JP6445265B2 - Medical image processing apparatus and medical image processing method - Google Patents

Description

  Embodiments described herein relate generally to a medical image processing apparatus and a medical image processing method that execute difference processing between a plurality of images.

  The difference process is a process for generating a difference image indicating a difference between a plurality of images. For example, an X-ray CT (computed tomography) apparatus performs a difference process between contrast image data collected by injecting a contrast agent and non-contrast image data collected before injecting the contrast agent, thereby removing the contrast agent from the contrast agent. It is possible to generate diagnostic image data in which the signal components are selectively extracted.

  Magnetic Resonance Imaging (MRI) device collects multiple image data corresponding to different cardiac time phases under ECG synchronization, and separates the arterial artery by performing differential processing between multiple images The collected blood vessel image data can be collected.

  Nuclear medicine diagnostic devices such as gamma cameras, SPECT (Single Photon Emission Computed Tomography) devices, and PET (Positron Emission Computed Tomography) devices, for example, perform differential processing on multiple medical images taken on different imaging dates. By doing so, it is possible to depict the progress or recovery state of the lesioned part contained in the subject.

  However, conventionally, since the number of times of incidence of gamma rays varies greatly between images depending on conditions such as a detection period, it may be difficult to depict a lesioned part included in a subject by difference processing.

  An object of the present embodiment is to provide a medical image processing apparatus and a medical image processing method that can improve the ability to depict a lesioned part included in a subject.

According to the embodiment, the medical image processing apparatus standardizes an acquisition unit that acquires a plurality of medical images representing a spatial accumulation distribution of radionuclides administered to a subject, and pixel values of each of the plurality of medical images. A normalization processing unit that executes normalization processing, and the acquisition unit detects a pair of annihilation radiations emitted by positron-electron pair annihilation emitted from the radionuclide, and simultaneously detects them. Counting the pair of annihilation radiations and storing them as coincidence counting information, creating the plurality of medical images using the coincidence counting information, and counting the coincidence counting of the pair of annihilation radiations in each of the plurality of medical images The number is acquired, and the count number is stored in association with the corresponding medical image, and the normalization processing unit uses the acquired count number to calculate pixel values of each of the plurality of medical images. To perform the normalization process of Kakuka.

1 is a block diagram illustrating a configuration of a medical image diagnostic apparatus according to an embodiment. 3 is a flowchart showing the flow of image processing by the medical image processing unit shown in FIG. 1. The figure which shows an example of the PET image output from the PET image memorize | stored in the memory | storage part shown in FIG. 1, the PET image output from the zero value-izing process part, and the normalization process part. The figure which shows an example of the difference image produced | generated from the PET image input into the difference process part shown in FIG. 1, and the said PET image. The figure which shows an example of the difference image produced | generated from the conventional PET image and the said PET image.

  Hereinafter, a medical image processing apparatus according to the present embodiment will be described with reference to the drawings.

  FIG. 1 is a block diagram illustrating a configuration of a medical image diagnostic apparatus 200 according to the present embodiment. In the present embodiment, a configuration of a PET (Positron Emission Computed Tomography) apparatus is shown as an example of the medical image diagnostic apparatus 200.

  A medical image diagnostic apparatus 200 shown in FIG. 1 includes a gamma ray collection unit 10, a reconstruction unit 20, a scan control unit 30, a communication unit 40, an input unit 50, a display unit 60, a storage unit 70, a medical image processing unit 80, and system control. Part 90 is provided.

  The gamma ray collection unit 10 collects gamma rays emitted from the positron nuclide administered to the subject under the control of the scan control unit 30. The gamma ray collection unit 10 includes a gantry 11 that is a gamma ray detection mechanism, a preprocessing unit 16, an energy discrimination unit 17, and a coincidence counter 18.

  The gantry 11 has a detector ring 12. A top plate on which the subject P can be placed is inserted into the opening of the detector ring 12. The top plate is supported by a bed (not shown) so as to be movable along the central axis of the detector ring 12. Here, it is assumed that the body axis (Z-axis) of the subject P placed on the top coincides with the central axis of the detector ring 12. Typically, a plurality of detector rings 12 are arranged in the gantry 11 along the Z axis. The detector ring 12 has a plurality of gamma ray detectors 13 arranged circumferentially around the Z axis.

