thereby realizing the attenuation, scatter and normalization correction problem of the scan data in the case of no CT, avoiding the radiation damage caused by CT scanning, without the need for registration of CT and PET images, simplifying the operation process, reducing the scanning time, and reducing the radiation damage to the scanned object and the operator.

[0034] Figure 1 is a schematic diagram of an application scenario of the image reconstruction system according to some embodiments of the present specification.

[0035] As Figure 1 shown, in some embodiments, the system 100 can include a scanning device 110, a processing device 120, a storage device 130, a terminal 140, and a network 150.

[0036] The scanning device 110 refers to a device that reproduces the structure inside the human body as an image using different media in the medical field. In some embodiments, the scanning device 110 can be any medical device that images or treats a specified body part of a patient by using a radionuclide, such as PET-CT, PET, Single-Photon Emission Computed Tomography (SPECT), SPECT-CT, PET-MR, etc. The scanning device 110 provided above is for illustrative purposes only and is not intended to limit the scope thereof. The detector in the scanning device 110 can receive radiation of a radiation source and measure the received radiation. In some embodiments, the scanning device 110 can transmit data and information related to the detector, such as the radiation photon energy value received by the detector, the output value of the detector, etc., to the processing device 120. In some embodiments, the scanning device 110 can acquire background coincidence event data (i.e., background radiation signal) of the device, target coincidence event data of a target object (e.g., a human body, a phantom, etc.) scanned, etc., and transmit the same to the processing device 120. In some embodiments, the scanning device 110 can perform normalization correction of the device according to the normalization correction factor determined by the processing device 120. The scanning device 110 can receive instructions, etc., transmitted by the physician through the terminal 140 and perform relevant operations, such as irradiation imaging, etc., according to the instructions. In some embodiments, the scanning device 110 can exchange data and / or information with other components (e.g., the processing device 120, the storage device 130, the terminal 140) in the system 100 through the network 150. In some embodiments, the scanning device 110 can be directly connected to other components in the system 100. In some embodiments, one or more components (e.g., the processing device 120, the storage device 130) in the system 100 can be included in the scanning device 110.

[0037] The processing device 120 can process data and / or information obtained from other devices or system components, perform the reconstruction method of images shown in some embodiments of the present specification based on the data, information and / or processing results, and complete one or more functions described in some embodiments of the present specification. For example, the processing device 120 can obtain a normalization correction factor of the acquisition device based on the background coincidence event data of the scanning device 110 and the target coincidence event data of the scanned target object, to perform normalization correction on the scanning device 110. For another example, the processing device 120 can obtain an attenuation chord based on the background coincidence event data of the scanning device 110, and perform at least one of attenuation correction, scatter correction, image reconstruction, etc. based on the attenuation chord and the target coincidence event data. In some embodiments, the processing device 120 can send the processed data, such as the normalization correction factor, the attenuation chord, etc., to the storage device 130 for storage. In some embodiments, the processing device 120 can obtain pre-stored data and / or information, such as the background coincidence event data, the target coincidence event data, various calculation formulas, etc., from the storage device 130, to be used for performing the reconstruction method of images shown in some embodiments of the present specification and / or the correction method of the scanning device, such as obtaining the normalization correction factor of the acquisition device, etc.

[0038] In some embodiments, the processing device 120 can include one or more sub-processing devices (e.g., single-core processing devices or multi-core multi-core processing devices). For example only, the processing device 120 can include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc., or any combination thereof.

[0039] The storage device 130 can store data or information generated by other devices. In some embodiments, the storage device 130 can store data and / or information collected by the scanning device 110, such as the background coincidence event data, the target coincidence event data, etc. In some embodiments, the storage device 130 can store data and / or information processed by the processing device 120, such as the normalization correction factor of the acquisition device, etc. The storage device 130 can include one or more storage components, each of which can be a separate device or a part of other devices. The storage device can be local or implemented through the cloud.

[0040] The terminal 140 can control the operation of the scanning device 110. A physician can issue an operation instruction to the scanning device 110 through the terminal 140 to make the scanning device 110 complete a designated operation, such as irradiating a designated body part of a patient for imaging. In some embodiments, the terminal 140 can instruct the processing device 120 to perform a reconstruction method of an image and / or a correction method of the scanning device as shown in some embodiments of the present specification. In some embodiments, the terminal 140 can receive a reconstructed image and the like from the processing device 120, and thus a physician can accurately determine the condition of a patient to effectively and targetedly examine and / or treat the patient. In some embodiments, the terminal 140 can be one or any combination of a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, a desktop computer, and other devices with input and / or output functions.

[0041] The network 150 can connect the components of the system and / or connect the system with external resource parts. The network 150 enables communication between the components and other parts outside the system, and facilitates exchange of data and / or information. In some embodiments, one or more components (e.g., the scanning device 110, the processing device 120, the storage device 130, the terminal 140) in the system 100 can send data and / or information to other components through the network 150. In some embodiments, the network 150 can be any one or more of a wired network or a wireless network.

[0042] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of the present specification. Various changes and modifications can be made by those of ordinary skill in the art under the guidance of the present specification. The features, structures, methods and other characteristics of the exemplary embodiments described in the present specification can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the processing device 120 can be based on a cloud computing platform, such as a public cloud, a private cloud, a community cloud, and a hybrid cloud. However, these changes and modifications will not depart from the scope of the present specification.

[0043] Figure 2 is a schematic diagram of an image reconstruction system according to some embodiments of the present specification.

[0044] As shown in Figure 2 some embodiments, the system 200 can include a data acquisition module 210, a chord diagram generation module 220, and an image reconstruction module 230.

[0045] In some embodiments, the data acquisition module 210 can be used to acquire background coincidence event data and / or target coincidence event data of a target object (e.g., a human body, a phantom, etc.).

[0046] In some embodiments, the data acquisition module 210 can acquire the background coincidence event data before, after or simultaneously with the acquisition of the target coincidence event data.

[0047] In some embodiments, the background coincidence event data can include background coincidence event data received by at least one combined response line, each of which is derived from two or more original response lines.

[0048] In some embodiments, the target coincidence event data can include normalized and corrected data, e.g., data after normalization and correction of coincidence event data of the target object using the normalization and correction factor obtained using the method shown in the flow 500.

[0049] In some embodiments, the chord diagram generation module 220 can be configured to estimate an initial attenuation chord diagram based on the background coincidence event data.

[0050] In some embodiments, the chord diagram generation module 220 can determine the initial attenuation chord diagram based on the background coincidence event data using a maximum likelihood estimation method.

[0051] In some embodiments, the image reconstruction module 230 can be configured to perform image reconstruction based on the initial attenuation chord diagram and the target coincidence event data to obtain a target image, i.e., a reconstructed image that meets a predetermined condition.

[0052] In some embodiments, the image reconstruction module 230 can reconstruct an attenuation map based on the initial attenuation chord diagram, determine a scatter estimate based on the attenuation map, perform scatter correction based on the target coincidence event data, the initial attenuation chord diagram and the scatter estimate to obtain a corrected initial image, and perform iterative updating of the corrected initial image based on the target coincidence event data and the initial attenuation chord diagram to meet a first predetermined condition, and take the corrected image that meets the first predetermined condition as the target image.

[0053] In some embodiments, the image reconstruction module 230 can reconstruct an attenuation map based on the initial attenuation chord diagram, determine a scatter estimate based on the attenuation map, perform scatter correction based on the target coincidence event data, the initial attenuation chord diagram and the scatter estimate to obtain a corrected initial image, and perform iterative updating of the corrected initial image and the initial attenuation chord diagram based on the target coincidence event data and the initial attenuation chord diagram to meet a second predetermined condition, and take the corrected image that meets the second predetermined condition as the target image.

[0054] In some embodiments, the image reconstruction module 230 can obtain a reconstructed initial image based on the initial attenuation chord diagram and the target coincidence event data, and perform iterative updating of the reconstructed initial image, the initial attenuation chord diagram, an attenuation map and a scatter estimate to meet a third predetermined condition, and take the updated image that meets the third predetermined condition as the target image.

[0055] In some embodiments, the image reconstruction module 230 can generate the updated image for the current iteration based on the updated image generated for the previous iteration, the attenuation chord generated for the previous iteration, and the scatter estimate generated for the previous iteration, wherein the updated image generated for the first iteration is the reconstructed initial image; determine the attenuation chord for the current iteration based on the updated image for the current iteration; determine the attenuation map for the current iteration based on the attenuation chord for the current iteration; and determine the scatter estimate for the current iteration based on the attenuation map for the current iteration.

