Nuclear medicine diagnosis device, medical image processing device, nuclear medicine diagnosis method and program

Abstract

To improve image quality.SOLUTION: A nuclear medicine diagnosis device according to an embodiment comprises a first acquisition unit, a second acquisition unit, an estimation unit, and a reconfiguration unit. The first acquisition unit acquires simultaneous enumeration data including a direct incident event in a gamma-ray detector and a scattering event in a subject. The second acquisition unit acquires an electron density function of the subject and geometric information on the gamma-ray detector. The estimation unit estimates, based on at least one of the electron density function and the geometric information, a first probability value corresponding to the direct incident event in the subject and a second probability value corresponding to the scattering event. The reconfiguration unit reconfigures a PET image based on the first probability value and the second probability value and the simultaneous enumeration data. The scattering event includes a multiple scattering event.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The embodiments disclosed in this specification and the drawings relate to a nuclear medicine diagnosis apparatus, a medical image processing apparatus, a nuclear medicine diagnosis method, and a program. [Background technology]

[0002] Many of the current PET devices estimate the radiation distribution within the subject and perform image reconstruction based on direct incidence events, which are gamma ray detection events that are incident on the detector without being scattered within the subject. Image reconstruction algorithms generally have a scatter correction function, but in many cases, the target of scatter correction is scattering up to one scattering. However, when the target of scatter correction is only one scattering, image quality may deteriorate.

[0003] On the other hand, in order to perform accurate scattered radiation correction to remove low-energy gamma ray detection events that have undergone multiple scattering, a high-emission scintillator and a large number of light-receiving cells corresponding to the high-emission scintillator may be required.

[0004] In addition, for example, in order to obtain high time resolution in TOF (Time Of Flight)-PET, a short-time emission scintillator is required as the scintillator, and a fast response and short recovery time of each cell may be required as the photodetector. However, a scintillator that satisfies such conditions can be expensive. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2012 / 0290519 Summary of the Invention [Problem to be solved by the invention]

[0006] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to improve image quality. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0007] A nuclear medicine diagnosis apparatus according to an embodiment includes a first acquisition unit, a second acquisition unit, an estimation unit, and a reconstruction unit. The first acquisition unit acquires coincidence data including a direct incidence event to a gamma ray detector and a scattering event in a subject. The second acquisition unit acquires an electron density function of the subject and geometric information of the gamma ray detector. The estimation unit estimates a first probability value corresponding to the direct incidence event in the subject and a second probability value corresponding to the scattering event based on at least one of the electron density function and the geometric information. The reconstruction unit reconstructs a PET image based on the first probability value, the second probability value, and the coincidence data. The scattering event includes a multiple scattering event. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of a nuclear medicine diagnosis apparatus according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating the background according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating a processing circuit according to the first embodiment. [Figure 4] FIG. 4 is a flowchart illustrating the procedure of the process performed by the nuclear medicine diagnosis apparatus according to the first embodiment. [Diagram 5] FIG. 5 is a diagram illustrating a processing circuit according to the second embodiment. [Figure 6] FIG. 6 is a flowchart illustrating the procedure of the process performed by the nuclear medicine diagnosis apparatus according to the second embodiment. [Figure 7]FIG. 7 is a diagram illustrating a processing circuit according to the third embodiment. [Figure 8] FIG. 8 is a flowchart illustrating the procedure of the process performed by the nuclear medicine diagnosis apparatus according to the third embodiment. [Figure 9] FIG. 9 is a diagram illustrating a processing circuit according to the fourth embodiment. [Figure 10] FIG. 10 is a flowchart illustrating the procedure of the process performed by the nuclear medicine diagnosis apparatus according to the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of a nuclear medicine diagnosis apparatus, a medical image processing apparatus, a nuclear medicine diagnosis method, and a program will be described in detail with reference to the drawings.

[0010] (First embodiment) Fig. 1 is a diagram showing the configuration of a PET device 100 as a nuclear medicine diagnosis device according to an embodiment. As shown in Fig. 1, the PET device 100 according to an embodiment includes a gantry device 10 and a console device 20 as a medical image processing device. The gantry device 10 includes a detector 1, a timing information acquisition circuit 102, a tabletop 103, a bed 104, and a bed driving unit 106.

[0011] Detector 1 is a detector that detects radiation by detecting scintillation light (fluorescence), which is light re-emitted when an annihilation gamma ray emitted from a positron in subject P interacts with a light emitter (scintillator) and the substance becomes excited and transitions back to the ground state. Detector 1 detects radiation energy information of the annihilation gamma ray emitted from a positron in subject P. Multiple detectors 1 are arranged in a ring shape surrounding the periphery of subject P, and are composed of, for example, multiple detector blocks.

[0012] An example of a specific configuration of the detector 1 is a photon-counting, Anger-type detector, which has, for example, a scintillator, a photodetector, and a light guide. That is, each of the pixels included in the detector 1 has a scintillator and a photodetector that detects the generated scintillation light.

[0013] The scintillator converts the annihilation gamma rays emitted from the positrons in the subject P and incident thereon into scintillation light (scintillation photons, optical photons), and outputs the light. The scintillator is formed of scintillator crystals suitable for TOF measurement and energy measurement, such as LaBr3 (Lanthanum Bromide), LYSO (Lutetium Yttrium Oxyorthosilicate), LSO (Lutetium Oxyorthosilicate), LGSO (Lutetium Gadolinium Oxyorthosilicate), or BGO, and is arranged, for example, two-dimensionally.

[0014] Examples of light detection elements that can be used include SiPMs (Silicon photomultipliers) and photomultiplier tubes. Photomultiplier tubes have a photocathode that receives scintillation light and generates photoelectrons, multiple dynodes that provide an electric field to accelerate the generated photoelectrons, and an anode that is an electron outlet, and amplify the scintillation light output from the scintillator and convert it into an electrical signal.

