WO2022166976A1 - Pet数据校正方法、装置、计算机设备以及pet图像重建方法 - Google Patents

Pet数据校正方法、装置、计算机设备以及pet图像重建方法 Download PDF

Info

Publication number
WO2022166976A1
WO2022166976A1 PCT/CN2022/075457 CN2022075457W WO2022166976A1 WO 2022166976 A1 WO2022166976 A1 WO 2022166976A1 CN 2022075457 W CN2022075457 W CN 2022075457W WO 2022166976 A1 WO2022166976 A1 WO 2022166976A1
Authority
WO
WIPO (PCT)
Prior art keywords
scattering
event
events
scatter
pet
Prior art date
Application number
PCT/CN2022/075457
Other languages
English (en)
French (fr)
Inventor
褚少平
Original Assignee
上海联影医疗科技股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 上海联影医疗科技股份有限公司 filed Critical 上海联影医疗科技股份有限公司
Priority to EP22749255.0A priority Critical patent/EP4289361A1/en
Priority to JP2023547840A priority patent/JP2024505727A/ja
Priority to US18/276,257 priority patent/US20240122567A1/en
Publication of WO2022166976A1 publication Critical patent/WO2022166976A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
    • A61B6/5282—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise due to scatter
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/037—Emission tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5205—Devices using data or image processing specially adapted for radiation diagnosis involving processing of raw data to produce diagnostic data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/16—Measuring radiation intensity
    • G01T1/20—Measuring radiation intensity with scintillation detectors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/29—Measurement performed on radiation beams, e.g. position or section of the beam; Measurement of spatial distribution of radiation
    • G01T1/2914—Measurement of spatial distribution of radiation
    • G01T1/2985—In depth localisation, e.g. using positron emitters; Tomographic imaging (longitudinal and transverse section imaging; apparatus for radiation diagnosis sequentially in different planes, steroscopic radiation diagnosis)

