JP4948985B2 - Medical image processing apparatus and method - Google Patents

Description

  The present invention relates to a technique for processing medical images, and more particularly to an image processing technique for directly comparing images obtained by photographing the same part of the same person by different methods.

As a technique for tomographic imaging of a human body for use in diagnosis by a doctor, for example, MRI (Magnetic Resonance Imaging), CT (Computed Tomography), PET (Positron Emission Tomography), and SPECT (Single Photo CT) are known. Yes. Each image has its own characteristics, such as an image whose shape can be accurately grasped and an image where the superiority or inferiority of a predetermined function can be grasped. And various processes are performed in order to make use of each characteristic according to the kind of image (for example, patent documents 1 and 2).
For example, conventionally, by comparing an image of a subject taken by the same technique with an image of a healthy person, a characteristic unique to each image is extracted, and this is used for diagnosis. For example, it is possible to detect that a specific part of the subject's brain is atrophied by comparing the subject and the healthy subject with respect to an image capable of grasping the morphology of the brain. Similarly, by comparing a subject with a healthy person for an image that can grasp the increase or decrease in cerebral blood flow, it is possible to grasp a region where blood flow is reduced in the subject's brain.
JP 2005-230456 A JP 2006-204641 A

  However, in such a case, it is possible to grasp that atrophy or blood flow reduction is occurring in the subject's brain, but it has not been possible to know which of atrophy and blood flow reduction is dominant.

  In this way, conventionally, from multiple types of images taken by different methods, it is only possible to understand the state of each subject that can be grasped independently, and it is possible to grasp the relationship between the states of subjects that can be grasped from each image. was difficult.

  Accordingly, an object of the present invention is to perform image processing for enabling the grasp of the relationship between the states of the subjects that can be grasped from each image by mutually comparing a plurality of types of images obtained by photographing the same part of the same person by different methods. The purpose is to provide technology.

  A medical image processing apparatus for processing a tomographic medical image obtained by imaging a human body according to an embodiment of the present invention includes first and second obtained by imaging a predetermined part of the same person by different first and second imaging methods, respectively. Storage means for storing the first and second normalized image data obtained by normalizing each of the images, and the first normalized image data and the second normalized image data. Computation means for performing computation, and display control means for performing processing for displaying an image of the predetermined part on a display device based on the computation result of the computation means.

  In a preferred embodiment, the first image may be a tomographic image showing a brain function, the second image may be a tomographic image showing a brain form, and the first and second normal images may be displayed. The normalized image data may be obtained by normalizing the first and second images in units of pixels. At this time, the calculation means can perform the predetermined calculation by comparing the first and second normalized image data in units of pixels.

  In a preferred embodiment, the tomographic image indicating the function of the brain may be a SPECT tomographic image or a PET tomographic image, and the tomographic image indicating the morphology of the brain may be an MRI tomographic image or a CT tomographic image.

  In a preferred embodiment, the first normalized image data is obtained by normalizing the SPECT tomographic image or the PET tomographic image to a Z value, and the second normalized image data is the MRI tomographic image or The CT tomographic image may be normalized to a Z value.

  In a preferred embodiment, evaluation is performed to extract an evaluation value indicating a distribution state of the feature region in each ROI when the feature region is extracted based on the calculation result data by the calculation unit and the predetermined part is divided into ROIs. Means may further be provided. At this time, the display control means can further perform processing for causing the display device to display an image of the predetermined part based on the evaluation value calculated by the evaluation means.

  In a preferred embodiment, the calculation means may calculate a difference between the first normalized image data and the second normalized image data.

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

  FIG. 1 is a functional configuration diagram of a medical image processing apparatus 1 according to the present embodiment.