[0015] In addition, the gantry device 10 generates counting information from the output signal of the detector 1 by the timing information acquisition circuit 102, and stores the generated counting information in the memory unit 130 of the console device 20. The detector 1 is divided into a plurality of blocks and is equipped with the timing information acquisition circuit 102.

[0016] The timing information acquisition circuit 102 converts the output signal of the detector 1 into digital data to generate counting information. This counting information includes the detection position, energy value, and detection time of the annihilation gamma ray. For example, the timing information acquisition circuit 102 identifies a plurality of photodetection elements that have converted the scintillation light into an electric signal at the same timing. Then, the timing information acquisition circuit 102 identifies a scintillator number (P) indicating the position of the scintillator into which the annihilation gamma ray is incident. The means for identifying the scintillator position into which the annihilation gamma ray is incident may be by performing a center of gravity calculation based on the position of each photodetection element and the intensity of the electric signal. In addition, when the element sizes of the scintillator and the photodetection element correspond to each other, the scintillator corresponding to the photodetection element from which the output is obtained may be identified as the scintillator position into which the annihilation gamma ray is incident.

[0017] Furthermore, the timing information acquisition circuit 102 identifies the energy value (E) of the annihilation gamma ray incident on the detector 1 by integrating the intensity of the electrical signal output from each photodetector element. The timing information acquisition circuit 102 also identifies the detection time (T) at which the detector 1 detects scintillation light due to the annihilation gamma ray. The detection time (T) may be an absolute time or the elapsed time from the start of imaging. In this way, the timing information acquisition circuit 102 generates counting information including the scintillator number (P), the energy value (E), and the detection time (T).

[0018] The timing information acquisition circuit 102 is realized by a circuit such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The timing information acquisition circuit 102 is an example of a timing information acquisition unit.

[0019] The top board 103 is a bed on which the subject P is placed, and is placed on the bed 104. The bed driving unit 106 moves the top board 103 under the control of the bed control function 105k of the processing circuit 150. For example, the bed driving unit 106 moves the top board 103 to move the subject P into the imaging opening of the gantry device 10.

[0020] The console device 20 accepts operations of the PET device 100 by an operator, controls the capturing of PET images, and reconstructs the PET images using the counting information collected by the gantry device 10. As shown in Fig. 1, the console device 20 includes a processing circuit 150, an input device 110, a display 120, and a storage unit 130. The various units included in the console device 20 are connected via a bus. Details of the processing circuit 150 will be described later.

[0021] The input device 110 is a mouse, a keyboard, or the like used by an operator of the PET device 100 to input various instructions and settings, and transfers the input instructions and settings to the processing circuitry 150. For example, the input device 110 is used to input an instruction to start imaging.

[0022] The display 120 is a monitor or the like referred to by the operator, and under the control of the processing circuit 150, displays the subject's respiratory waveform and PET images, as well as a GUI (Graphical User Interface) for receiving various instructions and settings from the operator.

[0023] The storage unit 130 stores various data used in the PET device 100. The storage unit 130 is composed of, for example, a memory, and is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, a hard disk, an optical disk, etc. The storage unit 130 stores counting information in which a scintillator number (P), an energy value (E), and a detection time (T) are associated with each other, coincidence information in which a set of counting information is associated with a coincidence number that is a serial number of the coincidence information, a reconstructed PET image, etc.

[0024] Next, the background of the embodiment will be described.

[0025] Many of the current PET devices estimate the radiation distribution within the subject and perform image reconstruction based on direct incidence events, which are basically gamma ray detection events that are incident on the detector without being scattered within the subject. Image reconstruction algorithms generally have a scatter correction function, but in many cases, the target of scatter correction is scattering up to one scattering. However, when the target of scatter correction is only one scattering, image quality may deteriorate.

[0026] For example, in Fig. 2, curve 30 shows the signal intensity of gamma rays incident on the detector as a function of energy. Region 32 is an energy region corresponding to direct incidence events and single scattering events. In contrast, region 31 is an energy region corresponding to multiple scattering. Events in region 31, which is an energy region corresponding to multiple scattering, have a smaller signal intensity itself and multiple types of events are superimposed compared to events in region 32, which is an energy region corresponding to direct incidence events and single scattering events, making it more difficult to perform scattering correction.

[0027] One method for accurately performing scatter correction to remove low-energy gamma-ray detection events that have undergone multiple scattering is to prepare a high-emission scintillator for accurately measuring the incident energy and a large number of light-receiving cells corresponding to the high-emission scintillator. In this case, for example, when a SiPM is used as a photodetector that converts scintillation photons into an electrical signal, a large number of thousands to tens of thousands of light-receiving cells are required to handle high-emission scintillation.

[0028] In addition, for example, to obtain high time resolution in TOF (Time Of Flight)-PET, a short-time emission scintillator is required as the scintillator, and a quick response and short recovery time are required for each cell of the photodetector. In addition, an electronic circuit for calculating energy based on the signal is required after the photodetector, and in many cases, it becomes necessary to develop an ASIC suitable for the device.

[0029] Thus, when performing scattered radiation correction including multiple scattering in TOF-PET, the energy measurement system constituting one PET device 100 becomes complicated and consists of a huge number of channels. In addition, since there is inherent variability in the operation of each channel, in order to correct these variability in operation and obtain measurement accuracy, a complicated procedure must be configured during operation. In addition, a scintillator that simultaneously satisfies the conditions of high light emission and short light emission time required for TOF-PET is expensive.

[0030] Therefore, if multiple scattering events can be effectively corrected without using a scintillator that simultaneously satisfies the conditions of high light output and short light emission time, the design constraints of the PET device due to the energy measurement system can be significantly reduced.

[0031] In view of the above background, the nuclear medicine diagnosis apparatus according to the embodiment corrects multiple scattering events in a PET apparatus by a new method. Specifically, the nuclear medicine diagnosis apparatus according to the embodiment acquires an electron density function of a subject and geometric information of a gamma ray detector, estimates a first probability value corresponding to a direct incidence event and a second probability value corresponding to a scattering event including a multiple scattering event based on the acquired data, and reconstructs a PET image based on the estimated probability values ​​and coincidence counting data.