  The plurality of gamma ray detectors 13 detect gamma rays emitted from positron nuclides injected into the subject P, and generate detection signals corresponding to the energy of the gamma rays. Since the plurality of gamma ray detectors 13 are arranged circumferentially around the body axis of the subject P, gamma rays can be detected in a plurality of directions (solid angles). Specifically, the gamma ray detector 13 includes a plurality of scintillators 14 and a plurality of photoelectric conversion elements 15. The scintillator 14 generates fluorescence when gamma rays are incident. The fluorescence is guided to the photoelectric conversion element 15 through a light guide (not shown). The photoelectric conversion element 15 receives the fluorescence from the scintillator 14 via the ride guide, amplifies the amount of the received fluorescence, and generates a detection signal corresponding to the amount of light. The photoelectric conversion element 15 outputs the generated detection signal to the preprocessing unit 16 and the energy discriminating unit 17.

  Here, in PET, positrons and electrons emitted from one positron nuclide administered to a subject are annihilated at the same time as a pair of annihilation radiations that are respectively emitted in opposite directions by 180 °. Detect with. A line connecting the incident positions of a pair of annihilation radiations is called LOR (Line of Response). PET discriminates this pair of annihilation radiation and generates a spatial distribution of positron nuclides.

The preprocessing unit 16 calculates the energy value of gamma rays incident on the gamma ray detector 13 based on the detection signal output from the gamma ray detector 13. The preprocessing unit 16 measures the detection time of the gamma rays incident on the gamma ray detector 13 based on the detection signal output from the gamma ray detector 13. Based on the pair of detection signals output from the gamma ray detector 13, the preprocessing unit 16 determines the position information of the scintillator 14 that has generated fluorescence based on the ratio of the amount of fluorescence received by the photoelectric conversion element 15, that is, The incident position of the pair of gamma rays is calculated . The preprocessing unit 16 outputs the gamma ray energy value information, detection time information, and incident position information to the energy discriminating unit 17.

  The energy discriminating unit 17 discriminates the detection signal output from the gamma ray detector 13 based on the energy value information output from the preprocessing unit 16. Specifically, the energy discriminating unit 17 discriminates a detection signal having an energy value within an energy window width of about ± 20% around the energy peak 511 keV of gamma rays. In addition, when an energy value is lower than an energy window lower limit (lower threshold, about 300 to 400 keV), it is regarded as a scattered radiation component of gamma rays. Moreover, when an energy value is higher than an energy window upper limit (upper limit threshold, about 600 to 850 keV), it is regarded as pile-up. Pile-up means that signals input simultaneously or at very short time intervals overlap each other.

The coincidence counter 18 counts a pair of detection signals discriminated by the energy discriminating unit 17 based on the detection time information output from the preprocessing unit 16 based on simultaneity. Specifically, the coincidence counter 18 counts, for each LOR, a pair of detection signals whose detection time difference is within a time window width of 2 nsec to 5 nsec from the gamma rays incident on the gamma ray detector 13. In the present embodiment, a value obtained by counting the pair of detected detection signals for each LOR is defined as coincidence counting information. The coincidence count information includes a data set (event data) that associates the incident position and the incident time of each of the pair of gamma rays that are generated for each event that represents an event in which gamma rays are incident on the scintillator 14. Here, the coincidence counter 18 obtains the number of coincidence counts for each of the plurality of medical images. That is, the coincidence counter 18 obtains the count number of event data generated for each event. In the present embodiment, the count number of event data generated for each event is described as the event number. The coincidence counter 18 obtains coincidence count information and event number information in a plurality of directions (solid angles) by repeatedly executing the above-described discrimination processing of the pair of detection signals. The coincidence counter 18 outputs coincidence counting information in the plurality of directions to the reconstruction unit 20 and a storage unit 70 described later. The coincidence counting information is stored in the storage unit 70 in association with the PET image reconstructed by the reconstruction unit 20 described later. The coincidence counter 18 outputs the event number information to the storage unit 70 described later. The event number information is stored in the storage unit 70 in association with the PET image reconstructed by the reconstruction unit 20 described later.

  The reconstruction unit 20 reconstructs a plurality of PET images using event data included in the coincidence counting information in a plurality of directions output from the coincidence counter 18. A PET image is a spatially integrated distribution of positron nuclides generated from coincidence information regarding gamma rays emitted from positron nuclides administered to a subject. Specifically, the reconstruction unit 20 performs arithmetic processing by applying a filtered back projection (FBP) or successive approximation reconstruction method, and reconstructs a PET image from the coincidence information. To do. The data of the reconstructed plurality of PET images is stored in the storage unit 70 described later in association with the coincidence counting information in the plurality of directions.

  The scan control unit 30 controls the gamma ray collection unit 10 so as to collect gamma rays according to a predetermined scan sequence. The scan sequence defines the collection timing of gamma rays.