Definitions

  • the present application relates to the technical field of medical imaging, and in particular, to a PET data correction method, apparatus, computer equipment, and PET image reconstruction method.
  • PET Positron Emission Tomography
  • PET systems generally include multiple detector modules, and each positron annihilation generates a pair of gamma photons. will hit two different detector modules, and the detector modules will detect them.
  • the gamma photon hits the scintillation crystal in the detector module, it is absorbed and then fluoresces, which reaches the photosensor, producing an electrical signal.
  • the positron annihilation produces gamma photons with an energy of 511keV.
  • the absorption of gamma photons by scintillation crystals is divided into several cases.
  • the first case is that the gamma photons are completely absorbed at the first point and the energy is fully deposited; the second In the first case, gamma photons are scattered at the first point, depositing part of the energy, and the remaining energy is absorbed at the second point; in the third case, gamma photons are scattered multiple times, resulting in energy deposition at multiple points.
  • the three gamma photon deposition scenarios produce different properties.
  • the traditional image reconstruction scheme does not distinguish the deposition of gamma photons, and all gamma photon events are indistinguishable. Using the same parameters for image reconstruction, the image quality will be affected by the scattering events and the number of scattering, resulting in inaccurate reconstruction results. less effective.
  • Embodiments of the present application provide a PET data correction method, device, computer equipment, and PET image reconstruction method, so as to at least solve the problems of inaccurate reconstruction results and poor imaging effects of traditional image reconstruction schemes in the related art.
  • an embodiment of the present application provides a PET data correction method, including:
  • the single event including a non-scattering event and a scattering event
  • the scatter event is corrected according to the second correction parameter.
  • the scattering characteristics include at least one of number of scatterings, spatial extent of scattering, maximum point deposition energy, and temporal distribution of scattering points.
  • the scattering feature includes a number of scattering
  • the classifying the scattering event based on the scattering feature includes:
  • the scattering events are divided into primary scattering events and multiple scattering events based on the number of scatterings.
  • the scattering characteristic includes a scattering spatial extent
  • the classifying the scattering event based on the scattering characteristic includes:
  • the scattering events are classified into close-range scattering events and long-range scattering events based on the scattering spatial extent.
  • the scattering feature includes a maximum point deposition energy
  • the classifying the scattering event based on the scattering feature includes:
  • the scattering events are divided into high energy scattering events and low energy scattering events based on the maximum point deposition energy.
  • the scattering feature includes a time distribution of scattering points
  • the classifying the scattering event based on the scattering feature includes:
  • the scattering events are divided into long-interval scattering events and short-interval scattering events based on the time distribution of the scattering points.
  • the method further includes:
  • Scatter recovery is performed on the scattering event.
  • an embodiment of the present application provides a PET image reconstruction method, including:
  • the coincident events are reconstructed according to the scattering features to obtain a PET image.
  • the reconstruction parameters of the scattering events are different from the reconstruction parameters of the non-scattering events, and the scattering events of different classifications The reconstruction parameters are different.
  • an embodiment of the present application provides a PET data correction device, including:
  • an event acquisition module configured to acquire a single event in the PET scanning process, where the single event includes a non-scattering event and a scattering event;
  • a first correction module configured to acquire a first correction parameter of the non-scattering event, and correct the non-scattering event according to the first correction parameter
  • a classification module configured to acquire scattering features of the scattering events, and classify the scattering events based on the scattering features
  • a parameter acquisition module configured to acquire second correction parameters of the scattering events of different classifications
  • a second correction module configured to correct the scattering event according to the second correction parameter.
  • an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, when the processor executes the computer program.
  • the PET data correction method, device, computer equipment, and PET image reconstruction method acquire a single event in the PET scanning process, where the single event includes a non-scattering event and a scattering event; a first correction parameter of the non-scattering event, the non-scattering event is corrected according to the first correction parameter; the scattering feature of the scattering event is acquired, and the scattering event is classified based on the scattering feature; Obtaining second correction parameters of the scattering events of different classifications; and correcting the scattering events according to the second correction parameters.
  • Distinguish and correct scattering events and non-scattering events respectively classify them according to the characteristics of scattering events, use corresponding correction parameters to correct different types of scattering events, and use the corrected scattering events and non-scattering events for image reconstruction,
  • the reconstruction result is more accurate and the imaging effect is better.
  • Fig. 1 is the schematic diagram of the whole deposition of energy at one point when the gamma photon of the application hits the scintillation crystal;
  • FIG. 2 is a schematic diagram of the multi-point energy deposition when the gamma photon of the application hits the scintillation crystal;
  • FIG. 3 is a schematic flowchart of a PET data correction method according to an embodiment of the application.
  • FIG. 4 is a schematic flowchart of a PET image reconstruction method according to an embodiment of the present application.
  • FIG. 5 is a structural block diagram of a PET data correction device according to an embodiment of the application.
  • FIG. 6 is a structural block diagram of a PET image reconstruction apparatus according to an embodiment of the application.
  • FIG. 7 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application.
  • Words like "connected,” “connected,” “coupled,” and the like referred to in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
  • the “plurality” referred to in this application refers to two or more.
  • “And/or” describes the association relationship between associated objects, indicating that there can be three kinds of relationships. For example, “A and/or B” can mean that A exists alone, A and B exist at the same time, and B exists alone.
  • the character “/” generally indicates that the associated objects are an “or” relationship.
  • the terms “first”, “second”, “third”, etc. involved in this application are only to distinguish similar objects, and do not represent a specific order for the objects.
  • PET Positron Emission Tomography
  • a pair of gamma photons in opposite directions will be generated.
  • the gamma photon hits the scintillation crystal, there is a certain probability that all the energy will be deposited.
  • there is also a certain probability of depositing part of the energy and the remaining energy is taken away by gamma photons and deposited at another point or points, as shown in Figure 2.
  • these two situations are not distinguished, and both are treated as the same event for correction and reconstruction, and the reconstruction results will be affected, resulting in inaccurate reconstruction results and poor imaging results.
  • FIG. 3 is a schematic flowchart of a PET data correction method according to an embodiment of the present application.
  • the PET data correction method includes:
  • the radionuclide decays in the human body and releases positrons.
  • the positrons collide with the electrons and annihilate during the movement.
  • two photons moving in opposite directions are generated and received by the detector.
  • the single-event data is obtained by statistics.
  • a photon received by the detector is called a single event. Specifically, if the photon is completely absorbed at the first point and the energy is completely deposited, it is a non-scattering event, and if the photon is scattered and energy is deposited at two or more points, it is a scattering event.
  • the first correction parameter of the non-scattering event belongs to the correction parameter of the conventional annihilation event, and the correction parameter and the correction process are not specifically limited.
  • gamma photons have different scattering characteristics according to different scattering conditions such as deposition energy, scattering times, etc., and the scattering events can be classified based on the scattering characteristics. It can be understood that corresponding tag information can be added to different types of scattering events according to the classification results.
  • statistical analysis can be performed on the data packets collected during the PET scanning process to obtain characteristics such as deposition energy and scattering times.
  • the scattering events are classified by the scattering features, and the second correction parameters of the scattering events of different classifications are respectively obtained based on the classification results, so as to be used for subsequent correction and image reconstruction of the scattering events of different classifications respectively.
  • the detector outputs the case According to the scattering times 1, 2, 3, and more classifications, use the type as part of the address to search for different lattice parameters stored in the memory, obtain the crystal number, and complete the lattice correction.
  • Time correction Take time correction as an example, collect the coincident data, classify the coincident data according to the number of scatterings, calculate the time offset parameters respectively, and store them in the memory of the detector. times, more times, etc., use the type as part of the address to find the time offset parameter, add to the detected gamma time arrival time parameter, and complete the time offset correction.
  • the energy correction parameters are the same as the above, collect single event data, obtain crystal energy spectra with different scattering times, calculate the energy correction parameters, and store them in the memory of the detector. , etc., use the type as part of the address to find the energy correction parameter and calculate the energy of the event according to the specified formula.
  • the second is determined according to the classification of the scattering events, so targeted corrections can be performed for different types of scattering events, the reconstruction result is more accurate, and the imaging effect is better.
  • the scattering characteristics include at least one of number of scatterings, spatial extent of scattering, maximum point deposition energy, and temporal distribution of scattering points.
  • the scattering events can be classified according to only one of the characteristics of the number of scattering, the spatial range of scattering, the maximum point deposition energy, and the time distribution of the scattering points. Scattering events are classified separately by two or more features in the point-time distribution. There is no specific limitation here, and it can be determined by the user according to actual needs.
  • scattering features of other dimensions may be extracted, which is not specifically limited here.
  • the second correction parameter is related to the scattering feature, for example, the second correction parameter is classified with the scattering space range as the classification criterion and correspondingly obtained, and the correction is the deviation of the scattering event in the position dimension; the maximum point deposition energy is used as the The classification standard is used to classify and correspond to the acquired second correction parameter, and the correction is the deviation of the scattering event in the energy dimension; the time distribution of the scattering point is used as the classification standard to classify and correspond to the acquired second correction parameter, and the correction is the time distribution of the scattering event. Dimensional bias.
  • the second correction parameter corresponding to each scattering feature is calculated in advance and stored in the database. After the scattering feature of the scattering event is obtained, the database is queried according to the scattering feature to obtain the corresponding first correction parameter. The second correction parameter is used to correct for scattering events. Specifically, how to obtain the second correction parameter according to different scattering characteristics is not specifically limited here, as long as the deviation corresponding to the scattering characteristics can be corrected.
  • the scatter feature includes a number of scatter
  • classifying the scatter event based on the scatter feature includes the steps of:
  • Scattering events are divided into primary scattering events and multiple scattering events based on the number of scatterings.
  • the scattering event undergoes only one scattering, that is, the gamma photon is scattered at the first point, part of the energy is deposited, and the remaining energy is absorbed at the second point, then the scattering event is classified as a scattering event ; If the scattering event undergoes two scattering, that is, the gamma photon is scattered at the first point, depositing some energy, scattered at the second point, depositing some energy, and the remaining energy is absorbed at the third point, then the The scattering event is classified as a secondary scattering event; if the scattering event undergoes multiple scattering, that is, gamma photons are scattered multiple times, resulting in energy deposition at multiple points, the scattering event is classified as a multiple scattering event, where multiple scattering Including more than three scatterings.
  • the scattering signature includes a scattering spatial extent
  • classifying the scattering event based on the scattering signature includes the steps of:
  • Scattering events are divided into close-range scattering events and long-range scattering events based on the spatial extent of scattering.
  • the scattering event is classified as a close-range scattering event; if the scattering space range of the scattering event is not within the preset scattering range, the scattering event is classified as a close-range scattering event. for long-range scattering events.
  • the preset scattering range may be set by the user according to the actual situation, which is not specifically limited here. Near-distance scattering events and long-distance scattering events have different effects on the determination of the location of the annihilation point. Distinguishing and correcting the scattering events through the scattering space range can reduce the deviation of the position dimension.
  • the scattering signature includes a maximum point deposition energy
  • classifying the scattering event based on the scattering signature includes the steps of:
  • Scattering events are divided into high-energy scattering events and low-energy scattering events based on the maximum point deposition energy.