[0032] This will improve image quality and greatly reduce design constraints imposed by the energy measurement system of the PET device. For example, it will become possible to design a PET device using a cheaper scintillator than currently available, and the performance of the PET device will be improved.

[0033] Next, the PET device 100 according to the first embodiment will be described in detail with reference to Fig. 3 and Fig. 4. Fig. 3 is a diagram illustrating the configuration of a processing circuit 150 included in the PET device 100 according to the first embodiment. Fig. 4 is a flowchart illustrating processing performed by the PET device 100 according to the embodiment.

[0034] 3, in the first embodiment, the processing circuit 150 has a detection data acquisition function 150a, a coincidence information data generation function 150b, a geometric information acquisition function 150c, an electron density function acquisition function 150d, a direct incidence probability calculation function 150e, a scattered incidence probability calculation function 150f, a system matrix generation function 150g, an initial image generation function 150h, a reconstruction function 150i, a system control function 150j, and a bed control function 150k. Note that the functions other than the system control function 150j and the bed control function 150k will be described in detail later with reference to FIG. 4.

[0035] In the embodiment, each processing function performed by the detection data acquisition function 150a, the coincidence information data generation function 150b, the geometric information acquisition function 150c, the electron density function acquisition function 150d, the direct incidence probability calculation function 150e, the scattered incidence probability calculation function 150f, the system matrix generation function 150g, the initial image generation function 150h, the reconstruction function 150i, the system control function 150j, and the bed control function 150k is stored in the storage unit 130 in the form of a program executable by a computer. The processing circuitry 150 is a processor that realizes a function corresponding to each program by reading and executing the program from the storage unit 130. In other words, the processing circuitry 150 in a state in which each program has been read has each function shown in the processing circuitry 105 of FIG. 3.

[0036] In FIG. 3, the processing functions performed by the detection data acquisition function 150a, the coincidence information data generation function 150b, the geometric information acquisition function 150c, the electron density function acquisition function 150d, the direct incidence probability calculation function 150e, the scattered incidence probability calculation function 150f, the system matrix generation function 150g, the initial image generation function 150h, the reconstruction function 150i, the system control function 150j, and the bed control function 150k are realized in a single processing circuit 150. However, the processing circuit 150 may be configured by combining a plurality of independent processors, and each processor may execute a program to realize the function. In other words, each of the above-mentioned functions may be configured as a program, and one processing circuit 150 may execute each program. As another example, a specific function may be implemented in a dedicated independent program execution circuit.

[0037] The term "processor" used in the above description means a circuit such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor realizes functions by reading and executing a program stored in the memory unit 130.

[0038] Note that the present invention is not limited to the processing circuit 150 in FIG. 3, and similar implementation is possible for the processing circuits 150 in FIGS. 5, 7, and 9 described below.

[0039] In addition, in FIG. 3, the detection data acquisition function 150a, the coincidence counting information data generation function 150b, and the initial image generation function 150h are examples of a first acquisition unit. Also, the geometric information acquisition function 150c and the electron density function acquisition function 150d are examples of a second acquisition unit. The direct incidence probability calculation function 150e, the scattered incidence probability calculation function 150f, and the system matrix generation function 150g are examples of an estimation unit. The reconstruction function 150i is an example of a reconstruction unit. The system control function 150j and the bed control function 150k are examples of a control unit.

[0040] The processing circuitry 105 controls the gantry device 10 and the console device 20 by using a system control function 105c, thereby performing overall control of the PET device 100. For example, the processing circuitry 105 controls imaging in the PET device 100 by using a system control unit 105c.

[0041] The processing circuit 105 controls the bed driving unit 105 by a bed control function 105k.

[0042] Next, the flow of processing executed by the PET device 100 according to the first embodiment will be described with reference to FIG. 4, while describing each function of the processing circuitry 150. In step S100, the processing circuitry 150 acquires an electron density function of the subject by the electron density function acquisition function 150d. As an example, the processing circuitry 150 acquires a CT image of the subject as an electron density function by the electron density function acquisition function 150d. As an example, the processing circuitry 150 acquires a CT image of the subject obtained as a result of imaging the subject by a CT device (not shown) as an electron density function of the subject by the electron density function acquisition function 150d. In addition, as another example, the nuclear medicine diagnosis device according to the embodiment is a PET-CT device having a function as a CT device, and the processing circuitry 150 may acquire a CT image of the subject obtained as a result of imaging the subject by CT imaging by the PET-CT device by the electron density function acquisition function 150d.

[0043] The data representing the electron density function of the subject is not limited to data obtained by CT imaging. As an example, the data representing the electron density function of the subject may be a magnetic resonance image. For example, the processing circuit 150 uses the electron density function acquisition function 150d to capture an image of the subject using an MRI device (not shown), and acquires, for example, a magnetic resonance image that has been subjected to segmentation processing as the electron density function of the subject.

[0044] In this manner, the PET device 100 according to the embodiment acquires the electron density function of the subject separately from the data acquired by PET imaging in step S100 by the electron density function acquisition function 150d. This allows the processing circuitry 150 to acquire basic information for estimating multiple scattering of gamma rays, and improves the accuracy of estimation of multiple scattering in the following steps compared to existing methods.

[0045] Next, in step S110, the processing circuit 150 acquires geometric information of the detector 1, which is a gamma ray detector, by the geometric information acquisition function 150c. Here, the geometric information of the detector 1 refers to information on the relative positional relationship of each of the detectors 1 and information on the positional relationship between the detector 1 and the subject P. As an example, the processing circuit 150 acquires information on the relative positional relationship of each of the detectors 1, which is stored in advance in the storage unit 130, from the storage unit 130 by the geometric information acquisition function 150c, thereby acquiring information on the relative positional relationship of each of the detectors 1. Also, the processing circuit 150 acquires information on the positional relationship between the detector 1 and the subject P by acquiring the position of the subject P through the bed driving unit 106 by the geometric information acquisition function 150c. Note that, for example, the processing of step S110 and the processing of step S100 may be executed in the reverse order. Also, in FIG. 10 described later, the processing of step S110 and the processing of step S100 may be executed in the reverse order.