[0056] Figure 3 is a schematic diagram of a correction system of a scanning device according to some embodiments of the present specification.

[0057] As Figure 3 shown, in some embodiments, the system 300 can include a data acquisition module 310, an event correction factor acquisition module 320, and a device correction factor determination module 330.

[0058] In some embodiments, the data acquisition module 310 can be configured to acquire background coincidence event data (e.g., first background coincidence event data, second background coincidence event data, etc.) and / or bulk coincidence event data.

[0059] In some embodiments, the data acquisition module 310 can acquire the first background coincidence event data by a blank scan.

[0060] In some embodiments, the data acquisition module 310 can acquire the background coincidence event data before, after, or simultaneously with the bulk coincidence event data.

[0061] In some embodiments, the event correction factor acquisition module 320 can be configured to determine a normalization correction factor corresponding to the background coincidence event based on the background coincidence event data. In some embodiments, the normalization correction factor determined based on the first background coincidence event data can be referred to as a reference normalization correction factor, and the normalization correction factor determined based on the second background coincidence event data can be referred to as a first normalization correction factor, wherein the reference normalization correction factor and the first normalization correction factor are the same except that they are based on different local coincidence data. In some embodiments, the reference normalization correction factor and the first normalization correction factor can be determined using the same method. In some embodiments, the first normalization correction factor can include at least one of a first geometric correction factor, a first crystal interference correction factor, a first axial profile correction factor, a first detector ring-to-detection efficiency correction factor, a first ring profile correction factor, and a first crystal detection efficiency correction factor.

[0062] In some embodiments, the event correction factor obtaining module 320 can perform the following steps in a loop until all the first normalization correction factors are determined, wherein in each round of the loop, one of the first geometric correction factor, the first crystal interference correction factor, the first axial profile correction factor, the first detector ring pair detection efficiency correction factor, the first ring profile correction factor, and the first crystal detection efficiency correction factor is determined: obtaining actual true coincidence counts corresponding to a current first normalization correction factor based on the second true coincidence event data; calculating theoretical true coincidence counts corresponding to the current first normalization correction factor; determining the current first normalization correction factor based on the actual true coincidence counts and the theoretical true coincidence counts; and wherein if there is a first normalization correction factor that has not been determined, correcting the actual true coincidence counts corresponding to the second true coincidence event data based on the determined current first normalization correction factor.

[0063] In some embodiments, the event correction factor obtaining module 320 can also determine second normalization correction factors corresponding to the Compton scatter events based on the Compton scatter event data. In some embodiments, the second normalization correction factors can include at least one of a second geometric correction factor, a second crystal interference correction factor, a second axial profile correction factor, a second detector ring pair detection efficiency correction factor, a second ring profile correction factor, and a second crystal detection efficiency correction factor.

[0064] In some embodiments, the event correction factor obtaining module 320 can perform the following steps in a loop until all the second normalization correction factors are determined, wherein in each round of the loop, one of the second geometric correction factor, the second crystal interference correction factor, the second axial profile correction factor, the second detector ring pair detection efficiency correction factor, the second ring profile correction factor, and the second crystal detection efficiency correction factor is determined: performing physical correction on the Compton scatter event data to obtain actual true coincidence counts corresponding to a current second normalization correction factor; calculating theoretical true coincidence counts corresponding to the current second normalization correction factor; determining the current second normalization correction factor based on the actual true coincidence counts and the theoretical true coincidence counts; and wherein if there is a second normalization correction factor that has not been determined, correcting the actual true coincidence counts corresponding to the Compton scatter event data based on the determined current second normalization correction factor.

[0065] In some embodiments, the event correction factor obtaining module 320 can determine a mapping relationship based on the first normalization correction factor and the second normalization correction factor. In some embodiments, the mapping relationship can include at least one of a first mapping relationship, a second mapping relationship, a third mapping relationship, a fourth mapping relationship, a fifth mapping relationship, and a sixth mapping relationship, wherein the first mapping relationship includes a mapping relationship between the first geometric correction factor and the second geometric correction factor, the second mapping relationship includes a mapping relationship between the first crystal interference correction factor and the second crystal interference correction factor, the third mapping relationship includes a mapping relationship between the first axial profile correction factor and the second axial profile correction factor, the fourth mapping relationship includes a mapping relationship between the first detector ring pair detection efficiency correction factor and the second detector ring pair detection efficiency correction factor, the fifth mapping relationship includes a mapping relationship between the first ring profile correction factor and the second ring profile correction factor, and the sixth mapping relationship includes a mapping relationship between the first crystal detection efficiency correction factor and the second crystal detection efficiency correction factor.

[0066] In some embodiments, the device correction factor determining module 330 can be configured to determine the normalization correction factor of the scanning device based on the reference normalization correction factor and the mapping relationship.

[0067] In some embodiments, the system 300 can further include a data correction module (not shown). The data correction module can perform normalization correction on the scanning device, i.e., perform normalization correction on the target coincidence event data of the target object (e.g., a human body, a phantom, etc.) based on the normalization correction factor of the scanning device; and perform image reconstruction based on the initial attenuation sinogram and the normalization corrected target coincidence event data to obtain a target image. Figure 3

[0068] Figure 4 is an exemplary flowchart of a method of image reconstruction according to some embodiments of the present specification.

[0069] As shown in Figure 4 , the flow 400 can include the following steps. In some embodiments, the flow 400 can be performed by the processing device 120.

[0070] Step 410, obtaining background coincidence event data and target coincidence event data of a target object. In some embodiments, step 410 can be performed by the data obtaining module 210.

[0071] The background coincidence event is a coincidence event generated by the spontaneous background radiation of the crystal in the scanning device, i.e., the radiation signal received by the coincidence event is generated by the background radiation. Common PET systems use LSO or LYSO crystals as scintillation crystals, which contain isotope Lu-176 that can generate spontaneous background radiation. Figure 6 The decay energy level diagram for Lu-176 is shown in FIG. 2.​Figure 6 As shown, the decay of Lu-176 includes simultaneous β decay and cascaded γ decay. The maximum energy of β decay is 589 keV, and the energies of gamma decay are 307 keV, 202 keV, and 88 keV. Utilizing the simultaneity and definite energies of β and γ decay, background radiation events, i.e., background coincidence events, can be identified. In some embodiments, the background radiation signal from a scanning device can be acquired to obtain background coincidence event data. In some embodiments, the background radiation energy (i.e., the background gamma decay energy) can be 307 keV, 202 keV, or 88 keV, etc.

[0072] In some embodiments, it can be achieved through, as shown in Figure 7 The process shown in step 700 is used to identify baseline coincidence events in the PET system.

[0073] like Figure 7 As shown, at a certain moment, detector 1 receives a single event A, and detector 2 receives a single event B. The energy and arrival time of single event A are EA and TA, respectively, and the energy and arrival time of single event B are EB and TB, respectively, and TA>TB (event B occurs before event A). If EA falls within energy window C, EB falls within energy window D, and TA-TB falls within time window E, then this event is recorded as a background coincidence event. Here, energy window C can be [307keV-3σ, 307keV+3σ], [202keV-3σ, 202keV+3σ], or [88keV-3σ, 88keV+3σ], etc.; σ is the standard deviation of the Gaussian energy distribution of the system; energy window D can be [0keV, 1000keV]; and time window E can be [0, ΔT′]. max +3σ′], where ΔT′ max σ′ represents the maximum possible absolute value of the time difference between the arrival of β decay and γ rays at the detector, and σ′ represents the standard deviation of the Gaussian time distribution of the system.

[0074] The target object, also referred to as a target scanning object, refers to a scanning object of a scanning device, for example, a living body, a phantom, etc. The living body can be a human body, a small animal, etc. The phantom can be a phantom of various materials and shapes, for example, a water phantom, a gel material phantom, a wooden phantom, a cylinder, a cuboid, etc. In some embodiments, the target object can be a human body and / or a water phantom. The target coincidence event data refers to coincidence events corresponding to the emission energy of a specific energy level, for example, coincidence events corresponding to 511 keV, coincidence events corresponding to 662 keV, etc. In some embodiments, the target object can be scanned by the scanning device to obtain the target coincidence event data of the target object, for example, a water phantom and / or a human body can be scanned to identify the coincidence events corresponding to 511 keV from all coincidence events of the water phantom and / or the human body. In some embodiments, the background coincidence event data and / or the target coincidence event data can be obtained from a storage device or by other means.

