CN100577107C - Method and device for displaying functional image - Google Patents

Abstract

A method and apparatus for displaying functional images, wherein in analyzing biological function information, since information obtained from a tomogram and information obtained from a plurality of functional images can be obtained from one image without sequentially moving the line of sight to observe a plurality of functional images or tomograms, and the severity of biological function abnormality can be easily determined, a plurality of functional images which are unique to each other and displayed with arbitrary gradient color patches are synthesized with arbitrary weights, images obtained by performing an operation between a plurality of functional images are displayed, or the functional images and the tomograms are superimposed with arbitrary weights. Further, the operator can arbitrarily set and change the application range of the weight, the display range of the gradient color scale, and the synthesis range.

Description

Functional image display method and device

technical field

The present invention relates to analysis or evaluation of living body function information based on cross-sectional images obtained from CT apparatuses, MRI apparatuses, and other imaging diagnostic apparatuses.

Background technique

When analyzing functional information of a living body, a plurality of functional information may be comprehensively considered for diagnosis. For example, in the analysis of cerebral perfusion function information, it is usually observed comprehensively from cerebral blood flow (Cerebral Blood Flow, or CBF) image, cerebral blood volume (Cerebral Blood Volume, or CBV) image, mean transit time (Mean Transit Time (MTT) images and other functional images and information obtained from cross-sectional images (for example, early CT signs, blood vessel course, tissue location, etc. in anatomical findings) are used for diagnosis.

As a method of displaying an image representing functional information of a living body, as disclosed in Japanese Unexamined Patent Publication No. 2002-282248, there is a method of integrating one functional image and a cross-sectional image and displaying it as one composite image. With this method, information obtained from a cross-sectional image and information obtained from a certain functional image are displayed on one image. For one parameter among blood flow, blood volume, average transit time, etc., the range of measured values is divided into a plurality of ranges, and a different hue is assigned to each range of measured values using a color map. However, since only one parameter can be displayed, there is a problem that it is not possible to comprehensively identify whether the abnormality, symptom, and risk of a living body are mild or severe (hereinafter referred to as "severity"). In addition, since the entire cross-sectional image is displayed in color, there is a problem that the information is too cumbersome and abnormal judgment is difficult.

As a method of observing multiple functional images obtained in multiple examinations, there is also a method of making differences in SPECT (Single PHoton Emission Computed Tomography) images and showing significant changes in standard brain MR The method of image synthesis is called SISCOM (Subtracted Ictal SPECT Co-Registered to MRI). In particular, this method is a method of obtaining functional images by making a difference in the SPECT images taken separately during seizures (ictal) and during seizures (interictal) of epileptic patients. At this time, an electrode-type electroencephalograph ( electroencephalogram (EEG)). The above-mentioned SISCOM only corresponds to SPECT, and is not applicable to functional images created from CT images or MR images. In other words, when superimposing a series of cross-sectional images captured by different imaging diagnostic equipment compared with imaging with a single imaging diagnostic equipment, it is necessary to make the CT image or MRI image a standard brain. Comparing is more difficult. In addition, there is a problem that the SPECT image and the MR image must be obtained simultaneously, which limits the patient's time to too long. When integrating SPECT images and MR images, SPECT images are deformed compared with standard brain images and aligned with the above MR images, so it is possible to lose the shape of the patient's original brain and lose important disease information. Especially when the patient's head is deformed, the loss of information about the condition is a serious problem.

Contents of the invention

The object of the present invention is to provide a single image diagnosis device (medical device: modality) that integrates information obtained from a cross-sectional image and information obtained from a plurality of functional images on one image, which can accurately assess the degree of severity. An imaging diagnostic device that facilitates judgment. Another object of the present invention is to provide an image diagnostic device and an image diagnostic method that can efficiently determine the severity by displaying only necessary information of a plurality of necessary functional image information, preventing judgment confusion caused by complicated information.

In addition, another object of the present invention is to provide a method for easily grasping changes in biological function information over time by creating functional images of CT images or MR images during multiple inspections from the original without losing the original shape of the inspection site. An image diagnosis device and an image diagnosis method capable of analyzing living body function information.

Furthermore, another object of the present invention is to provide an objective evaluation and analysis of changes in biological function information over time regardless of operator preferences even when the same data is analyzed by different operators. An image diagnosis device and an image diagnosis method.

