Nuclear medicine diagnosis device

Abstract

To perform synchronous reconfiguration to the periodic movement of a subject without using an external device.SOLUTION: A nuclear medicine diagnosis device analyzes a respiratory movement from list mode data of scanning in which the respiratory movement and heart beats occur, divides the list mode data for every respiratory phase to create first images, analyzes the heart beats for every respiratory phase, creates second images from data obtained by dividing the list mode data for every respiratory phase and data obtained by the division for every heart beat phase, acquires at least one of a first motion vector between the respiratory phases and a second motion vector between the heart beat phases, and applies the first motion vector to the second images and adds up the images, for every group of the second images different in the respiratory phase from each other and common in the heart beat phase, to create a first single phase image corresponding to a specific phase, or applies the motion vector to the second images and adds up the images, for every group of the second images different in the heart beat phase from each other and common in the respiratory phase, to create a second single phase image corresponding to the specific phase.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a nuclear medicine diagnostic device. [Background technology]

[0002] Image acquisition in a nuclear medicine imaging system may be affected by respiratory motion, cardiac pulsation, and the like. Synchronized reconstruction techniques, such as respiratory-gated reconstruction and cardiac-gated reconstruction, are known to suppress the effects of such periodic motion and improve image quality. For example, by measuring respiration and cardiac rhythm using an external device attached to the subject, data acquired by the nuclear medicine imaging system can be associated with respiratory phases and cardiac phases, and data of specific phases can be extracted and used for reconstruction processing, thereby obtaining nuclear medicine images in which the effects of respiratory motion and cardiac rhythm are suppressed. However, attaching an external device to the subject places a burden on both the operator (e.g., a technician) and the subject. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-187350 Summary of the Invention [Problem to be solved by the invention]

[0004] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to perform synchronous reconstruction with respect to the periodic movement of a subject without using an external device. 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 below can also be positioned as other problems. [Means for solving the problem]

[0005] A nuclear medicine diagnosis apparatus according to an embodiment includes a first analysis unit that analyzes respiratory movement based on list mode data obtained by performing a nuclear medicine scan on a subject in which respiratory movement and cardiac movement occur, a first image generation unit that generates a first image from each of divided data obtained by dividing the list mode data for each respiratory phase, which is a phase of the respiratory movement, a second analysis unit that analyzes cardiac movement for each respiratory phase based on the list mode data, a second image generation unit that generates a second image from each of divided data obtained by dividing phase data obtained by dividing the list mode data for each respiratory phase, for each cardiac phase, which is a phase of the cardiac movement, and a first mosaic image generation unit that generates a second image from each of divided data indicating a change between the respiratory phases. a vector acquisition unit that acquires at least one of a first motion vector and a second motion vector that indicates a change between phases in the cardiac phase; and a summation processing unit that applies the first motion vector to the second images for each group of second images having different respiratory phases but a common cardiac phase and sums them together to generate a first single-phase image corresponding to a specific phase of the respiratory phases, or applies the second motion vector to the second images for each group of second images having different cardiac phases but a common respiratory phase and sums them together to generate a second single-phase image corresponding to a specific phase of the cardiac phases. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a nuclear medicine diagnosis apparatus according to an embodiment. [Figure 2] FIG. 2 is a flowchart showing a series of processes performed by the processing circuit according to the first embodiment. [Figure 3A] FIG. 3A is a diagram showing an example of respiratory movement analysis according to the first embodiment. [Figure 3B] FIG. 3B is a diagram showing an example of respiratory movement analysis according to the first embodiment. [Figure 4A] FIG. 4A is a diagram showing an example of respiratory movement analysis according to the first embodiment. [Figure 4B]FIG. 4B is a diagram showing an example of respiratory movement analysis according to the first embodiment. [Figure 5] FIG. 5 is a diagram for explaining images for each respiratory phase according to the first embodiment. [Figure 6] FIG. 6 is a diagram illustrating motion vectors according to the first embodiment. [Figure 7] FIG. 7 is a diagram illustrating the phase data according to the first embodiment. [Figure 8] FIG. 8 is a diagram showing an example of the process of estimating a heartbeat waveform according to the first embodiment. [Figure 9] FIG. 9 is a diagram for explaining the process of generating a single phase image according to the first embodiment. [Figure 10] FIG. 10 is a diagram for explaining the process of generating a single phase image according to the second embodiment. [Figure 11] FIG. 11 is a diagram showing an example of the configuration of a medical information processing system according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, an embodiment of a nuclear medicine diagnosis apparatus will be described with reference to the drawings.

[0008] (First embodiment) First, a description will be given of the nuclear medicine diagnosis apparatus 10. The nuclear medicine diagnosis apparatus 10 is a medical imaging diagnostic apparatus (modality) capable of performing nuclear medicine scans. A nuclear medicine scan is a scan performed by administering a drug labeled with a radionuclide to a subject, and typical examples include a PET (Positron Emission Computed Tomography) scan and a SPECT (Single Photon Emission Computed Tomography) scan.

[0009] In this embodiment, a case where the nuclear medicine diagnosis apparatus 10 is a PET apparatus will be described as an example with reference to FIG. 1. FIG. 1 is a diagram showing an example of the configuration of the nuclear medicine diagnosis apparatus 10 according to the first embodiment. The nuclear medicine diagnosis apparatus 10 shown in FIG. 1 includes a gantry device 110 and a console device 120. The gantry device 110 includes a detector 130, a front-end circuit 112, a tabletop 113, a bed 114, and a bed driver 116.

[0010] The detector 130 is a detector that detects radiation. For example, the detector 130 is a detector that detects radiation by detecting scintillation light (fluorescence), which is light that is re-emitted when a substance that has become excited by the interaction of a light emitter with a gamma ray generated by the annihilation of a positron emitted from a drug administered to and accumulated in the subject P with an electron in the surrounding tissue, and then transitions back to the ground state. In an embodiment, the detector 130 can also detect Cherenkov light. The detector 130 detects radiation energy information of gamma rays generated by the annihilation of a positron emitted from a drug administered to and accumulated in the subject P with an electron in the surrounding tissue. A plurality of detectors 130 are arranged in a ring shape around the subject P, and each detector is composed of, for example, a plurality of detector blocks.

