Detector dead time correction method, system, device and storage medium

Abstract

The embodiments of the present application disclose a detector dead time correction method, system, device and storage medium. The method may include: obtaining raw scan data of a target object, the raw scan data being obtained by a detector of an imaging device receiving radiation photons; obtaining two first single event counting information under two energy windows or two or more first single event counting information under two or more energy windows related to the target object according to the raw scan data; determining a first dead time correction factor based on the two or more first single event counting information; and performing dead time correction on the raw scan data based on the first dead time correction factor.

Description

Technical Field

[0001] The present application relates to the technical field of medical equipment, and in particular to a detector dead time correction method, system, device, and storage medium. Background Art

[0002] Emission Computed Tomography (ECT) detector is a detection device used to measure or detect the energy of radiation photons. It is widely used in the field of nuclear medicine. Positron Emission Tomography (PET) / Single-Photon Emission Computed Tomography (SPECT) and other techniques all use ECT detectors. Dead time refers to the minimum processing time for the detector unit to process a single event. During the dead time, the detection unit will not process the second input single event. The resulting signal loss is called dead time loss. Dead time loss will cause the loss of photon counts (for example, single event counts), thereby underestimating the pixel value of the image and affecting the imaging quality. Therefore, it is necessary to perform dead time correction on the data obtained by the detector. Summary of the invention

[0003] One of the embodiments of the present application provides a dead time correction method for a detector, wherein the method includes: acquiring original scanning data of a target object, wherein the original scanning data is obtained by receiving radiation photons by a detector of an imaging device; acquiring, based on the original scanning data, two first single event counting information under two energy windows related to the target object or two or more first single event counting information under two or more energy windows; determining a first dead time correction factor based on the two or more first single event counting information; and performing dead time correction on the original scanning data based on the first dead time correction factor.

[0004] In some embodiments, the two or more energy windows include a first energy window and a second energy window, wherein the energy range of the second energy window is greater than the energy range of the first energy window.

[0005] In some embodiments, the energy range of the second energy window is used to select a first type of single event to determine incident information based on the original scanning data, and the first energy window reflects the energy range of a characteristic energy peak associated with the radioactive material injected into the target object.

[0006] In some embodiments, the energy range of the first energy window is used to select a second type of single event from the first type of single events for coincidence determination.

[0007] In some embodiments, the energy range of the first energy window is 430keV-650keV, and the energy of the second energy window is greater than 200keV.

[0008] In some embodiments, determining the first dead time correction factor based on the two or more first single event counting information includes: determining a first relationship value of the two or more first single event counting information; obtaining a correction relationship model, the correction relationship model including the relationship between the first relationship value of the two or more single event counting information and a mean value of the first dead time correction factor; determining a mean value of the first dead time correction factor based on the first relationship value and the correction relationship model; determining the first dead time correction factor based on the mean value of the first dead time correction factor.

[0009] In some embodiments, the first dead time correction factor includes two or more first correction parameters, each of which corresponds to a detection unit of the detector of the imaging device; the method also includes: obtaining a second dead time correction factor corresponding to an experimental object, the second dead time correction factor includes two or more second correction parameters, each of which corresponds to a detection unit of the imaging device, and the experimental object is used to obtain the correction relationship model; determining the mean of the two or more second correction parameters as the mean of the second dead time correction factor; determining the two or more first correction parameters based on the mean of the first dead time correction factor, the mean of the second dead time correction factor and the two or more second correction parameters.

[0010] One of the embodiments of the present application provides a dead time correction system for a detector, wherein the system includes: an acquisition module, used to acquire original scanning data of a target object, wherein the original scanning data is obtained by receiving radiation photons by a detector of an imaging device; a counting module, used to acquire two first single event counting information under two energy windows related to the target object or two or more first single event counting information under two or more energy windows according to the original scanning data; a determination module, used to determine a first dead time correction factor based on the two or more first single event counting information; and a correction module, used to perform dead time correction on the original scanning data based on the first dead time correction factor.

[0011] One of the embodiments of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any one of the above-mentioned methods for correcting the dead time of the detector is implemented.

[0012] One of the embodiments of the present application provides a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any one of the above-described methods for correcting the dead time of a detector.

[0013] The beneficial effects that may be brought about by the embodiments of the present application include: the dead time correction method of the detector provided in the embodiments of the present application, according to the original scanning data, obtains two or more first single event counting information under two or more energy windows related to the target object or two or more first single event counting information under two or more energy windows, determines a first dead time correction factor based on the two or more first single event counting information, and performs dead time correction on the original scanning data based on the first dead time correction factor. By introducing single event counting rates under multiple energy windows, the system can obtain a more accurate dead time correction effect for different scanned objects. The single event counting rates under multiple energy windows can simulate / approximately reflect the shape of the scanned object and the characteristics of the distribution of the radiation source, thereby improving the accuracy of calibration. The dead time correction factor is further automatically obtained through the correction relationship model, thereby improving the efficiency of correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present application will be further described in the form of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0015] Figure 1 is a schematic diagram of a dead time correction system of an exemplary detector according to some embodiments of the present application;

[0016] Figure 2 is an exemplary flow chart of a dead time correction method of a detector according to some embodiments of the present application;

[0017] Figure 3 is an exemplary flow chart of determining a first dead time correction factor according to some embodiments of the present application;

[0018] Figure 4 is an exemplary flow chart of dead time correction according to some embodiments of the present application;

[0019] Figure 5 is an exemplary flow chart of obtaining a correction model according to some embodiments of the present application;

[0020] Figure 6 is another exemplary flow chart of obtaining a correction model according to some embodiments of the present application;

[0021] Figure 7is an exemplary module diagram of a processing device for dead time correction of a detector according to some embodiments of the present application;

[0022] Figure 8 is a schematic diagram of exemplary hardware and / or software components of a computing device that can implement a processing device according to some embodiments of the present application;

[0023] Fig. 9 is a schematic diagram of energy ranges of a first energy window and a second energy window according to some embodiments of the present application;

[0024] Fig.10 is a schematic diagram of obtaining the dead time factor by linear fitting of high activity sampling points according to some embodiments of the present application;

[0025] Fig.11 A schematic diagram of obtaining background count rate by linear fitting of low activity sampling points according to some embodiments of the present application;

[0026] Fig.12 A schematic diagram of the distribution of single event count rates under two different energy windows according to some embodiments of the present application;

[0027] Fig.13 A schematic diagram showing how the ratio of single event count rates in two energy windows varies with the count rate in the second energy window according to some embodiments of the present application. DETAILED DESCRIPTION

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0029] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0030] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprises" and "includes" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0031] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, the various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0032] PET is a diagnostic technology that reflects the functions and metabolic status of organs and tissues in the human body at the molecular level. PET injects drugs containing positron radioactivity (such as 18F-FDG) into the human body. Since the metabolism of FDG is very similar to that of glucose, it can accumulate in cells that digest glucose, especially in rapidly growing tumor tissues, where drug uptake is different. The positrons emitted by 18F decay will undergo annihilation reactions with the negative electrons in the tissues, generating two gamma photons with equal energy (511keV) and opposite directions. By measuring these two photons with coincidence measurement technology through a detector array surrounding the human body, the position information of the positrons can be obtained, and then processed with image reconstruction software to obtain a tomographic image of the distribution of positrons in the human body.

