Detector event processing method and PET apparatus

Abstract

To improve the processing performance of detector events. [Solution] The method includes sorting multiple events detected by the photodetector of a PET apparatus by time, generating a first subseries of events by combining one or more subsequent events that fall within a certain time range from the acquisition time of the first event with the first event, generating a second subseries of events by combining one or more subsequent events that fall within a certain time range from the acquisition time of the second event with the second event, removing duplicate events from the second subseries with the first subseries, and generating a third subseries of events by combining one or more subsequent events that fall within a certain time range from the acquisition time of the third event that were not removed in the second subseries with the third event.

Description

TECHNICAL FIELD

[0001] Embodiments disclosed in the present specification and drawings relate to a detector event processing method and a PET apparatus.

BACKGROUND ART

[0002] Positron Emission Tomography (PET) is a functional imaging modality that can image biochemical processes in humans or animals through the use of radioactive tracers. In PET imaging, a tracer agent is administered to a patient to be imaged via injection, inhalation, or ingestion. After administration, the physical and biomolecular properties of the agent cause the agent to concentrate at specific locations within the patient's body. Here, the actual spatial distribution of the agent, the intensity of the agent accumulation region, and the dynamics of the process from administration to final excretion are all factors that may be clinically significant elements.

PRIOR ART DOCUMENTS

PATENT DOCUMENTS

[0003]

PATENT DOCUMENT 1

PATENT DOCUMENT 2

PATENT DOCUMENT 3

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0004] One of the problems to be solved by the embodiments disclosed in the present specification and drawings is to improve the processing performance of detector events. However, the problems to be solved by the embodiments disclosed in the present specification and drawings are not limited to the above-mentioned problem. Problems corresponding to the respective effects of the respective configurations shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0005] The detector event processing method according to the embodiment includes sorting a plurality of events detected by the photodetector of a PET apparatus by time, generating a first subseries of the plurality of events by combining one or more subsequent events that are within a certain spatial range and a certain time range from the acquisition time of the first event from the photodetector associated with the first event with the first event, generating a second subseries of the plurality of events by combining one or more subsequent events that are within a certain spatial range and a certain time range from the acquisition time of the second event from the photodetector associated with the second event with the second event, removing duplicate events that are included in both the first and second subseries from the second subseries, and generating a third subseries of the plurality of events by combining one or more subsequent events that are within a certain spatial range and a certain time range from the acquisition time of the third event from the photodetector associated with the third event that were not removed in the second subseries with the third event. [Brief explanation of the drawing]

[0006] [Figure 1] This flowchart shows a method for generating a single photodetector event according to one embodiment of the present disclosure. [Figure 2] This is a schematic diagram of sorting of photodetector events according to one embodiment of the present disclosure. [Figure 3A] (1) This is a schematic diagram of a single according to one embodiment of the present disclosure. [Figure 3B] (2) A schematic diagram of a single according to one embodiment of the present disclosure. [Figure 3C] (3) This is a schematic diagram of a single according to one embodiment of the present disclosure. [Figure 3D] (4) This is a schematic diagram of a single according to one embodiment of the present disclosure. [Figure 4] This is a table of sorting times according to one embodiment of the present disclosure. [Figure 5] This is a table of processing times according to one embodiment of the present disclosure. [Figure 6A] A perspective view of the PET apparatus of this disclosure is shown. [Figure 6B] This figure shows an example of the configuration of a PET apparatus that can be used in the technology described herein, according to one embodiment of the present disclosure. [Modes for carrying out the invention]

[0007] The detector event processing method and embodiments of the PET apparatus will be described in detail below with reference to the drawings.