[0011] The detector 130 typically comprises a scintillator crystal and a light detection surface comprising a photodetector element. The scintillator crystal may be made of a material suitable for generating Cherenkov light, such as bismuth germanium oxide (BGO), lead glass (SiO2+PbO), lead fluoride (PbF2), or a lead compound such as PWO (PbWO4). Alternatively, scintillator crystals such as LYSO (Lutetium Yttrium Oxyorthosilicate), LSO (Lutetium Oxyorthosilicate), LGSO (Lutetium Gadolinium Oxyorthosilicate), or BGO may be used. The photodetector element constituting the light detection surface may comprise, for example, a plurality of pixels, each of which may be, for example, a single photon avalanche diode (SPAD). The configuration of the detector 130 is not limited to the above example, and as an example, the photodetector element may be, for example, a SiPM (Silicon photomultiplier) or a photomultiplier tube. The scintillator crystal may be a monolithic crystal, and the photodetection surfaces made up of the photodetector elements may be arranged on, for example, six faces of the scintillator crystal.

[0012] Furthermore, the gantry device 110 generates counting (count number) information from the output signal of the detector 130 using the front-end circuit 112, and stores the generated counting information in the memory 124 of the console device 120. The detector 130 may be divided into multiple blocks and equipped with the front-end circuit 112.

[0013] The front-end circuit 112 converts the output signal from the detector 130 into digital data and generates counting information. This counting information includes, for example, the detection position, energy value, and detection time of the annihilation gamma ray. For example, the front-end circuit 112 identifies multiple photodetector elements that simultaneously converted scintillation light into electrical signals. The front-end circuit 112 then identifies a scintillator number (P) indicating the position of the scintillator onto which the annihilation gamma ray was incident. The position of the scintillator onto which the annihilation gamma ray was incident may be identified by performing a center of gravity calculation based on the position of each photodetector element and the intensity of the electrical signal. Furthermore, when the element sizes of the scintillators and the photodetector elements correspond to each other, for example, the scintillator corresponding to the photodetector element that produced the maximum output may be assumed to be the scintillator position onto which the annihilation gamma ray was incident, and the final identification may be performed taking into account inter-scintillator scattering.

[0014] The front-end circuit 112 also integrates the intensity of the electrical signal output from each photodetector element or measures the time (time over threshold) at which the electrical signal intensity exceeds a threshold, thereby identifying the energy value (E) of the annihilation gamma ray incident on the detector 130. The front-end circuit 112 also identifies the detection time (T) at which the detector 130 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 front-end circuit 112 generates counting information including the scintillator number (P), the energy value (E), and the detection time (T).

[0015] The front-end circuit 112 is realized by a circuit such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), or 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)).

[0016] The top board 113 is a bed on which the subject P is placed, and is placed on the bed 114. The bed driving unit 116 moves the top board 113 under the control of the control function 125a of the processing circuitry 125. For example, the bed driving unit 116 moves the top board 113 to move the subject P into the imaging opening of the gantry device 110.

[0017] The console device 120 accepts operations of the nuclear medicine diagnosis apparatus 10 by an operator, controls the execution of a nuclear medicine scan, and reconstructs a nuclear medicine image from the collected list mode data. When the nuclear medicine diagnosis apparatus 10 is a PET apparatus, the console device 120 controls the execution of a PET scan and reconstructs a PET image from the list mode data. As shown in FIG. 1 , the console device 120 includes a communication interface 121, an input interface 122, a display 123, a memory 124, and a processing circuit 125.

[0018] The communication interface 121 controls the transmission and communication of various data sent and received between the console device 120 and other devices or systems connected via a network. Specifically, the communication interface 121 is connected to the processing circuitry 125, and outputs data received from other devices or systems to the processing circuitry 125, or transmits data output from the processing circuitry 125 to other devices or systems. For example, the communication interface 121 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

[0019] The input interface 122 accepts various input operations from the operator, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 125. For example, the input interface 122 may be implemented by a mouse, keyboard, trackball, switch, button, joystick, a touchpad that allows input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, a voice input circuit, etc. The input interface 122 may also be implemented by a tablet terminal or the like that can wirelessly communicate with the console device 120. The input interface 122 may also be a circuit that accepts input operations from the operator using motion capture. For example, the input interface 122 can accept the operator's body movements, line of sight, etc. as input operations by processing signals acquired via a tracker and images collected about the operator. The input interface 122 is not limited to those that include physical operating components such as a mouse and keyboard. For example, an example of the input interface 122 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the console device 120 and outputs this electrical signal to the processing circuit 125.

[0020] The display 123 displays various types of information. For example, the display 123 displays various types of medical information such as nuclear medicine images collected from the subject P, respiratory waveforms, and cardiac waveforms. In addition, for example, the display 123 displays a GUI (Graphical User Interface) for receiving various instructions, settings, and the like from an operator via the input interface 122. For example, the display 123 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 123 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the console device 120 main body.

[0021] The memory 124 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. For example, the memory 124 stores various types of medical information and programs that enable circuits included in the console device 120 to realize their functions. The memory 124 may also be realized by a group of servers (cloud) connected to the console device 120 via a network NW.

[0022] The processing circuitry 125 functions as a control function 125a, a first analysis function 125b, a first image generation function 125c, a vector acquisition function 125d, a second analysis function 125e, a second image generation function 125f, and a summation processing function 125g, thereby controlling the operation of the entire console device 120. For example, the processing circuitry 125 functions as the control function 125a by reading out a program corresponding to the control function 125a from the memory 124 and executing it.

[0023] For example, the control function 125a controls the operation of various components included in the gantry device 110 to perform a nuclear medicine scan on the subject P and acquire list mode data indicating the radiation detection results. The control function 125a also controls display on the display 123. The control function 125a also controls the transmission and reception of each piece of data via the network. For example, the control function 125a transmits and registers the list mode data collected by the nuclear medicine scan and the nuclear medicine images reconstructed based on the list mode data to a PACS (Picture Archiving and Communication System) server.