[0033] In some embodiments, two photons with opposite movement directions generated after annihilation can be received by the detector assembly 112, and single event data is statistically obtained based on the data received by the detector assembly 112. Among them, the detector receiving a photon is called a single event. The coincidence event can be determined based on the single event data and the coincidence time window, and the PET image is reconstructed based on the coincidence event. Dead time refers to the minimum processing time for the detection unit of the detector to process a single event. During the dead time, the detection unit will not process the second input single event. The signal loss caused by this is called dead time loss. Generally, PET systems have a dead time of 1-10ns after each event. Dead time loss will cause the loss of photon counts (for example, single event counts), thereby underestimating the pixel value of the image and affecting the imaging quality. Therefore, it is necessary to perform dead time correction on the single event data lost due to dead time.

[0034] Figure 1 FIG. 1 is a schematic diagram of an exemplary detector dead time correction system 100 according to some embodiments of the present application. Figure 1 As shown, the detector dead time correction system 100 may include a scanning device 110, a network 120, a terminal 130, a processing device 140, and a storage device 150. The components of the detector dead time correction system 100 may be connected in various ways. For example, Figure 1As shown, scanning device 110 may be connected to processing device 140 via network 120. For another example, scanning device 110 may be directly connected to processing device 140 (as shown by the double-headed arrow in the dashed line connecting scanning device 110 and processing device 140). For another example, storage device 150 may be connected to processing device 140 directly or via network 120. As yet another example, terminal devices (e.g., 130-1, 130-2, 130-3, etc.) may be directly connected to processing device 140 (as shown by the double-headed arrow in the dashed line linking terminal 130 and processing device 140) or via network 120.

[0035] The scanning device 110 can scan the target object and / or generate scanning data (e.g., projection data) about the target object. In the present application, the target object may also be referred to as a target, a scanning object, or a detected object, and the above terms may be used interchangeably. In some embodiments, the target object may be a biological object such as a patient, an animal, etc., or an artificial object such as a phantom, etc. The target object may also be a specific part of a patient, such as an organ and / or tissue. When the target object needs to be scanned, it may be placed on the mobile platform 114, and as the mobile platform 114 moves longitudinally along the scanning device 110, it enters the scanning area 113. An exemplary scanning device 110 may be a medical imaging device, such as a PET device, a PET-CT device, a PET-MRI device, etc. The scanning device 110 may include a detector assembly 112. After the target object enters the scanning area 113, the detector assembly 112 may detect radiation events (e.g., electron annihilation events in the target object) occurring in the scanning area 113. In some embodiments, the detector assembly 112 may include one or more detection units. The detection units can be arranged in any suitable shape, such as a ring, an arc, a rectangle, an array, etc. or a combination thereof. For example, the detection units can be arranged along the radial direction of the scanning device 110 to form a detector ring. The arrangement of the detector assemblies 112 defines a scanning area 113.

[0036] The network 120 may include any suitable network that can facilitate information and / or data exchange of the dead time correction system 100 of the detector. In some embodiments, one or more components of the dead time correction system 100 of the detector (e.g., the scanning device 110, the terminal 130, the processing device 140, or the storage device 150) may communicate information and / or data with one or more other components of the dead time correction system 100 of the detector via the network 120. For example, the processing device 140 may obtain a signal of object attenuation from the scanning device 110 via the network 120. In some embodiments, the network 120 may be a wired network or a wireless network, etc., or any combination thereof. The network 120 may be and / or include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN), a wide area network (WAN), etc.), a wired network (e.g., an Ethernet network), a wireless network (e.g., a Wi-Fi network, a Li-Fi network, etc.), a cellular network (e.g., a Long Term Evolution (LTE) network), a frame relay network, a virtual private network (“VPN”), a satellite network, a telephone network, a router, a hub, a switch, a server computer, and / or any combination thereof. By way of example only, the network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth™ network, a ZigBee™ network, a near field communication (NFC) network, an ultra-wideband (UWB) network, a mobile communication (1G, 2G, 3G, 4G, 5G) network, a narrowband Internet of Things (NB-IoT), infrared communication, etc., or any combination thereof. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include wired and / or wireless network access points, such as base stations and / or Internet exchange points, through which one or more components of the detector dead time correction system 100 may connect to the network 120 to exchange data and / or information.

[0037] The terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, etc., or any combination thereof. In some embodiments, the terminal 130 may interact with other components in the dead time correction system 100 of the detector through a network. For example, the terminal 130 may send one or more control instructions to the scanning device 110 to control the mobile platform 114 to carry the target object into the scanning area 113, and control the detector component 112 to receive data. For another example, the terminal 130 may also receive data sent by the detector component 112. In some embodiments, the terminal 130 may receive information and / or instructions input by a user (e.g., a user of the dead time correction system 100 of the detector, such as a doctor), and send the received information and / or instructions to the scanning device 110 or the processing device 140 via the network 120. In some embodiments, the terminal 130 may be a part of the processing device 140. The terminal 130 and the processing device 140 may be integrated as a control device of the scanning device 110, such as an operating console. In some embodiments, the terminal 130 may be omitted.

[0038] The processing device 140 can process data and / or information obtained from the scanning device 110, the terminal 130 and / or the storage device 150. For example, the processing device 140 can obtain the collected scanning data from the scanning device (e.g., the detector 112). For another example, the processing device 140 can correct the relevant parameters of the scanning device 110 based on the above-obtained data, such as dead time correction. In some embodiments, the processing device 140 can be a single server or a server group. The server group can be centralized or distributed. In some embodiments, the processing device 140 can be local or remote. For example, the processing device 140 can access information and / or data stored in or obtained by the scanning device 110, the terminal 130 and / or the storage device 150 via the network 120. For another example, the processing device 140 can be directly connected to the scanning device 110 (e.g., the detector 112). Figure 1 The processing device 140 and the scanning device 110 are connected as shown by the double-headed arrow in the dotted line), the terminal 130 (as shown in FIG. Figure 1 In some embodiments, the processing device 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof. In some embodiments, the processing device 140 may be implemented on a computing device 800 having the computing device 800 described in the present application. Figure 8 One or more components shown.

[0039] The storage device 150 may store data and / or instructions. In some embodiments, the storage device 150 may store data obtained from the scanning device 110, the terminal 130, and / or the processing device 140. For example, the storage device 150 may store a dead time correction factor. In some embodiments, the storage device 150 may store data and / or instructions that the processing device 140 may execute or use to execute the exemplary methods described in the present application. For example, the storage device 150 may store instructions for the processing device 140 to execute the methods shown in the various flow charts. In some embodiments, the storage device 150 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid-state drives, etc. Exemplary removable storage devices may include flash drives, floppy disks, optical disks, memory cards, compact disks, tapes, etc. Exemplary volatile read-write memory may include random access memory (RAM). Exemplary RAMs may include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDRSDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero capacitance RAM (Z-RAM). Exemplary ROMs may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital universal disk ROM, etc. In some embodiments, the storage device 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, etc., or any combination thereof.

[0040] In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more components of the dead time correction system 100 for the detector (e.g., the scanning device 110, the processing device 140, the terminal 130, etc.). One or more components of the dead time correction system 100 for the detector may access data or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be directly connected to or communicate with one or more components of the dead time correction system 100 for the detector (e.g., the scanning device 110, the processing device 140, the terminal 130, etc.). In some embodiments, the storage device 150 may be part of the processing device 140.

[0041] In some embodiments, the dead time correction system 100 for the detector may further include one or more power supplies (e.g., a scanning device 110, a processing device 140, a terminal 130, a storage device 150, etc.) connected to one or more components of the dead time correction system 100 for the detector. Figure 1 not shown).