Another object of the present invention is to provide an image diagnosis device and an image diagnosis method that can grasp and analyze changes in biological function information over time by using any medical device such as a CT device or an MR device without using a plurality of medical devices.

That is, according to the first technical feature of the present invention, it is an image display device including: a mechanism for collecting image data of a subject; a mechanism for creating a cross-sectional image based on the image data; and calculating a plurality of biological functions from the cross-sectional image. information; a mechanism for creating a functional image for each of the above biological function information; a mechanism for synthesizing the above-mentioned functional image and the above-mentioned sectional image to create a composite image; and a mechanism for displaying the above-mentioned functional image, the above-mentioned sectional image, and the above-mentioned composite image The display mechanism is characterized in that the mechanism for making the above-mentioned synthesized image makes each of the above-mentioned living body function information correspond to different gradient color scales (gradation color scale), transforms at least a part of the above-mentioned each functional image, and synthesizes the transformed image. For each functional image, other areas in the functional image are displayed with any color not included in the gradient color scale or displayed as transparent.

In addition, one feature of the present invention further includes: a mechanism for performing calculations between the functional image at the first time and the functional image at the second time using at least one of four arithmetic operations to create a calculated image, and the above-mentioned synthesis The image means synthesizes the calculated image and the cross-sectional image to create a synthesized image.

According to the second technical feature of the present invention, in the image display device of the above-mentioned 1, the composite image can be displayed by any one of overlapping display, parallel display, or partial display.

According to a third technical feature of the present invention, in the image display device based on the feature 1 or 2 above, the ratio of the functional image in the part of the functional image is set to zero by means for creating the functional image.

According to the fourth technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 3, the means for creating the functional image can arbitrarily change the gradient color scale assigned to the biological function information.

According to a fifth technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 4, the means for creating the composite image includes a mechanism capable of arbitrarily setting the ratio of each functional image in the composite image to the cross-sectional image. .

According to the sixth technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 5, the mechanism for creating the above-mentioned functional image determines whether the image data value of the pixel unit is within or outside the specified range. The above-mentioned part of the area in the above-mentioned functional image

According to the seventh technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 6, the mechanism for creating the functional image determines an arbitrary region of interest in the functional image as the part of the functional image area.

According to the eighth technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 7, the mechanism for creating the functional image sets the pixel value that is the value of each pixel of the image data at a predetermined window level. The value in (window level) and window width corresponds to the transformation coefficient, and the above gradient color scale is determined based on the transformation coefficient.

According to a ninth technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 8, the mechanism for creating the functional image, the gradient color scale assigned to the functional image is determined by various lists as follows , wherein the list is such that a pixel value that is a value of each pixel of image data is associated with a transformation coefficient in each RGB.

According to the tenth technical feature of the present invention, in the image display device based on the above-mentioned features 1 to 9, the living body function information is at least one of blood flow function information represented by blood flow, blood volume, and average transit time. A sort of.

According to the eleventh technical feature of the present invention, it is an image display method, including: a step of obtaining a cross-sectional image of a subject by a cross-sectional image acquisition unit; A step of creating a functional image from the living body function information, combining the functional image and the cross-sectional image, and creating a composite image, displaying the functional image, the cross-sectional image, and the composite image Step; It is characterized in that in the step of creating the synthesized image, according to the evaluation value of the biological function information, a different gradient color scale is assigned to each of the biological function information, and the functional images are transformed and synthesized. Transformed images of each feature.

According to another feature of the present invention, it further includes: a step of calculating the functional image at the first time point and the functional image at the second time point to generate a calculated image, and the step of making a composite image is to combine the calculated The rear image is combined with the cross-sectional image to create a composite image.

According to the twelfth technical feature of the present invention, in the image display method of the eleventh technical feature, the composite image may be displayed by any one of overlapping display, parallel display or partial display.

According to a 13th technical feature of the present invention, in the image display method based on the 11th and 12th technical features, the step of creating the functional image is to set the ratio of the functional image in other regions within the functional image to zero.

According to the 14th technical feature of the present invention, in the image display method according to the 11th to 13th technical features, the step of creating the functional image includes changing a gradient color scale assigned to the biological function information image.