  The communication unit 40 communicates with an external device by wire or wireless. The external apparatus is, for example, a modality such as an X-ray CT apparatus, a server included in a system such as PACS (Picture Archiving and Communication System), or another workstation.

  The input unit 50 is an interface for inputting commands and the like according to user operations, and includes, for example, a keyboard, a mouse, a touch panel, a trackball, various buttons, and the like.

  The display unit 60 displays a PET image or the like on a display device. As a display device, a CRT display, a liquid crystal display (LCD), an organic EL display (OELD), a plasma display, or the like can be used as appropriate.

  The storage unit 70 is an HDD (Hard Disk Drive), an SSD (Solid State Drive), or the like that can store a relatively large amount of data. The storage unit 70 stores the data of a plurality of PET images reconstructed by the reconstruction unit 20, the coincidence count information in a plurality of directions output from the coincidence counter 18, and the event number information in association with each other. . The storage unit 70 also stores a dedicated program for generating PET images. The storage unit 70 uses an optical disk such as a magneto-optical disk, a CD (Compact Disc), a DVD (Digital Versatile Disc), and a Blu-ray (Blu-ray (registered trademark)) in addition to a magnetic disk such as an HDD. May be.

  The medical image processing unit 80 includes an extraction processing unit 1, a zero value processing unit 2, a normalization coefficient calculation unit 4, a normalization processing unit 3, and a difference processing unit 5.

  The extraction processing unit 1 extracts a region other than the subject such as a space or a bed from a plurality of PET images input from the storage unit 70 by threshold processing. In the present embodiment, a region extracted by the extraction processing unit 1 and a region other than the subject such as a bed is referred to as a background region.

  The zero value processing unit 2 executes zero value processing for replacing a pixel value of the background region extracted by the extraction processing unit 1 with a zero value for a plurality of PET images. The zero value processing unit 2 outputs a plurality of PET images in which the background region is zeroed to the normalization coefficient calculation unit 4.

The normalization processing unit 3 executes a normalization process for normalizing the pixel values of each of the plurality of PET images. The normalization processing unit 3 includes a normalization coefficient calculation unit 4.

  The normalization coefficient calculation unit 4 calculates the normalization coefficient for each of the plurality of PET images from the event number information associated with each of the plurality of PET images output from the zero value processing unit 2.

  The normalization processing unit 3 uses the normalization coefficient calculated by the normalization coefficient calculation unit 4 to perform normalization processing on each of the plurality of zero-valued PET images. As a result, the normalization processing unit 3 corrects the pixel values of each of the plurality of PET images that have been zeroized. The standardization processing unit 3 outputs a plurality of standardized PET images to the difference processing unit 5.

  The difference processing unit 5 performs difference processing on the plurality of PET images output from the standardization processing unit 3. As a result, the difference processing unit 5 generates a difference image between a plurality of standardized PET images. The difference processing unit 5 outputs the generated difference image to the display unit 60. Thereby, the difference image generated by the difference processing unit 5 is displayed on the display unit 60.

  The system control unit 90 controls the operations of the gamma ray collection unit 10, the reconstruction unit 20, the scan control unit 30, the communication unit 40, the input unit 50, the display unit 60, the storage unit 70, and the medical image processing unit 80.

  Here, the flow of image processing by the medical image diagnostic apparatus 200 according to the present embodiment will be described with a specific example.

  FIG. 2 is a flowchart showing the flow of image processing by the medical image processing unit 80 shown in FIG. FIG. 3 is a diagram illustrating an example of a PET image stored in the storage unit 70 illustrated in FIG. 1, a PET image output from the zero value processing unit 2, and a PET image output from the standardization processing unit 3.

  As shown in FIG. 2, the plurality of PET images stored in the storage unit 70 and the event number information associated with the plurality of PET images are input to the extraction processing unit 1 (step ST1). As illustrated in FIG. 3, for example, a plurality of PET images captured on different imaging dates regarding the same subject are input to the extraction processing unit 1. Note that a PET image with a new imaging date shown in FIG. 3A is a first PET image. Further, a PET image having an imaging date older than that of the PET image of FIG. 3A shown in FIG. 3B is set as a second PET image.

  In the medical image processing unit 80, the extraction processing unit 1 extracts a background region from the input plurality of PET images by threshold processing (step ST2). Specifically, the extraction processing unit 1 divides the PET image into a plurality of regions (for example, for each pixel of the PET image). The extraction processing unit 1 determines the volume fraction of each of the plurality of divided regions (for example, the volume content of positron nuclides). The extraction processing unit 1 extracts a region whose volume fraction is equal to or less than a predetermined threshold as a background region. For example, the extraction processing unit 1 extracts a region where the volume content of the positron nuclide is zero as a background region. The plurality of PET images from which the background region has been extracted by the extraction processing unit 1 are output to the zero value processing unit 2.