[0075] In some embodiments, the positron-electron annihilation coincidence events, i.e., target coincidence events, in a PET system can be identified by a process 800 as shown in Figure 8

[0076] As shown in Figure 8 At a certain time, the detector 1 of the PET device receives a single event A, and the detector 2 receives a single event B. The energy and arrival time of the single event A are EA and TA, respectively. The energy and arrival time of the single event B are EB and TB, respectively. TA>TB (event B occurs before event A). If EA and EB both fall within an energy window F, and TB-TA falls within a time window G, then the event is recorded as a target coincidence event. The energy window F can be taken as [511 keV-3σ, 511 keV+3σ]. σ is the standard deviation of the Gaussian energy distribution of the system. The time window G can be taken as [-ΔT max -3σ', ΔT max +3σ'], where ΔT max is the maximum possible absolute value of the time difference of a pair of 511 keV γ rays generated by a pair of positron-electron annihilation reaching the detector. σ' is the standard deviation of the Gaussian time distribution of the system.

[0077] In some embodiments, the coincidence event data can be obtained by collecting coincidence events. The coincidence event data can include the actual collected coincidence counts, the energy of the coincidence events, etc. In some embodiments, the background coincidence event data can be collected before, after or simultaneously with the target coincidence event data.

[0078] ​The attenuation coefficient of the same substance differs for gamma rays of different energies. In some embodiments, to perform attenuation and scattering correction on target coincidence event data, the spatial distribution of the attenuation coefficient μ corresponding to the 511 keV energy can be obtained. The relationship between this distribution and the attenuation coefficient μ′ corresponding to the background gamma decay energy can be expressed by the following formula:

[0079] μ=kμ′ (1)

[0080] Where k is a constant, and in some embodiments, k can be measured by measuring the energy E of a substance (e.g., water). γ The attenuation coefficient μ′ of gamma rays H2O and the attenuation coefficient μ for γ-rays with an energy of 511 keV H2O The result is shown in the following formula:

[0081]

[0082] Step 420: Estimate an initial attenuation chord graph based on the background coincidence event data. In some embodiments, step 420 may be performed by the chord graph generation module 220.

[0083] Because the background signal strength is weak, in order to increase the background event count received on a single response line and reduce statistical noise, in some embodiments, two or more original response lines can be merged into a single combined response line, for example, formed by connecting two or more crystal modules. In some embodiments, the background coincidence event data may include background coincidence event data received by at least one combined response line.

[0084] In some embodiments, a normalized correction factor corresponding to the background gamma photon energy can be generated through various methods (e.g., empty scan, maximum likelihood estimation, etc.). This involves obtaining a normalized correction factor corresponding to the background coincidence event based on the background coincidence event data, such as a reference normalized correction factor or a first normalized correction factor. In some embodiments, the actual background coincidence event data (i.e., the actual background coincidence count) can be normalized using the normalized correction factor corresponding to the background coincidence event to obtain the normalized background coincidence event data.

[0085] Blank scan refers to scanning the air directly without any target objects such as humans or phantoms. In this case, the target object can be considered as air. In some embodiments, target coincidence event data can be acquired through blank scan, and such target coincidence event data can also be called blank scan data. In some embodiments, for any response line, the normalization correction factor for that response line can be obtained by dividing the target coincidence event count obtained from the blank scan by the mean of the target coincidence event counts on all response lines.

[0086] In some embodiments, the normalization correction factor can be obtained by a maximum likelihood estimation method.

[0087] In some embodiments, the likelihood function L t which can be expressed as follows:

[0088]

[0089] where n' i i represents the detection efficiency of the response line to the background true events; subscript i represents the number of the response line; y' i i and respectively represent the measured count and the expected count of the background coincidence events on the i-th response line, where y' i i represents the background coincidence event count including the background true events, the background random events and the background scattering events, and experiencing actual physical effects such as decay effect, detector detection efficiency effect, etc.

[0090] In some embodiments, the measured count and the expected count of the background coincidence events on the i-th response line which can be expressed as follows:

[0091]

[0092] where b i i represents the background coincidence event count on the i-th response line, including the background true event count; s' i i represents the estimation of the background random and scattering event count on the i-th response line; n' has the same meaning as in equation (3).

[0093] In some embodiments, assuming that the background coincidence events are uniformly distributed on all response lines, the following equation is obtained:

[0094]

[0095] where N is the total number of response lines; b i has the same meaning as in equation (4); represents the mean of the background coincidence event count occurring on all response lines.

[0096] In some embodiments, based on equations (3)-(5), the update formula of the response line detection efficiency n can be as follows:

[0097]

[0098] where k is the iteration number, and the remaining symbols have the same meaning as in equations (3)-(5).

[0099] In some embodiments, the normalization correction factor NC' of any one of the response lines i The detection efficiency n of the response line can be expressed by the following formula: i The reciprocal of the detection efficiency n of the response line can be expressed by the following formula:

[0100]

[0101] In some embodiments, the normalization correction factor corresponding to the background coincidence event data can also be obtained by other methods. For example, the first normalization correction factor can be obtained by the method shown in Figure 12 , each of the normalization correction factors in the first normalization correction factor is multiplied, and the product is taken as the normalization correction factor corresponding to the background coincidence event data. For details about how to obtain the first normalization correction factor, refer to the related description of Figure 12 , which will not be described here again.

[0102] In some embodiments, the normalization correction factor corresponding to the 511 keV gamma photon, by which the target coincidence event data is normalized and corrected, can be obtained in various ways. For example, the second normalization correction factor can be obtained by the method shown in Figure 11 , and the second normalization correction factor is taken as the normalization correction factor corresponding to the 511 keV gamma photon. For details about how to obtain the second normalization correction factor, refer to the related description of Figure 11 , which will not be described here again.

[0103] The initial attenuation chord is the chord corresponding to the background gamma photon energy, i.e. the attenuation chord of the background coincidence event data. The attenuation chord can also be referred to as the attenuation effect chord, and the attenuation chord on any one of the response lines is used to represent the attenuation effect on the response line. In some embodiments, the initial attenuation chord can be determined based on the background coincidence event data by a maximum likelihood estimation method.

[0104] In some embodiments, in order to obtain the initial attenuation chord, a scanning object without injection of radioactive drugs is placed in the field of view of the PET system, a certain amount of background coincidence event data (for example, 307 keV, 202 keV or 88 keV background coincidence event data) is collected, wherein the background coincidence event data can also be referred to as background transmission data; and target coincidence event data (for example, 511 keV target coincidence event data) of the scanning object (i.e. target object) is collected.

[0105] In some embodiments, the likelihood function of the background coincidence (transmission) event data can be expressed by the following formula:

[0106]

[0107] where a represents the attenuation chord corresponding to the 511 keV gamma photon energy; subscript i represents the number of the response line; y' i represents the measured count of the background coincidence events on the i-th response line; y' i represents the expected count of the background coincidence events on the i-th response line; and y' i represents the estimated count of the background random and scatter events on the i-th response line. i and respectively represent the measured count and the expected count of the background coincidence events on the i-th response line, where y' i represents the measured count of the background coincidence events on the i-th response line; y' i represents the expected count of the background coincidence events on the i-th response line; and y' i represents the estimated count of the background random and scatter events on the i-th response line. i represents the count of the background coincidence events including the background true events, the background random events and the background scatter events, and experiencing the actual physical effects such as the attenuation effect, the detector detection efficiency effect, etc.

[0108] In some embodiments, the measured count and the expected count of the background coincidence events on the i-th response line can be represented by the following formula:

[0109]

[0110] where a and a' respectively represent the attenuation chord corresponding to the 511 keV and the background gamma decay energy (307 keV, 202 keV or 88 keV); b i represents the count of the background coincidence events on the i-th response line, including the count of the background true events; s' i represents the estimation of the count of the background random and scatter events on the i-th response line, where the background random estimation can be estimated by the delayed coincidence event data, for example, directly using or using the delayed coincidence data after noise reduction (for example, smoothing or other methods), and the background scatter estimation can be estimated by the analytical method or the Monte Carlo simulation method.

[0111] In some embodiments, the relationship between the attenuation coefficient μ corresponding to the 511 keV and the attenuation coefficient μ' corresponding to the background gamma decay energy can be represented by the following formula:

[0112] μ' = ημ (10)

[0113] where η in the formula (9) and (10) is the same, η = 1 / k, and k is a constant in the formula 1.

[0114] In some embodiments, the attenuation chord a corresponding to the 511 keV and the attenuation chord a' corresponding to the background gamma decay energy can be represented by the following formula:

[0115]

[0116] where l represents the system matrix without time of flight (TOF); subscript i represents the number of the response line; subscript j represents the number of the voxel; and the meanings of the remaining symbols are the same as in the formula (9)-(10).