  As shown in the figure, the medical image processing apparatus 1 includes a SPECT data storage unit 11, an MRI data storage unit 13, a normalization processing unit 21 for normalizing SPECT data and MRI data, and normalized data. SPECT normalized data storage unit 15 and MRI normalized data storage unit 17 to be stored, comparison operation unit 22 for performing comparison operation on normalized SPECT data and MRI data, and operation data storage unit 18 for storing the result of comparison operation And ROI data storage unit 19 for storing ROI data for dividing the image area into ROI (Region Of Interest), and extracting the feature area based on the operation data, and the extent of the feature area in the ROI A spread calculation unit 23 for calculating and a display control unit 25 are provided.

  The medical image processing apparatus 1 according to the present embodiment is configured by, for example, a general-purpose computer system, and individual components or functions in the medical image processing apparatus 1 described below are executed by, for example, executing a computer program. Realized.

  The SPECT data storage unit 11 stores raw data of a SPECT image of a subject taken by a SPECT tomography apparatus (not shown).

  The MRI data storage unit 13 stores raw data of an MRI image of the same subject that is imaged by an MRI tomography apparatus (not shown).

  In the present invention, processing is performed using SPECT images and MRI images obtained by photographing the same part of the same person. In the embodiment described below, processing for a tomographic image of the brain (head) of a subject will be described, but the present invention can also be applied to images other than the brain.

  The SPECT and MRI raw image data stored in the SPECT data storage unit 11 and the MRI data storage unit 13 are both voxel data. In the case of raw data, the voxel value of each voxel is a measured value measured by each imaging device.

  Therefore, the normalization processing unit 21 normalizes each raw data. The processing performed by the normalization processing unit 21 includes normalization of a shape that matches the shape of the brain indicated by the raw data with a so-called standard brain, and normalization of a value that normalizes the value of the subject data based on the healthy person data. Including. Here, the value normalization process based on the healthy person data may be, for example, a process of converting the voxel data of the subject normalized to the standard brain by shape normalization into a Z value based on the healthy person data. Further, when converting to a Z value, it may be converted to a t value and then converted from a t value to a Z value. In addition, healthy person data are the average value and standard deviation for every voxel of voxel data of many healthy persons.

  For detailed processing of shape normalization and conversion to a Z value, refer to, for example, Japanese Patent Application Laid-Open Nos. 2005-230456 and 2006-204641.

  The SPECT normalized data storage unit 15 stores the normalized data of the SPECT image of the subject that has been normalized by the normalization processing unit 21.

  The MRI normalized data storage unit 17 stores the normalized data of the MRI image of the subject that has been normalized by the normalization processing unit 21.

  In the present embodiment, the SPECT normalized data storage unit 15 stores data obtained by converting the SPECT image data of the subject into Z values for each voxel. The MRI normalized data storage unit 17 stores data obtained by converting the MRI image data of the subject into Z values for each voxel.

  FIG. 2 shows an example of the data structure of Z value data common to the SPECT normalized data storage unit 15 and the MRI normalized data storage unit 17. That is, the Z value data is configured to form N XY cross sections in the Z-axis direction, where the left-right direction of the head is the X-axis, the front-rear direction is the Y-axis, and the up-down direction is the Z-axis. Each Z value is set in each voxel of each XY cross section. Each voxel corresponds to a pixel when displaying each XY image. This data structure is the same as the data structure of raw data.

  Referring again to FIG. 1, the comparison calculation unit 22 performs a predetermined calculation on the Z value stored in the SPECT normalized data storage unit 15 and the Z value stored in the MRI normalized data storage unit 17. . For example, the comparison calculation unit 22 subtracts the other Z value from one of the Z values. This subtraction is performed for each corresponding voxel (pixel).

  Here, the processing performed by the comparison calculation unit 22 is not necessarily limited to subtraction. For example, the ratio of each Z value corresponding to each voxel may be taken, or each Z value such as four arithmetic operations, logical operations, and correlations may be applied to a predetermined function. Then, the comparison calculation unit 22 stores the calculation result in the calculation data storage unit 18.

  The calculation data storage unit 18 stores Z value difference data, which is the calculation result of the comparison calculation unit 22. The data structure of the difference data stored in the calculation data storage unit 18 is common to the Z value data shown in FIG.