  • the maximum point deposition energy is the energy of the scattering point with the most deposition energy in the gamma photon scattering process. If the maximum point deposition energy of the scattering event is greater than the preset deposition energy, the scattering event is a high-energy scattering event; if the maximum point deposition energy of the scattering event is less than or equal to the preset deposition energy, the scattering event is a low-energy scattering event. It can be understood that the preset deposition energy can be set by the user according to actual needs, which is not specifically limited here. High-energy scattering events and low-energy scattering events have different effects on the determination of the position of the annihilation point. Distinguishing and correcting the scattering events by the maximum point deposition energy can reduce the deviation of the energy dimension.
  • the scatter feature includes a time distribution of scatter points, and classifying the scatter event based on the scatter feature includes the steps of:
  • Scattering events are divided into long-interval scattering events and short-interval scattering events based on the time distribution of scattering points.
  • the scattering event is classified as a long-interval scattering event; if the minimum time interval between two adjacent scattering points of a scattering event is If it is less than or equal to the preset time interval, the scattering event is classified as a short-interval scattering event.
  • the preset time interval can be set by the user according to the actual situation, which is not specifically limited here. Long-interval scattering events and short-interval scattering events have different effects on the determination of the position of the annihilation point. Distinguishing and correcting the scattering events through the time distribution of the scattering points can reduce the deviation of the time dimension.
  • scattering events are classified separately according to two or more characteristics of number of scattering, spatial extent of scattering, maximum point deposition energy, and temporal distribution of scattering points. For example, the scattering events are divided into primary scattering events and multiple scattering events according to the number of scattering, and the scattering events are divided into close scattering events and long-distance scattering events based on the scattering space range, and the scattering events are obtained based on the scattering times and the scattering space range respectively. the second correction parameter.
  • the scatter event can be corrected based on only the second correction parameter obtained from a single scatter feature, or two or more second correction parameters can be used simultaneously to correct the scatter event.
  • the correction of scattering events is to modify the attribute information such as the position, energy, and time of the particle to make it more accurate.
  • Two second correction parameters can be used to correct the same attribute at the same time. For example, a time can be obtained by looking up the scattering distance table. Offset correction value, another time offset correction value is obtained by scattering times, the two can be superimposed to correct the time together. If different properties are corrected, the correction is performed separately for different properties. For example, for the position of the particle, a position offset correction value is obtained by looking up the table of scattering distance, and for the energy of the particle, an energy offset is obtained by looking up the table by the scattering distance. Correction value, both of which correct the particle's position and energy, respectively.
  • the following steps are further included after acquiring the scattering feature of the scattering event:
  • the scatter event is a single event that does not meet the energy threshold.
  • the scatter event needs to be restored to a single event that meets the energy threshold.
  • the energy threshold is obtained through pre-experiments, and is an optimal value for theoretically classifying whether a single event is a scattering event.
  • FIG. 4 is a schematic flowchart of a PET image reconstruction method according to an embodiment of the present application.
  • the PET image reconstruction method includes:
  • a single event in the PET scanning process includes a non-scattering event and a scatter event.
  • the non-scatter event can be directly used for image reconstruction or can be corrected for image reconstruction.
  • the scatter event needs to be corrected by the above PET data correction method. Can be used for image reconstruction.
  • the corrected scattering events and non-scattering events constitute all the single events acquired in the PET scanning process, and the single events are matched to obtain the matching events.
  • the radionuclide decays in the human body and releases positrons.
  • the positrons collide with electrons and annihilate during the movement.
  • two photons moving in opposite directions are generated and received by the detector.
  • the data that the detector receives a pair of photons is called coincidence event data.
  • the detector is composed of many crystals, and the data that each crystal receives a photon is called single-event data. It can be understood that by matching the single events corresponding to the matched photons received by the two crystals, a matching event corresponding to a pair of photons can be obtained.
  • the partial coincidence events include scattering events, and reconstruction parameters are respectively selected for reconstruction according to the scattering features corresponding to the scattering events, so that the reconstruction result is more accurate and the imaging effect is better.
  • a single event in a PET scanning process is acquired, and a single event includes a non-scattering event and a scattering event; the first correction parameter of the non-scattering event is acquired, and the first correction parameter The non-scattering events are corrected; the scattering features of the scattering events are obtained, and the scattering events are classified based on the scattering features; second correction parameters of different classified scattering events are obtained; and the scattering events are corrected according to the second correction parameters.
  • Distinguish and correct scattering events and non-scattering events respectively classify them according to the characteristics of scattering events, use corresponding correction parameters to correct different types of scattering events, and use the corrected scattering events and non-scattering events for image reconstruction,
  • the reconstruction result is more accurate and the imaging effect is better.
  • the reconstruction parameters of the scattering events are different from the reconstruction parameters of the non-scattering events, and the reconstruction parameters of the scattering events of different classifications are different.
  • the TOF performance of the event will be used in the reconstruction to reconstruct the distribution of the possible locations of the event.
  • the TOF performance of non-scattering events is significantly different.
  • the two are reconstructed with independent TOF performance parameters.
  • Different types of scattering events are reconstructed with different TOF performance parameters, and then the reconstruction results can be superimposed to improve image performance.
  • This embodiment also provides a PET image reconstruction device and a PET data correction device, the devices are used to implement the above-mentioned embodiments and optional implementation manners, which have been described and will not be repeated.
  • the terms “module,” “unit,” “subunit,” etc. may be a combination of software and/or hardware that implements a predetermined function.
  • the apparatus described in the following embodiments is preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.
  • FIG. 5 is a structural block diagram of a PET data correction device according to an embodiment of the present application. As shown in FIG. 5 , the device includes:
  • the event acquisition module 10 is used to acquire a single event in the PET scanning process, and the single event includes a non-scattering event and a scattering event.
  • a first correction module 20 configured to acquire a first correction parameter of the non-scattering event, and correct the non-scattering event according to the first correction parameter
  • the classification module 30 is configured to acquire the scattering features of the scattering events, and classify the scattering events based on the scattering features.
  • the classification module 30 is further configured to classify the scattering events into primary scattering events and multiple scattering events based on the scattering times.
  • the classification module 30 is further configured to classify the scattering events into close-range scattering events and long-range scattering events based on the spatial range of scattering.
  • the classification module 30 is further configured to classify the scattering events into high-energy scattering events and low-energy scattering events based on the maximum point deposition energy.
  • the classification module 30 is further configured to classify the scattering events into long-interval scattering events and short-interval scattering events based on the time distribution of the scattering points.
  • the parameter acquisition module 40 is configured to acquire second correction parameters of scattering events of different classifications.
  • the second correction module 50 is configured to correct the scattering event according to the second correction parameter.
  • the PET data correction device further includes: a scattering recovery module.
  • Scatter recovery module for scatter recovery of scatter events.
  • FIG. 6 is a structural block diagram of a PET image reconstruction apparatus according to an embodiment of the present application. As shown in FIG. 6 , the apparatus includes:
  • the scatter event acquisition module 60 is configured to acquire scatter events and non-scatter events corrected by the above-mentioned PET data correction method.
  • a coincidence event acquisition module 70 for obtaining coincidence events according to the corrected scatter events and non-scatter events.
  • the reconstruction module 80 is used for reconstructing the coincident events according to the scattering features to obtain a PET image.
  • the reconstruction parameters of the scattering events are different from the reconstruction parameters of the non-scattering events, and the reconstruction parameters of the scattering events of different classifications are different.
  • each of the above modules may be functional modules or program modules, and may be implemented by software or hardware.
  • the above-mentioned modules may be located in the same processor; or the above-mentioned modules may also be located in different processors in any combination.
  • FIG. 7 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application.
  • the computer device may include a processor 91 and a memory 92 storing computer program instructions.
  • processor 91 may include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), or may be configured as one or more integrated circuits implementing the embodiments of the present application.
  • CPU central processing unit
  • ASIC Application Specific Integrated Circuit
  • memory 92 may include mass storage for data or instructions.
  • the memory 92 may include a hard disk drive (Hard Disk Drive, abbreviated as HDD), a floppy disk drive, a solid state drive (referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape, or universal serial A Universal Serial Bus (USB for short) drive or a combination of two or more of these.
  • Memory 92 may include removable or non-removable (or fixed) media, where appropriate. Where appropriate, memory 92 may be internal or external to the data processing device.
  • the memory 92 is a non-volatile (Non-Volatile) memory.
  • the memory 92 includes a read-only memory (Read-Only Memory, referred to as ROM for short) and a random access memory (Random Access Memory, referred to as RAM for short).
  • the ROM can be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, referred to as PROM), an erasable PROM (Erasable Programmable Read-Only Memory, referred to as EPROM), an electrically erasable Except PROM (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), Electrically Rewritable ROM (Electrically Alterable Read-Only Memory, referred to as EAROM) or Flash (FLASH) or a combination of two or more of these.
  • the RAM may be Static Random-Access Memory (SRAM for short) or Dynamic Random Access Memory (DRAM for short), where DRAM may be a fast page Mode dynamic random access memory (Fast Page Mode Dynamic Random Access Memory, referred to as FPMDRAM), extended data output dynamic random access memory (Extended Date Out Dynamic Random Access Memory, referred to as EDODRAM), synchronous dynamic random access memory (Synchronous Dynamic Random-Access Memory, referred to as SDRAM) and so on.
  • SRAM Static Random-Access Memory
  • DRAM Dynamic Random Access Memory
  • SDRAM synchronous dynamic random access memory
  • the memory 92 may be used to store or cache various data files required for processing and/or communication use, and possibly computer program instructions executed by the processor 91 .
  • the processor 91 reads and executes the computer program instructions stored in the memory 92 to implement any one of the PET data correction methods and the PET image reconstruction methods in the foregoing embodiments.
  • the computer device may also include a communication interface 93 and a bus 90 .
  • the processor 91 , the memory 92 , and the communication interface 93 are connected through the bus 90 to complete the mutual communication.
  • the communication interface 93 is used to implement communication between modules, apparatuses, units, and/or devices in the embodiments of the present application.
  • the communication interface 93 can also implement data communication with other components such as: external devices, image/data acquisition devices, databases, external storage, and image/data processing workstations.
  • the bus 90 includes hardware, software, or both, coupling the components of the computer device to each other.
  • the bus 90 includes but is not limited to at least one of the following: a data bus (Data Bus), an address bus (Address Bus), a control bus (Control Bus), an expansion bus (Expansion Bus), and a local bus (Local Bus).
  • the bus 90 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (Front Side Bus) , referred to as FSB), Hyper Transport (Hyper Transport, referred to as HT) interconnect, Industry Standard Architecture (Industry Standard Architecture, referred to as ISA) bus, wireless bandwidth (InfiniBand) interconnect, low pin count (Low Pin Count, referred to as LPC bus, memory bus, Micro Channel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local Bus (VLB) bus or other suitable bus or a combination of two or more of these.
  • Bus 90 may include one or more buses, where appropriate. Although embodiments of this application describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.
  • the computer device can execute the PET data correction method and the PET image reconstruction method in the embodiments of the present application based on the acquired computer program instructions, thereby implementing the PET data correction method and the PET image reconstruction method described in conjunction with FIG. 3 .
  • the embodiments of the present application may provide a computer-readable storage medium for implementation.
  • Computer program instructions are stored on the computer-readable storage medium; when the computer program instructions are executed by the processor, any one of the PET data correction methods and the PET image reconstruction methods in the foregoing embodiments is implemented.
  • the single event includes a non-scattering event and a scattering event;
  • the parameter corrects the non-scattering events; obtains the scattering features of the scattering events, and classifies the scattering events based on the scattering features; obtains second correction parameters of the different classified scattering events; and corrects the scattering events according to the second correction parameters.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Biomedical Technology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Optics & Photonics (AREA)
  • Pathology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Biophysics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Surgery (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Nuclear Medicine (AREA)
  • Measurement Of Radiation (AREA)