[0117] In some embodiments, the attenuation sinogram a corresponding to the background gamma photon energy can be estimated first by a maximum likelihood estimation method i Then the attenuation sinogram a corresponding to the 511 keV gamma photon can be obtained i = (a' i ) 1 / η .

[0118] At step 430, image reconstruction is performed based on the initial attenuation sinogram and the target coincidence event data to obtain a target image. In some embodiments, step 430 can be performed by the image reconstruction module 230.

[0119] In some embodiments, the image reconstruction can be performed based on the initial attenuation sinogram and the target coincidence event data.

[0120] In some embodiments, the target coincidence event data can include normalized corrected data. In some embodiments, the actual target coincidence event data (i.e. actual true coincidence counts) can be corrected by using the normalization correction factor corresponding to the target coincidence event. For example, the second normalization correction factor can be obtained by the method shown in FIG. 6, each of the items in the second normalization correction factor is multiplied, the product is taken as the normalization correction factor corresponding to the target coincidence event, and the actual true coincidence counts are corrected by using the normalization correction factor. Figure 11

[0121] In some embodiments, the attenuation sinogram (e.g. the initial attenuation sinogram) can be updated based on the background coincidence event data and / or the target coincidence event data. In some embodiments, the updating of the attenuation sinogram can be performed iteratively.

[0122] In some embodiments, the initial attenuation sinogram can be updated based on the background coincidence event data by a maximum likelihood estimation method to obtain an updated initial attenuation sinogram. For example, the updating is performed by equations (12) and (13).

[0123] In some embodiments, if L t in equation (8) is a convex function, the attenuation sinogram (e.g. the initial attenuation sinogram) can be updated by the following equation:

[0124]

[0125] where k is the iteration number, and the rest of the symbols have the same meaning as in equations (8)-(11).

[0126] In some embodiments, if L t in equation (8) is a non-convex function, the attenuation sinogram can be updated by the following equation:

[0127] ​

[0128] wherein, c i n (a i n ) denotes a replacement function (i.e., a proxy function) q i n of the curvature of the function f n is the iteration number; the subscript + outside the brackets means that when the value inside the brackets is less than 0, it is equal to 0, otherwise it is equal to the value inside the brackets.

[0129] In some embodiments, the decay sinogram (e.g., the initial decay sinogram) can be updated (e.g., updated by equation (19) and equation (20)) based on the background coincidence event data and the target coincidence event data by the maximum likelihood estimation method to obtain an updated decay sinogram, wherein the target coincidence event data can be obtained by placing the scan object to which the radiopharmaceutical has been injected into the field of view of the PET system and then collecting coincidence data, while the background coincidence event data can be collected simultaneously.

[0130] In some embodiments, the likelihood function of the target coincidence event data can be expressed by the following equation:

[0131]

[0132] wherein λ denotes the image; a denotes the decay sinogram corresponding to 511 keV; the subscript i denotes the number of the response line; the subscript t denotes the number of the time window (TOF-bin); y it and denote the measured count and the expected count of the target coincidence event in the tth time window (TOF-bin) of the ith response line, respectively.

[0133] In some embodiments, the expected count of the target coincidence event in the tth TOF-bin of the ith response line can be expressed by the following equation:

[0134]

[0135] wherein s it denotes the estimate of the sum of the scatter and random event counts in the tth TOF-bin of the ith response line; P it is a function, which can be seen from equation (17); the remaining symbol meanings are the same as in equation 14.

[0136] In some embodiments, the decay sinogram corresponding to 511 keV of the ith response line can be expressed by the following equation:

[0137]

[0138] where μ denotes the attenuation coefficient corresponding to 511 keV; I denotes the system matrix without TOF; and subscript j denotes the number of voxels.

[0139] In some embodiments, P it can be expressed as follows:

[0140]

[0141] where c denotes the system matrix with TOF, and the remaining symbols have the same meaning as in equations (14)-(16).

[0142] In some embodiments, the likelihood function of the background coincidence event data can be shown as equations (8)-(11). In some embodiments, the total likelihood function L(λ, a) can be obtained according to equations (8)-(11) and equations (14)-(17), as shown in the following equation:

[0143] L(λ, a) = L e (λ, a) + aL t (a) (18)

[0144] where a is a constant greater than 0; L e (λ, a) is the likelihood function of the target coincidence event data; L t (a) is the likelihood function of the background coincidence event data, L t (a) indicates that a' in equation (8) is replaced by a.

[0145] In some embodiments, if L in equation (18) is a convex function, the updating formula of the attenuation chord diagram can be as follows:

[0146]

[0147] where k is the iteration number, and the remaining symbols have the same meaning as in equations (8)-(11) and equations (14)-(17).

[0148] In some embodiments, if L in equation (18) is a non-convex function, the updating formula of the attenuation chord diagram can be as follows:

[0149]

[0150] where, c i n (a i n ) is the curvature of the replacement function q i n , where, n is the number of iterations.

[0151] In some embodiments, the attenuation effect correction factor can be obtained from the attenuation sinogram, and the target coincidence event data is corrected for attenuation according to the attenuation effect correction factor.

[0152] In some embodiments, the attenuation effect correction factor AC i The reciprocal of the attenuation effect of the response line can be expressed by the following formula:

[0153]

[0154] Wherein, a i represents the attenuation sinogram corresponding to 511 keV.

[0155] In some embodiments, the target coincidence event data can be corrected for scatter based on the attenuation sinogram of the target coincidence event data. In some embodiments, the attenuation map can be obtained by processing the attenuation sinogram, and the scatter estimate can be determined by scatter estimation through the attenuation map.

[0156] In some embodiments, the following steps can be performed in a loop until the scatter estimate meets a preset stopping condition: updating the image based on the target coincidence event data, the random estimate and the current scatter estimate, wherein the first scatter estimate is 0, and the random estimate can be directly obtained from the delayed coincidence data or obtained after noise reduction; obtaining the scatter estimate by analytical calculation or Monte Carlo simulation based on the image and the attenuation map of the previous step; correcting the target coincidence event data for scatter based on the scatter estimate; comparing the target coincidence event data corrected for scatter with the target coincidence event data to obtain an amplification factor, and updating the scatter estimate based on the amplification factor. In some embodiments, the preset stopping condition can include at least one of reaching a fixed number of iterations, the difference between the images of adjacent two iterations being less than a preset threshold, the difference between the attenuation maps of adjacent two iterations being less than a preset threshold, the difference between the scatter estimates of adjacent two iterations being less than a preset threshold, etc.

[0157] The attenuation map is a reconstructed image based on the attenuation sinogram, such as an attenuation coefficient spatial distribution map, etc. In some embodiments, the attenuation sinogram can be converted into an attenuation coefficient line integral sinogram, and the attenuation coefficient spatial distribution map can be obtained by reconstruction using an algorithm (such as MLEM, OSEM, etc.) using the attenuation coefficient line integral sinogram. In some embodiments, the attenuation coefficient spatial distribution map can be used for scatter estimation.

[0158] In some embodiments, the attenuation coefficient line integral sinogram can be obtained based on the attenuation sinogram of the target coincidence event data. In some embodiments, the attenuation coefficient line integral sinogram ln(a i), as shown in the following equation:

[0159]

[0160] wherein the symbol meanings are the same as in equation (16).

[0161] In some embodiments, an image of the target object can be reconstructed based on the target coincidence event data and the initial attenuation sinogram. In some embodiments, an initial image can be reconstructed from the target coincidence event data. The initial image refers to a first reconstructed image without any correction (e.g., attenuation correction, scatter correction, etc.) and image update. In some embodiments, the reconstructed image (e.g., the initial image, the updated initial image, etc.) can be updated to obtain an updated reconstructed image.

[0162] In some embodiments, the reconstructed image can be updated by the following equation:

[0163]

[0164] wherein h denotes the iteration number; λ denotes the reconstructed image; ξ denotes the index of the voxel, and the rest of the symbol meanings are the same as in equations (8)-(11) and equations (14)-(17).

[0165] In some embodiments, the initial image can be iteratively updated to obtain the target image. In some embodiments, at least one of the initial attenuation sinogram, the attenuation map, the scatter estimate, etc. can be iteratively updated while the initial image is iteratively updated. For example only, the image can be iteratively updated as shown in Figure 13 the loop of steps 1350; the image and the initial attenuation sinogram can be iteratively updated as shown in Figure 14 the loop of steps 1450 and 1460; the image, the initial attenuation sinogram, the attenuation map, and the scatter estimate can be iteratively updated as shown in Figure 15 the loop of steps 1530-1560.