  The ROI data storage unit 19 stores ROI data for dividing a target image area into ROIs. In the present embodiment, for example, the ROI data storage unit 19 configures each ROI for each ROI in order to divide the brain region into a plurality of ROIs in each of the N XY cross sections described above. Pixels are defined. The ROI data can be designated as appropriate by the user.

  The spread calculation unit 23 extracts a predetermined feature region based on the calculation data, and calculates an evaluation value indicating how much the feature region is spread within each ROI. For example, the spread calculation unit 23 extracts a region where the value of the calculation data is greater than or less than a predetermined threshold as a feature region. Here, since the Z value can be either positive or negative, the difference can be either positive or negative. Therefore, a positive value threshold value and a negative value threshold value may be set, respectively, and a positive value feature region and a negative value feature region may be extracted. This threshold value may be determined in advance or specified by the user.

  Then, the spread calculation unit 23 refers to the ROI data storage unit 19 and calculates an evaluation value for each ROI.

For example, the spread calculation unit 23 calculates a first evaluation value (hereinafter referred to as EXTENT). This EXTENT is an evaluation value indicating the ratio of the area of the feature region in each ROI to the area of each ROI. EXTENT _m of the mth ROI _m is determined by the following equation. EXTENT _m may be calculated for each of a positive value feature region and a negative value feature region, or may be calculated together with a positive value feature region and a negative value feature region. Good.
EXTENT _m = area of the area / ROI _m overall characteristic region within ROI _m

Further, the spread calculation unit 23 may further calculate the following second evaluation value (hereinafter referred to as a specific pixel average value). This specific pixel average value is an average value of pixels in which the value of calculation data is larger than a predetermined value (for example, 0) in each ROI. particular pixel average value _m of m-th ROI _m is determined in the following equation.
The number of pixels greater than the value of the value prescribed operation data in a particular pixel average value _{m =} ROI pixel value sum / ROI larger pixels than the value of the value prescribed operation data in _{m m}

  The display control unit 25 causes the display device 3 to display a tomographic image of the brain based on the respective data stored in the SPECT normalized data storage unit 15, the MRI normalized data storage unit 17, and the calculation data storage unit 18. .

  FIG. 3 is an example of a brain tomographic image and a brain surface image displayed on the display device 3. That is, the display control unit 25 includes the SPECT image Z value stored in the SPECT normalized data storage unit 15, the Z value of the MRI image stored in the MRI normalized data storage unit 17, and the calculation data storage unit 18. Each of the images 100, 200, and 300 is displayed on the display device 3 based on the Z value difference data stored in the display device 3. In each of the images 100, 200, and 300, images of the same cross section are displayed so that they can be compared with each other. Here, the display control unit 25 may change the display mode such as changing the display color according to the value of each voxel, that is, the Z value or the difference between the Z values.

  In FIG. 3, only two cross-sectional views are shown in each of the images 100, 200, and 300, but more cross-sectional views are displayed.

  Here, in the image based on the Z value of the SPECT image (hereinafter referred to as the Z value SPECT image) 100 as shown in FIG. Is displayed in a different display mode. In addition, in the Z-value image 200 of the MRI image (hereinafter referred to as the Z-value MRI image) 200, the area 210 in which the volume of the brain tissue is enlarged or reduced as compared with the healthy person is different from other areas. Is displayed. That is, from the Z-value images 100 and 200, increase / decrease in blood flow and increase / decrease in volume can be grasped, respectively.

  However, if the volume decreases (that is, atrophy), the blood flow on the image decreases according to the degree of the decrease, or conversely, if the volume increases (that is, enlarges), the degree of the increase depends on the degree. Usually, blood flow increases on the image. Therefore, for example, even if a region where the blood flow is reduced in the Z-value SPECT image 100 can be confirmed, the blood flow on the image is reduced from the image 100 even though the volume of the brain tissue is not reduced. It is not known whether the blood flow on the image is reduced as the volume of the brain tissue is reduced. On the other hand, even if a portion where the volume is reduced can be confirmed in the Z-value MRI image 200, it is not known from the image 200 whether the function of the portion is lowered.