[0042] Figure 2 2 is an exemplary flow chart of a dead time correction method 200 for a detector according to some embodiments of the present application. In some embodiments, the process 200 can be executed by a processing logic, which can include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. Figure 2 One or more operations in the illustrated process 200 for detector dead time correction may be performed by Figure 1 For example, the process 200 may be stored in the storage device 150 in the form of instructions and called and / or executed by the processing device 140. In some embodiments, the process 200 may be executed by the processing device 800. Figure 2 As shown, process 200 may include the following operations.

[0043] Step 210 , acquiring raw scan data of the target object, where the raw scan data is obtained by a detector of an imaging device receiving radiation photons. In some embodiments, step 210 may be performed by an acquisition module 710 .

[0044] In some embodiments, the imaging device refers to a medical imaging device (eg, scanning device 110 ), such as a PET device, a PET-CT device, a PET-MRI device, or the like.

[0045] In some embodiments, the target object may be a living thing such as a patient, an animal, etc., or may be an artificial object such as a phantom, etc. The target object may also be a specific part of a patient, for example, an organ and / or a tissue.

[0046] In some embodiments, the raw scan data may include data generated by scanning a portion of an organ or tissue of the target object in a scan area.

[0047] In some embodiments, two photons with opposite movement directions generated after annihilation can be received by the detector assembly 112, and single event data is statistically obtained based on the data received by the detector assembly 112. The reception of one photon by the detector assembly 112 is referred to as a single event. In some embodiments, the single event data may include at least one of a signal value, energy deposition, incident position, and incident time.

[0048] In some embodiments, the raw scan data may include single event data related to the target object received by the detector assembly 112. For example, the raw scan data may include all single event data related to the target object received by the detector assembly 112. For another example, the raw scan data may include single event data related to the target object received by the detector assembly 112 with an energy value greater than a threshold.

[0049] In some embodiments, the raw scan data may be obtained by scanning a scanning component (e.g., a scanning rack) of an imaging device. In some embodiments, the raw scan data may be obtained by a transmission component from the scanning component of the imaging device to a reconstruction computer. For example, the raw scan data may be obtained by a ring sliding component between a scanning rack of the imaging device and a reconstruction computer. For another example, the raw scan data may be obtained by means of a network or an interface. The raw scan data may also be obtained by other means, and the present application does not limit the means for obtaining the raw scan data.

[0050] In some embodiments, the scanning area (e.g., scanning area 113) can be defined by a plurality of detection units in a detector of the imaging device. For example, a plurality of detection units are arranged in sequence to form an annular cylinder. The target object will be scanned inside the annular cylinder. In some embodiments, the raw scan data can be pre-stored in a storage device (e.g., a built-in memory or an external memory of the processing device 140 or the processing device 700, such as the storage device 150), and the acquisition module 710 can acquire the raw scan data by communicating with the storage device.

[0051] Step 220, based on the raw scan data, obtain two or more (multiple) first single event counting information under two or more energy windows related to the target object. In some embodiments, step 220 can be performed by the counting module 720. In some embodiments, two first single event counting information under two energy windows related to the target object or two or more first single event counting information under two or more energy windows can be obtained. In some embodiments, one energy window corresponds to one first single event counting information.

[0052] In some embodiments, the single event counting information may include single event counts or single event count rates. The first single event counting information may include single event counts or single event count rates associated with the target object under two or more (multiple) energy windows.

[0053] In some embodiments, the single event count rate may refer to the number of single events (e.g., radiation events) received per unit time (e.g., a certain moment or a certain time period). In some embodiments, the first single event count rate may refer to the single event count rate associated with the target object under two or more energy windows. For example, the first single event count rate may refer to the single event count under two or more energy windows among the single events associated with the target object received at a certain moment or a certain time period. For another example, the first single event count rate may refer to the single event count per unit time among the single events associated with the target object under two or more energy windows.

[0054] The energy window may refer to an energy range. The energy window may be represented by an energy value range (e.g., 120-160 keV) or an energy peak value combined with a percentage of up and down changes (e.g., 140 keV ± 20%). It is understood that the energy window may also be represented by other forms, which are not limited in this application.

[0055] In some embodiments, the two or more energy windows may include a first energy window and a second energy window, wherein the energy range of the second energy window may be greater than the energy range of the first energy window. In some embodiments, the energy range of the first energy window may be included in the energy range of the second energy window.

[0056] In some embodiments, the energy range of the second energy window can be used to select a first type of single event to determine the incident information from the single events included in the original scan data. The incident information may include but is not limited to at least one of the incident position, energy deposition, and incident time of the single event. For example, the energy range of the second energy window can be used to select a first type of single event to determine the energy deposition from the single events included in the original scan data. In some embodiments, the first energy window can reflect the energy range where the characteristic energy peak associated with the radioactive material injected into the target object is located. The characteristic energy peak can reflect the energy value corresponding to the single event received by the detector assembly 112 whose number is greater than a certain threshold. For example, in a PET imaging system, the characteristic energy peak corresponds to 511keV. In some embodiments, for PET imaging, the energy range of the first energy window can be used to select a second type of single event from the first type of single event for compliance determination.

[0057] Before scanning, the target object will be injected with a radioactive source (e.g., a radioactive tracer isotope). When the radioactive tracer isotope decays, one or more positrons can be emitted. After these positrons propagate over a short distance (e.g., about 1-3 mm) inside the target object, they will interact with electrons, annihilate and produce photons (e.g., a positron annihilation will produce a pair of gamma photons, each with an energy of 511 keV). The annihilation event can be referred to as a radiation event. At the same time, the same pair of gamma photons will move in opposite directions and can be received by the detection unit of the imaging device (e.g., scanning device 110). The moving path of the pair of gamma photons can be referred to as a response line, and the two detection units that receive the pair of gamma photons are located at both ends of the response line. The reception or detection of the same pair of gamma photons by two detection units located on the same response line can be referred to as a true coincidence event (or simply a true event). The two detection units can be referred to as a detection unit pair. In the present application, the detection unit pair can be referred to as a "detection module". Similarly, any detection unit of the imaging device can also receive one of the gamma photon pairs. The reception or detection of a gamma photon by any detection unit can be called a single radiation event (or simply a single event). Therefore, any detection unit can also be called a "detection module". After receiving the gamma photon, the detection unit of the imaging device can record the time when the gamma photon is received and convert the optical signal into an electrical signal, such as a timing pulse. These can be considered as scanning data collected by the detection module. Subsequently, the single event electrical signal, such as a timing pulse, will be transmitted to the coincidence circuit for identification, and a coincidence judgment will be made according to the coincidence time window to determine the coincidence event.

[0058] In some embodiments, the energy range of the first energy window may be 430keV-650keV, and the energy of the second energy window may be greater than 200keV. Fig. 9 As shown in FIG. , it is a schematic diagram of the energy range of the first energy window and the second energy window. Fig. 9 As shown, for PET imaging, the energy range of the first energy window is 430keV-650keV, which can reflect the energy range of the 511keV characteristic energy peak 610. The energy range of the second energy window is greater than 200keV.

[0059] In some embodiments, a single event with an energy value (energy deposition) under the first energy window and the second energy window can be selected in the raw scan data, and the single event count rate under the first energy window and the second energy window can be counted as the first single event count rate. For example, the single event count under the first energy window and the second energy window is divided by the acquisition time of the original scan to obtain the corresponding first single event count rate. In some embodiments, a single event corresponding to a certain time point or a certain time period can be selected in the raw scan data, and the single event under the first energy window and the second energy window can be selected as the first single event count rate.

[0060] Step 230 , determining a first dead time correction factor based on two or more first single event counting information. In some embodiments, step 230 may be performed by the determination module 730 .