[0166] In some embodiments, the iterative update can be stopped when a preset iteration stopping condition (e.g., a fixed iteration number, a difference between the images in two adjacent iterations is less than a threshold, a difference between the attenuation effect sinograms in two adjacent iterations is less than a threshold, etc.) is satisfied.

[0167] In some embodiments of the present specification, the initial attenuation chord diagram and the normalization correction factor are obtained based on the background coincidence event data, and the PET data is normalized based on the normalization correction factor. The PET data is corrected for attenuation, scatter and image updating based on the target coincidence event data and the initial attenuation chord diagram. The problems of attenuation, scatter and normalization correction of the PET data without CT are solved. The image can be reconstructed without relying on CT scan data, thereby eliminating the radiation damage of CT scanning, simplifying the scanning operation process, reducing the scanning time, and reducing the radiation damage of the entire PET system to the patient and the equipment operator. The quality of the reconstructed image is greatly improved through multiple iterations of image updating, so that the final image can well meet the diagnostic requirements.

[0168] Figure 5 FIG. 5 is an exemplary flowchart of a correction method of a scanning device according to some embodiments of the present specification.

[0169] As shown in FIG. 5, the flow 500 can include the following steps. In some embodiments, the flow 500 can be performed by the processing device 120. Figure 5

[0170] Step 510, obtaining first background coincidence event data. In some embodiments, the step 510 can be performed by the data acquisition module 210 or the data acquisition module 310.

[0171] The first background coincidence event data refers to the background coincidence event data obtained when the patient is scanned, i.e., the background coincidence event data obtained by the scanning device in the diagnostic application process. The second background coincidence event data refers to the background coincidence event data obtained when the normalization correction factor of the scanning device is obtained, i.e., the background coincidence event data obtained in the normalization factor calibration process of the scanning device. In some embodiments, the background coincidence event data can be obtained by acquiring the background radiation signal of the scanning device. The phantom coincidence event data refers to the target coincidence event data of the target object being a phantom (e.g., a water phantom, a gel material phantom, etc.). In some embodiments, the phantom coincidence event data can be obtained by scanning the phantom with the scanning device. In some embodiments, the second background coincidence event data and the phantom coincidence event data can be obtained in the calibration process of the normalization correction factor of the scanning device. In some embodiments, the first background coincidence event data and the target coincidence event data of the target object being a patient can be obtained when the scanning device scans the patient. In some embodiments, the normalization correction factor of the scanning device can be obtained through the calibration process of the normalization correction factor of the scanning device before the scanning device scans the patient.

[0172] ​In some embodiments, the second background coincidence event data can be acquired before, after or simultaneously with the acquisition of the model coincidence event data. More information about how to acquire the background coincidence event data and the model coincidence event data can be found in the description of step 410, which will not be repeated here.

[0173] At step 520, a reference normalization correction factor corresponding to a background coincidence event is determined based on the first background coincidence event data. In some embodiments, step 520 can be performed by the event correction factor acquisition module 320.

[0174] The reference normalization correction factor is a normalization correction factor corresponding to a background coincidence event in the first background coincidence event data, and can be used to perform normalization correction on the first background coincidence event data. The first normalization correction factor is a normalization correction factor corresponding to a background coincidence event in the second background coincidence event data, and can be used to perform normalization correction on the second background coincidence event data. In some embodiments, the normalization correction factor can be decomposed into at least two terms based on a component normalization correction method. In some embodiments, the first normalization correction factor can include at least one of a first geometric correction factor, a first crystal interference correction factor, a first axial profile correction factor, a first detector ring pair detection efficiency correction factor, a first ring profile correction factor, and a first crystal detection efficiency correction factor.

[0175] In some embodiments, the normalization correction factor corresponding to a background coincidence event can be determined based on the background coincidence event data (e.g., the first background coincidence event data, the second background coincidence event data, etc.). For example, the reference normalization correction factor is determined based on the first background coincidence event data. For another example, the first normalization correction factor is determined based on the second background coincidence event data.

[0176] In some embodiments, the following steps can be performed by a loop to determine all the first normalization correction factors, wherein each round of the loop determines one of the first normalization correction factors in the first geometric correction factor, the first crystal interference correction factor, the first axial profile correction factor, the first detector ring pair detection efficiency correction factor, the first ring profile correction factor, and the first crystal detection efficiency correction factor: based on the second background coincidence event data, obtaining an actual background coincidence count corresponding to a current first normalization correction factor, the first normalization correction factor determined in the current round of the loop is referred to as the current first normalization correction factor; calculating a theoretical background coincidence count corresponding to the current first normalization correction factor; determining the current first normalization correction factor based on the actual background coincidence count and the theoretical background coincidence count; and if there is a first normalization correction factor that has not been determined, correcting the actual background coincidence count corresponding to the second background coincidence event data based on the determined current first normalization correction factor.

[0177] like Figure 12 As shown, after collecting background coincidence event data, the actual background coincidence count can be obtained; the theoretical background coincidence count is calculated; a first geometric correction factor is generated based on the theoretical and actual background coincidence counts; then the actual background coincidence count is corrected using the first geometric correction factor; and the above steps are repeated to obtain a first crystal interferometry correction factor, a first axial profile correction factor, a first detector loop pair detection efficiency correction factor, a first circumferential profile correction factor, and a first crystal detection efficiency correction factor, respectively. In each round of these cycles, the actual background coincidence count used is the actual background coincidence count corrected by the first normalized correction factor obtained in the previous round. For example, when generating the first crystal interferometry correction factor, the actual background coincidence count corrected by the first geometric correction factor is used; when generating the first axial profile correction factor, the actual background coincidence count corrected by the first crystal interferometry correction factor is used.

[0178] The second normalization correction factor is the normalization correction factor corresponding to the 511keV gamma photon, that is, the normalization correction factor corresponding to the phantom coincidence event data. It can be used to normalize and correct the target coincidence event data, where the target object can be a human body or a phantom, etc. In some embodiments, the second normalization correction factor may include at least one of the following: a second geometric correction factor, a second crystal interference correction factor, a second axial profile correction factor, a second detector loop pair detection efficiency correction factor, a second circumferential profile correction factor, and a second crystal detection efficiency correction factor. In some embodiments, the second normalization correction factor may include a correction factor corresponding to each correction factor in the first normalization correction factor, for example, the second geometric correction factor corresponds to the first geometric correction factor, the second crystal interference correction factor corresponds to the first crystal interference correction factor, etc.

[0179] Since the elements in the background coincidence count corresponding to each correction factor included in the normalization correction factor are different, in the process of iteratively determining all first normalization correction factors and all second normalization correction factors, the actual background coincidence count used for different rounds of iteration actually corresponds to the values ​​of different elements, and the theoretical background coincidence count also corresponds to the values ​​of different elements.

[0180] In some embodiments, a second normalized correction factor corresponding to a phantom conformity event can be determined based on the phantom conformity event data.

[0181] In some embodiments, the following steps can be performed iteratively to determine all second normalization correction factors, wherein each iteration determines one of the following second normalization correction factors: second geometric correction factor, second crystal interference correction factor, second axial profile correction factor, second detector loop pair detection efficiency correction factor, second circumferential profile correction factor, and second crystal detection efficiency correction factor. The second normalization correction factor determined in the current iteration is referred to as the current second normalization correction factor: physical correction is performed on the phantom coincidence event data to obtain the actual true coincidence count corresponding to the current second normalization correction factor; the theoretical true coincidence count corresponding to the current second normalization correction factor is calculated; based on the actual true coincidence count and the theoretical true coincidence count, the current second normalization correction factor is determined; wherein, if there are second normalization correction factors that have not been determined, the actual true coincidence count corresponding to the phantom coincidence event data is corrected based on the determined current second normalization correction factor.

[0182] like Figure 11 As shown, after acquiring phantom coincidence event data, the data can be physically corrected (e.g., measurement deviation correction) to obtain the actual true coincidence count; the theoretical true coincidence count is calculated; a second geometric correction factor is generated based on the theoretical and actual true coincidence counts; then the actual true coincidence count is corrected using the second geometric correction factor; and the above steps are repeated to obtain the second crystal interference correction factor, the second axial profile correction factor, the second detector loop pair detection efficiency correction factor, the second circumferential profile correction factor, and the second crystal detection efficiency correction factor, respectively. In each round of these cycles, the actual true coincidence count used is the actual true coincidence count corrected by the second normalized correction factor obtained in the previous round. For example, when generating the second crystal interference correction factor, the actual true coincidence count corrected by the second geometric correction factor is used; when generating the second axial profile correction factor, the actual true coincidence count corrected by the second crystal interference correction factor is used.