  In the medical image processing unit 80, the zero value processing unit 2 executes zero value processing for replacing the pixel values of the background region with zero values for the plurality of PET images output from the extraction processing unit 1. (Step ST3). Thereby, the medical image processing unit 80 can clearly depict the subject region on the PET image. FIG. 3C and FIG. 3D show an example of a zero-valued first PET image and a zero-valued second PET image. The plurality of zero-valued PET images are output to the normalization coefficient calculation unit 4.

  In the medical image processing unit 80, the standardization coefficient calculation unit 4 calculates standardization coefficients for a plurality of PET images output from the zero value processing unit 2 from the input event number information (step ST4).

  In the medical image processing unit 80, each of a plurality of PET images output from the zero value processing unit 2 using the standardization coefficient calculated by the standardization coefficient calculation unit 4 by the standardization processing unit 3. Normalization processing is executed (step ST5).

  The normalization process is to multiply each pixel value of each PET image by a normalization coefficient calculated from the number of events. Here, the number of events associated with the first PET image is 300, and the number of events associated with the second PET image is 150. In addition, a case where a normalization process is performed in which the number of events of each of the first PET image and the second PET image is set to 100 will be described.

  The normalization coefficient calculation unit 4 calculates a normalization coefficient 100/300 = 1/3 for the first PET image from the number of events 300 associated with the first PET image. In addition, the normalization coefficient calculation unit 4 calculates the normalization coefficient 100/150 = 2/3 for the second PET image from the number of events 150 associated with the second PET image.

  The normalization processing unit 3 multiplies each pixel value of the first PET image by the normalization coefficient 1/3. Further, the normalization processing unit 3 multiplies each pixel value of the second PET image by the normalization coefficient 2/3. The pixel value represents the degree of integration in each pixel of the positron nuclide administered to the subject.

  Thereby, the medical image processing unit 80 can generate the standardized first PET image shown in FIG. 3E and the standardized second PET image shown in FIG. Become. The normalized first and second PET images are output to the difference processing unit 5.

  Here, the number of events may be estimated from a PET image. For example, the medical image processing unit 80 may include an estimation unit that estimates the coincidence count information from the PET image, and further estimates the number of events in a specific imaging range from the estimated coincidence count information. In the embodiment, as a normalization method, there is a method of normalizing a pixel value for each displayed pixel by a normalization coefficient calculated from the estimated number of events.

  In the medical image processing unit 80, the difference processing unit 5 performs difference processing on the plurality of PET images output from the standardization processing unit 3 (step ST6). FIG. 4 is a diagram illustrating an example of a PET image input to the difference processing unit 5 illustrated in FIG. 1 and a difference image generated from the PET image. The difference processing unit 5 makes a difference between the standardized first PET image shown in FIG. 4A and the standardized second PET image shown in FIG. As a result, the difference processing unit 5 generates a difference image shown in FIG. The generated difference image is output to the display unit 60. Thereby, for example, a differential image that clearly depicts a lesioned part such as a tumor is displayed on the display unit 60.

  According to the above configuration, in the medical image processing unit 80 according to the present embodiment, the normalization coefficient calculation unit 4 calculates the first PET from the number of events associated with each of the first PET image and the second PET image. A normalization coefficient for each of the image and the second PET image is calculated. The normalization processing unit 3 performs normalization processing on each of the first PET image and the second PET image using the calculated normalization coefficient. The difference processing unit 5 performs difference processing on the standardized first PET image and the standardized second PET image.

  Here, the case where the images are differentiated by the medical image processing apparatus according to the present embodiment is compared with the case where the images are differentiated by the conventional medical image processing apparatus.

  FIG. 5 is a diagram illustrating an example of a conventional PET image and a difference image generated from the PET image. As shown in FIG. 5, the conventional medical image processing apparatus is difficult to depict a lesioned part included in a subject when a plurality of PET images having greatly different numbers of gamma rays are different between PET images. is there.

  On the other hand, in the medical image processing unit 80 according to the present embodiment, the normalization coefficient calculation unit 4 calculates the normalization coefficient for each of the plurality of PET images from the number of events respectively associated with the plurality of PET images. In the medical image processing unit 80, the normalization processing is performed on the plurality of PET images by using the standardization coefficient for each of the plurality of PET images calculated by the standardization processing unit 3. Thereby, the difference processing unit 5 can output a difference image in which a lesion part included in the subject is accurately depicted.

  Therefore, the medical image processing unit 80 can improve the rendering ability of the lesioned part included in the subject.