According to the 15th technical feature of the present invention, in the image display method based on the 11th to 14th technical features, the step of creating the composite image includes setting each functional image in the composite image and the cross-sectional image. Ratio setting procedure.

According to the 16th technical feature of the present invention, in the image display method based on the 11th to 15th technical features, the step of creating the above-mentioned functional image is based on whether the value of the image data of the above-mentioned pixel unit is within or outside the specified range. The above-mentioned partial area in the above-mentioned functional image is specified.

According to the 17th technical feature of the present invention, in the image display method based on the 11th to 16th technical features, the step of creating the above-mentioned functional image is to use any region of interest in the above-mentioned functional image as the above-mentioned part of the above-mentioned functional image region to decide.

According to the 18th technical feature of the present invention, in the image display method based on the 11th to 17th technical features, the step of creating the above-mentioned functional image is to set the pixel value as the value of each pixel of the image data within a predetermined window The values in Horizontal and WindowWidth correspond to the transform coefficients upon which the gradient color scale described above is determined.

According to the 19th technical feature of the present invention, in the image display method based on the 11th to 18th technical features, using the step of creating the above-mentioned functional image, the gradient color scale assigned to the above-mentioned functional image is determined by various lists as follows In the above-mentioned list, a pixel value that is a value of each pixel of image data is associated with a transformation coefficient in each RGB.

According to the twentieth technical feature of the present invention, in the image display method based on the 11th to 19th technical features, the above-mentioned living body function information is blood flow function information represented by blood flow, blood volume and average transit time, etc. at least one.

Description of drawings

FIG. 1 is a block diagram of a method and device for displaying functional images according to the present invention.

Figure 2 is a flow chart from data acquisition to composite image display.

FIG. 3 is a schematic diagram of a method of calculating a transform coefficient.

FIG. 4 is a diagram showing the structure of a table for function image.

Fig. 5 is a schematic diagram of a list for functional images.

FIG. 6 is a diagram showing the configuration of a list for cross-sectional imaging.

Fig. 7 is a sample image of a hybrid function image.

Fig. 8 is a sample image of projecting a hybrid functional image on a cross-sectional image.

FIG. 9 is a flow chart from data acquisition to composite image display in Embodiment 2 of the present invention.

FIG. 10 is a diagram illustrating an example of an ROI setting method in an example of inter-image calculation in Embodiment 2 of the present invention.

Fig. 11 is an image of MTT function before treatment in Example 2 of the present invention.

Fig. 12 is an MTT functional image after treatment in Example 2 of the present invention.

Fig. 13 is an image obtained by synthesizing MTT difference images and CT cross-sectional images before and after treatment in Example 2 of the present invention.

Figure 14 is a functional image of CBV before treatment.

Figure 15 is a functional image of CBV after treatment.

FIG. 16 is an image obtained by combining CBV and MTT difference images before and after treatment with CT cross-sectional images related to the above-mentioned FIGS. 11 , 12 , 14 , and 15 .

Detailed ways

Hereinafter, preferred embodiments of the method and device for displaying functional images of the present invention will be described in detail with reference to the drawings.

[Example 1]

FIG. 1 is a diagram showing a preferred embodiment of a method and device for displaying a functional image of the present invention. The functional image display method and device of the present invention refer to a cross-sectional image data acquisition mechanism 1 such as a CT device or an MRI device, such as an X-ray attenuation signal or an echo signal emitted by magnetic resonance. It consists of a computer 2 for controlling the acquisition mechanism 1 and various calculations, a console such as a mouse or a keyboard, and a display method 4 such as a monitor. The computer 2 is loaded with a program for controlling the acquisition mechanism 1 , a program for performing image reconstruction and the like to create a cross-sectional image, a program for analyzing and drawing biological function information, and a program for creating a composite image. When constituting the functional image display method and device of the present invention, each of the above-mentioned programs may be installed in one computer, or may be installed in a plurality of computers for each type of operation.

Fig. 2 is a flow chart from data collection to display of composite images of the method and device for displaying functional images of the present invention. This flow is realized by software built into the computer 2 in FIG. 1 or an external computer not shown.

This embodiment will be described in accordance with this flow chart. First, X-ray attenuation data or magnetic susceptibility signal intensity data is collected by the collection mechanism 1 controlled by the control program installed in the computer 2 (step 201).