[0024] Similarly, the processing circuit 125 functions as a first analysis function 125b, a first image generation function 125c, a vector acquisition function 125d, a second analysis function 125e, a second image generation function 125f, and a summing processing function 125g. The first analysis function 125b is an example of a first analysis unit. The first image generation function 125c is an example of a first image generation unit. The vector acquisition function 125d is an example of a vector acquisition unit. The second analysis function 125e is an example of a second analysis unit. The second image generation function 125f is an example of a second image generation unit. The summing processing function 125g is an example of a summing processing unit. Details of the processing by the first analysis function 125b, the first image generation function 125c, the vector acquisition function 125d, the second analysis function 125e, the second image generation function 125f, and the summing processing function 125g will be described later.

[0025] 1, each processing function is stored in the memory 124 in the form of a program executable by a computer. The processing circuitry 125 is a processor that realizes the function corresponding to each program by reading and executing the program from the memory 124. In other words, the processing circuitry 125 in a state in which a program has been read has the function corresponding to the read program.

[0026] 1, the control function 125a, the first analysis function 125b, the first image generation function 125c, the vector acquisition function 125d, the second analysis function 125e, the second image generation function 125f, and the summation processing function 125g are described as being realized by a single processing circuit 125, but the processing circuit 125 may be configured by combining multiple independent processors, and each processor may execute a program to realize the functions. Furthermore, each processing function of the processing circuit 125 may be realized by being appropriately distributed or integrated into a single or multiple processing circuits.

[0027] The processing circuitry 125 may also realize its functions by using a processor of an external device connected via a network NW. For example, the processing circuitry 125 reads and executes a program corresponding to each function from the memory 124, and realizes each function shown in FIG. 1 by using a group of servers (cloud) connected to the nuclear medicine diagnosis apparatus 10 via the network NW as a computational resource.

[0028] The above describes an example configuration of the nuclear medicine diagnosis device 10. Depending on the imaging range, the list mode data acquired by the nuclear medicine scan may be affected by subject movement such as respiratory movement or cardiac movement. If the reconstruction process is performed without taking into account the influence of such movement, blurring may occur in the area of ​​the reconstructed nuclear medicine image where movement has occurred, which may hinder diagnosis.

[0029] Respiratory-gated reconstruction and cardiac-gated reconstruction are known as techniques for improving the image quality of nuclear medicine images by reducing the effects of respiratory motion and cardiac pulsation. Specifically, when performing synchronous reconstruction, an external device such as a respiratory-gated monitor or an ECG-gated monitor is attached to the subject before the start of a nuclear medicine scan. This allows the external device to measure the subject's respiration and heart rate while collecting list-mode data through a nuclear medicine scan, and associate the list-mode data with the phases of the respiration and heart rate.

[0030] Then, by extracting data of a specific phase from the list mode data and using it for reconstruction processing, it is possible to obtain nuclear medicine images in which the effects of respiration and heartbeat are suppressed. For example, since the movement of the subject due to respiration is small at the end-of-expiration phase, by extracting data corresponding to the end-of-expiration phase from the list mode data and using it for reconstruction processing, it is possible to obtain respiratory-gated images in which the effects of respiratory motion are suppressed.

[0031] However, using an external device for synchronous reconstruction imposes a burden on both the operator and the subject. For example, the process of attaching and detaching the external device to the subject deteriorates the workflow and increases the examination time. Furthermore, when electrodes of the external device are attached to the skin of the subject P, the electrodes may have an adverse effect on the skin. In addition, costs are incurred for purchasing and maintaining the external device.

[0032] For this reason, a method for performing synchronous reconstruction by analyzing list-mode data without using an external device is being studied. This method is also called device-less synchronous reconstruction or data-driven synchronous reconstruction.

[0033] However, list mode data may be affected by multiple periodic movements with different frequencies, such as respiratory movement and cardiac pulsation, making it difficult to perform device-less synchronous reconstruction with high accuracy. For example, when performing device-less respiratory-gated reconstruction, a respiratory waveform is estimated based on list mode data, but the accuracy of the respiratory waveform estimation may be reduced due to the influence of cardiac pulsation. Furthermore, because respiratory movement is generally larger than cardiac pulsation, respiratory movement is dominantly detected in the analysis of list mode data. In other words, cardiac-gated reconstruction is particularly difficult when performing device-less synchronous reconstruction.

[0034] In contrast, the nuclear medicine diagnosis apparatus 10 enables synchronous reconstruction with respect to the periodic movement of the subject P without using an external device, by processing by the processing circuitry 125, which will be described in detail below.

[0035] A series of processes performed by the processing circuitry 125 is shown in Fig. 2. Fig. 2 is a flowchart showing a series of processes performed by the processing circuitry 125 according to the first embodiment.

[0036] First, the control function 125a acquires list mode data (step S101). The list mode data is counting information collected from the subject P by a nuclear medicine scan, and is also referred to as raw data. Although the specific format of the list mode data is not particularly limited, the list mode data includes at least information on the detection time.

[0037] For example, the control function 125a acquires list mode data by controlling the operations of various components included in the gantry device 110 to execute a nuclear medicine scan on the subject P. Alternatively, the control function 125a may acquire list mode data collected in advance by the nuclear medicine diagnosis device 10 or another nuclear medicine diagnosis device. For example, the control function 125a may acquire list mode data registered in a PACS server via a network.

[0038] Next, first analysis function 125b analyzes respiratory motion based on the list mode data (step S102). That is, first analysis function 125b analyzes respiratory motion based on list mode data obtained by performing a nuclear medicine scan on a subject in which respiratory motion and cardiac motion occur.

[0039] 2, heartbeat analysis is performed in step S105, which follows step S102, which is the respiratory motion analysis, as will be described later. When analyzing two periodic motions, the period of the first periodic motion, which is the object of analysis first, must be longer than the period of the second periodic motion. In other words, when analyzing two body motions, respiratory motion and heartbeat, the respiratory motion is the object of the first analysis, and the heartbeat is the object of the second analysis.

[0040] For example, the first analysis function 125b extracts a portion of the list mode data in the time direction as partial data and estimates a respiratory waveform based on a reconstructed image reconstructed from the extracted partial data. That is, the first analysis function 125b estimates a respiratory waveform based on a plurality of time-divided reconstructed images. For example, the first analysis function 125b can estimate a respiratory waveform by analyzing the correlation between the reconstructed images, for example, by principal component analysis. The estimated respiratory waveform may be displayed on the display 123 under the control of the control function 125a.