[0008] The following disclosure provides many different embodiments, or examples, for carrying out different features of the subject matter provided. Specific examples of components and arrangements are described below for the sake of simplicity of this disclosure. Of course, these are merely examples and are not intended to limit the scope. For example, forming a first feature over or on a second feature in the following description may include embodiments in which the first and second features are formed in direct contact, or it may include embodiments in which additional features are formed between the first and second features, even if the first and second features are not in direct contact. Furthermore, reference numbers and / or letters may be repeated in various embodiments in this disclosure. This repetition is for the sake of simplification and clarity and does not in itself indicate relationships between the various embodiments and / or configurations described. Furthermore, spatially relative terms such as “top,” “bottom,” “beneath,” “below,” “lower,” “above,” and “upper” are used herein to facilitate explanation and may describe the relationship between one element or another element(s) or feature(s) and a feature(s) as shown in the figures. Spatially relative terms are intended to encompass different orientations of the device during use or operation, in addition to the orientations shown in the figures. The system may be oriented in other ways (e.g., rotated 90 degrees or in other orientations), and the spatially relative descriptions used herein may be interpreted accordingly.

[0009] As used herein, the term “plural” is defined as two or more. As used herein, the term “another” is defined as at least two or more. As used herein, the terms “including” and / or “having” are defined as comprising (i.e., open language). Throughout this specification, any reference to “one embodiment,” “a particular embodiment,” “embodiment,” “example,” or similar terms means that a particular feature, structure, or characteristic described in relation to an embodiment is included in at least one embodiment of this disclosure. Thus, not all such phrases or occurrences throughout this specification necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any suitable manner without being limited to one or more embodiments.

[0010] In one embodiment, the disclosure is directed toward assembling singles of events generated by a photodetector in a positron emission tomography (PET) system. A photodetector (or photosensor) in a PET system can generate an electrical output signal based on gamma-ray interactions (hits) with a detector crystal. In one embodiment, the output signal may be a current pulse generated in response to a detected gamma photon. An annihilation event generates two gamma photons, each of which can be detected by one or more photodetectors. One or more photodetector hits (photodetector events) corresponding to a single gamma photon can be grouped into a single through a processing method. Since the singles may include hits that are temporally and spatially related, they are likely to be the result of a single photon interacting with a photodetector.

[0011] Hits can be temporally related if they occur within the same time window. Hits can be spatially related if they occur on photodetectors within the same region of a PET system. For example, photodetectors may be located in a sensor array. Hits on adjacent sensors, or hits occurring across multiple sensors, can be grouped into a single based on spatial proximity. Ordering and grouping hits into singles is useful for identifying annihilation events and for identifying coincidence pairs of events corresponding to the same annihilation event. Singles can indicate the patient's anatomical structure for image reconstruction from PET data.

[0012] Photodetector events can include spatial data in the form of encoding used by the photodetector that generates the event. Photodetector events can be received in a linear, highly ordered list. However, because a large number of events can occur at high frequency in a single PET scan, photodetector events in a sequence may not be received in the same chronological order in which they occurred. Customized hardware can be developed to receive and process photodetector events. However, hardware is inherently static and cannot be scaled up or down after installation, or modified to accommodate changes in algorithms. Developing and manufacturing customized hardware also incurs costs.

[0013] The present disclosure is directed to systems and methods for sorting and grouping photodetector events to assemble singles using a parallel computing environment. In one embodiment, the methods described herein may be implemented on one or more Graphics Processing Unit (GPU) cores. This method can leverage the multi-threading capabilities of the GPU core to process a list of events using a plurality of threads. Each thread can process the list in a parallel pass, which allows events across the entire list to be grouped simultaneously and at high speed. Each thread in the process can access and write to shared memory during parallel processing. In one embodiment, each thread can access a list of events stored in memory to assemble singles from the events. In one embodiment, the method can be pipelined across multiple GPUs to achieve high throughput of event processing. In one embodiment, the method may be implemented in a hardware (e.g., Field Programmable Gate Array (FPGA)) configuration.