[0046] Next, in step S120, the processing circuit 150 uses the direct incidence probability calculation function 150e to calculate a first probability value, which is the probability that a gamma ray will directly enter the detector 1, based on the electron density of the subject acquired by the electron density function acquisition function 150d in step S100 and the geometric information of the detector 1 acquired by the geometric information acquisition function 150c in step S110. Here, the direct incidence of a gamma ray on the detector 1 means that the number of times that the generated gamma ray pair is scattered on the way to the detector 1 is 0.

[0047] In step S120, the processing circuitry 150 performs calculations assuming that a unit amount of radioactivity is emitted from each voxel set in the detector field of view, and the actual amount of radioactivity for each voxel is estimated in step S150.

[0048] The processing circuit 150 estimates a first probability value corresponding to a direct incidence event in the subject when a voxel in the detector field of view is assumed to have a unit radioactivity based on at least one of the electron density function of the subject and the geometric information of the gamma ray detector, using the direct incidence probability calculation function 150e. As an example, the processing circuit 150 calculates a first probability value, which is a probability of direct incidence to each of the detectors 1 when a voxel in the detector field of view is assumed to have a unit radioactivity, using, for example, a radiation transport equation, a Monte Carlo simulation, a neural network, or the like, based on at least one of the electron density function of the subject and the geometric information of the gamma ray detector.

[0049] Furthermore, the processing circuit 150 calculates the probability that a gamma ray will be incident on the detector 1 after being scattered by the scattering incidence probability calculation function 150f based on the electron density of the subject acquired by the electron density function acquisition function 150d in step S100 and the geometric information of the detector 1 acquired by the geometric information acquisition function 150c in step S110. Here, "through scattering" includes both incidence through single scattering and multiple scattering.

[0050] In other words, the processing circuit 150 estimates, by the scattering incidence probability calculation function 150f, a second probability value corresponding to a scattering event, including a multiple scattering event, in the subject, when it is assumed that a voxel in the detector field of view has a unit radioactivity, based on at least one of the electron density function of the subject and the geometric information of the gamma ray detector. Specifically, the processing circuit 150 uses the scattering incidence probability calculation function 150f to calculate the scattering incidence probability of the detector 1, when it is assumed that a voxel in the detector field of view has a unit radioactivity, by, for example, a radiation transport equation, a Monte Carlo simulation, a neural network, or the like.

[0051] Next, the processing circuit 150 generates a system matrix H by the system matrix generation function 150g based on the direct incidence probability calculated by the direct incidence probability calculation function 150e and the scattered incidence probability calculated by the scattered incidence probability calculation function 150f. Here, the system matrix H is expressed as follows: ij It is a matrix expressed as:

[0052] In the first and second embodiments, the effect of scattering is expressed as a scattering matrix incorporated into the system matrix, whereas in the third embodiment, the effect of scattering is expressed as a scattering term separate from the system matrix, for example, by using a shifted poission model or the like.

[0053] In step S130, the processing circuit 150 acquires counting information from the timing information acquisition circuit 102. Specifically, the processing circuit 150 acquires gamma ray detection data from the timing information acquisition circuit 102 by the detection data acquisition function 150a. Here, the gamma ray detection data acquired by the processing circuit 150 from the timing information acquisition circuit 102 by the detection data acquisition function 150a is counting information including, for example, the scintillator number (P), the energy value (E), and the detection time (T). Such counting information includes both data due to direct incidence and data due to scattered incidence.

[0054] Next, the processing circuit 150 generates coincidence counting information data using the coincidence counting information data generating function 150b based on the counting information acquired by the detection data acquiring function 150a.

[0055] That is, in step S130, the processing circuit 150 acquires coincidence data including direct incidence events to the gamma ray detector 1 and scattering events at the subject P by the detection data acquisition function 150a and the coincidence information data generation function 150b.

[0056] Next, in step S140, the processing circuitry 150 sets an initial distribution of the subject's radioactivity distribution by the initial image generation function 150h. As an example, the processing circuitry 150 creates an average radioactivity distribution by performing an averaging operation on the PET images of a plurality of subjects by the initial image generation function 150b, and sets the average radioactivity distribution as the initial distribution. As another example, the processing circuitry 150 sets an initial distribution by the initial image generation function 150h in which a certain amount of radioactivity is distributed in the CT image of the subject acquired in step S100 at a location where the electron density is equal to or higher than a certain threshold, and no radioactivity is distributed in other locations. As another example, the processing circuitry 150 sets the electron density distribution calculated from the CT image of the subject acquired in step S100 as the initial distribution of the subject's radioactivity distribution by the initial image generation function 150h.

[0057] Next, in step S150, the processing circuit 150 uses the image reconstruction function 150i to estimate the radioactivity distribution of the subject based on the initial distribution of the subject's radioactivity set in step S140, the system matrix generated in step S120, and the coincidence data acquired in step S130, and reconstructs a PET image to generate a reconstructed image. In other words, the processing circuit 150 uses the image reconstruction function 150i to reconstruct a PET image based on the initial distribution of the subject's radioactivity set in step S140, the first probability value and the second probability value estimated in step S120, and the coincidence data acquired in step S130. As an example, the processing circuit 150 uses the image reconstruction function 150i to reconstruct a PET image based on the initial distribution of the subject's radioactivity set in step S140, the system matrix based on the first probability value and the second probability value estimated in step S120, and the coincidence data acquired in step S130. As an example, the processing circuit 150 uses the image reconstruction function 150i to estimate the radioactivity distribution of the subject that reproduces the coincidence counting information acquired in step S130 based on the initial distribution of the subject's radioactivity set in step S140 and the system matrix generated in step S120, using the maximum likelihood method, a neural network, or the like, and generates a reconstructed image.