[0041] An example of respiratory motion analysis will now be described with reference to Figures 3A and 3B. In Figure 3A, a time interval T11 is set as the time interval for respiratory motion analysis, and a time width T12 is set as the time width for respiratory motion analysis. The time width is the size in the time direction of partial data extracted from list mode data. The smaller the time width, the better the temporal resolution of the analysis, but the greater the influence of statistical noise. The time interval is the frequency at which partial data is extracted from list mode data, and is the sampling interval in the time direction. First analysis function 125b extracts partial data having a time width T12 from the list mode data at each time interval T11, and estimates a respiratory waveform based on a reconstructed image reconstructed from the extracted partial data.

[0042] In Figures 3A and 3B, the time interval T11 and time width T12 are set short enough to detect heartbeat, which is a high-frequency movement compared to respiratory movement. For example, the time interval T11 and time width T12 are set to approximately 0.2 seconds. The respiratory waveform estimation results obtained when analysis was performed under these settings are shown in Figure 3B as a solid line graph. The output from the respiratory-gated monitor is also shown in Figure 3B as a dashed line graph. The output from the respiratory-gated monitor shown in Figure 3B is described as an approximation of the actual respiratory waveform to the extent that sufficient accuracy can be achieved for respiratory-gated reconstruction. The analysis results shown in Figure 3B exhibit numerous peaks that do not exist in the actual respiratory waveform due to the influence of heartbeats, and therefore cannot be said to be highly accurate as a respiratory waveform estimation result.

[0043] To suppress the influence of heartbeats on the estimation of the respiratory waveform, it is possible to set the time interval T11 and the time width T12 long. For example, it is possible to set the time interval T11 and the time width T12 to about 1.0 second. If the time width T12 is long and each partial data contains data for about one cycle of the heartbeat or more, the influence of the heartbeat is smoothed, and a smooth graph can be obtained as the estimated respiratory waveform. However, such an estimation result is rough, obtained at a sparse sampling interval, and cannot be said to be a highly accurate estimation result of the respiratory waveform.

[0044] Therefore, as shown in Fig. 4A, the first analysis function 125b may set a time interval T21 as the time interval for respiratory motion analysis and a time width T22 longer than the time interval T21 as the time width for respiratory motion analysis. For example, the first analysis function 125b may set the time interval T21 to approximately 0.2 seconds and the time width T22 to approximately 1.0 seconds. This allows the respiratory waveform to be accurately estimated at a sufficient sampling interval while smoothing the influence of heartbeats to obtain a smooth graph, as shown by the solid line graph in Fig. 4B, for example.

[0045] In Figure 4B, the solid line graph, which is the respiratory waveform estimated without a device, and the dashed line graph, which is the output from the respiratory-gated monitor, roughly match. In other words, by estimating the respiratory waveform with the settings shown in Figure 4A, it is possible to perform respiratory-gated reconstruction with the same level of accuracy as when using a respiratory-gated monitor.

[0046] Next, the first image generation function 125c generates an image for each respiratory phase (step S103). That is, the first image generation function 125c performs respiratory-gated reconstruction. The processing of step S103 will be described with reference to FIG. 5. FIG. 5 is a diagram for explaining images for each respiratory phase according to the first embodiment. Note that while FIG. 5 illustrates a case in which five phases are included in a respiratory cycle, this can be changed as appropriate. That is, the number of phases included in a respiratory cycle and the definition of each phase can be changed as appropriate, and may be automatically adjusted on the device side or manually changed by an operator.

[0047] The graph on the right side of Fig. 5 shows the respiratory waveform estimated in step S102, and the rectangles superimposed on the graph indicate the time ranges corresponding to each respiratory phase. For example, the four rectangles superimposed on the upper graph of the three graphs in Fig. 5 indicate the time range corresponding to the first respiratory phase. The first image generation function 125c divides the list mode data into data included in the time range indicated by these four rectangles, and reconstructs an image I11 of the first respiratory phase from the divided data.

[0048] Similarly, the first image generation function 125c generates images for each phase, such as an image I12 for the second respiratory phase and an image I15 for the fifth respiratory phase. Although not shown in Fig. 5, the first image generation function 125c also generates images for the third respiratory phase and the fourth respiratory phase in the same manner.

[0049] In this embodiment, images I11, I12, I15, and the images of the third and fourth respiratory phases (not shown) are examples of first images. That is, the first image generation function 125c generates first images from each of the divided data obtained by dividing the list mode data for each respiratory phase. First images such as image I11 are images obtained by smoothing, for example, approximately one cycle of the heartbeat.

[0050] Next, the vector acquisition function 125d acquires a motion vector indicating a change between respiratory phases based on the first image generated in step S103 (step S104). The motion vector indicating a change between respiratory phases is also referred to as a first motion vector. The processing of step S104 will be described with reference to FIG. 6. FIG. 6 illustrates an example in which the first respiratory phase is used as a reference phase and motion vectors that convert between the first respiratory phase and each phase are acquired.

[0051] For example, compared to image I11 in the first respiratory phase, image I12 in the second respiratory phase shows changes in the positions of each feature point, such as a change in the position of the heart relative to the diaphragm. The vector acquisition function 125d acquires motion vectors to offset such changes. For example, the vector acquisition function 125d can perform registration processing between images and acquire the inverse transformation of the registration processing as a motion vector. The vector acquisition function 125d may perform the registration processing or the motion vector acquisition processing itself using a machine learning technique (such as a neural network).

[0052] For example, if the position coordinates of a certain feature point are (x1, y1, z1) in image I11 and (x2, y2, z2) in image I12, (x1-x2, y1-y2, z1-z2) can be acquired as the motion vector of the feature point. Such position coordinates may be defined by setting the origin on image I11, or may be defined by setting the origin on the imaging space, such as the tabletop 113 or the floor. The feature point may be, for example, an anatomical feature point on the heart, diaphragm, etc.

[0053] The motion vector may be acquired for each position on the image (e.g., each pixel) or for each feature point. Although an example of acquiring a motion vector that converts between a reference phase and another phase has been described with reference to FIG. 6, the vector acquisition function 125d may acquire motion vectors that convert between each phase. For example, the vector acquisition function 125d may acquire a motion vector that converts between the first respiratory phase and another phase, a motion vector that converts between the second respiratory phase and another phase, a motion vector that converts between the third respiratory phase and another phase, a motion vector that converts between the fourth respiratory phase and another phase, and a motion vector that converts between the fifth respiratory phase and another phase.