[0014] Figure 1 is a flowchart of method 1000 for assembling a single according to one embodiment. In step 1001, a batch of photodetector signals can be received and decoded into photodetector events. Each photodetector event can correspond to an output signal from a photodetector in the PET system. Since the photodetector events are received from the photodetectors in a First-In, First-Out (FIFO) queue, they can be strictly ordered in the list. However, given that the photodetectors are spatially distributed within the PET system, the order of events in the queue may not be strictly chronological. In one embodiment, the FIFO queue can be received from the photodetectors via Remote Data Memory Access (RDMA), e.g., PCIe (Peripheral Component Interconnect Express), Ethernet®. In one embodiment, the completion of the transfer of photodetector signals may trigger the remaining steps of the method. In step 1200, the photodetector events can be sorted in time through one or more sort iterations. In other words, in step 1200, the processing circuit, processor 770, sorts multiple events detected by the gamma-ray-detecting photodetector of the PET apparatus 700 by time. The sorting step 1200 can be repeated until the number of sorting iterations is reached in 1290. Once the number of sorting iterations is reached in 1290, the method can proceed to step 1300. In step 1300, each event in the sorted list can be assigned to a thread, and each thread can assemble a single by searching forward for events spatially and temporally related to the assigned event and adding them to form a single. Thus, each event in the list is the initial event in the single of subsequent events. The single of each thread can be considered a subseries of photodetector events, which can be refined in further steps to produce the final single of photodetector events.For example, the processor 770, which is a processing circuit, generates a first subseries of events by combining one or more subsequent events that fall within a certain spatial range and a certain time range from the acquisition time of the first event, obtained from a photodetector associated with the first event, with the first event, and generates a second subseries of events by combining one or more subsequent events that fall within a certain spatial range and a certain time range from the acquisition time of the second event, obtained from a photodetector associated with the second event, with the second event. The steps of combining with the first event and combining with the second event are performed by parallel software threads. The first time range is based on the clock resolution of the photodetector associated with the first event. The first subseries precedes the second subseries.

[0015] At step 1310, each thread may execute a backward pass through its own single, and may delete events that are part of the single of other threads corresponding to previous events. As an example, the processor 770 which is a processing circuit deletes duplicate events that are included in both the first sub-series and the second sub-series from the second sub-series. Step 1310 removes duplicates between singles across different threads. As a result, the initial event of each thread's single may become different (updated) after step 1310. At step 1320, each thread may execute a forward pass through the singles of other threads starting from the updated initial event of its own single, and combine temporally similar singles. The combined single can be processed by the thread corresponding to the earliest initial event among the combined singles that are combined to form the combined single. As an example, after deleting the duplicate events, the processor 770 which is a processing circuit generates a third sub-series of a plurality of events by combining the third event with one or more subsequent events that are within a certain spatial range from the photodetector associated with the third event that was not deleted in the second sub-series and within a certain time range from the acquisition time of the third event. Here, typically, the second sub-series precedes the third sub-series.

[0016] In one embodiment, steps 1310 and 1320 can be repeated until the number of grouping iterations is reached. In one embodiment, the number of grouping iterations can be 4. Each iteration can combine or recombine singles to remove duplicates and appropriately group events based on their temporal and spatial origins. By repeating steps 1310 and 1320, it is possible to ensure that singles become the longest associated event while preventing duplicates between singles. When the number of grouping iterations is reached, or when the singles are stable in 1390, method 1000 can be terminated in step 1400. The processing circuit, processor 770, may display the progress of the completion of the first event combination, the second event combination, the removal of duplicate events, and the combination with the third event. The processing circuit, processor 770, may also reconstruct the image based on the first subseries and the third subseries.

[0017] Figure 2 is an explanatory diagram of a sort step 1200 according to one embodiment. In one embodiment, sorting can be a multithreaded process. A computing core (e.g., a GPU core) can sort a list of photodetector events using a sort network. In one embodiment, each thread can sort the number of events in the list using the sort network. In one embodiment, sort step 1200 may include a first sort pass 1210 and a second sort pass 1220. Sorting step 1200 may include the number of iterations of the first and second sort passes.