[0058] Next, in step S160, the processing circuitry 150 causes the system control function 150j to display the reconstructed image generated in step S150 on the display 120.

[0059] As described above, in the first embodiment, the electron density function of the subject and the geometric information of the gamma ray detector are acquired, and based on these acquired data, a first probability value corresponding to a direct incidence event and a second probability value corresponding to a scattering event including a multiple scattering event are estimated, and a PET image is reconstructed based on these estimated probability values ​​and coincidence counting data. According to the first embodiment, it is possible to configure a PET device that performs imaging and reconstruction making use of the subject information contained in the scattered rays. In addition, since the electron density function of the subject and the geometric information of the gamma ray detector are available, an expensive energy measurement circuit to be implemented in the PET to measure the energy of the detected gamma rays is no longer necessary, and the degree of freedom in design is increased.

[0060] As one example, it is possible to manufacture a nuclear medicine diagnostic device that can obtain high-quality PET images using a scintillator that is less expensive than conventional ones. As another example, the method according to the embodiment makes it possible to employ a scintillator that has advantageous properties for TOF functions, such as a low amount of light emission but a very fast response to gamma ray incidence, thereby expanding the range of scintillator options.

[0061] In addition, in the method according to the first embodiment, the generated system matrix handles the scattering process and the attenuation process in a consistent manner, so that there is no need to manually tune the relative strength between the scattering process and the attenuation process, as in the conventional method. In addition, in the method according to the first embodiment, since the image reconstruction is performed for all events including multiple scattering, an image with suppressed statistical noise can be obtained. In addition, in the method according to the first embodiment, low-exposure imaging can be expected by reducing the dosage of the radiopharmaceutical.

[0062] Second embodiment Next, the second embodiment will be described with reference to Figs. 5 and 6. In the first embodiment, the electron density function of the subject and the geometric information of the gamma ray detector are acquired, a system matrix is ​​estimated based on these acquired data, and a PET image is reconstructed based on the estimated system matrix. The second embodiment is common in that the electron density function of the subject and the geometric information of the gamma ray detector are acquired, and a system matrix is ​​calculated based on these acquired data, but in detail, the system matrix is ​​calculated by a procedure different from that of the first embodiment. That is, the direct incidence contribution part of the system matrix is ​​a part that does not depend on the shape of the electron density function of the subject, so that the processing circuit separately performs a calculation in advance for the direct incidence contribution part of the system matrix and generates the system matrix by adding the scattered incidence contribution part later. This can simplify the calculation of the system matrix.

[0063] The configuration of the processing circuit 150 in the second embodiment is shown in Fig. 5. In Fig. 5, the functions other than the direct incidence probability calculation function 150e are the same as those in the first embodiment, so detailed description will be omitted. Regarding the direct incidence probability calculation function 150e, in the first embodiment, the processing circuit 150 estimates the direct incidence probability using both the geometric information of the detector and the electron density function of the subject, but in the second embodiment, the processing circuit 150 estimates the direct incidence probability using only the geometric information of the detector.

[0064] In addition, in FIG. 5, similarly to FIG. 3, the detection data acquisition function 150a, the coincidence information data generation function 150b, and the initial image generation function 150h are an example of a first acquisition unit. Also, the geometric information acquisition function 150c and the electron density function acquisition function 150d are an example of a second acquisition unit. The direct incidence probability calculation function 150e, the scattered incidence probability calculation function 150f, and the system matrix generation function 150g are an example of an estimation unit. The reconstruction function 150i is an example of a reconstruction unit. The system control function 150j and the bed control function 150k are an example of a control unit.

[0065] 6 shows a flow of processing executed by the PET device 100 according to the second embodiment. Here, the contents of the processing other than steps S70, S90, and S120 are the same as those in the first embodiment, so repeated explanations will be omitted.

[0066] First, in step S70, the processing circuit 150 acquires detector geometric information by the geometric information acquisition function 150c. The process of step S70 is the same as that of step S110 in the first embodiment, but in the second embodiment, the process is performed at this timing.

[0067] Next, in step S90, the processing circuit 150 calculates the direct incidence probability, which is the first probability value, by the direct incidence probability calculation function 150e based on the detector geometric information acquired by the geometric information acquisition function 150c in step S70. At this time, the processing circuit 150 calculates the direct incidence probability, which is the first probability value, by the direct incidence probability calculation function 150e without using the electron density function of the subject. Next, in step S90, the processing circuit 150 calculates the contribution of the system matrix corresponding to the direct incidence probability, which is the first probability value, based on the calculated first probability value by the system matrix generation function 150g. That is, the processing circuit 150 generates a system matrix of the direct incidence probability based on the detector geometric information by the system matrix generation function 150g.

[0068] Subsequently, in steps S100 and S110, the same processes as in the first embodiment are performed, and the processing circuit 150 acquires the electron density function of the subject by the electron density function acquisition function 150d.

[0069] Next, in step S120, the processing circuit 150 performs the same processing as in the first embodiment by using the scattering incidence probability calculation function 150e to calculate the scattering incidence probability, which is the second probability value. Next, the processing circuit 150 generates a system matrix by using the system matrix generation function 150g based on the direct incidence probability, which is the first probability value calculated in advance in step S90, and the scattering incidence probability, which is the second probability value calculated in step S120 in the same manner as in the first embodiment. After that, in steps S130 to S160, the same processing as in the first embodiment is performed.

[0070] In this way, in the second embodiment, the processing circuitry 150 reconstructs a PET image using the reconstruction function 150i based on a system matrix based on a first probability value calculated in advance prior to acquiring the electron density function of the subject. This eliminates the need to calculate the direct incidence probability of the system matrix for each subject, and simplifies the calculation of the system matrix. This reduces the calculation load in the reconstruction process.

[0071] (Third embodiment) In the first and second embodiments, the case where the effect of scattering is expressed by being incorporated into the value of the system matrix as a scattering matrix has been described. However, the embodiments are not limited to this. In the third embodiment, for example, a ShiftedPoissonModel or the like is adopted, and the effect of scattering is expressed as a scattering term that is a correction term for the system matrix.