  On the other hand, according to the difference image (hereinafter referred to as difference image) 300 of the SPECT and MRI Z values, the region where the blood flow is strongly reduced against the volume reduction or the volume reduction against the blood flow reduction It can be seen that the region 310 is getting stronger. This means that by directly comparing the Z value of the SPECT image and the Z value of the MRI image, it is possible to easily grasp whether or not there is dissociation in the increase and decrease in volume and blood flow. That is, according to the difference between the Z value of the SPECT image and the Z value of the MRI image, it is possible to grasp which of the volume and the blood flow has a great influence on the brain function. Furthermore, even if the Z value of the SPECT image and the Z value of the MRI image are each independently in the normal range, an abnormal region may be detected by obtaining the difference between the two.

  In this way, by directly comparing the Z value of the SPECT image and the Z value of the MRI image, it is easy to grasp the pathological condition that is hidden in the single Z value SPECT image 100 and the Z value MRI image 200 and difficult to grasp. And more advanced diagnosis support becomes possible.

  Further, the display control unit 25 causes the display device 3 to display a brain tomographic image based on the EXTENT for each ROI calculated by the spread calculation unit 23.

  FIG. 4 is a display example of a tomographic image of the brain based on EXTENT for each ROI. Here, an EXTENT image 400 based on a positive value feature region and an EXTENT image 500 based on a negative value feature region are displayed.

  For example, when the Z value of the SPECT image can take a positive or negative value centered on 0, the larger the value, the greater the blood flow rate. When the Z value of the MRI image can take a positive or negative value centered on 0 In addition, it is assumed that the volume increases as the value increases. At this time, consider a case where the comparison calculation unit 22 calculates the difference data by subtracting the Z value of the SPECT image from the Z value of the MRI image. When the difference data obtained at this time is a positive value, the larger the absolute value is, the lower the blood flow is with respect to the volume. On the other hand, when the difference data has a negative value, the larger the absolute value, the smaller the volume with respect to the blood flow.

  Therefore, EXTENT (positive) 400 based on the feature region where the difference data is positive indicates the extent of the portion where the blood flow rate is reduced with respect to the volume. EXTENT (negative) 500 based on a feature region in which the difference data is negative indicates the extent of expansion of a portion having a small volume with respect to the blood flow. Again, the ROI display mode may be changed according to the value of EXTENT. Also from this screen, as described above, it becomes easy to grasp a disease state that has been difficult to grasp in the past, and more advanced diagnosis support is possible.

  FIG. 5 is a flowchart showing a processing procedure of the medical image processing apparatus 1 according to the present embodiment. A processing procedure is demonstrated along this.

  First, the normalization processing unit 21 processes the SPECT image data in the SPECT data storage unit 11 and the MRI image data in the MRI data storage unit 13 to calculate the Z value of the SPECT image and the Z value of the MRI image, respectively (S11). ). The Z value of the SPECT image and the Z value of the MRI image are respectively stored in the SPECT normalized data storage unit 15 and the MRI normalized data storage unit 17 (S12).

  The comparison calculation unit 22 subtracts the Z value of the SPECT image from the Z value of the MRI image, and stores this difference data in the calculation data storage unit 18 (S13, S14).

  The spread calculation unit 23 calculates EXTENT for each ROI based on the difference data (S15).

  The spread calculation unit 23 calculates a specific pixel average value for each ROI based on the difference data (S16). Note that if the EXTENT and the specific pixel average value are not calculated, steps S15 and S16 may be skipped.

  The display control unit 25 displays an image based on the Z value of the SPECT image and the MRI image and the difference data of the Z value on the display device 3 (S17). At this time, an image based on the EXTENT and the specific pixel average value may be further displayed.

  The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.