Abstract

一种PET数据校正方法、装置、计算机设备以及PET图像重建方法，其中，该PET数据校正方法包括：获取PET扫描过程中的单事件，单事件包括非散射事件以及散射事件(S101)；获取非散射事件的第一校正参数，根据第一校正参数对非散射事件进行校正(S102)；获取散射事件的散射特征，并基于散射特征对散射事件进行分类(S103)；获取不同分类的散射事件的第二校正参数；根据第二校正参数对散射事件进行校正(S104)。该PET数据校正方法使得重建结果更加准确，成像效果更好。

Description

PET数据校正方法、装置、计算机设备以及PET图像重建方法
相关申请
本申请要求2021年2月8日申请的，申请号为202110171084.5，发明名称为“PET数据校正方法、装置、计算机设备以及PET图像重建方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及医学成像技术领域，特别是涉及一种PET数据校正方法、装置、计算机设备以及PET图像重建方法。
背景技术
正电子放射断层成像(Positron Emission Tomography，PET)通过测量正电子湮灭产生的一对伽马光子来进行成像，PET系统一般包含多个探测器模块，每次正电子湮灭产生的一对伽马光子会打在两个不同的探测器模块上，探测器模块对其进行探测。伽马光子击中探测器模块中的闪烁晶体后，会被吸收，然后产生荧光，到达光电传感器，产生电信号。正电子湮灭产生的是能量为511keV的伽马光子，闪烁晶体吸收伽马光子分为多种情况，第一种情况是伽马光子在第一个点就被完全吸收，能量全部沉积；第二种情况是伽马光子在第一个点发生散射，沉积部分能量，剩余能量在第二个点被吸收；第三种情况就是伽马光子发生多次散射，在多个点产生能量沉积。三种伽马光子的沉积情况产生的特性不一样。传统图像重建方案不区分伽马光子沉积的情况，所有的伽马光子事件不加区分，采用同样的参数进行图像重建，图像质量会受到散射事件以及散射次数的影响，导致重建结果不准确，成像效果较差。
目前针对相关技术中传统图像重建方案重建结果不准确，成像效果较差的问题，尚未提出有效的解决方案。
发明内容
本申请实施例提供了一种PET数据校正方法、装置、计算机设备以及PET图像重建方法，以至少解决相关技术中传统图像重建方案重建结果不准确，成像效果较差的问题。
第一方面，本申请实施例提供了一种PET数据校正方法，包括：
获取PET扫描过程中的单事件，所述单事件包括非散射事件以及散射事件；
获取所述非散射事件的第一校正参数，根据所述第一校正参数对所述非散射事件进行校正；
获取所述散射事件的散射特征，并基于所述散射特征对所述散射事件进行分类；
获取不同分类的所述散射事件的第二校正参数；
根据所述第二校正参数对所述散射事件进行校正。
在其中一些实施例中，所述散射特征包括散射次数、散射空间范围、最大点沉积能量以及散射点时间分布中的至少一种。
在其中一些实施例中，所述散射特征包括散射次数，所述基于所述散射特征对所述散射事件进行分类包括：
基于所述散射次数将所述散射事件分为一次散射事件以及多次散射事件。
在其中一些实施例中，所述散射特征包括散射空间范围，所述基于所述散射特征对所述散射事件进行分类包括：
基于所述散射空间范围将所述散射事件分为近距离散射事件以及远距离散射事件。
在其中一些实施例中，所述散射特征包括最大点沉积能量，所述基于所述散射特征对所述散射事件进行分类包括：
基于所述最大点沉积能量将所述散射事件分为高能散射事件以及低能散射事件。
在其中一些实施例中，所述散射特征包括散射点时间分布，所述基于所述散射特征对所述散射事件进行分类包括：
基于所述散射点时间分布将所述散射事件分为长间隔散射事件以及短间隔散射事件。
在其中一些实施例中，所述获取所述散射事件的散射特征之后还包括：
对所述散射事件进行散射恢复。
第二方面，本申请实施例提供了一种PET图像重建方法，包括：
获取经上述PET数据校正方法校正得到的散射事件以及非散射事件；
根据经校正的散射事件和非散射事件获取符合事件；
根据所述散射特征对所述符合事件进行重建，获取PET图像。
在其中一些实施例中，所述根据所述散射特征对所述符合事件进行重建的过程中，所述散射事件的重建参数与所述非散射事件的重建参数不同，不同分类的所述散射事件的重建参数不同。
第三方面，本申请实施例提供了一种PET数据校正装置，包括：
事件获取模块，用于获取PET扫描过程中的单事件，所述单事件包括非散射事件以及散射事件；
第一校正模块，用于获取所述非散射事件的第一校正参数，根据所述第一校正参数对所述非散射事件进行校正；
分类模块，用于获取所述散射事件的散射特征，并基于所述散射特征对所述散射事件进行分类；
参数获取模块，用于获取不同分类的所述散射事件的第二校正参数；
第二校正模块，用于根据所述第二校正参数对所述散射事件进行校正。
第四方面，本申请实施例提供了一种计算机设备，包括存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上述第一方面所述的PET数据校正方法以及第二方面所述的PET图像重建方法。
相比于相关技术，本申请实施例提供的PET数据校正方法、装置、计算机设备以及PET图像重建方法，通过获取PET扫描过程中的单事件，所述单事件包括非散射事件以及散射事件；获取所述非散射事件的第一校正参数，根据所述第一校正参数对所述非散射事件进行校正；获取所述散射事件的散射特征，并基于所述散射特征对所述散射事件进行分类；获取不同分类的所述散射事件的第二校正参数；根据所述第二校正参数对所述散射事件进行校正。对散射事件和非散射事件进行区分并分别校正，根据散射事件的特征进行分类，并对不同类的散射事件采用对应的校正参数进行校正，采用校正后的散射事件和非散射事件进行图像重建，重建结果更加准确，成像效果更好。
本申请的一个或多个实施例的细节在以下附图和描述中提出，以使本申请的其他特征、目的和优点更加简明易懂。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1为本申请的伽马光子击中闪烁晶体时在一点能量全部沉积的示意图；
图2为本申请的伽马光子击中闪烁晶体时在多点能量沉积的示意图；
图3为本申请一实施例的PET数据校正方法的流程示意图；
图4为本申请一实施例的PET图像重建方法的流程示意图；
图5为本申请一实施例的PET数据校正装置的结构框图；
图6为本申请一实施例的PET图像重建装置的结构框图；
图7为本申请一实施例的计算机设备的硬件结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行描述和说明。应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。基于本申请提供的实施例，本领域普通技术人员在没有作出创造性劳动的前提下所获得的所有其他实施例，都属于本申请保护的范围。
显而易见地，下面描述中的附图仅仅是本申请的一些示例或实施例，对于本领域的普通技术人员而言，在不付出创造性劳动的前提下，还可以根据这些附图将本申请应用于其他类似情景。此外，还可以理解的是，虽然这种开发过程中所作出的努力可能是复杂并且冗长的，然而对于与本申请公开的内容相关的本领域的普通技术人员而言，在本申请揭露的技术内容的基础上进行的一些设计，制造或者生产等变更只是常规的技术手段，不应当理解为本申请公开的内容不充分。
在本申请中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域普通技术人员显式地和隐式地理解的是，本申请所描述的实施例在不冲突的情况下，可以与其它实施例相结合。
除非另作定义，本申请所涉及的技术术语或者科学术语应当为本申请所属技术领域内具有一般技能的人士所理解的通常意义。本申请所涉及的“一”、“一个”、“一种”、“该”等类似词语并不表示数量限制，可表示单数或复数。本申请所涉及的术语“包括”、“包含”、“具有”以及它们任何变形，意图在于覆盖不排他的包含；例如包含了一系列步骤或模块(单元)的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可以还包括没有列出的步骤或单元，或可以还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。本申请所涉及的“连接”、“相连”、“耦接”等类似的词语并非限定于物理的或者机械的连接，而是可以包括电气的连接，不管是直接的还是间接的。本申请所涉及的“多个”是指两个或两个以上。“和/或”描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。字符“/”一般表示前后关联对象是一种“或”的关系。本申请所涉及的术语“第一”、“第二”、“第三”等仅仅是区别类似的对象，不代表针对对象的特定排序。
正电子放射断层成像(Positron Emission Tomography，PET)中，当正电子发生衰变时，会产生方向相反的一对伽马光子，伽马光子击中闪烁晶体时，有一定的概率将全部能量沉积，如图1所示，也有一定的概率沉积部分能量，剩余的能量由伽马光子带走，在另一点或多点沉积下来，如图2所示。在传统方案中，这两种情况不做区分，都当作同一种 事件进行校正和处理重建，重建结果会受到影响，导致重建结果不准确，成像效果较差。
请参阅图3，图3为本申请一实施例的PET数据校正方法的流程示意图。
在本实施例中，PET数据校正方法包括：
S101，获取PET扫描过程中的单事件，单事件包括非散射事件以及散射事件。