[0072] The configuration of a processing circuit 150 in the third embodiment is shown in Fig. 7. In Fig. 7, the functions other than the scattering term calculation function 150l are the same as those in the second embodiment, so detailed explanations will be omitted.

[0073] Here, j is a subscript indicating the j-th LOR, and the scattering term S calculated by the processing circuit 150 using the scattering term calculation function 150l is j is given, for example, by the following equation (1).

[0074]

number

[0075] where i and m are subscripts representing the ith and mth voxel, respectively, k is the subscript representing the kth iteration in image reconstruction, and λ i k represents the estimated value of the radioactivity in the i-th voxel in the k-th iteration, and H ij is the system matrix between the ith voxel and the jth LOR, and g j is the number of events detected by the detector in the jth LOR. That is, the scattering term S calculated by the processing circuit 150 using the scattering term calculation function 150l j is a parameter related to scattered radiation, and is a correction term for the system matrix introduced for each LOR during PET image reconstruction.

[0076] In addition, in FIG. 7, the detection data acquisition function 150a, the coincidence counting information data generation function 150b, and the initial image generation function 150h are examples of a first acquisition unit. Also, the geometric information acquisition function 150c and the electron density function acquisition function 150d are examples of a second acquisition unit. The direct incidence probability calculation function 150e, the scattered incidence probability calculation function 150f, and the system matrix generation function 150g are examples of an estimation unit. The scattering term calculation function 150i is an example of an identification unit. The reconstruction function 150i is an example of a reconstruction unit. The system control function 150j and the bed control function 150k are examples of a control unit.

[0077] 8 shows a flow of processing executed by the PET device 100 according to the third embodiment. Here, the contents of the processing other than step S90, step S125, and step S150 are the same as those of the second embodiment, so repeated explanations will be omitted.

[0078] As in the second embodiment, in step S90, the processing circuit 150 uses the direct incidence probability calculation function 150e to estimate the direct incidence probability, which is a first probability value, based on the detector geometric information acquired by the geometric information acquisition function 150c in step S70, and generates a system matrix based on the detector geometric information using the system matrix generation function 150g.

[0079] Here, in the third embodiment, unlike the second embodiment in which scattering is incorporated into the components of the system matrix, the effect of scattering is expressed as a scattering term that is a correction term for the system matrix, and is therefore not an element of the system matrix. Therefore, in step S90, the processing circuit 150 has already generated the system matrix by the system matrix generating function 150g.

[0080] In step S125, the processing circuit 150 calculates a second probability, which is the probability that a gamma ray will enter the detector after single scattering or multiple scattering, using the scattering incidence probability calculation function 150f based on the electron density of the subject acquired by the electron density function acquisition function 150d in step S100 and the geometric information of the detector 1 acquired by the geometric information acquisition function 150c in step S70.

[0081] In other words, the processing circuit 150 estimates a second probability value corresponding to a scattering event including a multiple scattering event in the subject based on the electron density function of the subject and the geometric information of the gamma ray detector by the scattering incidence probability calculation function 150f. Specifically, the processing circuit 150 calculates the scattering incidence probability due to a scattering event including a multiple scattering event by, for example, a radiation transport equation, a Monte Carlo simulation, a neural network, etc., by the scattering incidence probability calculation function 150f. Next, the processing circuit 150 performs a scattering calculation by the scattering term calculation function 150l based on the detector geometric information acquired in step S70, the direct incidence probability which is the first probability value estimated in step S90, the electron density function of the subject acquired in step S100, and the initial estimated value of the radioactivity distribution of the subject, and identifies a scattering term which is a parameter related to the scattered rays. Here, the scattering term which is a parameter related to the scattering term includes a multiple scattering term.

[0082] Furthermore, in step S150, processing circuit 150 estimates the value of the left side of equation (1) by, for example, evaluating the right side of equation (1) using reconstruction function 150i, and estimates the radioactivity distribution of the subject based on the initial estimate of the radioactivity distribution of the subject set in step S140, the system matrix generated in step 90, and the scattering term, which is a parameter related to scattered rays generated in step S125, and reconstructs a PET image.

[0083] For example, in step S100, the data acquired by the processing circuitry 150 using the electron density function 150d is not limited to a CT image, and may be, for example, a magnetic resonance image. As an example, the processing circuitry 150 performs segmentation processing on the magnetic resonance image using a segmentation function (not shown) to automatically extract / determine a region. Next, the processing circuitry 150 converts the magnetic resonance signal value to a CT value for each extracted / determined region of the magnetic resonance image, and generates a magnetic resonance image having a signal value similar to that of the CT image. The processing circuitry 150 acquires the magnetic resonance image as an image showing the electron density of the subject using the electron density function 150d.

[0084] Furthermore, the processing circuitry 150 may perform, for example, a semantic segmentation process on the CT image of the subject acquired in step S100 using the system matrix generation function 150g to identify a scattering region and estimate the second probability value based on the identified scattering region, thereby reducing the calculation load associated with image reconstruction.

[0085] As described above, in the third embodiment, the processing circuit 150 calculates the scattering term, which is a parameter related to scattered rays, including multiple scattering, based on the detector geometric information and the electron density function by the scattering term calculation function 150i, and reconstructs a PET image using the separately calculated system matrix and scattering term. In this way, by calculating the term including multiple scattering as the scattering term separately from the system matrix and performing reconstruction, it becomes possible to perform PET reconstruction with high accuracy for data including scattering including multiple scattering.

[0086] (Fourth embodiment) Next, a fourth embodiment will be described. When generating coincidence data, the coincidence data may be generated by removing noise from the output result from the gamma ray detector using a low-pass filter or a peak value discriminator (discriminator). Here, for example, the parameter value used by the peak value discriminator for peak value cutoff may differ for each detector. In the fourth embodiment, image reconstruction is performed using the parameter value used for peak value cutoff for each detector, thereby further improving the image quality of the reconstructed PET image.