[0183] In some embodiments, a mapping relationship between the first normalized correction factor and the second normalized correction factor can be determined based on the first normalized correction factor and the second normalized correction factor, wherein the mapping relationship represents the correspondence between the first normalized correction factor and the second normalized correction factor. In some embodiments, during the calibration process of the normalized factor of the scanning device, the first normalized correction factor can be obtained based on the second background coincidence event data, the second normalized correction factor can be obtained based on the phantom coincidence event data, and then the mapping relationship between the first normalized correction factor and the second normalized correction factor can be determined.

[0184] In some embodiments, the mapping relationship between the first normalization correction factors and the second normalization correction factors can include a mapping relationship between each of the first normalization correction factors and a corresponding one of the second normalization correction factors. In some embodiments, the mapping relationship between the first normalization correction factors and the second normalization correction factors can include at least one of a first mapping relationship, a second mapping relationship, a third mapping relationship, a fourth mapping relationship, a fifth mapping relationship, and a sixth mapping relationship, where the first mapping relationship includes a mapping relationship between the first geometric correction factors and the second geometric correction factors, the second mapping relationship includes a mapping relationship between the first crystal interference correction factors and the second crystal interference correction factors, the third mapping relationship includes a mapping relationship between the first axial profile correction factors and the second axial profile correction factors, the fourth mapping relationship includes a mapping relationship between the first pair-of-detector-ring detection efficiency correction factors and the second pair-of-detector-ring detection efficiency correction factors, the fifth mapping relationship includes a mapping relationship between the first ring-wise profile correction factors and the second ring-wise profile correction factors, and the sixth mapping relationship includes a mapping relationship between the first crystal detection efficiency correction factors and the second crystal detection efficiency correction factors.

[0185] In some embodiments, for each of the first normalization correction factors and a corresponding one of the second normalization correction factors, a mapping relationship between the two can be obtained by various methods (e.g., curve fitting, interpolation, other methods, etc.).

[0186] In some embodiments, the mapping relationship between the first normalization correction factors and the second normalization correction factors can be expressed by a ratio of the first normalization correction factors and the second normalization correction factors. For example, the first mapping relationship can be a ratio of the first geometric correction factors to the second geometric correction factors, or a ratio of the second geometric correction factors to the first geometric correction factors.

[0187] In some embodiments, based on each of the first normalization correction factors and a corresponding one of the second normalization correction factors, a mapping relationship corresponding to the two can be determined. For example, as shown in FIG. 6, a mapping relationship between the first geometric correction factors and the second geometric correction factors can be determined based on the first geometric correction factors and the second geometric correction factors. Figure 9As shown, after the acquisition of the phantom coincidence event data, the second geometric correction factor, the second crystal interference correction factor, the second axial profile correction factor, the second detector ring pair detection efficiency correction factor, the second ring profile correction factor, and the second crystal detection efficiency correction factor can be generated based on the phantom coincidence event data. After the acquisition of the background coincidence event data, the first geometric correction factor, the first crystal interference correction factor, the first axial profile correction factor, the first detector ring pair detection efficiency correction factor, the first ring profile correction factor, and the first crystal detection efficiency correction factor can be generated based on the background coincidence event data. After the determination of all the first normalization correction factors and the second normalization correction factors, the corresponding mapping relationship can be generated based on each of the first normalization correction factors and the corresponding one of the second normalization correction factors. For example, the first mapping relationship is generated based on the second geometric correction factor and the first geometric correction factor. For another example, the second mapping relationship is generated based on the second crystal interference correction factor and the first crystal interference correction factor.

[0188] At step 530, the normalization correction factors of the scanning device are determined based on the reference normalization correction factors and the mapping relationship. In some embodiments, step 530 can be performed by the device correction factor determination module 330.

[0189] The normalization correction factors of the scanning device refer to the correction factors that are directly used for the normalization correction of the target coincidence event data (e.g., the phantom coincidence event data, the target coincidence event data in which the target object is a human body, etc.). In some embodiments, the reference normalization correction factors can be considered as equivalent to the first normalization correction factors in the calibration process of the scanning device during the clinical scanning process of the scanning device. In some embodiments, the normalization correction factors of the scanning device can include a plurality of correction factors, wherein each of the correction factors corresponds to one of the first normalization correction factors. In some embodiments, the normalization correction factors of the scanning device can include at least one of a device geometric correction factor, a device crystal interference correction factor, a device axial profile correction factor, a device detector ring pair detection efficiency correction factor, a device ring profile correction factor, and a device crystal detection efficiency correction factor.

[0190] In some embodiments, the normalization correction factor of the scanning device can be determined based on a first normalization correction factor and a mapping relationship. For example, the mapping relationship can be represented as a function graph, where the first geometric correction factor can be an abscissa / ordinate, and the abscissa / ordinate corresponding to the first normalization correction factor is determined as the normalization correction factor of the scanning device. Alternatively, the mapping relationship can be the ratio of a second normalization correction factor to a first normalization correction factor, and the product of the first normalization correction factor and the mapping relationship is determined as the normalization correction factor of the scanning device. In some embodiments, the mapping relationship between the first and second normalization correction factors can be obtained through the normalization correction factor calibration process of the scanning device. A reference normalization correction factor is obtained based on the first background coincidence event data when the scanning device scans the patient. Then, the normalization correction factor of the scanning device is determined based on this reference normalization correction factor and the mapping relationship, where the reference normalization correction factor can correspond to the first normalization correction factor in the mapping relationship, and the normalization correction factor of the scanning device can correspond to the second normalization correction factor in the mapping relationship.

[0191] In some embodiments, the correction factor corresponding to each correction factor in the first normalized correction factor and its corresponding mapping relationship can be determined from the normalized correction factors of the scanning device. For example... Figure 10 As shown, after collecting background coincidence event data, a first geometric correction factor, a first crystal interference correction factor, a first axial profile correction factor, a first detector ring pair detection efficiency correction factor, a first circumferential profile correction factor, and a first crystal detection efficiency correction factor can be generated based on the background coincidence event data. Then, based on the determined first, second, third, fourth, fifth, and sixth mapping relationships, the device geometric correction factor, device crystal interference correction factor, device axial profile correction factor, device detector ring pair detection efficiency correction factor, device circumferential profile correction factor, and device crystal detection efficiency correction factor are determined. For example, the device geometric correction factor can be determined based on the first geometric correction factor and the first mapping relationship. As another example, the device crystal interference correction factor can be determined based on the first crystal interference correction factor and the second mapping relationship.

[0192] In some embodiments, the normalized correction factor of the scanning device can be expressed as a numerical value or parameter, etc., and the product of all the correction factors of the normalized correction factor of the scanning device can be determined as the normalized correction factor of the scanning device. For example, the normalized correction factor of the scanning device includes the device geometry correction factor, the device crystal interference correction factor, and the device axial profile correction factor, then the normalized correction factor of the scanning device = device geometry correction factor × device crystal interference correction factor × device axial profile correction factor.

[0193] In some embodiments, the target coincidence event data can be normalized according to a correction factor of the scanning device, and a target image can be reconstructed based on the normalized target coincidence event data.

[0194] In some embodiments of the present specification, a mapping relationship of the normalization correction factors of the background coincidence event data and the phantom coincidence event data is obtained, and a normalization correction factor of the device is obtained based on the background coincidence data and the mapping relationship without CT scanning data, thereby simplifying the process of device normalization correction, reducing the radiation damage to patients and operators, and the method is simple and easy to operate, and has good universality. Meanwhile, by dividing the normalization correction factor into multiple items for separate calculation, various factors are considered more comprehensively, and the normalization correction factor obtained has good accuracy and high reliability.

[0195] Figure 13 FIG. 1 is a schematic diagram of an image reconstruction method according to some embodiments of the present specification.

[0196] As shown in FIG. 1, in some embodiments, the process 1300 can include the following steps. In some embodiments, the process 1300 can be performed by the processing device 120. Figure 13

[0197] Step 1310, obtaining background coincidence event data and target coincidence event data of a target object. In some embodiments, step 1310 can be performed by the data acquisition module 210.

[0198] In some embodiments, the background coincidence event data and the target coincidence event data of the target object can be obtained by various means such as a scanning device. For more information on how to obtain the background coincidence event data and the target coincidence event data of the target object, please refer to the related description of step 410, which will not be repeated here.

[0199] Step 1320, estimating an initial attenuation chord diagram. In some embodiments, step 1310 can be performed by the chord diagram generation module 220.