[0018] For example, Figure 2 shows a first sort pass 1210 in which each thread can sort a first group of four events in a first window using a four-element sort network. Each thread's event window can be distinguished without overlapping with another thread's window. The sort network can compare events in pairs and rearrange events based on the pair comparison until the events are temporally ordered. The maximum number of comparisons the sort network performs depends on the number of elements being sorted. In one embodiment, each thread can write the sorted event group to an output buffer, so that another thread can read that sorted event group from memory in a second pass.

[0019] In the second sort pass 1220, each thread can sort a second group of four events in the second window using a four-element sort network. In one embodiment, the second window may overlap with the first window. For example, the second event group may share half of its events with the first event group. The embodiment in Figure 2 shows the second group of four events for each thread in the second sort pass 1220, where the first two events of the second group for each thread are the latter two events of the first group in the first sort pass 1210.

[0020] In one embodiment, the first sort pass 1210 and the second sort pass 1220 can be repeated sequentially in each iteration. In each iteration, the thread can sort the events in the first window followed by the events in the second window. It is understood that the events in each window may be different after each sort iteration. The list is sorted iteratively, with each pass moving earlier events to the top of the list and later events to the bottom. In one embodiment, at each step, the group of events to be sorted is received by the thread via an input buffer. After the event group has been sorted, the thread can swap the input and output buffers, outputting the sorted events for other threads to access and receive the next group of events to sort.

[0021] In one embodiment, the first sort pass 1210 and the second sort pass 1220 can be repeated a number of times equal to the number of sort iterations. In one embodiment, the number of sort iterations can be approximately 80. The initial order of the list may already correspond to a rough chronological order based on when events were received from the photodetector. As a result, it is not necessary to compare each event in the list with all other events in the list in order to sort the list chronologically. The sorting can be roughly localized based on the size of the group and the number of sort iterations. By limiting the number of iterations, the computational load and complexity of the sorting process can be limited, while the repeated sorting can leverage the order of the events to produce a well-sorted list. Therefore, the execution time of the sort step 1200 can be kept constant.

[0022] Figure 3A shows the initialization of a single in the first grouping step 1300 ("initialization step"). After the list is sorted in step 1200, each event in the list can be assigned as an initial event to a thread of a multithreaded processor. For example, the first thread can be assigned the first event as its initial event, the second thread can be assigned the second event as its initial event, the third thread can be assigned the third event as its initial event, and so on. In step 1300, each thread can perform a forward scan to access events that follow the initial event in the ordered list. Each thread can combine subsequent events into a single if the subsequent events are spatially and temporally related to the initial event. In one embodiment, the forward scan can be restricted to a time window. For example, a thread can access subsequent events from the thread's initial event until it reaches an event outside the time window. Events outside the time window do not need to be processed because they are not the same single as the initial event.

[0023] In Figure 3A, the first thread can group the first, second, and third events into a single S1 because the second and third events are spatially and temporally related to the first event. The second thread can group the second, third, and fourth events into a single S2 because the third and fourth events are spatially and temporally related to the second event. The third thread can group the third, fourth, and fifth events into a single S3. The fourth thread can group the fourth and fifth events into a single S4. The fifth thread can group the fifth event into a single S5.

[0024] In one embodiment, events can be spatially related if they are generated by adjacent photodetectors. In one embodiment, events can be spatially related if they are generated by photodetectors within a spatial region or area. The spatial region can be, for example, a number of photodetectors. In one embodiment, events can be temporally related if they are within a given time window. The time window can be set based on the clock resolution of the PET detector. As an example, the time window may be on a nanosecond to picosecond scale. Adjacent events may not be part of the same single because they are not spatially and temporally related. For example, a first event and a second event following the first event may fall within a given time window. However, the first and second events cannot be added to the same single because the second event may originate from a photodetector that is not adjacent to the photodetector that generated the first event. In another example, the first and second events may be generated by adjacent photodetectors, but they may not be in the same single because they were not generated within a given time window.