[0061] The dead time correction factor can be used to correct the radiation events (true events or single events) collected by the detector of the imaging device. For example, the signal loss caused by the dead time can be estimated and restored according to the dead time correction factor. In some embodiments, the first dead time correction factor can refer to a dead time correction factor determined based on two or more first single event counting information.

[0062] In some embodiments, a first relationship value of two or more first single event counting information may be determined, and a correction relationship model may be obtained, wherein the correction relationship model may include a relationship between the relationship value of the two or more single event counting information and the first dead time correction factor mean value, and the first dead time correction factor mean value may be determined according to the first relationship value and the correction relationship model, and the first dead time correction factor may be determined according to the first dead time correction factor mean value. For more descriptions on determining the first dead time correction factor, see Figure 3 And its description will not be repeated here.

[0063] Step 240 , performing dead time correction on the raw scan data based on the first dead time correction factor. In some embodiments, step 240 may be performed by the correction module 740 .

[0064] In some embodiments, the original scan data may be subjected to dead time correction based on the first dead time correction factor to obtain corrected scan data. For example, the signal loss caused by the dead time in the original scan data may be estimated and restored based on the first dead time correction factor. In some embodiments, the correction operation may include but is not limited to one or a combination of random correction, scatter correction, attenuation correction, etc.

[0065] It is understandable that only one correction may not yield the best correction result or one that meets the preset standard. Therefore, performing dead time correction on the original scan data based on the first dead time correction factor also includes performing dead time iterative correction on the original scan data based on the first dead time correction factor, so that the corrected scan data meets the preset standard, thereby improving the quality and accuracy of the reconstructed PET image.

[0066] In some embodiments, a coincidence determination can be performed based on the corrected scan data to determine a coincidence event, and an image reconstruction can be performed based on the coincidence event to obtain a PET image of the target object.

[0067] It should be noted that the above description of the process 200 is only for example and illustration, and does not limit the scope of application of the present application. For those skilled in the art, various modifications and changes can be made to the process 200 under the guidance of the present application. However, these modifications and changes are still within the scope of the present application.

[0068] Figure 3 is an exemplary flow chart of determining the first dead time correction factor according to some embodiments of the present application. In some embodiments, process 300 may be performed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. Figure 3 One or more operations in the illustrated process 300 for determining the first dead time correction factor may be performed by Figure 1 For example, process 300 may be stored in the form of instructions in storage device 150 and called and / or executed by processing device 140. In some embodiments, process 300 may be executed by processing device 700 (or determination module 730). Figure 3 As shown, process 300 may include the following operations.

[0069] Step 310 , determining a first relationship value of two or more first single event counting information. Step 310 may be performed by the first relationship value determining unit 732 .

[0070] In some embodiments, the first relationship value may refer to an operation value between two or more first single event counting information, reflecting the comparative relationship between the two or more first single event counting information. In some embodiments, the first relationship value may be a ratio between two or more first single event counting information. As an example only, the two first single event counting information corresponding to the first energy window and the second energy window are A and B, respectively, then the ratio f between the first single event counting information A and B can be determined, for example, f = A / B or f = B / A.

[0071] In some embodiments, the first relationship value may also be a calculated value obtained by adding, subtracting, multiplying, or dividing two or more first single event count information. For example, at a certain time t, the two first single event count information corresponding to the first energy window and the second energy window are A and B, respectively, and the first relationship value may be A+B, AB, BA, (AB) / B, (BA) / B, or A*B, etc.

[0072] Step 320 , obtaining a correction relationship model, the correction relationship model including the relationship between the first relationship value of two or more single event counting information and the first dead time correction factor mean value. Step 320 may be performed by the model obtaining unit 734 .

[0073] In some embodiments, the correction relationship model may be represented in the form of a function, a table, or a curve.

[0074] In some embodiments, the correction relationship model can be obtained by the following method: for each of two or more experimental objects of different shapes or sizes, obtain two or more second single event counting information under two or more energy windows of the experimental object (the same as the two or more energy windows in step 220), and determine the second relationship values ​​of the two or more second single event counting information; obtain the mean value of the second dead time correction factor corresponding to the experimental object, and determine the correction relationship model according to the second relationship value and the mean value of the second dead time correction factor corresponding to each experimental object.

[0075] In some embodiments, the calibration model may be obtained in advance and stored in a storage device of the system 100 (e.g., the storage device 150, the memory 820 of the processing device 140, etc.). When executing step 320, the processing device 140 may obtain the calibration model from the storage device. For more information about obtaining the calibration model, see Figure 5 And its description will not be repeated here.

[0076] Step 330 , determining a first dead time correction factor mean value according to the first relationship value and the correction relationship model. Step 330 may be performed by the mean value determination unit 736 .

[0077] In some embodiments, the correction relationship model may reflect the relationship between the first relationship value and the first dead time correction factor mean value, and the first dead time correction factor mean value may be determined by the correction relationship model according to the first relationship value.

[0078] Step 340 , determining a first dead time correction factor according to the first dead time correction factor mean value. Step 340 may be performed by the first dead time correction factor determination unit 738 .

[0079] In some embodiments, the first dead time correction factor may include two or more first correction parameters, wherein each first correction parameter may correspond to a detection unit of the detector assembly of the imaging device, that is, the first dead time correction factor includes the first correction parameter corresponding to each detection unit.

[0080] In some embodiments, the first correction parameter can be determined by the following method: obtaining a second dead time correction factor corresponding to the experimental object, the second dead time correction factor can include two or more second correction parameters, each second correction parameter corresponds to a detection unit of the PET imaging device, and the experimental object can be used to obtain a correction relationship model; determining the mean of the two or more second correction parameters as the mean of the second dead time correction factor; determining the two or more first correction parameters based on the mean of the first dead time correction factor, the mean of the second dead time correction factor and the two or more second correction parameters.

[0081] In some embodiments, the correction coefficient is determined according to the mean of the first dead time correction factor and the mean of the second dead time correction factor; and two or more first correction parameters are determined according to two or more second correction parameters and the correction coefficient. In some embodiments, the correction coefficient may include the ratio of the mean of the first dead time correction factor and the second dead time correction factor. For example, the mean of the first dead time correction factor is E, and the ratio of the second dead time correction factor is F, then the correction coefficient is E / F. In some embodiments, the first correction parameter can be obtained by multiplying the second correction parameter and the correction coefficient. For example, for the same detection unit, the second correction parameter corresponding to the experimental object is G, and the correction coefficient is H, then the first correction parameter corresponding to the target object is I=G*H.

[0082] It should be noted that the above description of process 300 is only for example and illustration, and does not limit the scope of application of the present application. For those skilled in the art, various modifications and changes can be made to process 300 under the guidance of the present application. However, these modifications and changes are still within the scope of the present application.

[0083] Figure 4 is an exemplary flow chart of dead time correction according to some embodiments of the present application. Figure 4 As shown, in the dead time correction link in the actual scanning, the original scanning data can be first obtained (such as scanning the target object on site or retrieving the original scanning data stored in the storage device 150 and / or the terminal 130), and then based on two or more first single event counting information under two or more energy windows in the original scanning data, the first relationship value of the two or more first single event counting information is obtained (such as the ratio between the two or more first single event counting information), and the correction model is obtained. The mean of the dead time factor lookup table is calculated according to the first relationship value, and the mean of the dead time factor lookup table is applied to the dead time factor lookup table corresponding to the experimental object used to obtain the correction model to obtain the final dead time correction factor, and then the original scanning data is dead-time corrected according to the final dead time correction factor to obtain the corrected scanning data, and the corrected scanning data can be converted into a chord diagram or List-Mode data for image reconstruction.