[0200] In some embodiments, the initial attenuation chord diagram can be estimated based on the background coincidence event data. For more information on how to obtain the initial attenuation chord diagram, please refer to the related description of step 420, which will not be repeated here.

[0201] Step 1330, reconstructing an attenuation map. In some embodiments, steps 1330-1370 can be performed by the image reconstruction module 230.

[0202] In some embodiments, the attenuation map can be reconstructed based on the initial attenuation chord diagram. For more information on how to reconstruct the attenuation map, please refer to the related description of step 430, which will not be repeated here. ​

[0203] Step 1340, determining a scatter estimation.

[0204] In some embodiments, the scatter estimation can be determined based on the attenuation map. In some embodiments, an initial image can be obtained based on the target coincidence event data and a random estimation, where the random estimation can be obtained directly from the delayed coincidence data or after denoising; and the scatter estimation can be obtained based on the initial image and the attenuation map by analytical calculation or Monte Carlo simulation, etc.

[0205] In some embodiments, after the scatter estimation is determined, step 1350 and step 1360 can be performed in a loop.

[0206] Step 1350, updating the image.

[0207] Step 1360, determining whether a first preset condition is met.

[0208] Step 1370, ending.

[0209] In some embodiments, the scatter estimation can be used to perform scatter correction to obtain a corrected initial image. Specifically, the target coincidence event data can be corrected based on the scatter estimation, and then the initial image (i.e., the reconstructed image) can be corrected based on the scatter corrected target coincidence event data to obtain the corrected initial image.

[0210] In some embodiments, the corrected initial image can be iteratively updated based on the target coincidence event data and the initial attenuation map to meet the first preset condition. If the first preset condition is met, step 1370 is performed to end the iteration, and the corrected image that meets the first preset condition is taken as the target image. If the first preset condition is not met, step 1350 is returned to perform the next iteration. In some embodiments, the corrected initial image can be updated in various ways, for example, the image can be updated by formula (23), etc.

[0211] In some embodiments, the first preset condition can include at least one of reaching a fixed number of iterations and a difference between images in adjacent two iterations being less than a preset threshold, etc.

[0212] Figure 14 is a schematic diagram of a method for reconstructing an image according to some embodiments of the present specification.

[0213] As shown in Figure 14 some embodiments, flow 1400 can include the following steps. In some embodiments, flow 1400 can be performed by processing device 120.

[0214] Step 1410, obtaining background coincidence event data and target coincidence event data of a target object.

[0215] Step 1420, estimating an initial attenuation sinogram.

[0216] Step 1430, reconstructing an attenuation map. In some embodiments, steps 1430-1480 can be performed by the image reconstruction module 230.

[0217] Step 1440, determining a scatter estimate.

[0218] In some embodiments, steps 1410-1440 can be the same as steps 1310-1340, which will not be repeated here.

[0219] In some embodiments, after determining the scatter estimate, steps 1450 to 1470 can be performed in a loop.

[0220] Step 1450, updating an image.

[0221] Step 1460, updating the initial attenuation sinogram.

[0222] Step 1470, determining whether a second preset condition is reached.

[0223] Step 1480, ending.

[0224] In some embodiments, the corrected initial image and the initial attenuation sinogram can be iteratively updated based on the target coincidence event data and the initial attenuation sinogram to meet the second preset condition, if the second preset condition is met, step 1480 is performed to end the iteration, and the corrected image that meets the second preset condition is taken as the target image; if the second preset condition is not met, step 1450 is returned to perform the next iteration. For details about how to obtain the corrected initial image and how to update the image, please refer to the related description of Figure 13 , which will not be repeated here.

[0225] In some embodiments, the second preset condition can include at least one of reaching a fixed number of iterations, a difference between images in adjacent two iterations being less than a preset threshold, and a difference between adjacent two initial attenuation sinograms being less than a preset threshold.

[0226] In some embodiments, the initial attenuation sinogram corresponding to the target coincidence event data can be updated in various ways, for example, by formula (19) and formula (20).

[0227] Figure 15 is a schematic diagram of an image reconstruction method according to some embodiments of the present specification.

[0228] As Figure 15As shown, in some embodiments, the process 1500 can include the following steps. In some embodiments, the process 1500 can be performed by the processing device 120.

[0229] At step 1510, the background coincidence event data and the target coincidence event data of the target object are acquired.

[0230] At step 1520, the initial attenuation chord map is estimated.

[0231] In some embodiments, the steps 1510-1520 can be the same as or similar to the steps 1310-1320, which will not be repeated here.

[0232] In some embodiments, the reconstructed initial image can be obtained based on the initial attenuation chord map and the target coincidence event data. Specifically, the target coincidence event data can be attenuation corrected according to the initial attenuation chord map, and then the initial image can be reconstructed according to the attenuation corrected target coincidence event data to obtain the reconstructed initial image.

[0233] In some embodiments, the reconstructed initial image, the initial attenuation chord map, the attenuation map and the scatter estimate can be iteratively updated to meet a third preset condition, and the updated image that meets the third preset condition is taken as the target image.

[0234] In some embodiments, the third preset condition can include at least one of reaching a fixed number of iterations, a difference between images in adjacent two iterations being less than a preset threshold, a difference between adjacent two initial attenuation chord maps being less than a preset threshold, a difference between adjacent two attenuation maps being less than a preset threshold, and a difference between adjacent two scatter estimates being less than a preset threshold.

[0235] In some embodiments, after the scatter estimate is determined, the steps 1530 to 1570 can be executed in a loop. In some embodiments, the steps 1530-1580 can be performed by the image reconstruction module 230.

[0236] At step 1530, the image is updated.

[0237] In some embodiments, the updated image of the current iteration can be obtained based on the updated image generated in the previous iteration, the attenuation chord map generated in the previous iteration and the scatter estimate generated in the previous iteration, wherein the updated image generated in the first iteration is the reconstructed initial image. In some embodiments, the updated image of the current iteration can be obtained in various ways, for example, the updated image of the current iteration can be obtained by updating the updated image generated in the previous iteration according to formula (23).

[0238] At step 1540, the initial attenuation chord map is updated.

[0239] In some embodiments, the attenuation map of the current iteration can be determined based on the updated image of the current iteration in various ways, for example, by updating the attenuation map of the previous iteration according to formula (19) and formula (20) to obtain the attenuation map of the current iteration.

[0240] At step 1550, the attenuation map is reconstructed.

[0241] In some embodiments, the attenuation map of the current iteration can be determined based on the attenuation chord of the current iteration in various ways, for example, by calculating the attenuation map of the current iteration according to formula (22) based on the attenuation chord of the current iteration.

[0242] At step 1560, the scatter estimation is updated.

[0243] In some embodiments, the scatter estimation of the current iteration can be determined based on the attenuation map of the current iteration in various ways. For more information about how to obtain the scatter estimation based on the attenuation map, please refer to the relevant description of step 430, which will not be repeated here.

[0244] At step 1570, it is determined whether the third preset condition is met.

[0245] At step 1580, the iteration is ended.

[0246] In some embodiments, at the end of each iteration, it can be determined whether the third preset condition is met. If the third preset condition is met, step 1580 is executed to end the iteration, and the corrected image that meets the second preset condition is taken as the target image. If the third preset condition is not met, step 1530 is returned to continue the next iteration.

[0247] It should be noted that the above description of the processes 400, 500, 700, 800, 1300, 1400, and 1500 is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to the processes 400, 500, 700, 800, 1300, 1400, and 1500 under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification. For example, the order of steps for determining the first normalization correction factor and the second normalization correction factor in the process 500 can be arbitrarily exchanged.

[0248] The beneficial effects that the embodiments of the present specification can bring include but are not limited to: (1) obtaining an initial attenuation chord diagram and a normalization correction factor from the background coincidence event data, and performing normalization correction and attenuation correction, scatter correction, and image updating on the PET data based on the normalization correction factor and the initial attenuation chord diagram, so as to realize the attenuation, scatter and normalization of the PET data without CT, to realize image reconstruction without relying on CT scan data, to simplify the scanning operation process, to reduce the scanning time, and to reduce the radiation damage to the patient and the equipment operator of the entire PET system; (2) greatly improving the quality of the reconstructed image through multiple iterative updates of the image, so that the reconstructed image can well meet the diagnostic requirements, and greatly improve the diagnostic quality; (3) obtaining the mapping relationship of the normalization correction factors of the background coincidence event data and the phantom coincidence event data, and then obtaining the normalization correction factor of the device, without CT scanning, simplifying the process of device normalization correction, also reducing the radiation damage to the patient and the operator, and the method is simple and easy to operate, and has good universality; (4) by dividing the normalization correction factor into multiple items for separate calculation, the consideration of various factors is more comprehensive, and the obtained normalization correction factor has good accuracy and high reliability. It should be noted that different embodiments can have different beneficial effects, and in different embodiments, the beneficial effects that can be produced can be any one or a combination of the above, or any other beneficial effects that can be obtained.