[0025] Figure 3A shows five singles generated by five threads in step 1300. The threads operate in parallel and can construct each single by forward-passing through an ordered list of events. Each single may contain at least an initial and a final event. In one embodiment, a thread may write to shared memory to mark or indicate events combined with a single. A single event may be stored in multiple singles. For example, the third event in Figure 3A is contained in the first single of the first thread, the second single of the second thread, and the third single of the third thread. However, overlaps between singles can hinder image reconstruction because it becomes more difficult to identify concurrent counting events that span different singles.

[0026] Therefore, the second grouping step 1310 ("deletion step") can remove duplicate events from a single. In step 1310, each thread can identify duplicate events among other threads. Each thread can perform a backward pass or scan of events within its single to identify events that are also grouped in other singles. For example, thread 2 in Figure 3A generates a single S2 which includes event 2 as the initial event and event 4 as the ending event. Thread 2 can access singles generated by threads that have earlier initial events (e.g., thread 1) and can identify whether events 2-4 are included in those threads' singles. Thread 2 can then delete the duplicate events (e.g., event 2 and event 3) from S2.

[0027] Figure 3B shows the threads in Figure 3A after the second grouping step 1310. Duplicate events are removed from each single. In the example in Figure 3B, the duplicate events removed in S2-S5 include the initial events of each single. However, initial events do not necessarily overlap with other singles, considering that adjacent events may not be added to the same single if they are not spatially and temporally related.

[0028] In one embodiment, each thread can identify and delete duplicate events in parallel to reduce the time required to complete the second grouping step 1310. Each thread can independently perform the process of identifying and deleting duplicate events without inter-thread communication. As a result, the same event(s) can be deleted from multiple threads, as shown in Figure 3B. For example, S2 has only one event, event 4, after step 1310. Since it is unlikely that a single contains only one event, a correction step can be performed to combine a single that is too short with other events that are spatially and temporally related.

[0029] A third grouping step 1320 ("extension step") may be a modification step in which events are added to a single in a forward pass. In step 1320, each thread can access a sorted list of events and add events that are spatially and temporally related to the earliest event in the thread's single. The earliest event in a thread's single may refer to the earliest non-overlapping event remaining after step 1310. The earliest event may or may not be the initial event assigned to the thread. The criteria for adding events to a single can be the same in step 1320 as in step 1300. In one embodiment, the forward scan in step 1320 may also be restricted to a time window. For example, a thread can access subsequent events from its earliest event until it reaches an event outside the time window. Events outside the time window do not need to be processed because they do not make up the same single as the earliest event.

[0030] Figure 3C shows the thread in Figure 3B after the third grouping step 1320. Events 4 and 5 are linked in S2 because they are spatially and temporally related. The third grouping step allows the thread to continue building singles after removing duplicate events. In one embodiment, a single can be limited to a maximum number of elements. The maximum number of elements can be a predetermined number that can be set based on the PET system, allowing for flexibility in the grouping process.

[0031] Adding events to singles in the third grouping step 1320 may result in new overlaps between singles. For example, Figure 3C shows that event 5 is added to S2, and S2 now overlaps with S3. In one embodiment, the method may include repeating grouping steps 1310 and 1320 to remove overlaps and continue adding to singles. Figure 3D shows an example of events after repeating steps 1310 and 1320. As shown in Figure 3D, repeating grouping step 1310 with the single in Figure 3C removes the overlapping event 5 from S3. The events depicted in Figure 3D are grouped into two separate singles, the first single S1 containing events 1-3, and the second single S2 containing (at least) events 4-5. The singles are non-overlapping and comprehensively group all events that are spatially and temporally related.

[0032] By repeating grouping steps 1310 and 1320, the size of a single can be increased within the size limit, while ensuring that the single contains only spatially and temporally relevant events. The backward pass in step 1310 removes unnecessary duplicate events from the single. The thread can then add events to the single in the second forward pass 1320 without being constrained by unnecessary duplicate events. Without combining these two steps, the single could be redundant and / or might not capture all spatially and temporally relevant events resulting from photon interactions.