[0084] Figure 5 500 is an exemplary flow chart of obtaining a correction model according to some embodiments of the present application. In some embodiments, process 500 can be performed by processing logic, which can include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. Figure 5 One or more operations in the process 500 for obtaining a calibration model may be performed by Figure 1 For example, process 500 may be stored in the form of instructions in storage device 150 and called and / or executed by processing device 140. In some embodiments, process 500 may be executed by processing device 700 (or determination module 730). Figure 5 As shown, process 500 may include the following operations.

[0085] Step 510 , for each of the two or more experimental objects, obtain two or more second single event count information under two or more energy windows of the experimental object. Step 510 may be performed by the model acquisition unit 734 .

[0086] In some embodiments, the experimental object may be placed in a scanning device 110 for scanning a target object to perform a decay experiment to obtain the second single event count information, or the experimental object may be placed in a device of the same type as the scanning device 110 to perform a decay experiment to obtain the second single event count information. In some embodiments, if the experimental object has previously performed a decay experiment in the scanning device 110 or a device of the same type as the scanning device 110, the experimental data of the experimental object may be obtained from a storage device (e.g., a storage device of the system 100 or an external device), and the second single event count information may be obtained based on the experimental data.

[0087] In some embodiments, the experimental object may include a living being such as a patient, an animal, etc., or may include an artificial object such as a phantom, etc. At least one of the shapes, types, and sizes of the two or more experimental objects may be different.

[0088] In some embodiments, the phantom may include a uniform phantom, and its shape may be various, for example, a cylinder, a cube, a sphere, an ellipsoid, etc. The length of the phantom may be less than the axial length of the imaging device. The axial direction may refer to the direction in which the patient is pushed forward or away from the scanning area 113. In some embodiments, the axial length of the imaging device refers to the size of the axial field of view (FOV) of the imaging device. For example, when the axial length of the imaging device is 2m, the length of the uniform phantom may be set to 0.3m, 0.6m, 1m, etc., which is less than the axial length of 2m. This uniform phantom that is smaller than the axial length of the imaging device is more convenient in terms of processing, storage, transportation, infusion, and placement. The radiation source infused into the uniform phantom can be evenly distributed in the phantom, and the phantom can be more accurately placed in the center of the field of view (axial field of view or radial field of view) of the imaging device, so as to improve the accuracy of calibration.

[0089] In some embodiments, the second single event count information may include single event counts or single event count rates associated with the experimental object under two or more energy windows. The second single event count rate may refer to the single event count rate associated with the experimental object under two or more energy windows. For example, the second single event count rate may refer to the single event counts under two or more energy windows in a single event associated with the experimental object received at a certain moment or a certain time period. For another example, the second single event count rate may refer to the single event count per unit time in a single event associated with the experimental object under two or more energy windows.

[0090] Step 520 , determining a second relationship value of two or more second single event counting information. Step 520 may be performed by the model acquisition unit 734 .

[0091] In some embodiments, the second relationship value may refer to an operation value between two or more second single event counting information, reflecting the comparative relationship between the two or more second single event counting information. In some embodiments, the second relationship value may be a ratio between two or more second single event counting information. As an example only, the two second single event counting information corresponding to the first energy window and the second energy window are C and D, respectively, then the ratio f between the second single event counting information C and D can be determined, for example, f = C / D or f = D / C.

[0092] In some embodiments, the second relationship value may also be a calculated value obtained by adding, subtracting, multiplying or dividing two or more second single event count information. For example, at a certain time t, the two second single event count information corresponding to the first energy window and the second energy window are C and D, respectively, then the first relationship value may be C+D, CD, DC, (CD) / D, (DC) / D, or C*D, etc.

[0093] Step 530 , obtaining a second dead time correction factor mean value corresponding to the experimental object. Step 530 may be performed by the model acquisition unit 734 .

[0094] In some embodiments, for each experimental object, a second dead time correction factor corresponding to the experimental object can be obtained. The second dead time correction factor can include two or more second correction parameters, each second correction parameter corresponding to a detection unit of a detector assembly of an imaging device. The second dead time correction factor mean refers to the average value of all or part of the second correction parameters in the second dead time correction factor.

[0095] In some embodiments, the second dead time correction factor can be obtained through a radioactive phantom decay experiment based on a model of the change of the single event count rate of the detection unit with the activity of the radiation source. For example, the second dead time correction factor corresponding to the experimental object can be obtained based on a paralysis model (for example, the imaging device cannot accept the next event when processing the current event) or a non-paralysis model (for example, the imaging device can accept the next event when processing the current event).

[0096] For example, for a detection unit, assume that the single event count rate it receives obeys the paralysis model: m = ne -λτ Or non-paralysis model: m = n + n b -m(n+n b )τ, where m represents the single event count rate measured by the detection unit, n represents the single event count rate incident on the detection unit, τ represents the second correction parameter corresponding to the detection unit, and n b represents the background count rate, and λ represents the decay constant.

[0097] In a decay experiment, at different time points t i Collect multiple sets of data, and the single event count rate measured by the detection unit of each set of data is recorded as m i Since the single event count rate measured by the detection unit is proportional to the activity of the radiation source, it can conform to the exponential decay change, for example, (n0 represents the initial single event count rate), and it is brought into the above non-paralysis model, then the non-paralysis model can be transformed into:

[0098]

[0099] For m i Greater than 10 times n b The sampling point can be ignored, and the influence of the background count rate can be ignored. Then formula (1) can be transformed into:

[0100]

[0101] In formula (2), m iAs an independent variable, After transformation as the dependent variable, a standard linear equation can be obtained. After linear fitting, the slope -n0τ and intercept n0 can be obtained, and the second correction parameter τ can be calculated.

[0102] like Fig.10 As shown, the horizontal axis is m i , the vertical axis is M i and t i Substitution Fig.10 The coordinate system shown forms the sampling points, such as Fig.10 As shown, for m i Greater than 10 times n b A linear fitting is performed on the sampling points to obtain a fitting result L. According to the fitting result L, the slope -n0τ and the intercept n0 can be obtained, and then the second correction parameter τ corresponding to the detection unit can be obtained.

[0103] The paralysis model is derived in a similar way. For example, Substituting into the paralysis model and ignoring the background count rate, the linear equation system becomes:

[0104] λτ+ln(m)=-n0τe -λτ +ln(n0). (3)

[0105] In some embodiments, the second dead time correction factor may be represented in the form of a second dead time correction factor lookup table. For example, the second dead time correction factor lookup table may include the type of the experimental object (e.g., a uniform phantom), the shape of the experimental object, the position (and / or number) of each detection unit of the detector assembly, and the second correction parameter corresponding to each detection unit.

[0106] In some embodiments, the above method may be used to determine a second correction parameter for each detection unit, thereby determining a second dead time correction factor.

[0107] It is understandable that the second dead time correction factor of the experimental object may also be determined according to other methods, and the present application does not limit the method for determining the second dead time correction factor.

[0108] In some embodiments, the background count rate may refer to the single event count rate caused by other factors in the same environment, except for the radioactivity of the experimental object. In the present application, the background count rate may refer to the single event count rate of the detection unit when no experimental object is placed. The background count rate is related to the type of the detection unit, and since the type of the detection unit of the PET imaging device is known, the background count rate is also known. For example, since the dead time effect of the detection unit is relatively weak when the activity is low, the background count rate n can be obtained by linear fitting using the 10 measurement results with the lowest activity. b In some embodiments, the background count rate may be determined in advance before determining the second dead time correction factor, and then the background count rate may be stored in a storage device, such as a built-in memory or an external memory of the processing device 700. In some embodiments, the background count rate may be determined during the process of determining the second dead time correction factor.