示例性地，在进行PET扫描时放射性核素在人体内衰变，并释放出正电子，正电子在运动过程中与电子碰撞发生湮灭，湮灭后产生两个运动方向相反的光子被探测器接收，根据探测器接收到的数据，统计得到单事件数据。其中，将探测器接收到一个光子称为一个单事件。具体的，若光子在第一个点就被完全吸收，能量全部沉积，则为非散射事件，若光子发生散射，在两个或多个点沉积能量，则为散射事件。
S102，获取非散射事件的第一校正参数，根据第一校正参数对非散射事件进行校正；
示例性地，非散射事件的第一校正参数属于常规湮灭事件的校正参数，对于该校正参数以及校正过程不作具体限定。
S103，获取散射事件的散射特征，并基于散射特征对散射事件进行分类。
示例性地，伽马光子在散射过程中根据沉积能量、散射次数等散射状况不同，具有不同的散射特征，基于散射特征可以将散射事件进行分类。可以理解的，可以根据分类结果对不同类别的散射事件添加相应的标记信息。
具体的，对PET扫描过程中收集的数据包，可以进行统计分析，得到沉积能量、散射次数等特征，根据统计的特征及其数据来源，对各个数据包依据特征添加对应标记。
S104，获取不同分类的散射事件的第二校正参数。
可以理解的，由于散射事件与非散射事件的能量分辨率、时间分辨率等特性不同，采用相同的参数对散射事件与非散射事件进行校正与图像重建，会导致图像重建准确度不高，效果不佳。因此，通过散射特征对散射事件进行分类，并基于分类结果分别获取不同分类的散射事件的第二校正参数，以用于后续分别对不同分类散射事件的校正与图像重建。
具体的，以格子参数为例，将分类的数据分开，单独形成洪水图(flood map),根据洪水图分布画出格子，将不同类型的格子参数存储到探测器的存储器中，探测器输出事例时按散射次数1次，2次，3次，更多次分类，用类型作为地址一部分查找存储器中存储的不同的格子参数，得到晶体编号，完成格子校正。
以时间校正为例，采集符合数据，将符合数据也按散射次数分类，分别计算其时间偏移参数，存储到探测器的存储器中，探测器输出事例时按散射次数1次，2次，3次，更多次等，用类型作为地址一部分来查找时间偏移参数，与探测到的伽马时间到达时间参数相加，完成时间偏移校正。
能量校正参数和上述相同，采集单事件数据，得到不同散射次数的晶体能谱，计算能量校正参数，存储到探测器的存储器中，探测器输出事例时按散射次数1次，2次，3次，更多次等，用类型作为地址一部分来查找能量校正参数，并按指定公式计算出事件的能量。
S105，根据第二校正参数对散射事件进行校正。
示例性地，第二根据散射事件的分类进行确定，因此可针对不同类别的散射事件分别进行针对性的校正，重建结果更加准确，成像效果更好。
在另一个实施例中，散射特征包括散射次数、散射空间范围、最大点沉积能量以及散射点时间分布中的至少一种。
可以理解的，可以仅根据散射次数、散射空间范围、最大点沉积能量以及散射点时间分布中的一种特征对散射事件进行分类，也可以根据散射次数、散射空间范围、最大点沉积能量以及散射点时间分布中的两种或多种特征分别对散射事件进行分类。此处不作具体限定，可以由用户根据实际需求进行确定。
在其他实施例中，可以提取其他维度的散射特征，此处不作具体限定。
示例性地，第二校正参数与散射特征相关，例如，以散射空间范围作为分类标准进行分类并对应获取的第二校正参数，校正的为散射事件在位置维度的偏差；以最大点沉积能量作为分类标准进行分类并对应获取的第二校正参数，校正的为散射事件在能量维度的偏差；以散射点时间分布作为分类标准进行分类并对应获取的第二校正参数，校正的为散射事件在时间维度的偏差。
在另一个实施例中，每一散射特征对应的第二校正参数均预先完成计算并存储在数据库中，当获取到散射事件的散射特征后，根据散射特征在数据库中进行查询，获取对应的第二校正参数，对散射事件进行校正。具体的，如何根据不同的散射特征获取第二校正参数此处不作具体限定，只需能够校正散射特征对应的偏差即可。
在另一个实施例中，散射特征包括散射次数，基于散射特征对散射事件进行分类包括以下步骤：
基于散射次数将散射事件分为一次散射事件以及多次散射事件。
在本实施例中，若散射事件仅经过一次散射，即伽马光子在第一个点发生散射，沉积部分能量，剩余能量在第二个点被吸收，则将该散射事件归为一次散射事件；若散射事件经过两次散射，即伽马光子在第一个点发生散射，沉积部分能量，在第二个点发生散射，沉积部分能量，剩余能量在第三个点被吸收，则将该散射事件归为二次散射事件；若散射事件经过多次散射，即伽马光子发生多次散射，在多个点产生能量沉积，则将该散射事件归为多次散射事件，其中多次散射包括三次以上的散射。
在另一个实施例中，散射特征包括散射空间范围，基于散射特征对散射事件进行分类包括以下步骤：
基于散射空间范围将散射事件分为近距离散射事件以及远距离散射事件。
示例性地，若散射事件的散射空间范围在预设散射范围内，则将该散射事件归为近距离散射事件；若散射事件的散射空间范围不在预设散射范围内，则将该散射事件归为远距离散射事件。其中，预设散射范围可以由用户根据实际情况进行设定，此处不作具体限定。近距离散射事件以及远距离散射事件对湮灭点的位置的确定有不同的影响，通过散射空间范围区分散射事件并校正，可减少位置维度的偏差。
在另一个实施例中，散射特征包括最大点沉积能量，基于散射特征对散射事件进行分类包括以下步骤：
基于最大点沉积能量将散射事件分为高能散射事件以及低能散射事件。
示例性地，最大点沉积能量为伽马光子散射过程中沉积能量最多的散射点的能量。若散射事件的最大点沉积能量大于预设沉积能量，则该散射事件为高能散射事件；若散射事件的最大点沉积能量小于或等于预设沉积能量，则该散射事件为低能散射事件。可以理解的，预设沉积能量可以由用户根据实际需求进行设定，此处不作具体限定。高能散射事件以及低能散射事件对湮灭点的位置的确定有不同的影响，通过最大点沉积能量区分散射事件并校正，可减少能量维度的偏差。在另一个实施例中，散射特征包括散射点时间分布，基于散射特征对散射事件进行分类包括以下步骤：
基于散射点时间分布将散射事件分为长间隔散射事件以及短间隔散射事件。
示例性地，伽马光子在两个相邻散射点发生能量沉积的时间点之间存在一定的时间间隔。若散射事件的两个相邻散射点之间的最小时间间隔大于预设时间间隔，则将该散射事件归为长间隔散射事件；若散射事件的两个相邻散射点之间的最小时间间隔小于或等于预设时间间隔，则将该散射事件归为短间隔散射事件。可以理解的，预设时间间隔可以由用户根据实际情况进行设定，此处不作具体限定。长间隔散射事件以及短间隔散射事件对湮灭点的位置的确定有不同的影响，通过散射点时间分布区分散射事件并校正，可减少时间维度的偏差。
在另一个实施例中，根据散射次数、散射空间范围、最大点沉积能量以及散射点时间分布中的两种或多种特征分别对散射事件进行分类。例如，根据散射次数将散射事件分为一次散射事件以及多次散射事件，基于散射空间范围将散射事件分为近距离散射事件以及远距离散射事件，并分别基于散射次数以及散射空间范围获取散射事件的第二校正参数。
示例性地，可以仅基于单一散射特征获取的第二校正参数对散射事件进行校正，也可 以同时使用两个或多个第二校正参数对散射事件进行校正。
对散射事件进行校正是针对粒子的位置、能量、时间等属性信息进行修改，使其更准确，可以同时使用两个第二校正参数对同一个属性进行校正，比如通过散射距离查表得到一个时间偏移校正值，通过散射次数得到另一个时间偏移校正值，两者可以进行叠加，一起对时间进行校正。若是对不同属性进行校正，则针对不同属性单独进行校正，例如，针对粒子的位置，通过散射距离查表得到一个位置偏移校正值，针对粒子的能量，通过散射距离查表得到一个能量偏移校正值，两者分别对粒子的位置和能量进行校正。
在另一个实施例中，获取散射事件的散射特征之后还包括以下步骤：
对散射事件进行散射恢复。
示例性地，散射事件为不满足能量阈值的单事件，在对散射事件进行校正并基于校正后的散射事件进行图像重建之前，还需要将散射事件恢复为满足能量阈值的单事件。具体的，能量阈值通过预先的实验得到，为理论上划分单事件是否为散射事件的最佳数值。
请参阅图4，图4为本申请一实施例的PET图像重建方法的流程示意图。
在本实施例中，PET图像重建方法包括：
S201，获取经上述PET数据校正方法校正得到的散射事件以及非散射事件。
示例性地，PET扫描过程中的单事件包括非散射事件以及散射事件，非散射事件可以直接用于图像重建，也可经校正用于图像重建，散射事件需要经上述PET数据校正方法校正后才可用于图像重建。
S202，根据经校正的散射事件和非散射事件获取符合事件。
可以理解的，经校正的散射事件和非散射事件组成了PET扫描过程中获取到的所有单事件，将单事件进行符合，得到符合事件。
具体地，在进行PET扫描时，放射性核素在人体内衰变，并释放出正电子，正电子在运动过程中与电子碰撞发生湮灭，湮灭后产生两个运动方向相反的光子被探测器接收，探测器接收到一对光子的数据称为符合事件数据。探测器由许多的晶体构成，每个晶体接收到一个光子的数据称为单事件数据。可以理解的，将两个晶体接收到的匹配的光子对应的单事件进行符合，即可得到一对光子对应的符合事件。
S203，根据散射特征对符合事件进行重建，获取PET图像。
示例性地，部分符合事件中包含散射事件，根据散射事件对应的散射特征分别选用重建参数进行重建，以使重建结果更加准确，成像效果更好。
上述PET数据校正方法以及PET图像重建方法，通过获取PET扫描过程中的单事件，单事件包括非散射事件以及散射事件；获取所述非散射事件的第一校正参数，根据所述第 一校正参数对所述非散射事件进行校正；获取散射事件的散射特征，并基于散射特征对散射事件进行分类；获取不同分类的散射事件的第二校正参数；根据第二校正参数对散射事件进行校正。对散射事件和非散射事件进行区分并分别校正，根据散射事件的特征进行分类，并对不同类的散射事件采用对应的校正参数进行校正，采用校正后的散射事件和非散射事件进行图像重建，重建结果更加准确，成像效果更好。