[0033] In one embodiment, the grouping steps 1310 and 1320 can be repeated a predetermined number of grouping iterations. The number of grouping iterations can be set based on the PET system, allowing for flexibility in the grouping process. In an embodiment, the number of grouping iterations can be related to the maximum number of events per single. For example, as the maximum number of events increases, the number of iterations can be increased in each iteration to handle greater duplication and additions.

[0034] In one embodiment, the grouping steps 1310 and 1320 can be repeated until the singles are stable and no longer change when passing through further elements. In one embodiment, once the stability condition is met, the grouping process can be terminated in the final deletion step to ensure that there are no overlaps among the singles.

[0035] In one embodiment, each thread can maintain input and output buffers to avoid data dependencies and keep the data to be read (input) and the data to be written (output) separate. In one embodiment, the thread does not perform read-modify-write operations while executing parallel paths in steps 1310 and 1320. In one embodiment, the input and output buffers can be swapped after each iteration to update the list of events. In one embodiment, the threads can be synchronized after each pass (step). In one embodiment, the threads may further include processing buffers. In one embodiment, the threads can be composed of one or more CUDA streams to hide data transfer time.

[0036] In one embodiment, the method may include providing updates on the completion progress of the assembly method. For example, the method may include a total number of steps, including the number of sorting iterations and the number of grouping iterations. In one embodiment, the number of sorting iterations and / or grouping iterations may be a predetermined number. In one embodiment, the number of sorting iterations and / or grouping iterations may be received as input (e.g., user input). In one embodiment, the PET detector system may be configured to display or otherwise indicate the progress or status of the assembly method. For example, the progress of the assembly method may be shown as a percentage of the total steps completed. The progress may be updated as the method progresses.

[0037] Method 1000 can improve the efficiency of a single configuration compared to conventional methods. Both the sorting step 1200 and the grouping steps 1300-1320 can contribute to speeding up execution time. As an example, sorting step 1200, which uses 80 sort iterations, can be performed in 10 6 While this can be executed in 0.172ms (milliseconds) for an event, the conventional full sort method may take 3.75ms. Figure 4 shows 10 6 This table shows experimental results when the number of sort iterations is varied for a sample of events. A limited number of sort iterations (e.g., 20 to 100 iterations) can sort an ordered list of events much faster than a full sort algorithm where the remaining events (% Extra) are few.

[0038] Figure 5 shows 10 6 This is a table of experimental results for grouping steps 1300-1320 on a sorted sample of events. 10 6The event sample can be sorted in 80 iterations using sort step 1200. The grouping process in Figure 5 may include repeating the delete step 1310 and the expansion step 1320 until a single stabilizes. As an example, a single stabilizes after going through steps 1300-1320 three times. In one embodiment, the initialization step and the subsequent expansion step may take 0.124 ms, the delete steps may take 0.129 ms each, totaling 0.258 ms, and the expansion step may take 0.154 ms. 6 The total assembly time to group the events into a single is approximately 0.536ms. This assembly time is significantly faster than conventional methods, which, even with an optimized CPU or CPU-GPU combination, can take 10 6 This event could take approximately 992ms.

[0039] In one embodiment, using the method described herein, 10 6 The total time to process the events may be approximately 1.158 ms. This total processing time may include approximately 0.280 ms for decoding the photodetector events, approximately 0.172 ms for sorting the events, approximately 0.536 ms for assembling the singles, and approximately 0.250 ms for additional processing such as data transfer. If the events are encoded with 16 bytes of data, the method of this disclosure can assemble the singles with a throughput of approximately 13.8 gigabytes (GB) / second. Conventional methods are far less efficient, with throughputs of 10-16 MB / second.

[0040] Figures 6A and 6B show non-limiting embodiments of a PET apparatus 700 in which method 1000 can be carried out. The PET apparatus 700 includes a number of gamma-ray detectors (GRDs) (e.g., GRD1, GRD2 to GRDN) each configured as a rectangular detector module. According to one embodiment, the detector ring includes 40 GRDs. In another embodiment, there are 48 GRDs, and a larger number of GRDs is used to produce a larger bore size for the PET apparatus 700.