[0109] like Fig.11 As shown in the coordinate system, the horizontal axis represents the activity of the radiation source, and the vertical axis represents the single event count rate measured by the detection unit, wherein the single event count rate measured by the detection unit is proportional to the activity of the radiation source, and the 10 measurement results with the lowest activity (for example, Fig.11 Taking the sampling point in the box 730 as an example, the background count rate n can be obtained when performing linear fitting. b .

[0110] In some embodiments, the experimental object may be placed in a scanning device 110 for scanning a target object or a device of the same type as the scanning device 110 to perform a decay experiment to obtain the second dead time correction factor. In some embodiments, if the experimental object has previously undergone a decay experiment in the scanning device 110 or a device of the same type as the scanning device 110, experimental data of the experimental object may be obtained from a storage device (e.g., a storage device of the system 100 or an external device), and the second dead time correction factor may be obtained based on the experimental data, thereby obtaining the second dead time correction factor based on the second dead time correction factor mean. In some embodiments, obtaining the second single event count information of the experimental object and determining the second dead time correction factor may be based on the same decay experiment of the experimental object.

[0111] Step 540 , determining the correction relationship model according to the second relationship value and the second dead time correction factor mean value corresponding to each of the experimental objects. Step 540 may be performed by the model acquisition unit 734 .

[0112] In some embodiments, the second relationship value and the second dead time correction factor mean corresponding to the two or more experimental objects can be fitted to obtain a correction model. Among them, the fitting method adopted may include interpolation, least squares method, quadratic polynomial fitting, cubic polynomial fitting, semi-logarithmic fitting regression, Log-Log fitting regression, Logit-log fitting regression, four-parameter fitting, etc., or other suitable fitting methods. This application does not limit the specific method of fitting. The correction model can represent the corresponding relationship between the first dead time correction factor mean and the first relationship value.

[0113] It should be noted that the above description of process 500 is only for example and illustration, and does not limit the scope of application of the present application. For those skilled in the art, various modifications and changes can be made to process 500 under the guidance of the present application. However, these modifications and changes are still within the scope of the present application.

[0114] Figure 6 It is another exemplary flow chart for obtaining a correction model according to some embodiments of the present application. For a motif (such as motif 1), the mean of the second dead time correction factor corresponding to motif 1 is obtained, and the second relationship value of two or more second single event count information corresponding to motif 1 is calculated. It is determined whether there are other motifs. If there are other motifs (such as motif 2), the mean of the second dead time correction factor corresponding to motif 2 is obtained, and the second relationship value of two or more second single event count information corresponding to motif 2 is calculated, and it is determined again whether there is a new motif until there are no other motifs. The correction model is obtained by fitting the second dead time correction factor mean and the second relationship value corresponding to different motifs, thereby unifying the correction model based on the single event count rate under different motifs or different radiation source distribution conditions. Among them, the fitting method used may include interpolation, least squares method, quadratic polynomial fitting, cubic polynomial fitting, semi-logarithmic fitting regression, Log-Log fitting regression, Logit-log fitting regression, four-parameter fitting, etc., or other suitable fitting methods. The specific method of fitting is not limited in this application.

[0115] Figure 7 is a block diagram of a processing device 700 for detector dead time correction according to some embodiments of the present application. Figure 7 As shown, the processing device 700 may include an acquisition module 710 , a counting module 720 , a determination module 730 , and a correction module 740 .

[0116] In some embodiments, the acquisition module 710 may be used to acquire raw scan data of the target object, where the raw scan data is obtained by a detector of an imaging device receiving radiation photons.

[0117] In some embodiments, the counting module 720 can be used to obtain two or more first single event counting information under two or more energy windows related to the target object according to the original scan data. In some embodiments, the two or more energy windows include a first energy window and a second energy window, wherein the energy range of the second energy window is greater than the energy range of the first energy window. In some embodiments, the energy range of the second energy window is used to select a first type of single event to determine the incident information according to the original scan data, and the first energy window reflects the energy range of the characteristic energy peak related to the radioactive material injected into the target object. In some embodiments, the energy range of the first energy window is used to select a second type of single event from the first type of single event for compliance determination. In some embodiments, the energy range of the first energy window is 430keV-650keV, and the energy of the second energy window is greater than 200keV.

[0118] In some embodiments, the determination module 730 can be used to determine a first dead time correction factor based on the two or more first single event counting information. In some embodiments, the first dead time correction factor includes two or more first correction parameters, each of which corresponds to a detection unit of the detector of the imaging device.

[0119] In some embodiments, the determination module 730 further includes a first relationship value determination unit 732 , a model acquisition unit 734 , a first mean value determination unit 736 and a first dead time correction factor unit 738 .

[0120] In some embodiments, the first relationship value determining unit 732 may be used to determine a first relationship value of the two or more first single event counting information. In some embodiments, the first relationship value is a ratio between the two or more first single event counting information.

[0121] In some embodiments, the model acquisition unit 734 may be used to acquire a correction relationship model, where the correction relationship model includes a relationship between the first relationship value of the two or more single event counting information and a first dead time correction factor mean value.

[0122] In some embodiments, the first mean value determining unit 736 may be configured to determine a first dead time correction factor mean value according to the first relationship value and the correction relationship model.

[0123] In some embodiments, the first dead time correction factor unit 738 may be configured to determine the first dead time correction factor according to the first dead time correction factor mean.

[0124] In some embodiments, the determination module 730 may also include a second dead time correction factor unit, used to obtain a second dead time correction factor corresponding to the experimental object, the second dead time correction factor including two or more second correction parameters, each of the second correction parameters corresponding to one of the detection units of the imaging device, and the experimental object is used to obtain the correction relationship model; a second mean determination unit, used to determine the mean of the two or more second correction parameters as the mean of the second dead time correction factor; a correction coefficient determination unit, used to determine the correction coefficient according to the mean of the first dead time correction factor and the mean of the second dead time correction factor; a first correction parameter determination unit, used to determine the two or more first correction parameters according to the two or more second correction parameters and the correction coefficient.

[0125] In some embodiments, the determination module 730 can also be used to obtain, for each of two or more experimental objects, two or more second single event counting information under the two or more energy windows of the experimental object; determine the second relationship value of the two or more second single event counting information; obtain the mean value of the second dead time correction factor corresponding to the experimental object; and determine the correction relationship model based on the second relationship value corresponding to each of the experimental objects and the mean value of the second dead time correction factor.

[0126] In some embodiments, the correction module 740 may be configured to perform dead time correction on the raw scan data based on the first dead time correction factor.

[0127] For a detailed description of each module of the processing device 700 for detector dead time correction, reference may be made to the flowchart section of this specification, for example, Figures 1 to 6 Related instructions.

[0128] It should be understood that Figure 7The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of this specification. Not only can the hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, it can also be implemented with software such as executed by various types of processors, and it can also be implemented by a combination of the above hardware circuits and software (for example, firmware).

[0129] It should be noted that the above description of the sequence annotation system and its modules is only for convenience of description, and this specification cannot be limited to the scope of the embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules without deviating from this principle, or to form a subsystem connected with other modules. For example, the acquisition module 710 and the counting module 720 can be a module for realizing the function of data acquisition. For another example, the determination module 730 and the correction module 740 can be a module having the functions of the above two modules at the same time. For example, each module can share a storage module, and each module can also have its own storage module. Such variations are all within the scope of protection of this specification.

[0130] Figure 8 1 is a schematic diagram of exemplary hardware and / or software components of a computing device that can implement the processing device 140 according to some embodiments of the present application. Figure 8 As shown, computing device 800 may include a processor 810 , memory 820 , input / output (I / O) 830 , and communication port 840 .