[0087] The configuration of a processing circuit 150 in the fourth embodiment is shown in Fig. 9. In Fig. 9, functions other than a cutoff processing function 150n and a cutoff parameter setting function 150m are similar to those in the first embodiment, so detailed description thereof will be omitted.

[0088] The cutoff processing function 150n is a function that performs noise cut processing on the count data obtained by the detector data acquisition function 150a using a low-pass filter or a pulse height discriminator. As an example, the processing circuit 150 uses the cutoff processing function 150n to remove count data that is less than a defined pulse height cutoff parameter from the count data obtained by, for example, the gamma ray detector data acquisition function 150a. For example, if the count data obtained by the detector data acquisition function 150a is less than the defined pulse height cutoff parameter, the output count data is 0.

[0089] Moreover, the processing circuit 150 sets the value of the cutoff parameter by a cutoff parameter setting function 150m.

[0090] In addition, in FIG. 9, the detection data acquisition function 150a, the coincidence information data generation function 150b, the cutoff parameter setting function 150m, the cutoff processing function 150n, and the initial image generation function 150h are an example of a first acquisition unit. Also, the geometric information acquisition function 150c and the electron density function acquisition function 150d are an example of a second acquisition unit. The direct incidence probability calculation function 150e, the scattered incidence probability calculation function 150f, and the system matrix generation function 150g are an example of an estimation unit. The reconstruction function 150i is an example of a reconstruction unit. The system control function 150j and the bed control function 150k are an example of a control unit.

[0091] 10 shows a flow of processing executed by the PET device 100 according to the fourth embodiment. Here, the contents of the processing other than steps S80, S85, S120, S130, and S150 are the same as those of the first embodiment, so repeated explanations will be omitted.

[0092] In step S80, the processing circuit 150 sets a peak value cutoff parameter by the cutoff parameter setting function 150m. Also, in step S130, the processing circuit 150 acquires gamma ray detection data as counting information from the timing information acquisition circuit 102 by the detection data acquisition function 150a, as in the first embodiment. Next, the processing circuit 150 generates coincidence counting information data by the coincidence counting information data generation function 150b based on the counting information acquired by the gamma ray detection data acquisition function 150a and the peak value parameter set in step S80. That is, the processing circuit 150 generates coincidence counting data by applying a peak value cutoff to the output result from the gamma ray detector by the coincidence counting information data generation function 150b.

[0093] On the other hand, in step S85, the processing circuit 150 notifies the peak value cutoff parameters set in step S80 to the scattered incidence calculation units such as the direct incidence probability calculation function 150e and the scattered incidence probability calculation function 150f.

[0094] In step S120, the processing circuit 150 calculates the direct incidence probability by the direct incidence probability calculation function 150e and calculates the scattered incidence probability based on the scattered incidence probability calculation function 150f, and the processing circuit 150 estimates the direct incidence probability as a first probability value by the direct incidence probability calculation function 150e based on the value of the peak value parameter notified in step S85. In addition, the processing circuit 150 estimates the scattered incidence probability as a second probability value by the scattered incidence probability calculation function 150f based on the value of the peak value parameter notified in step S85.

[0095] Furthermore, the processing circuit 150 generates a system matrix by the system matrix generation function 150g based on the direct incidence probability calculated by the direct incidence probability calculation function 150e and the scattered incidence probability calculated by the scattered incidence probability calculation function 150f, and at this time, generates the system matrix based on the value of the peak value parameter notified in step S85. That is, the processing circuit 150 reflects the difference in the cutoff parameter for each detector in the system matrix generated in step S120.

[0096] Furthermore, in step S150, processing circuit 150, using image reconstruction function 150i, estimates the radioactivity distribution of the subject based on the initial distribution of the subject's radioactivity set in step S140, the system matrix generated in step S120, the coincidence counting data acquired in step S130, and the peak value parameters set by the cutoff parameter setting function, and reconstructs a PET image to generate a reconstructed image.

[0097] As described above, in the fourth embodiment, scattering evaluation and image reconstruction are performed using the parameter values ​​used for the peak value cutoff for each detector, thereby making it possible to further improve the image quality of the reconstructed PET image.

[0098] According to at least one of the embodiments described above, image quality can be improved.

[0099] Regarding the above embodiment, the following supplementary notes are disclosed as one aspect and optional features of the invention.

[0100] (Appendix 1) A nuclear medicine diagnosis apparatus according to one aspect of the present invention includes a first acquisition unit, a second acquisition unit, an estimation unit, and a reconstruction unit. The first acquisition unit acquires coincidence data including a direct incidence event on a gamma ray detector and a scattering event in a subject. The second acquisition unit acquires an electron density function of the subject and geometric information of the gamma ray detector. The estimation unit estimates a first probability value corresponding to the direct incidence event in the subject and a second probability value corresponding to the scattering event based on at least one of the electron density function and the geometric information. The reconstruction unit reconstructs a PET image based on the first probability value, the second probability value, and the coincidence data. The scattering events include multiple scattering events.

[0101] (Appendix 2) The estimator may estimate the first probability value using a radiative transport equation, a Monte Carlo simulation, or a neural network.

[0102] (Appendix 3) The estimator may estimate the second probability value using a radiative transport equation, a Monte Carlo simulation, or a neural network.

[0103] (Appendix 4) The reconstruction unit may reconstruct the PET image based on a system matrix that is based on the first probability value.

[0104] (Appendix 5) The reconstruction unit may reconstruct the PET image based on a system matrix based on the first probability value and the second probability value.

[0105] (Appendix 6) The estimation unit may estimate the first probability value based on the geometric information, and estimate a second probability value based on the geometric information and the electron density function.

[0106] (Appendix 7) An identification unit that identifies a parameter related to scattered radiation based on the geometric information and the electron density function, A reconstruction unit may reconstruct the PET image based on the parameters.

[0107] (Appendix 8) The reconstruction unit may reconstruct the PET image using a Shifted Poisson Model.