[0054] Next, the second analysis function 125e performs heartbeat analysis for each respiratory phase based on the list mode data (step S105). Specifically, the second analysis function 125e first divides the list mode data into groups for each respiratory phase. For example, as shown in FIG. 7, the second analysis function 125e divides the list mode data into groups for the time range of the second respiratory phase to obtain second respiratory phase data. The second respiratory phase data is an example of phase data.

[0055] Furthermore, the second analysis function 125e extracts a portion of the phase data in the time direction as partial data and estimates a cardiac waveform based on a reconstructed image reconstructed from the extracted partial data. Here, the phase data is respiratory-synchronized data, and the influence of respiratory motion is eliminated or suppressed. Therefore, by estimating the cardiac waveform based on the phase data, the influence of respiratory motion during cardiac analysis can be eliminated or suppressed, and the cardiac waveform can be estimated with high accuracy. The estimated cardiac waveform may be displayed on the display 123 under the control of the control function 125a.

[0056] For example, as shown in Fig. 7, the second analysis function 125e extracts partial data of a time width T32 for each time interval T31 from the second respiratory phase data. The time interval T31 and the time width T32 are set to be short enough to allow detection of cardiac beats. For example, the time interval T31 and the time width T32 are set to about 0.2 seconds.

[0057] 7, the time interval T31 and the time width T32 are illustrated as having the same length, but the embodiment is not limited to this. For example, the time interval T31 may be set to approximately 0.1 seconds, and the time width T32 may be set to approximately 0.2 seconds. This makes it possible to accurately estimate the heartbeat waveform at a shorter sampling interval while suppressing the influence of statistical noise from becoming excessively large.

[0058] Although FIG. 7 illustrates an example in which data included in the time range of the second respiratory phase is divided to acquire second respiratory phase data, the second analysis function 125e can similarly acquire phase data for other respiratory phases. For example, the second analysis function 125e can acquire first respiratory phase data by dividing data included in the time range of the first respiratory phase from list mode data. Furthermore, the second analysis function 125e can extract a portion of the first respiratory phase data in the time direction as partial data and estimate a cardiac waveform based on a reconstructed image reconstructed from the extracted partial data. That is, the second analysis function 125e can estimate a cardiac waveform for each respiratory phase.

[0059] In estimating the cardiac waveform for each respiratory phase, only data near the heart may be considered. For example, the second analysis function 125e performs a process of cutting out a rectangular region including the heart from the time-division reconstructed image I21 as image I31, as shown by the dashed line in Fig. 8, for all time-division reconstructed images, including time-division reconstructed image I22 and time-division reconstructed image I23. The time-division reconstructed image I21, time-division reconstructed image I22, and time-division reconstructed image I23 are examples of reconstructed images based on partial data. The second analysis function 125e then estimates the cardiac waveform based on all clipped images, including clipped images I31, I32, and I33.

[0060] Next, the second image generation function 125f generates an image from each of the divided data obtained by dividing the phase data for each cardiac phase (step S106). For example, the second image generation function 125f divides the phase data for each cardiac phase based on the cardiac waveform estimated in step S105, and generates an image from each of the divided data. The image generated in step S106 is an example of the second image.

[0061] In the following, a case where one cardiac cycle contains eight phases will be described, but as with the respiratory phases, this can be changed as appropriate. That is, the number of phases contained in one cardiac cycle and the definition of each phase can be changed as appropriate, and they can be automatically adjusted on the device side or manually changed by the operator.

[0062] For example, the second image generation function 125f divides the second respiratory phase data shown in Fig. 7 into eight cardiac phases and generates an image for each cardiac phase. For example, as shown in Fig. 8, the second image generation function 125f generates eight images, namely, an image of the first cardiac phase, an image of the second cardiac phase, an image of the third cardiac phase, an image of the fourth cardiac phase, an image of the fifth cardiac phase, an image of the sixth cardiac phase, an image of the seventh cardiac phase, and an image of the eighth cardiac phase.

[0063] As described above, the second image generation function 125f generates eight images for each cardiac phase based on the second respiratory phase data. Similarly, the second image generation function 125f generates eight images for each cardiac phase based on the first respiratory phase data, eight images for each cardiac phase based on the third respiratory phase data, eight images for each cardiac phase based on the fourth respiratory phase data, and eight images for each cardiac phase based on the fifth respiratory phase data. This allows the second image generation function 125f to generate 40 images with different combinations of respiratory phases and cardiac phases, as shown in FIG. 9.

[0064] It is conceivable to directly use the images shown in Fig. 9 for diagnosis such as cardiac function analysis. However, each image shown in Fig. 9 is generated based on a small number of time-divided counts, and is therefore subject to statistical noise. Specifically, each image shown in Fig. 9 is data on a count number that is about 1 / 40 of the total count number obtained by a nuclear medicine scan, and the image quality is not high.

[0065] It is also known that the movement of the subject due to breathing is small in the end-expiration phase. Therefore, for example, if the first respiratory phase is the end-expiration phase, the eight images in the first respiratory phase are relatively less likely to be blurred and are suitable for diagnosis. In other words, even if the 40 images shown in FIG. 9 are provided, the 32 images other than those in the first respiratory phase may not be used for diagnosis. It is preferable to avoid unnecessary calculations that would generate images that will not be used for diagnosis.

[0066] As described above, it is not preferable from the viewpoint of image quality and calculation efficiency to provide the 40 images shown in Fig. 9 as they are. Therefore, the summation processing function 125g applies a motion vector to each image for each group of images having different respiratory phases but a common cardiac phase, and sums them together to generate a single phase image corresponding to a specific respiratory phase (step S107).

[0067] For example, the summing function 125g groups five images of the first cardiac phase shown in FIG. 9 and applies a motion vector to each image to sum them. Specifically, the summing function 125g applies a motion vector that converts between the first and second respiratory phases to the image of the second respiratory phase. The summing function 125g also applies a motion vector that converts between the first and third respiratory phases to the image of the third respiratory phase. The summing function 125g also applies a motion vector that converts between the first and fourth respiratory phases to the image of the fourth respiratory phase. The summing function 125g also applies a motion vector that converts between the first and fourth respiratory phases to the image of the fifth respiratory phase. Furthermore, the summation processing function 125g generates a single phase image corresponding to the first respiratory phase by summing the image of the first respiratory phase with the image of the second respiratory phase, the image of the third respiratory phase, the image of the fourth respiratory phase, and the image of the fifth respiratory phase after applying the motion vector.