[0131] The processor 810 can execute computer instructions (program code) and perform the functions of the processing device 140 according to the techniques described herein. Computer instructions may include routines, programs, objects, components, signals, data structures, processes, modules, and functions that perform specific functions described herein. For example, the processor 810 can obtain raw scan data from the storage device 150 and / or the terminal 130. In some embodiments, the processor 810 may include a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device, and any circuit and processor capable of performing one or more functions, etc., or any combination thereof.

[0132] For illustrative purposes only, only one processor is described in the computing device 800. However, it should be noted that the computing device 800 in the present application may also include multiple processors, so the operations of the method described in the present application that are performed by one processor may also be performed by multiple processors jointly or individually. For example, if both operations A and B are performed in the processor of the present application of the computing device 800, it should be understood that operation A and step B may also be performed jointly or individually by two different processors in the computing device 800 (e.g., the first processor performs operation A, the second processor performs operation B, or the first and second processors perform operations A and B together).

[0133] For example only, the processor 810 may receive instructions to follow a PET scanning protocol for a target object. For example, the processor 810 may provide certain control signals to control the detection unit 112 to be turned on and off.

[0134] In some embodiments, the processor 810 may acquire raw scan data of the target object in a scan area of ​​the imaging device.

[0135] In some embodiments, the processor 810 can also obtain data from one or more detection modules. For example, the processor 900 can obtain radiation events received by each detection module. Specifically, each detection module receives a radiation event and generates an electrical signal in response to the radiation event. The processor 900 can collect electrical signals from each detection module. In some embodiments, each detector can count the received radiation events, such as a second count rate.

[0136] In some embodiments, the processor 810 may also determine the dead time and other related parameters of the PET imaging device based on the acquired scan data. The dead time related correction parameters of the PET imaging device include the dead time correction factor and the dead time of the imaging device.

[0137] The memory 820 may store data / information acquired from the scanning device 110, the terminal 130, the storage device 150, or any other component of the dead time correction system 100 of the detector. In some embodiments, the memory 820 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. For example, the mass storage device may include a magnetic disk, an optical disk, a solid-state drive, etc. The removable storage device may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. The volatile read-write memory may include a random access memory (RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double data rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and a zero capacitance RAM (Z-RAM), etc. Exemplary ROM may include a shielded ROM (MROM), a programmable ROM (PROM), a erasable programmable ROM (PEROM), an electronically erasable programmable ROM (EEPROM), an optical disk ROM, or a digital universal disk ROM, etc. In some embodiments, the memory 820 may store one or more programs and / or instructions to perform the exemplary methods described in this application. For example, the memory 820 may store a program for processing the device 140, which is used to determine the dead time related parameters of the device based on the acquired scanning data. For another example, the memory 820 may store a program related to controlling the mobile platform 114.

[0138] I / O 830 can input or output signals, data or information. In some embodiments, I / O 830 can enable a user to interact with processing device 810. In some embodiments, I / O 830 can include input devices and output devices. Exemplary input devices can include a keyboard, a mouse, a touch screen, a microphone, a trackball, etc., or a combination thereof. Exemplary output devices can include a display device, a speaker, a printer, a projector, etc., or any combination thereof. Exemplary display devices can include a liquid crystal display (LCD), a display based on a light emitting diode (LED), a flat panel display, a curved display, a television device, a cathode ray tube (CRT), etc., or any combination thereof.

[0139] By way of example only, a user (e.g., an operator) may input data related to an object (e.g., a patient) to be imaged / scanned via the I / O 830. The data related to the object may include identification information (e.g., name, age, gender, medical history, contract information, physical examination results, etc.) and / or test information including the nature of the PET scan that must be performed. The user may also input parameters required for the operation of the scanning device 110, such as image contrast and / or ratio, region of interest (ROI), slice thickness, imaging type, or any combination thereof. The I / O may also display a PET image generated based on the sampled data.

[0140] The communication port 840 can be connected to a network (e.g., network 120) to facilitate data communication. The communication port 840 can establish a connection between the processing device 140 and the scanning device 110, the terminal 130, or the storage device 150. The connection can be a wired connection, a wireless connection, or a combination of the two, which enables data transmission and reception. The wired connection may include a cable, an optical cable, a telephone line, etc., or any combination thereof. The wireless connection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, a mobile network (e.g., 3G, 4G, 5G, etc.), etc., or a combination thereof. In some embodiments, the communication port 840 may be a standardized communication port, such as RS232, RS485, etc. In some embodiments, the communication port 840 may be a specially designed communication port. For example, the communication port 840 may be designed according to the Digital Imaging and Communications in Medicine (DICOM) protocol.

[0141] The above dead time correction is described by taking the PET system as an example. It is understandable that the dead time correction method proposed in this application can also be applied to the single-photon emission computed tomography (SPECT) imaging system. For example, Figure 1 The system 100 in the embodiment may be a SPECT imaging system. The scanning device 110 may be a SPECT scanning device 110, including a SPECT device, a SPECT-CT device, and the like.

[0142] For SPECT, the patient first needs to take a radioactive isotope drug. After the drug reaches the fault position required for imaging, due to radioactive decay, gamma photons will be emitted from the fault. The detection unit of the detector assembly detects the gamma photons coming in along a projection line, and the detected high-energy gamma rays are converted into scintillation light through the scintillator. The optical signal is converted into an electrical signal through the photoelectric sensor, and then the electrical signal is converted into a digital signal, and the image is reconstructed based on the digital signal. Among them, the detector receiving a photon is called a single event. Different types of radiation sources can emit gamma photons with different energies.

[0143] Since both PET and SPECT systems use ECT detectors, the dead time correction method for SPECT systems is similar to that for PET systems. Figure 2-Figure 6 The calibration model is obtained and the dead time correction of the SPECT system is performed.

[0144] As an example, for a SPECT system, at least one of the shape, type, size, and radiation source of the experimental object used to obtain the calibration model can be different. In some embodiments, a calibration model can correspond to the same radiation source or different types of radiation sources.

[0145] As an example, the two or more energy windows may include a first energy window and a second energy window, wherein the energy range of the second energy window may be greater than the energy range of the first energy window. In some embodiments, the energy range of the first energy window may be included in the energy range of the second energy window. In some embodiments, the energy range of the second energy window may be used to select a first type of single event to determine the incident information from the single event included in the raw scan data. The incident information may include but is not limited to at least one of the incident position, energy deposition, and incident time of the single event. For example, the energy range of the second energy window may be used to select a first type of single event to determine the energy deposition from the single event included in the raw scan data. In some embodiments, the first energy window may reflect the energy range of the characteristic energy peak associated with the radioactive material injected into the target object. The characteristic energy peak may reflect the energy value corresponding to the single event whose number is greater than a certain threshold received by the detector assembly 112. For example, if the radioactive material is 99mTc, the characteristic energy peak corresponds to 140keV. The energy range of the first energy window may be 140keV±20%. For another example, if the radioactive material is 133Ba, the characteristic energy peaks are 302keV and 356keV. The energy range of the first energy window may include the two characteristic energy peaks, or the two energy windows may be determined to represent the two energy peaks respectively.

[0146] In some embodiments, when more than two energy windows are selected, for example, if the radioactive material is 133Ba, two first energy windows can be selected to represent two corresponding characteristic energy peaks, and a second energy window can be selected to include the two first energy windows. The single event count rate corresponding to the two first energy windows is C 1A and C 2A , the single event count rate corresponding to the second energy window is C2, and C 1A and C 2A The ratio of C2, r1 = C 1A / C2, r2 = C 2A / C2, taking the arithmetic mean or weighted mean of r1 and r2 as the first relationship value.