[0249] The above detailed description has described only a few implementations of the application, and many modifications, improvements, and corrections can be made by those skilled in the art without departing from the spirit and scope of the present specification. Such modifications, improvements, and corrections are suggested in the present specification, so they still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0250] At the same time, the present specification uses specific words to describe the embodiments of the present specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0251] Furthermore, the order of the processing elements and sequences described in this specification are not intended to be construed as a limitation, unless specifically stated, but are included to provide a complete description of one or more embodiments of the present specification. Regardless of the particular sequence of processing elements and sequences, however, the description herein of a process should be understood to include any and all combinations of one or more elements of a process independently selected from each sequence. For example, although the system components described above can be implemented by hardware devices, they can also be implemented by software solutions, such as installing the described system on an existing server or mobile device.

[0252] Similarly, it is to be noticed that the term "comprising", used in the description, is not intended to exclude other elements or steps. It is to be understood that the description and the examples are intended to be illustrative, but not limiting, of the scope of the present specification. Thus, the scope of the present specification should be given by the appended claims, along with their full scope of equivalents, and not by an restricting interpretation of the description or the examples.

[0253] Some embodiments use numerical designations to describe components, quantities of attributes. It is to be understood that such numerical designations used in the description of embodiments are, in some examples, modified by the adjectives "about", "approximately", or "generally". Unless otherwise stated, "about", "approximately", or "generally" indicates that the stated numerical value is allowed ±20% variation. Accordingly, in some embodiments, numerical values used in the specification and claims are approximations that can vary depending on the desired properties of the individual embodiments. In some embodiments, numerical values should be considered in the context of the number of significant figures used in the specification and claims. Although the numerical ranges and parameters setting forth the broadest scope of the embodiments herein are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to give a general understanding of the embodiments.

[0254] Each patent, patent application, patent publication, and other material cited in this specification is hereby incorporated by reference in its entirety for the purpose of describing and disclosing the materials described in the documents in connection with the embodiments of the present specification. Citation of a document is not an admission that it is prior art with respect to the present specification. Citation of a document herewith provides open admission that the document is incorporated by reference. Descriptions, definitions, and / or terminology in the present specification may be used in addition to the descriptions, definitions, and / or terminology used in the documents incorporated by reference in order to provide a complete description of the embodiments of the present specification and are not intended to be limiting.

[0255] Finally, it should be understood that the embodiments described herein are only given by way of example and that other modifications can occur to persons skilled in the art. Therefore, the scope of the present description is not intended to be limited to the embodiments described herein but is only limited by the claims that follow.

Claims

1. A calibration method for a scanning device, comprising: Obtain the first baseline data that matches the event data; Based on the first background coincidence event data, determine the reference normalization correction factor corresponding to the background coincidence event; The normalization correction factor of the scanning device is determined based on the reference normalization correction factor and the mapping relationship. The normalization correction factor of the scanning device is used to normalize and correct the target coincidence event data. The mapping relationship represents the correspondence between the first normalization correction factor and the second normalization correction factor. The first normalization correction factor corresponds to the background coincidence event, and the second normalization correction factor corresponds to the phantom coincidence event.

2. The method of claim 1, further comprising: Obtain the second background conformity event data and the motif conformity event data; Based on the second background conformity event data, the first normalization correction factor is determined; Based on the phantom conformity event data, the second normalization correction factor is determined; The mapping relationship is determined based on the first normalization correction factor and the second normalization correction factor.

3. The method as described in claim 2, wherein obtaining the second background coincidence event data includes: The second background coincidence event data is collected before, after, or simultaneously with the collection of the phantom coincidence event data.

4. The method as described in claim 2, The first normalization correction factor includes at least one of the following: a first geometric correction factor, a first crystal interference correction factor, a first axial profile correction factor, a first detector ring pair detection efficiency correction factor, a first circumferential profile correction factor, and a first crystal detection efficiency correction factor; The second normalized correction factor includes at least one of the following: a second geometric correction factor, a second crystal interference correction factor, a second axial profile correction factor, a second detector ring pair detection efficiency correction factor, a second circumferential profile correction factor, and a second crystal detection efficiency correction factor; The mapping relationship includes at least one of a first mapping relationship, a second mapping relationship, a third mapping relationship, a fourth mapping relationship, a fifth mapping relationship, and a sixth mapping relationship. The first mapping relationship includes the mapping relationship between the first geometric correction factor and the second geometric correction factor. The second mapping relationship includes the mapping relationship between the first crystal interference correction factor and the second crystal interference correction factor. The third mapping relationship includes the mapping relationship between the first axial profile correction factor and the second axial profile correction factor. The fourth mapping relationship includes the mapping relationship between the first detector ring pair detection efficiency correction factor and the second detector ring pair detection efficiency correction factor. The fifth mapping relationship includes the mapping relationship between the first circumferential profile correction factor and the second circumferential profile correction factor. The sixth mapping relationship includes the mapping relationship between the first crystal detection efficiency correction factor and the second crystal detection efficiency correction factor.

5. The method as described in claim 4, wherein determining the first normalization correction factor corresponding to the background coincidence event based on the second background coincidence event data includes: The following steps are repeated until all the first normalized correction factors are determined, wherein in each iteration, one of the following first normalized correction factors is determined: the first geometric correction factor, the first crystal interference correction factor, the first axial profile correction factor, the first detector loop pair detection efficiency correction factor, the first circumferential profile correction factor, and the first crystal detection efficiency correction factor: Based on the second background coincidence event data, the actual background coincidence count corresponding to the current first normalized correction factor is obtained; Calculate the theoretical background coincidence count corresponding to the current first normalized correction factor; Based on the actual background coincidence count and the theoretical background coincidence count, the current first normalization correction factor is determined; If the first normalization correction factor has not been determined, the actual background conformity count corresponding to the second background conformity event data is corrected based on the determined current first normalization correction factor.

6. The method of claim 4, wherein determining the second normalized correction factor corresponding to the phantom conformity event based on the phantom conformity event data includes: The following steps are repeated until all the second normalized correction factors are determined, wherein in each iteration, one of the following second normalized correction factors is determined: the second geometric correction factor, the second crystal interference correction factor, the second axial profile correction factor, the second detector loop pair detection efficiency correction factor, the second circumferential profile correction factor, and the second crystal detection efficiency correction factor: The phantom coincidence event data is physically corrected to obtain the actual true coincidence count corresponding to the current second normalized correction factor; Calculate the theoretical true coincidence count corresponding to the current second normalization correction factor; The current second normalization correction factor is determined based on the actual true coincidence count and the theoretical true coincidence count; If the second normalization correction factor has not been determined, the actual true coincidence count corresponding to the motif coincidence event data is corrected based on the determined current second normalization correction factor.

7. The method as described in claim 1, wherein obtaining the first background coincidence event data includes: The first baseline data matching the event was obtained by air scanning.

8. An image reconstruction method, comprising: Obtain baseline event data and target event data for the target object; The normalized correction factor of the scanning device is obtained by using the method described in any one of claims 1-7, and the target conformity event data is corrected according to the normalized correction factor; as well as The target image is obtained by reconstructing the image based on the corrected target coincidence event data.

9. The method of claim 8, further comprising: A first normalization correction factor is obtained using the method described in any one of claims 2-6, and the background conformance event data is corrected according to the first normalization correction factor. The corrected background conformance event data is then used for image reconstruction.

10. A calibration system for a scanning device, comprising a background data acquisition module, an event correction factor acquisition module, and a device correction factor determination module; The data acquisition module is used to acquire the first baseline coincidence event data; The event correction factor acquisition module is used to determine the reference normalization correction factor corresponding to the background conformity event based on the first background conformity event data. The device correction factor determination module is used to determine the normalization correction factor of the scanning device based on the reference normalization correction factor and the mapping relationship. The normalization correction factor of the scanning device is used to normalize and correct the target conformance event data. The mapping relationship represents the correspondence between the first normalization correction factor and the second normalization correction factor. The first normalization correction factor corresponds to the background conformance event, and the second normalization correction factor corresponds to the phantom conformance event.

11. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer performs the method as described in any one of claims 1 to 7.

Images