[0068] As described above, the summation processing function 125g can group five images in the first cardiac phase to generate a single phase image corresponding to the first respiratory phase. Such a single phase image is data of the count number corresponding to the five images in the first cardiac phase, and the influence of statistical noise is mitigated, improving image quality.

[0069] Similarly, the summing function 125g can group five images in the second cardiac phase to generate a single phase image corresponding to the first respiratory phase. Similarly, the summing function 125g can generate single phase images corresponding to the first respiratory phase for the third to eighth cardiac phases as well.

[0070] That is, the summing function 125g can generate a single-phase image corresponding to the first respiratory phase for each of the eight cardiac phases. These eight single-phase images are cardiac-gated images and can be used for diagnoses such as cardiac function analysis. Furthermore, these eight single-phase images were generated using the 40 images shown in FIG. 9 without waste, and each has high image quality based on a sufficient number of counts. That is, the eight single-phase images generated by the summing function 125g can improve the efficiency of diagnoses such as cardiac function analysis.

[0071] The single-phase image generated by the summing processing function 125g is displayed on the display 123 under the control of, for example, the control function 125a. Also, for example, the single-phase image generated by the summing processing function 125g is sent to and stored in the PASC server under the control of the control function 125a.

[0072] 9 illustrates an example of generating a single-phase image corresponding to the first respiratory phase, but it is also possible to generate single-phase images corresponding to other respiratory phases. For example, the summation processing function 125g can generate a single-phase image corresponding to the second respiratory phase by applying a motion vector that converts between the second respiratory phase and other phases to each group of images with a common cardiac phase and summing them. In other words, the summation processing function 125g can generate a cardiac-gated image for any respiratory phase.

[0073] The respiratory phase for which a single-phase image is to be generated may be preset or may be arbitrarily selected by the operator. For example, the respiratory phase for which a single-phase image is to be generated may be selected as the phase at the end of expiration, when the subject's movement due to breathing is smallest. Alternatively, the respiratory phase for which a single-phase image is to be generated may be selected as the respiratory phase for which an X-ray CT (Computed Tomography) image used for attenuation correction and scatter correction of nuclear medicine images is captured. Multiple respiratory phases for which a single-phase image is to be generated may be selected.

[0074] 2 is merely an example and can be modified as appropriate. For example, the process of acquiring the first motion vector in step S104 may be performed after the processes of heartbeat analysis in step S105 and generation of the second image in step S106, or may be performed in parallel with these steps.

[0075] As described above, the nuclear medicine diagnosis apparatus 10 according to the first embodiment includes a first analysis function 125b, a first image generation function 125c, a vector acquisition function 125d, a second analysis function 125e, a second image generation function 125f, and a summation processing function 125g. The first analysis function 125b analyzes respiratory motion based on list mode data obtained by performing a nuclear medicine scan on a subject in which respiratory motion and cardiac motion occur. The first image generation function 125c generates a first image from each of the divided data obtained by dividing the list mode data for each respiratory phase. The vector acquisition function 125d acquires motion vectors indicating changes between respiratory phases based on the first image. The second analysis function 125e analyzes cardiac motion for each respiratory phase based on the list mode data. The second image generation function 125f generates a second image from each of the divided data obtained by dividing the phase data obtained by dividing the list mode data for each respiratory phase for each cardiac phase. Furthermore, the summation processing function 125g applies a motion vector to the second images for each group of second images having different respiratory phases but a common cardiac phase, and sums them to generate a single phase image corresponding to a specific respiratory phase. With this configuration, the nuclear medicine diagnosis apparatus 10 can perform synchronous reconstruction without using an external device, even for a subject that experiences multiple movements such as respiratory movement and cardiac movement.

[0076] Furthermore, the second analysis function 125e according to the first embodiment analyzes the heartbeat for each respiratory phase based on the list mode data. For example, the second analysis function 125e extracts partial data from phase data obtained by dividing the list mode data for each respiratory phase, and estimates the heartbeat waveform based on a reconstructed image reconstructed from the extracted partial data. This allows the second analysis function 125e to eliminate or suppress the influence of respiratory motion during the analysis of the heartbeat, thereby enabling the heartbeat waveform to be estimated with high accuracy.

[0077] That is, as described above, respiratory motion is larger than cardiac motion, and respiratory motion is detected as a dominant component when list mode data is analyzed as is, making it difficult to estimate a cardiac waveform without using an external device. In contrast, the nuclear medicine diagnosis apparatus 10 according to the first embodiment analyzes cardiac motion using phase data obtained by dividing list mode data into respiratory phases, thereby enabling accurate estimation of a cardiac waveform without using an external device and achieving data-driven cardiac synchronization reconstruction.

[0078] Furthermore, the second analysis function 125e according to the first embodiment extracts a portion in the time direction of phase data obtained by dividing list mode data for each respiratory phase as partial data, extracts a region corresponding to the heart from a reconstructed image reconstructed from the extracted partial data as a cutout image, and estimates a heartbeat waveform based on the cutout image. This configuration allows the heartbeat to be analyzed by extracting only data near the heart, thereby reducing calculation costs and improving analysis accuracy.

[0079] (Second embodiment) In the first embodiment, an example has been described in which a single-phase image corresponding to a specific phase among respiratory phases is generated for each cardiac phase as the single-phase image shown in step S107 in Fig. 2. In contrast, in the second embodiment, a modified example of the single-phase image generated in step S107 will be described.

[0080] The nuclear medicine diagnosis apparatus 10 according to the second embodiment can be configured in the same manner as the nuclear medicine diagnosis apparatus 10 shown in Fig. 1, with some differences in the processing by the processing circuitry 125. Below, only the processing by the processing circuitry 125 that differs from that of the first embodiment will be described, and duplicated descriptions will be omitted. In other words, unless otherwise specified, the descriptions given in the first embodiment also apply to the second embodiment.