[0147] The dead time correction factor determined based on the model that the single event count rate in a specific energy window varies with the activity of the radiation source cannot fully cover all the links that cause the dead time of the system electronics (for example, the ratio of the single event count rate to the total number of events in a single energy window will vary with the size of the scanned object or the intensity distribution of the radiation source). This leads to quantitative deviations in the system dead time factor calibrated by the standard phantom decay experiment when reconstructing images of other phantoms or human data. In addition, due to the baseline recovery problem of the electronic circuit, the energy peak of the detector output signal is constantly drifting for the same scanned object at different activities. On the other hand, the detector itself will also cause the energy peak of the detector output signal to drift continuously due to the coupling problem of the photoconductive material (such as silicone oil) between the scintillator and the photoelectric conversion device. This results in different dead time correction factors for the same phantom at different scanning times or different scanning dose conditions.

[0148] Different shapes of scanned objects, radiation source distributions, different scanning times, and different scanning doses will lead to different ratios of single event counts to the total number of events within a single energy window. This results in the inability to solve all types of scanned objects or model mismatch problems caused by changes in detection efficiency due to system state drift (energy peak drift) when only single event count information within a single energy window is used to correct the dead time effect of the system.

[0149] In the present application, a dead time correction method based on single event counting information under two or more energy windows is adopted. First, dead time factor lookup table calculations are performed on a variety of phantoms to obtain different lookup tables; and the ratio of single event counting information in two or more energy windows under the experiment of each phantom is calculated. Then, the relationship between the mean of different lookup tables and the ratio of single event counting information is fitted. In the dead time correction link in actual scanning, the mean of the dead time factor lookup table is first calculated based on the ratio of single event counting information in two or more energy windows in the collected data, and the mean of the dead time factor lookup table is applied to the lookup table to obtain the final dead time correction factor.

[0150] The ratio between the single event count rate and the number of events actually processed by the detector in a certain energy window varies with the shape of different scanned objects, the distribution of radiation sources, different scanning times, and different scanning doses. The reason is that the proportion of scattered events, signal pile-up effects, and circuit multiplexing effects of different scanned objects are different. Therefore, the ratio between the single event count rate and the number of events actually processed by the detector in a certain energy window can reflect the characteristics of different situations. The above method simulates the ratio between the single event count rate and the number of events actually processed by the detector in a certain energy window by the ratio of the single event count information in two or more energy windows to reflect the characteristics of different situations (for example, the shape of different scanned objects, the distribution of radiation sources, different scanning times, and different scanning doses), so that the system can identify different situations through the ratio, thereby determining the corresponding dead time correction factor. For example, the above second energy window (for example, the energy range of the second energy window is used to select a single event to determine the incident information (for example, energy deposition)) can be used to approximate the number of all events actually processed by the detector assembly. Although the single event counts in the energy range of the second energy window are not all event counts entering the detector electronics, they already include those events that require more computing resources of the detector. As an example, multiple measurements were performed on different phantoms (such as the uniform phantom and the National Electrical Manufacturers Association standard noise equivalent count rate (NEC) phantom). The measurement results show that the ratio of the single event count rate in the second energy window of the NEC phantom to the single event count rate in the first energy window is greater than the ratio of the single event count rate in the second energy window of the uniform water phantom to the single event count rate in the first energy window, and the NEC phantom remains stable under different radiation source activities. The distribution of single event count rates in two different energy windows is shown in Figure 2. Fig.12 As shown in Figure 1, NEC#1NEC#2NEC#3 represents three measurements of the NEC model, and Unif#1Unif#2 represents two measurements of the uniform water model. The ratio of the single event count rate under two different energy windows varies with the count rate under the second energy window. Fig.13 As shown. Fig.12 and Fig.13 It can be seen from the figure that the ratio between the single event count rate in the first energy window and the single event count rate in the second energy window can distinguish different scan objects.

[0151] The beneficial effects that may be brought about by the embodiments of the present application include but are not limited to: (1) By introducing single event count rates under multiple energy windows, the present application can enable the system to obtain a relatively accurate dead time correction effect for different scanned objects; (2) The ratio of single event count rates under multiple energy windows can simulate / approximately reflect the shape of the scanned object and the characteristics of the radiation source distribution, thereby improving the accuracy of calibration; (3) Through the correction relationship model, the dead time correction factor can be automatically obtained, thereby improving the efficiency of correction. It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0152] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0153] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0154] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0155] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0156] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0157] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0158] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

Claims

1. A method for correcting the dead time of a detector, wherein: The method comprises: Acquiring original scanning data of the target object, wherein the original scanning data is obtained by receiving radiation photons from a detector of an imaging device; Acquire, according to the original scan data, two first single event counting information in two energy windows or more than two first single event counting information in more than two energy windows related to the target object; Determining a first dead time correction factor based on the two or more first single event counting information includes: Determine a first relationship value of the two or more first single event counting information; Acquire a correction relationship model, wherein the correction relationship model includes a relationship between a first relationship value of the two or more single event counting information and a first dead time correction factor mean value; Determining a first dead time correction factor mean value according to the first relationship value and the correction relationship model; determining the first dead time correction factor according to the first dead time correction factor mean; Based on the first dead time correction factor, dead time correction is performed on the original scan data.

2. The method according to claim 1, characterized in that The two or more energy windows include a first energy window and a second energy window, wherein the energy range of the second energy window is greater than the energy range of the first energy window.

3. The method according to claim 2, characterized in that The energy range of the second energy window is used to select and determine a first type of single event of incident information according to the original scanning data, and the first energy window reflects the energy range where a characteristic energy peak associated with the radioactive material injected into the target object is located.

4. The method according to claim 3, characterized in that The energy range of the first energy window is used to select a second type of single event from the first type of single events for coincidence determination.

5. The method according to claim 2, characterized in that: The energy range of the first energy window is 430keV-650keV, and the energy of the second energy window is greater than 200keV.

6. The method according to claim 1, characterized in that The first dead time correction factor includes two or more first correction parameters, each of which corresponds to a detection unit of a detector of the imaging device; The method further comprises: Acquire a second dead time correction factor corresponding to an experimental object, wherein the second dead time correction factor includes two or more second correction parameters, each of the second correction parameters corresponds to one of the detection units of the imaging device, and the experimental object is used to acquire the correction relationship model; Determining an average of the two or more second correction parameters as an average of the second dead time correction factors; The two or more first correction parameters are determined according to the first dead time correction factor mean, the second dead time correction factor mean and the two or more second correction parameters.

7. A detector dead time correction system, wherein: The system comprises: An acquisition module, used to acquire original scan data of the target object, wherein the original scan data is obtained by receiving radiation photons from a detector of an imaging device; A counting module, used for acquiring two first single event counting information in two energy windows or more than two first single event counting information in more than two energy windows related to the target object according to the original scanning data; A determination module, configured to determine a first dead time correction factor based on the two or more first single event counting information, comprising: Determine a first relationship value of the two or more first single event counting information; Acquire a correction relationship model, wherein the correction relationship model includes a relationship between a first relationship value of the two or more single event counting information and a first dead time correction factor mean value; Determining a first dead time correction factor mean value according to the first relationship value and the correction relationship model; determining the first dead time correction factor according to the first dead time correction factor mean; A correction module is used to perform dead time correction on the original scan data based on the first dead time correction factor.

8. A dead time correction device for a detector, characterized in that: The device comprises: at least one storage medium storing computer instructions; At least one processor executes the computer instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when a computer reads the computer instructions, the computer executes the method according to any one of claims 1 to 6.

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