[0108] (Appendix 9) The first acquisition unit applies a peak value cutoff to an output result from the gamma ray detector to generate the coincidence count data; The estimation unit may estimate the first probability value and the second probability value based on a parameter used for the peak value cutoff.

[0109] (Appendix 10) The estimation unit may perform a segmentation process to identify a scattering region, and estimate the second probability value based on the identified scattering region. The segmentation process may be a semantic segmentation process.

[0110] (Appendix 11) The electron density function may be a CT image.

[0111] (Appendix 12) The electron density function may be generated from a magnetic resonance image.

[0112] (Appendix 13) A medical image processing device provided in one aspect of the present invention includes a first acquisition unit, a second acquisition unit, an estimation unit, and a reconstruction unit. The first acquisition unit acquires coincidence data including a direct incidence event on a gamma ray detector and a scattering event in a subject. The second acquisition unit acquires an electron density function of the subject and geometric information of the gamma ray detector. The estimation unit estimates a first probability value corresponding to the direct incidence event in the subject and a second probability value corresponding to the scattering event based on at least one of the electron density function and the geometric information. The reconstruction unit reconstructs a PET image based on the first probability value, the second probability value, and the coincidence data. The scattering events include multiple scattering events.

[0113] (Appendix 14) A nuclear medicine diagnostic method provided in one aspect of the present invention is a nuclear medicine diagnostic method executed by a nuclear medicine diagnostic device, comprising: acquiring coincidence data including a direct incidence event on a gamma ray detector and a scattering event in a subject; acquiring an electron density function of the subject and geometric information of the gamma ray detector; estimating a first probability value corresponding to the direct incidence event in the subject and a second probability value corresponding to the scattering event based on at least one of the electron density function and the geometric information; and reconstructing a PET image based on the first probability value, the second probability value, and the coincidence data, wherein the scattering event includes a multiple scattering event.

[0114] (Appendix 15) A program provided in one aspect of the present invention causes a computer to execute a process of acquiring coincidence data including direct incidence events on a gamma ray detector and scattered events including multiple scattering events in a subject, acquiring an electron density function of the subject and geometric information of the gamma ray detector, estimating a first probability value corresponding to the direct incidence event in the subject and a second probability value corresponding to the scattered event based on at least one of the electron density function and the geometric information, and reconstructing a PET image based on the first probability value, the second probability value, and the coincidence data.

[0115] Although some embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0116] 150 Processing circuit 150a Detection data acquisition function 150b Coincidence information generation function 150c Geometric information acquisition function 150d electron density function acquisition function 150e Direct incidence probability calculation function 150f Scattering incidence probability calculation function 150g System matrix generator 150h Initial image generation function 150i reconfiguration function 150k bed control function

Claims

1. a first acquisition unit that acquires coincidence data including a direct incidence event to the gamma ray detector and a scattering event including a multiple scattering event in the subject; A second acquisition unit that acquires an electron density function of the subject and geometric information of the gamma ray detector; an estimation unit that estimates a first probability value corresponding to the direct incidence event and a second probability value corresponding to the scattering event including a multiple scattering event in the subject based on at least one of the electron density function and the geometric information; a reconstruction unit that reconstructs a PET image based on the first probability value, the second probability value, and the coincidence data.

2. The nuclear medicine diagnosis apparatus according to claim 1 , wherein the reconstruction unit reconstructs the PET image based on a system matrix that is based on the first probability value.

3. The nuclear medicine diagnosis apparatus according to claim 1 , wherein the reconstruction unit reconstructs the PET image based on a system matrix based on the first probability value and the second probability value.

4. The nuclear medicine diagnosis apparatus according to claim 1 , wherein the estimation unit estimates the first probability value based on the geometric information, and estimates the second probability value based on the geometric information and the electron density function.

5. An identification unit that identifies a parameter related to scattered radiation based on the geometric information and the electron density function, The nuclear medicine diagnosis apparatus according to claim 2 , wherein the reconstruction unit reconstructs the PET image based on the parameters.

6. The first acquisition unit applies a peak value cutoff to an output result from the gamma ray detector to generate the coincidence count data; The nuclear medicine diagnosis apparatus according to claim 1 , wherein the estimation unit estimates the first probability value and the second probability value based on a parameter used for the peak value cutoff.

7. The nuclear medicine diagnosis apparatus according to claim 1 , wherein the estimation unit performs a segmentation process to identify a scattering region, and estimates the second probability value based on the identified scattering region.

8. a first acquisition unit that acquires coincidence data including a direct incidence event to the gamma ray detector and a scattering event including a multiple scattering event in the subject; A second acquisition unit that acquires an electron density function of the subject and geometric information of the gamma ray detector; an estimation unit that estimates a first probability value corresponding to the direct incidence event and a second probability value corresponding to the scattering event including a multiple scattering event in the subject based on at least one of the electron density function and the geometric information; a reconstruction unit that reconstructs a PET image based on the first probability value, the second probability value, and the coincidence data. Medical imaging equipment.

9. A nuclear medicine diagnosis method performed by a nuclear medicine diagnosis apparatus, comprising: Acquire coincidence counting data including a direct incidence event on the gamma ray detector and a scattering event including a multiple scattering event at the subject; acquiring an electron density function of the object and geometric information of the gamma ray detector; estimating a first probability value corresponding to the direct incidence events and a second probability value corresponding to the scattering events including multiple scattering events in the object based on at least one of the electron density function and the geometric information; A nuclear medicine diagnostic method comprising: reconstructing a PET image based on the first probability value, the second probability value, and the coincidence count data.

10. On the computer, Acquire coincidence counting data including a direct incidence event on the gamma ray detector and a scattering event including a multiple scattering event at the subject; acquiring an electron density function of the object and geometric information of the gamma ray detector; estimating a first probability value corresponding to the direct incidence events and a second probability value corresponding to the scattering events including multiple scattering events in the object based on at least one of the electron density function and the geometric information; A program for executing a process of reconstructing a PET image based on the first probability value, the second probability value, and the coincidence count data.

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