在一些实施例中，根据散射特征对符合事件进行重建的过程中，散射事件的重建参数与非散射事件的重建参数不同，不同分类的散射事件的重建参数不同。
具体的，以TOF(Time of flight，飞行时间)重建为例，在重建时会用到事件的TOF性能，重建事件可能发生位置的分布，TOF性能好，其可能分布的范围就小，散射和非散射事件的TOF性能有明显不同，两者采用独立的TOF性能参数进行重建，不同分类的散射事件采用不同的TOF性能参数进行重建，再将重建结果叠加可以提升图像性能。
需要说明的是，在上述流程中或者附图的流程图中示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
本实施例还提供了一种PET图像重建装置以及PET数据校正装置，该装置用于实现上述实施例及可选实施方式，已经进行过说明的不再赘述。如以下所使用的，术语“模块”、“单元”、“子单元”等可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。
图5是根据本申请实施例的PET数据校正装置的结构框图，如图5所示，该装置包括：
事件获取模块10，用于获取PET扫描过程中的单事件，单事件包括非散射事件以及散射事件。
第一校正模块20，用于获取非散射事件的第一校正参数，根据第一校正参数对非散射事件进行校正；
分类模块30，用于获取散射事件的散射特征，并基于散射特征对散射事件进行分类。
分类模块30，还用于基于散射次数将散射事件分为一次散射事件以及多次散射事件。
分类模块30，还用于基于散射空间范围将散射事件分为近距离散射事件以及远距离散射事件。
分类模块30，还用于基于最大点沉积能量将散射事件分为高能散射事件以及低能散射事件。
分类模块30，还用于基于散射点时间分布将散射事件分为长间隔散射事件以及短间隔散射事件。
参数获取模块40，用于获取不同分类的散射事件的第二校正参数。
第二校正模块50，用于根据第二校正参数对散射事件进行校正。
PET数据校正装置，还包括：散射恢复模块。
散射恢复模块，用于对散射事件进行散射恢复。
图6是根据本申请实施例的PET图像重建装置的结构框图，如图6所示，该装置包括：
散射事件获取模块60，用于获取经上述PET数据校正方法校正得到的散射事件以及非散射事件。
符合事件获取模块70，用于根据经校正的散射事件和非散射事件获取符合事件。
重建模块80，用于根据散射特征对符合事件进行重建，获取PET图像。
在一些实施例中，重建模块80进行图像重建时，散射事件的重建参数与所述非散射事件的重建参数不同，不同分类的所述散射事件的重建参数不同。
需要说明的是，上述各个模块可以是功能模块也可以是程序模块，既可以通过软件来实现，也可以通过硬件来实现。对于通过硬件来实现的模块而言，上述各个模块可以位于同一处理器中；或者上述各个模块还可以按照任意组合的形式分别位于不同的处理器中。
另外，结合图3描述的本申请实施例的PET数据校正方法以及PET图像重建方法可以由计算机设备来实现。图7为根据本申请实施例的计算机设备的硬件结构示意图。
计算机设备可以包括处理器91以及存储有计算机程序指令的存储器92。
具体地，上述处理器91可以包括中央处理器(CPU)，或者特定集成电路(Application Specific Integrated Circuit，简称为ASIC)，或者可以被配置成实施本申请实施例的一个或多个集成电路。
其中，存储器92可以包括用于数据或指令的大容量存储器。举例来说而非限制，存储器92可包括硬盘驱动器(Hard Disk Drive，简称为HDD)、软盘驱动器、固态驱动器(Solid State Drive，简称为SSD)、闪存、光盘、磁光盘、磁带或通用串行总线(Universal Serial Bus，简称为USB)驱动器或者两个或更多个以上这些的组合。在合适的情况下，存储器92可包括可移除或不可移除(或固定)的介质。在合适的情况下，存储器92可在数据处理装置的内部或外部。在特定实施例中，存储器92是非易失性(Non-Volatile)存储器。在特定实施例中，存储器92包括只读存储器(Read-Only Memory，简称为ROM)和随机存取存储器(Random Access Memory，简称为RAM)。在合适的情况下，该ROM可以是掩模 编程的ROM、可编程ROM(ProgrammableRead-Only Memory，简称为PROM)、可擦除PROM(Erasable Programmable Read-Only Memory，简称为EPROM)、电可擦除PROM(Electrically Erasable Programmable Read-Only Memory，简称为EEPROM)、电可改写ROM(Electrically Alterable Read-Only Memory，简称为EAROM)或闪存(FLASH)或者两个或更多个以上这些的组合。在合适的情况下，该RAM可以是静态随机存取存储器(Static Random-Access Memory，简称为SRAM)或动态随机存取存储器(Dynamic Random Access Memory，简称为DRAM)，其中，DRAM可以是快速页模式动态随机存取存储器(Fast Page Mode Dynamic Random Access Memory，简称为FPMDRAM)、扩展数据输出动态随机存取存储器(Extended Date Out Dynamic Random Access Memory，简称为EDODRAM)、同步动态随机存取内存(Synchronous Dynamic Random-Access Memory，简称SDRAM)等。
存储器92可以用来存储或者缓存需要处理和/或通信使用的各种数据文件，以及处理器91所执行的可能的计算机程序指令。
处理器91通过读取并执行存储器92中存储的计算机程序指令，以实现上述实施例中的任意一种PET数据校正方法以及PET图像重建方法。
在其中一些实施例中，计算机设备还可包括通信接口93和总线90。其中，如图7所示，处理器91、存储器92、通信接口93通过总线90连接并完成相互间的通信。
通信接口93用于实现本申请实施例中各模块、装置、单元和/或设备之间的通信。通信接口93还可以实现与其他部件例如：外接设备、图像/数据采集设备、数据库、外部存储以及图像/数据处理工作站等之间进行数据通信。
总线90包括硬件、软件或两者，将计算机设备的部件彼此耦接在一起。总线90包括但不限于以下至少之一：数据总线(Data Bus)、地址总线(Address Bus)、控制总线(Control Bus)、扩展总线(Expansion Bus)、局部总线(Local Bus)。举例来说而非限制，总线90可包括图形加速接口(Accelerated Graphics Port，简称为AGP)或其他图形总线、增强工业标准架构(Extended Industry Standard Architecture，简称为EISA)总线、前端总线(Front Side Bus，简称为FSB)、超传输(Hyper Transport，简称为HT)互连、工业标准架构(Industry Standard Architecture，简称为ISA)总线、无线带宽(InfiniBand)互连、低引脚数(Low Pin Count，简称为LPC)总线、存储器总线、微信道架构(Micro Channel Architecture，简称为MCA)总线、外围组件互连(Peripheral Component Interconnect，简称为PCI)总线、PCI-Express(PCI-X)总线、串行高级技术附件(Serial Advanced Technology Attachment，简称为SATA)总线、视频电子标准协会局部(Video Electronics Standards Association Local Bus，简称为VLB)总线或其他合适的总线或者两个或更多个以上这些的组合。在合适的 情况下，总线90可包括一个或多个总线。尽管本申请实施例描述和示出了特定的总线，但本申请考虑任何合适的总线或互连。
该计算机设备可以基于获取到的计算机程序指令，执行本申请实施例中的PET数据校正方法以及PET图像重建方法，从而实现结合图3描述的PET数据校正方法以及PET图像重建方法。
另外，结合上述实施例中的PET数据校正方法以及PET图像重建方法，本申请实施例可提供一种计算机可读存储介质来实现。该计算机可读存储介质上存储有计算机程序指令；该计算机程序指令被处理器执行时实现上述实施例中的任意一种PET数据校正方法以及PET图像重建方法。
上述PET数据校正方法、装置、计算机设备以及PET图像重建方法，通过获取PET扫描过程中的单事件，单事件包括非散射事件以及散射事件；获取非散射事件的第一校正参数，根据第一校正参数对非散射事件进行校正；获取散射事件的散射特征，并基于散射特征对散射事件进行分类；获取不同分类的散射事件的第二校正参数；根据第二校正参数对散射事件进行校正。根据散射事件的特征进行分类，并对不同类的散射事件采用对应的校正参数进行校正，采用校正后的散射事件和非散射事件进行图像重建，重建结果更加准确，成像效果更好。
以上所述实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (11)