  In the present embodiment, the configuration of the PET apparatus is shown as the medical image diagnostic apparatus 200, but the present invention is not limited to this. The medical image processing unit 80 of the medical image diagnostic apparatus 200 according to the present embodiment can be applied to a PET-CT apparatus, a SPECT apparatus, a gamma camera, and the like.

  In the present embodiment, the coincidence counter 18 obtains the number of events, and the event number information is stored in the storage unit 70 in association with the PET image. However, the present invention is not limited to this. In the present embodiment, for example, the normalization coefficient calculation unit 4 may obtain the number of events from the coincidence count information stored in the storage unit 70.

  In the present embodiment, the case where the filter-corrected back projection method or the successive approximation reconstruction method is applied as the method for reconstructing the PET image in the reconstruction unit 20 of the medical image diagnostic apparatus 200 has been described. Absent. The medical image diagnostic apparatus 200 estimates a generation point of a pair of gamma rays from a difference between detection times of the pair of gamma rays at a pair of incident positions connected by the LOR. The reconstruction unit 20 may reconstruct the spatial accumulation distribution of positron nuclides, that is, a PET image, by repeatedly executing processing for estimating the generation point of a pair of gamma rays. A method of estimating the generation point of a pair of gamma rays from the difference in detection time of the pair of gamma rays is called TOF (Time of Flight).

  Further, the normalization processing unit 3 performs a normalization process for normalizing pixel values using event number information associated with the plurality of PET images or event number information estimated from the plurality of PET images. Yes, but not limited to this. The normalization processing unit 3 may execute a normalization process for normalizing each pixel value of each of the plurality of medical images using the total pixel value information of each of the plurality of PET images.

  As mentioned above, although embodiment of this invention was described, this embodiment is shown as an example and is not intending limiting the range of invention. The novel embodiment can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. This embodiment and its modifications are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

  DESCRIPTION OF SYMBOLS 200 ... Medical diagnostic imaging apparatus, 10 ... Gamma ray collection part, 11 ... Gantry, 12 ... Detector ring, 13 ... Gamma ray detector, 14 ... Scintillator, 15 ... Photoelectric conversion element, 16 ... Pre-processing part, 17 ... Energy discrimination part , 18 ... coincidence counter, 20 ... reconstruction unit, 30 ... scan control unit, 40 ... communication unit, 50 ... input unit, 60 ... display unit, 70 ... storage unit, 80 ... medical image processing unit, 1 ... extraction process , 2... Zero value processing unit, 3... Normalization processing unit, 4... Normalization coefficient calculation unit, 5.

Claims (5)

An acquisition unit for acquiring a plurality of medical images representing a spatial accumulation distribution of radionuclides administered to a subject;
A normalization processing unit that performs a normalization process for normalizing pixel values of each of the plurality of medical images ,
The acquisition unit detects a pair of annihilation radiations emitted by positron-electron pair annihilation emitted from the radionuclide, counts the pair of annihilation radiations detected simultaneously, stores them as coincidence information, The plurality of medical images are created using the coincidence information, and the count number of the coincidence counting of the pair of annihilation radiations in each of the plurality of medical images is acquired, and the count number is stored in association with the corresponding medical image. And
The standardization processing unit is a medical image processing apparatus that executes a normalization process for normalizing pixel values of each of the plurality of medical images using the acquired count number .   The medical image processing apparatus according to claim 1, further comprising a difference processing unit that executes a difference process between the plurality of medical images that have been subjected to the normalization process. An extraction processing unit that extracts a region other than the subject by threshold processing from each of the plurality of medical images;
Further comprising a zero value processing unit for replacing the pixel value of the extracted region with a zero value;
The medical image processing apparatus according to claim 1, wherein the normalization processing unit applies a normalization process of pixel values of each of a plurality of medical images processed by the zero value processing unit.   The medical image processing apparatus according to claim 1, wherein the medical image is a PET image, a SPECT image, or an image captured by a gamma camera. Obtain multiple medical images representing the spatial accumulation of radionuclides administered to the subject,
A medical image processing method for executing a normalization process for normalizing pixel values of each of the plurality of medical images ,
A pair of annihilation radiations emitted by positron-electron pair annihilation emitted from the radionuclide is detected, and the pair of annihilation radiations detected at the same time is counted and stored as coincidence information, and the coincidence information is used. Creating the plurality of medical images, obtaining a count number of the simultaneous counting of the pair of annihilation radiations in each of the plurality of medical images, storing the count number in association with the corresponding medical image,
A medical image processing method for executing a normalization process for normalizing pixel values of each of the plurality of medical images using the acquired count number .