[0041] Each GRD may include a two-dimensional array of individual detector crystals that absorb gamma rays and emit scintillation photons. These scintillation photons can be detected by a two-dimensional array of photomultiplier tubes (PMTs) also located within the GRD. An optical conductor can be placed between the array of detector crystals and the PMTs.

[0042] Alternatively, scintillation photons can be detected by an array of silicon photomultiplier tubes (SiPMs), where each individual detector crystal may contain a corresponding SiPM.

[0043] Each photodetector (e.g., PMT or SiPM) can generate an analog signal indicating when a scintillation event occurs, as well as the energy of the gamma ray that generates the detection event. Furthermore, photons emitted from one detector crystal can be detected by two or more photodetectors, and based on the analog signals generated by each photodetector, the detector crystal corresponding to the detection event can be determined, for example, using Anger logic and crystal decoding.

[0044] Figure 6B shows a schematic diagram of a PET scanner system having a gamma-ray (gamma-ray) photon count detector (GRD) positioned to detect gamma rays emitted from the subject's obstetric ophthalmological junction (OBJ). The GRD can measure the timing, position, and energy corresponding to the detection of each gamma ray. In one embodiment, the gamma-ray detector is arranged in a ring shape, as shown in Figures 6A and 6B. The detector crystal may be a scintillator crystal having individual scintillator elements arranged in a two-dimensional array, and the scintillator elements may be any known scintillator material. The PMTs may be arranged so that light from each scintillator element is detected by multiple PMTs, enabling Anger arithmetic and crystal decoding of the scintillation event.

[0045] Figure 6B shows an example of the arrangement of the PET scanner 700, where the subject OBJ to be imaged is placed on the bed 716, and the GRD modules GRD1 to GRDN are arranged circumferentially around the subject OBJ and the bed 716. The GRDs can be fixedly connected to annular components 720 which are fixedly connected to the gantry 740. The gantry 740 houses many of the components of the PET scanner. The gantry 740 of the PET scanner also includes an opening through which the subject OBJ and the bed 716 can pass, allowing the GRDs to detect gamma rays emitted in the opposite direction from the subject OBJ due to annihilation events, and to determine the coincidence of gamma ray pairs using timing and energy information.

[0046] Figure 6B also shows the circuitry and hardware for acquiring, storing, processing, and distributing gamma-ray detection data. This circuitry and hardware includes a processor 770, a network controller 774, a memory 778, and a Data Acquisition System (DAS) 776. The PET imaging system also includes data channels for routing detection measurement results from the GRD to the DAS 776, processor 770, memory 778, and network controller 774. The DAS 776 can control the acquisition, digitization, and routing of detection data from the detector. In one embodiment, the DAS 776 controls the movement of the patient bed 716. The processor 770 is a processing circuitry that performs functions including image reconstruction from detection data, pre-reconstruction processing of detection data, and post-reconstruction processing of image data, as described herein. The processor 770 can perform the determination of calibration parameters in the calibration phase and / or the application of energy correction in the correction phase.

[0047] The processor 770 can be configured to perform various steps and variations of the method described herein. The processor 770 may include a CPU that can be implemented as individual logic gates, such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other Complex Programmable Logic Device (CPLD). The FPGA or CPLD implementation may be coded in VHDL, Verilog®, or other hardware description language, and the code may be stored directly in the electronic memory within the FPGA or CPLD, or in separate electronic memory. Furthermore, the memory may be non-volatile, such as ROM, EPROM, EEPROM, or flash memory. The memory may also be volatile, such as static RAM or dynamic RAM, and a processor, such as a microcontroller or microprocessor, may be provided to manage the electronic memory and manage the interaction between the FPGA or CPLD and the memory.