  1. 一种PET数据校正方法，其特征在于，所述方法包括：
    获取PET扫描过程中的单事件，所述单事件包括非散射事件以及散射事件；
    获取所述非散射事件的第一校正参数，根据所述第一校正参数对所述非散射事件进行校正；
    获取所述散射事件的散射特征，并基于所述散射特征对所述散射事件进行分类；
    获取不同分类的所述散射事件的第二校正参数；
    根据所述第二校正参数对所述散射事件进行校正。
  2. 根据权利要求1所述的PET数据校正方法，其特征在于，所述散射特征包括散射次数、散射空间范围、最大点沉积能量以及散射点时间分布中的至少一种。
  3. 根据权利要求1所述的PET数据校正方法，其特征在于，所述散射特征包括散射次数，所述基于所述散射特征对所述散射事件进行分类包括：
    基于所述散射次数将所述散射事件分为一次散射事件以及多次散射事件。
  4. 根据权利要求1所述的PET数据校正方法，其特征在于，所述散射特征包括散射空间范围，所述基于所述散射特征对所述散射事件进行分类包括：
    基于所述散射空间范围将所述散射事件分为近距离散射事件以及远距离散射事件。
  5. 根据权利要求1所述的PET数据校正方法，其特征在于，所述散射特征包括最大点沉积能量，所述基于所述散射特征对所述散射事件进行分类包括：
    基于所述最大点沉积能量将所述散射事件分为高能散射事件以及低能散射事件。
  6. 根据权利要求1所述的PET数据校正方法，其特征在于，所述散射特征包括散射点时间分布，所述基于所述散射特征对所述散射事件进行分类包括：
    基于所述散射点时间分布将所述散射事件分为长间隔散射事件以及短间隔散射事件。
  7. 根据权利要求1所述的PET数据校正方法，其特征在于，所述获取所述散射事件的散射特征之后还包括：
    对所述散射事件进行散射恢复。
  8. 一种PET图像重建方法，其特征在于，所述方法包括：
    获取经权利要求1-7任一项所述的PET数据校正方法校正得到的散射事件以及非散射事件；
    根据经校正的散射事件和非散射事件获取符合事件；
    根据所述散射特征对所述符合事件进行重建，获取PET图像。
  9. 根据权利要求8所述的PET图像重建方法，其特征在于，所述根据所述散射特征对所述符合事件进行重建的过程中，所述散射事件的重建参数与所述非散射事件的重建参数不同，不同分类的所述散射事件的重建参数不同。
  10. 一种PET数据校正装置，其特征在于，所述装置包括：
    事件获取模块，用于获取PET扫描过程中的单事件，所述单事件包括非散射事件以及散射事件；
    第一校正模块，用于获取所述非散射事件的第一校正参数，根据所述第一校正参数对所述非散射事件进行校正；
    分类模块，用于获取所述散射事件的散射特征，并基于所述散射特征对所述散射事件进行分类；
    参数获取模块，用于获取不同分类的所述散射事件的第二校正参数；
    第二校正模块，用于根据所述第二校正参数对所述散射事件进行校正。
  11. 一种计算机设备，包括存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至7中任一项所述的PET数据校正方法或如权利要求8所述的PET图像重建方法。
PCT/CN2022/075457 2021-02-08 2022-02-08 Pet数据校正方法、装置、计算机设备以及pet图像重建方法 WO2022166976A1 (zh)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP22749255.0A EP4289361A1 (en) 2021-02-08 2022-02-08 Pet data correction method and apparatus, computer device and pet image reconstruction method
JP2023547840A JP2024505727A (ja) 2021-02-08 2022-02-08 Ｐｅｔデータ補正方法、装置、コンピュータ機器及びｐｅｔ画像再構成方法
US18/276,257 US20240122567A1 (en) 2021-02-08 2022-02-08 Pet-data correction method and device, computer apparatus, and pet-image reconstruction method