[0081] The processing circuitry 125 according to the second embodiment executes the same processes as steps S101, S102, S103, S105, and S106 shown in Fig. 2 to generate an image for each respiratory phase and each cardiac phase. For example, the processing circuitry 125 generates 40 images corresponding to a combination of eight cardiac phases and five respiratory phases, as in the case shown in Fig. 9.

[0082] In the second embodiment, step S104 shown in Fig. 2 may be omitted. That is, the vector acquisition function 125d according to the second embodiment does not need to acquire the first motion vector indicating a change between respiratory phases.

[0083] Furthermore, the vector acquisition function 125d according to the second embodiment acquires a second motion vector indicating a change between cardiac phases based on the second image. For example, the vector acquisition function 125d may perform registration processing between the second images having the same respiratory phase but different cardiac phases, and acquire the inverse transformation of the registration processing as the second motion vector. The vector acquisition function 125d may perform the registration processing or the process of acquiring the second motion vector itself using a machine learning technique (such as a neural network).

[0084] As an example, the summation processing function 125g according to the second embodiment generates, for each respiratory phase, a single-phase image corresponding to a specific cardiac phase as the single-phase image in step S107. Specifically, for each group of images having different cardiac phases but a common respiratory phase, the summation processing function 125g applies a second motion vector indicating a change between cardiac phases to each image and sums them to generate a single-phase image corresponding to a specific cardiac phase.

[0085] For example, as shown in Fig. 10, the summing function 125g groups eight images in the first respiratory phase to generate a single-phase image corresponding to the first cardiac phase. Similarly, the summing function 125g generates single-phase images corresponding to the first cardiac phase for the second to fifth respiratory phases. That is, the summing function 125g can generate a single-phase image corresponding to the first cardiac phase for each of the five respiratory phases. These five single-phase images are respiratory-gated images and can be used for diagnosis, such as pulmonary function analysis.

[0086] As another example, the summation processing function 125g according to the second embodiment generates a single image corresponding to a specific respiratory phase and a specific cardiac phase as the single phase image in step S107.

[0087] Specifically, the summing function 125g generates eight single-phase images corresponding to the first respiratory phase by applying a first motion vector that converts between the first respiratory phase and other phases to each group of images having different respiratory phases but the same cardiac phase, and summing the images, as in the case shown in Fig. 9. Furthermore, the summing function 125g applies a second motion vector that converts between the first cardiac phase and other phases to the eight single-phase images corresponding to the first respiratory phase, and sums the images, thereby generating a single image corresponding to the first respiratory phase and the first cardiac phase.

[0088] In such a case, the vector acquisition function 125d may acquire the second motion vector directly from the second image, which is an image for each respiratory phase and each cardiac phase, or may acquire it based on a single phase image corresponding to a specific respiratory phase.

[0089] For example, first, second images are generated for each respiratory phase and each cardiac phase by the process of step S106 in FIG. 2. Next, the vector acquisition function 125d acquires a second motion vector indicating a change between cardiac phases based on the second images. The summation processing function 125g applies the first motion vector to each group of second images having different respiratory phases but the same cardiac phase, and sums them to generate eight single-phase images corresponding to the first respiratory phase. The summation processing function 125g then applies the second motion vector to the eight single-phase images corresponding to the first respiratory phase, and sums them to generate a single image corresponding to the first respiratory phase and the first cardiac phase.

[0090] As another example, after second images for each respiratory phase and each cardiac phase are generated, the summing function 125g applies a first motion vector to each group of second images having different respiratory phases but the same cardiac phase, thereby generating eight single-phase images corresponding to the first respiratory phase. Next, the vector acquisition function 125d acquires a second motion vector indicating changes between cardiac phases based on the eight single-phase images. Furthermore, the summing function 125g applies the second motion vector to the eight single-phase images corresponding to the first respiratory phase, thereby generating a single image corresponding to the first respiratory phase and the first cardiac phase.

[0091] Alternatively, the processing circuitry 125 applies a second motion vector that converts between the first cardiac phase and other phases to each group of images having different cardiac phases but a common respiratory phase, and sums the images to generate five single-phase images corresponding to the first cardiac phase, as in the case shown in Fig. 10. Furthermore, the summation processing function 125g applies a first motion vector that converts between the first respiratory phase and other phases to the five single-phase images corresponding to the first cardiac phase, and sums the images to generate a single image corresponding to the first respiratory phase and the first cardiac phase.

[0092] Such single images are high-quality images generated based on a larger number of counts. Although an example of generating single images corresponding to the first respiratory phase and the first cardiac phase has been described, single images corresponding to other respiratory phases and cardiac phases can also be generated in a similar manner. For example, when a single image is generated for each cardiac phase, these multiple single images become cardiac-gated images. Furthermore, when a single image is generated for each respiratory phase, these multiple single images become respiratory-gated images.

[0093] (Third embodiment) In addition to the first and second embodiments described above, the present invention may be embodied in various different forms.

[0094] 1 illustrates the nuclear medicine diagnosis apparatus 10 as a PET apparatus. However, the embodiments are not limited to this, and the above-described embodiments can be similarly applied to cases where the nuclear medicine diagnosis apparatus 10 is a SPECT apparatus, for example.

[0095] In the above-described embodiment, the control function 125a, the first analysis function 125b, the first image generation function 125c, the vector acquisition function 125d, the second analysis function 125e, the second image generation function 125f, and the summation processing function 125g are executed in the processing circuitry 125 included in the nuclear medicine diagnosis apparatus 10. However, the embodiment is not limited to this, and some or all of the functions of the processing circuitry 125 may be executed in a processing circuit included in an apparatus different from the nuclear medicine diagnosis apparatus 10.

[0096] 11 shows a medical information processing system 1 including a nuclear medicine diagnosis apparatus 10 and a medical information processing apparatus 20. In the medical information processing system 1, the nuclear medicine diagnosis apparatus 10 and the medical information processing apparatus 20 are connected via a network NW, for example, and a processing circuit 25 included in the medical information processing apparatus 20 may perform processing similar to that of the processing circuit 125.

[0097] 11 includes a communication interface 21, an input interface 22, a display 23, a memory 24, and a processing circuit 25. The communication interface 21, the input interface 22, the display 23, and the memory 24 can be configured in the same manner as the communication interface 121, the input interface 122, the display 123, and the memory 124 shown in Fig. 1, and therefore description thereof will be omitted. The processing circuit 25 also includes a control function 25a, a first analysis function 25b, a first image generation function 25c, a vector acquisition function 25d, a second analysis function 25e, a second image generation function 25f, and a summation processing function 25g.