  For example, in the above-described embodiment, the SPECT image that is a functional image and the MRI image that is a morphological image are used, but other images may be compared. For example, functional images or morphological images may be compared. In other words, PET images, f-MRI images, electroencephalogram images, or CT images may be compared in an appropriate combination, or different reagents (eg, sugar metabolism preparations, blood flow preparations, receptor preparations, etc.) may be added and separated. You may compare the image | photographed image.

It is a functional lineblock diagram of medical image processing device 1 concerning one embodiment of the present invention. An example of the data structure of Z value data is shown. An example of the tomographic image of the brain displayed on the display device 3 is shown. The example of a display of the tomographic image of the brain based on EXTENT according to ROI is shown. 4 is a flowchart showing a processing procedure of the medical image processing apparatus 1.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Medical image processing apparatus 3 Display apparatus 11 SPECT data storage part 13 MRI data storage part 15 SPECT normalization data storage part 17 MRI normalization data storage part 18 Operation data storage part 19 ROI data storage part 21 Normalization processing part 22 Comparison calculation Unit 23 spread calculation unit 25 display control unit

Claims (8)

A medical image processing apparatus for processing a tomographic medical image obtained by photographing a human body,
The same person a predetermined site is acquired by shooting with different first and second imaging method, the second image is the first image and the form image is a functional image, respectively normalizing means for normalizing ,
Storage means for storing the first and second normalized image data normalized by the normalization means;
A calculation means for performing a predetermined calculation using the first normalized image data and the second normalized image data;
Display control means for performing processing for displaying on the display device an image based on the first normalized image data, an image based on the second normalized image data, and an image based on the calculation result of the calculation means A medical image processing apparatus. Before Symbol first and second normalized image data, said first and second image is obtained by each normalized pixel,
2. The medical image processing apparatus according to claim 1, wherein the calculation unit compares the first and second normalized image data for each pixel and performs the predetermined calculation. The first image is a SPECT (Single Photon Emission CT) tomographic image or a PET (Positron Emission Tomography) tomographic image;
The medical image processing apparatus according to claim 1, wherein the second image is an MRI (Magnetic Resonance Imaging) tomographic image or a CT (Computed Tomography) tomographic image. The first normalized image data is obtained by normalizing the first image to a Z value,
The medical image processing apparatus according to claim 1, wherein the second normalized image data is obtained by normalizing the second image to a Z value. Evaluation means for extracting a characteristic area based on the calculation result data by the calculation means and calculating an evaluation value indicating a distribution state of the characteristic area in each ROI when the predetermined part is divided into ROIs (Region Of Interest) Further comprising
The medical image processing apparatus according to claim 1, wherein the display control unit further performs processing for causing the display device to display an image of the predetermined part based on the evaluation value calculated by the evaluation unit. The calculation means calculates a difference between a Z value related to the first normalized image data and a Z value related to the second normalized image data,
The display control means is based on an image based on a Z value related to the first normalized image data, an image based on a Z value related to the second normalized image data, and a difference calculated by the calculating means. The medical image processing apparatus according to claim 4, wherein an image is displayed on the display device. A medical image processing method for processing a tomographic medical image obtained by photographing a human body,
The same person a predetermined site is acquired by shooting with different first and second imaging method, the steps of the second image is a first image and a form image are normalized respectively a functional image,
Storing the first and second normalized image data obtained by the normalization in a storage unit;
Performing a predetermined calculation using the first normalized image data and the second normalized image data;
And a step of performing a process for causing a display device to display an image based on the first normalized image data, an image based on the second normalized image data, and an image based on the calculation result. Image processing method. A computer program for processing a tomographic image of a human body,
The same person a predetermined site is acquired by shooting with different first and second imaging method, the steps of the second image is a first image and a form image are normalized respectively a functional image,
Storing the first and second normalized image data obtained by the normalization in a storage unit;
Performing a predetermined calculation using the first normalized image data and the second normalized image data;
Performing a process for causing a display device to display an image based on the first normalized image data, an image based on the second normalized image data, and an image based on the calculation result. A computer program for executing medical image processing.