[0048] Alternatively, the CPU within the processor 770 may execute a computer program comprising a set of computer-readable instructions that perform various steps of the method described herein, the program being stored in any of the non-transient electronic memory and / or hard disk drives, CDs, DVDs, flash drives, or other known storage media described above. Furthermore, the computer-readable instructions may be provided as utility applications, background daemons, or components of an operating system, or a combination thereof, and may be executed in cooperation with processors such as Intel Xeon® processors or AMD® Opteron® processors, and Microsoft® Vista®, UNIX®, Solaris®, Linux®, Apple®, MAC-OS®, and other operating systems known to those skilled in the art. Furthermore, the CPU may be implemented as multiple processors that operate concurrently and cooperatively to execute instructions.

[0049] The memory 778 may be a hard disk drive, CD-ROM drive, DVD drive, flash drive, RAM, ROM, or other electronic storage device known in the art.

[0050] Network controllers 774, such as the Intel® Ethernet® PRO network interface card manufactured by Intel Corporation in the United States, can interface between various parts of the PET imaging system. Furthermore, network controller 774 can also interface with an external network. As can be understood, the external network can be a public network such as the Internet, or a private network such as a LAN or WAN network, or any combination thereof, and may include PSTN® or ISDN® subnetworks. The external network can be wired, such as an Ethernet® network, or wireless, such as a cellular network including EDGE, 3G, 4G, and 5G wireless cellular systems. The wireless network may be WiFi®, Bluetooth®, or other known wireless communication forms.

[0051] According to at least one embodiment described above, the processing performance of detector events can be improved.

[0052] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be implemented in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments are possible without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0053] 700 PET equipment 740 Gantry 770 processor 778 memory

Claims

1. Multiple events detected by the photodetector of the PET device are sorted by time. A first subseries of events is generated by combining one or more subsequent events that fall within a first time range, which is a certain spatial range and a certain time range from the acquisition time of the first event, with the first event, from a photodetector associated with the first event. A second subseries of the plurality of events is generated by combining one or more subsequent events that are within a certain spatial range and within a certain time range from the acquisition time of the second event, obtained from a photodetector associated with the second event, with the second event. From the second subseries, remove duplicate events that are included in both the first and second subseries. After deleting the duplicate events, a third subseries of the multiple events is generated by combining one or more subsequent events from a photodetector associated with the third event that was not deleted in the second subseries, which are within a certain spatial range and within a certain time range from the acquisition time of the third event, with the third event. Includes a detector event processing method.

2. The detector event processing method according to claim 1, wherein the step of combining with the first event and the step of combining with the second event are performed by parallel software threads.

3. The detector event processing method according to claim 1, wherein the first time range is based on the clock resolution of a photodetector associated with the first event.

4. The detector event processing method according to claim 1, further comprising displaying the completion progress of the merging of the first event, the merging of the second event, the deletion of the duplicate event, and the merging with the third event.

5. The detector event processing method according to claim 1, further comprising reconstructing an image based on the first subseries and the third subseries.

6. The detector event processing method according to claim 1, wherein the first subseries is located before the second subseries, and the second subseries is located before the third subseries.

7. A photodetector that detects gamma rays, Multiple events detected by the aforementioned photodetector are sorted by time. A first subseries of events is generated by combining one or more subsequent events that fall within a first time range, which is a certain spatial range and a certain time range from the acquisition time of the first event, with the first event, from a photodetector associated with the first event. A second subseries of the plurality of events is generated by combining one or more subsequent events that are within a certain spatial range and within a certain time range from the acquisition time of the second event, obtained from a photodetector associated with the second event, with the second event. From the second subseries, remove duplicate events that are included in both the first and second subseries. After deleting the duplicate events, a processing circuit generates a third subseries of the plurality of events by combining one or more subsequent events from a photodetector associated with the third event that was not deleted in the second subseries, which are within a certain spatial range and within a certain time range from the acquisition time of the third event, with the third event. Equipped with a PET scanner.

8. The PET apparatus according to claim 7, wherein the first time range is based on the clock resolution of a photodetector associated with the first event.

9. The PET apparatus according to claim 7, wherein the processing circuit displays the progress of the completion of the merging of the first event, the merging of the second event, the deletion of the duplicate event, and the merging with the third event on a display unit.

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