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110171084.5 2021-02-08
CN202110171084.5A CN112998732B (zh) 2021-02-08 2021-02-08 Pet数据校正方法、装置、计算机设备以及pet图像重建方法

Publications (1)

Publication Number Publication Date
WO2022166976A1 true WO2022166976A1 (zh) 2022-08-11

Family

ID=76384305

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/075457 WO2022166976A1 (zh) 2021-02-08 2022-02-08 Pet数据校正方法、装置、计算机设备以及pet图像重建方法

Country Status (5)

Country Link
US (1) US20240122567A1 (zh)
EP (1) EP4289361A1 (zh)
JP (1) JP2024505727A (zh)
CN (1) CN112998732B (zh)
WO (1) WO2022166976A1 (zh)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112998732B (zh) * 2021-02-08 2023-07-18 上海联影医疗科技股份有限公司 Pet数据校正方法、装置、计算机设备以及pet图像重建方法
CN113506355B (zh) * 2021-09-10 2021-12-03 苏州瑞派宁科技有限公司 散射校正方法、装置、成像系统及计算机可读存储介质

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008000190A (ja) * 2006-06-20 2008-01-10 Toshiba Corp Ｘ線診断装置およびｘ線診断装置におけるデータ処理方法
CN104508513A (zh) * 2012-07-30 2015-04-08 皇家飞利浦有限公司 高空间分辨率模式固态正电子发射断层摄影(pet)扫描器
US20150289825A1 (en) * 2012-11-07 2015-10-15 Massachusetts Institute Of Technology Inter-detector scatter enahnced emission tomography
CN109658472A (zh) * 2018-12-21 2019-04-19 上海联影医疗科技有限公司 处理正电子发射计算机断层扫描图像数据的系统和方法
CN110327067A (zh) * 2019-06-10 2019-10-15 东软医疗系统股份有限公司 图像重建方法、装置、终端设备及pet系统
CN110934604A (zh) * 2019-12-03 2020-03-31 上海联影医疗科技有限公司 康普顿散射序列恢复方法、装置、存储介质和pet成像系统
CN112998732A (zh) * 2021-02-08 2021-06-22 上海联影医疗科技股份有限公司 Pet数据校正方法、装置、计算机设备以及pet图像重建方法

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6109498B2 (ja) * 2011-07-05 2017-04-05 東芝メディカルシステムズ株式会社 超音波診断装置及び超音波診断装置制御プログラム
DE112015002809T5 (de) * 2014-06-13 2017-03-16 Siemens Medical Solutions Usa, Inc. Multiple Emissionsenergien bei Einzelphotonen-Emissionscomputertomographie
CN106491153B (zh) * 2016-12-29 2017-10-27 上海联影医疗科技有限公司 一种pet散射校正方法、pet成像方法及pet成像系统
US10410383B2 (en) * 2017-08-26 2019-09-10 Uih America, Inc. System and method for image data processing in positron emission tomography
WO2019063760A1 (en) * 2017-09-28 2019-04-04 Koninklijke Philips N.V. DISPERSION CORRECTION BASED ON DEEP LEARNING
WO2019149621A1 (en) * 2018-01-31 2019-08-08 Koninklijke Philips N.V. Scatter correction for positron emission tomography (pet)
CN109875592B (zh) * 2019-01-10 2023-06-30 北京永新医疗设备有限公司 一种pet和spect同时成像的方法、装置和系统
CN110063742B (zh) * 2019-04-30 2024-01-02 上海联影医疗科技股份有限公司 散射校正方法、装置、计算机设备和存储介质
CN112053411A (zh) * 2020-08-27 2020-12-08 东软医疗系统股份有限公司 一种pet图像的重建方法及装置

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008000190A (ja) * 2006-06-20 2008-01-10 Toshiba Corp Ｘ線診断装置およびｘ線診断装置におけるデータ処理方法
CN104508513A (zh) * 2012-07-30 2015-04-08 皇家飞利浦有限公司 高空间分辨率模式固态正电子发射断层摄影(pet)扫描器
US20150289825A1 (en) * 2012-11-07 2015-10-15 Massachusetts Institute Of Technology Inter-detector scatter enahnced emission tomography
CN109658472A (zh) * 2018-12-21 2019-04-19 上海联影医疗科技有限公司 处理正电子发射计算机断层扫描图像数据的系统和方法
CN110327067A (zh) * 2019-06-10 2019-10-15 东软医疗系统股份有限公司 图像重建方法、装置、终端设备及pet系统
CN110934604A (zh) * 2019-12-03 2020-03-31 上海联影医疗科技有限公司 康普顿散射序列恢复方法、装置、存储介质和pet成像系统
CN112998732A (zh) * 2021-02-08 2021-06-22 上海联影医疗科技股份有限公司 Pet数据校正方法、装置、计算机设备以及pet图像重建方法

Also Published As

Publication number Publication date
CN112998732B (zh) 2023-07-18
US20240122567A1 (en) 2024-04-18
EP4289361A1 (en) 2023-12-13
JP2024505727A (ja) 2024-02-07
CN112998732A (zh) 2021-06-22

Similar Documents

Publication Publication Date Title
WO2022166976A1 (zh) Pet数据校正方法、装置、计算机设备以及pet图像重建方法
US10319118B2 (en) Reconstructing PET image
US10930031B2 (en) Synchronization method and system for single event list type data in PET imaging
US7417231B2 (en) Fourier re-binning of time-of-flight positron emission tomography data
US10078141B2 (en) Timestamping detected radiation quanta
US11288847B2 (en) Double scatter simulation for improved reconstruction of positron emission tomography data
US10006808B2 (en) Adjusting gain of detector
CN109887048B (zh) Pet散射校正方法、图像重建方法、装置及电子设备
JPWO2012077468A1 (ja) ガンマ線を利用する画像化装置、画像信号処理装置およびガンマ線測定データの画像処理方法
WO2016046703A2 (en) Time of flight calibration in digital positron emission tomography
Lage et al. Recovery and normalization of triple coincidences in PET
CN106199674A (zh) 正电子放射断层摄影方法及正电子放射断层摄影装置
JP2020529616A (ja) 陽電子放出断層撮影における狭いエネルギー窓カウントから再構成された放出画像推定を使用した散乱補正
WO2023000756A1 (zh) 测距方法与装置、终端及非易失性计算机可读存储介质
US11925498B2 (en) Reconstructing image
CN111568452A (zh) Pet系统状态检测方法、装置、计算机设备和存储介质
EP3552046A1 (en) Hybrid tof and non-tof pet systems with joint tof and non-tof image reconstruction
CN110934604B (zh) 康普顿散射序列恢复方法、装置、存储介质和pet成像系统
CN112690818B (zh) Pet探测器的晶体位置查找表的校正方法及pet系统
CN111728625B (zh) Pet系统时间同步方法、装置、计算机设备和存储介质
CN107908363B (zh) 基于cuda的pet符合事件筛选的方法、系统及装置
CN112068188A (zh) Pet探测器能量峰位漂移的实时校准方法
JP4317124B2 (ja) 画像処理システムおよび画像処理方法
CN107908361B (zh) 针对全数字pet的符合事件筛选的方法及装置
CN108648807B (zh) 一种图像重建方法和装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22749255

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 18276257

Country of ref document: US

Ref document number: 2023547840

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2022749255

Country of ref document: EP

Effective date: 20230908