[0098] The control function 25a, for example, controls the transmission and reception of each piece of data via the network NW. For example, the control function 25a acquires list mode data collected by the nuclear medicine diagnosis apparatus 10 via the network NW. Furthermore, for example, the control function 25a transmits the list mode data and nuclear medicine images reconstructed based on the list mode data to a PACS server and registers them. The control function 25a also controls display on the display 123. The first analysis function 25b, the first image generation function 25c, the vector acquisition function 25d, the second analysis function 25e, the second image generation function 25f, and the summation processing function 25g are similar to the first analysis function 125b, the first image generation function 125c, the vector acquisition function 125d, the second analysis function 125e, the second image generation function 125f, and the summation processing function 125g shown in FIG. 1, respectively.

[0099] The term "processor" used in the above description refers to circuits such as a CPU, GPU, ASIC, and programmable logic device (e.g., simple programmable logic device (SPLD), complex programmable logic device (CPLD), and field programmable gate array (FPGA)). If the processor is, for example, a CPU, the processor realizes its function by reading and executing a program stored in a memory circuit. On the other hand, if the processor is, for example, an ASIC, instead of storing a program in a memory circuit, the function is directly incorporated into the processor circuit as a logic circuit. Note that each processor in the embodiments is not limited to being configured as a single circuit per processor, but may be configured as a single processor by combining multiple independent circuits to realize its function. Furthermore, multiple components in each figure may be integrated into a single processor to realize its function.

[0100] The components of each device according to the above-described embodiments are conceptual and functionally independent, and are not necessarily physically configured as shown in the drawings. In other words, the specific form of distribution and integration of each device is not limited to that shown in the drawings, and all or part of each device can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0101] The methods described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer or a workstation. This program can be distributed via a network such as the Internet. The medical information processing program can also be recorded on a non-transitory computer-readable recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, or DVD, and executed by being read from the recording medium by a computer.

[0102] According to at least one of the embodiments described above, synchronous reconstruction for periodic motion of a subject can be performed without using an external device.

[0103] Although several 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 within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0104] 10: Nuclear medicine diagnostic equipment 110: Mounting device 112: Front-end circuit 113: Top plate 114:Bed 116: Bed drive unit 120: Console device 121: Communication interface 122: Input interface 123: Display 124: Memory 125: Processing circuit 125a: Control function 125b: 1st analysis function 125c: First image generation function 125d: Vector acquisition function 125e:Second analysis function 125f: Second image generation function 125g: Total processing function 130: Detector 20: Medical information processing device 21: Communication interface 22: Input interface 23: Display 24: Memory 25: Processing circuit 25a: Control function 25b: 1st analysis function 25c: First image generation function 25d: Vector acquisition function 25e:Second analysis function 25f: Second image generation function 25g: Total processing function

Claims

1. a first analysis unit that analyzes respiratory motion based on list mode data obtained by performing a nuclear medicine scan on a subject in which respiratory motion and cardiac motion occur; a first image generator that generates a first image from each of divided data obtained by dividing the list mode data for each respiratory phase, which is a phase of the respiratory movement; a second analyzer that analyzes the heartbeat for each respiratory phase based on the list mode data; a second image generator configured to generate second images from each of the divided data obtained by dividing the list mode data into phase data for each respiratory phase and dividing the phase data into cardiac phases that are the phases of the cardiac beats; a vector acquisition unit that acquires at least one of a first motion vector indicating a change between the respiratory phases and a second motion vector indicating a change between the cardiac phases; a summation processing unit that applies the first motion vector to the second images for each group of second images having different respiratory phases but a common cardiac phase and sums them together to generate a first single-phase image corresponding to a specific phase of the respiratory phases, or applies the second motion vector to the second images for each group of second images having different cardiac phases but a common respiratory phase and sums them together to generate a second single-phase image corresponding to a specific phase of the cardiac phases; A nuclear medicine diagnostic device comprising:

2. 2. The nuclear medicine diagnosis apparatus according to claim 1, wherein the summation processing unit generates the first single phase image by applying the first motion vector to the second images for each group of the second images having different respiratory phases but a common cardiac phase and summing them, further generates the second single phase image by applying the second motion vector to the first single phase image and summing them, or generates the second single phase image by applying the second motion vector to the second images for each group of the second images having different cardiac phases but a common respiratory phase and summing them, and further generates a single image corresponding to a specific phase of the respiratory phases and a specific phase of the cardiac phases by applying the first motion vector to the second single phase images and summing them.

3. the first analysis unit extracts a portion of the list mode data in a time direction as partial data, and estimates a respiratory waveform that is a waveform of the respiratory movement based on a reconstructed image reconstructed from the extracted partial data; The nuclear medicine diagnosis apparatus according to claim 1 , wherein the first image generating unit generates the first image from each of divided data obtained by dividing the list mode data for each respiratory phase based on the respiratory waveform.

4. 4. The nuclear medicine diagnosis apparatus according to claim 3, wherein the first analysis unit extracts the partial data having a time width of a first length from the list mode data for each time interval of a second length shorter than the first length.

5. the second analysis unit extracts a portion of the phase data in a time direction as partial data, and estimates a cardiac waveform that is a waveform of the cardiac beat based on a reconstructed image reconstructed from the extracted partial data; The nuclear medicine diagnosis apparatus according to claim 1 , wherein the second image generating unit generates the second image from each of divided data obtained by dividing the phase data for each cardiac phase based on the cardiac waveform.

6. the vector acquisition unit acquires the first motion vector based on the first image; 2. The nuclear medicine diagnosis apparatus according to claim 1, wherein the summation processing unit applies the first motion vector to the second images for each group of the second images having different respiratory phases but a common cardiac phase, and sums the second images to generate a first single-phase image corresponding to a specific phase among the respiratory phases.

7. the vector acquisition unit acquires the second motion vector based on the second image; 2. The nuclear medicine diagnosis apparatus according to claim 1, wherein the summation processing unit applies the second motion vector to the second images for each group of the second images having different cardiac phases and a common respiratory phase, and sums the second images to generate a second single-phase image corresponding to a specific phase among the cardiac phases.

Images