In step 201, through the acquisition mechanism 1 controlled by the control program on the computer 2, the echo signals emitted by X-ray attenuation signals or magnetic resonance are collected. For example, if the data acquisition device is a CT device, if the biofunction information to be analyzed is head perfusion information, after injecting a contrast-enhancing substance such as an iodine-based contrast agent into the patient 5, focus on the specific organ or organ into which the substance flows. By performing real-time imaging (that is, dynamic imaging) of the site, data necessary for analysis of living body function information can be collected.

In step 202 , a cross-sectional image is created using a program such as image reconstruction mounted on the computer 2 .

In step 203, the cross-sectional image created in step 202 is displayed.

In step 204, for example, a pixel value P, which is a parameter representing the biological function information, is calculated using, for example, an analysis program for biological function information mounted on the computer 2 . Representative parameters of the parameters mentioned here include Cerebral Blood Flow (CBF) images, Cerebral Blood Volume (CBV) images, and Mean Transit Time (MTT) images. .

The calculation of the parameters is preferably performed for each pixel of the cross-sectional image to prevent a decrease in resolution. However, when the calculation needs to be terminated urgently, such as when the diagnosis of biological function information is urgently performed, the calculation can be performed by reducing the image, or multiple pixels can be used. Do the math.

In step 205 , the calculation result obtained in step 204 is plotted using a plotting program installed in the computer 2 , thereby creating a functional image.

In step 206 , the functional image created in step 205 is displayed on the display unit 4 . Wherein, in step 206, not only the functional image is displayed, but the functional image and the cross-sectional image may also be displayed together if necessary. However, when the composite image is continued to be created, the image display may not be performed here.

In step 207 , a composite image is created using a composite image creation program installed in the computer 2 as will be described later.

In step 208 , the composite image is displayed on the display unit 4 . However, in step 208, not only the image is displayed, but at least two of the synthesized image, the functional image, and the cross-sectional image may be displayed together if necessary.

Regarding the flowchart of Fig. 2, when the X-ray attenuation data or the magnetic susceptibility signal strength data have been collected, the X-ray attenuation data or the magnetic susceptibility signal strength have been read from storage mechanisms 6 such as hard disks built in or externally connected to the computer 2 After the data is collected, steps after step 202 can be performed.

Furthermore, with regard to the flowchart of FIG. 2 , when the cross-sectional image has been created, after the cross-sectional image is read from the storage device 5 such as a hard disk built in or externally connected to the computer 2, the steps after step 203 may be executed.

Furthermore, with regard to the flowchart of FIG. 2 , when the functional image has been created, after the functional image is read from a storage mechanism 5 such as a hard disk built in or externally connected to the computer 2, the functional image and tomographic images are also read as necessary. image, and then the steps after step 206 can be performed.

Next, a method of creating a composite image in step 207 will be described. In this embodiment, the description will be made assuming that the total number of function images, which are types of biological function information on a certain organ, is M.

In addition, the grayscale number is a positive integer, such as 8 bits (256 grayscales), 12 bits (4096 grayscales), 16 bits (65536 grayscales), and 32 bits (4294967296 grayscales).

Hereinafter, hue refers to hue, chroma, lightness, or a combination of at least two of them. In addition, the gradient color scale refers to dividing the range of the maximum value and the minimum value of pixel values into at least one stage, and assigning different hues to each stage, that is, it is a continuum of hues.

(1) Transformation from pixels to transformation coefficients

An image called a hybrid function image will be described. This mixed functional image is an image synthesized by superimposing a plurality of functional images in which biological function information is displayed in correspondence with a gradient color scale. Here, the used tone is different between function images. Wherein, in the functional image as the original image of the mixed functional image, only a specific region in the image is displayed with a gradient color scale. In this case, the areas other than the specific area may be displayed in any specific color. When creating a mixed functional image, the conversion coefficient C is calculated based on the pixel value P of the functional image, the display window level value WL, and the display window width value WW. The conversion coefficient C is obtained, for example, by the following Equation 1 with reference to FIG. 3 . Here, WL represents the window level, and WW represents the window width. The concentration displayed is centered on the window level and distributed within the range of WW/2 up and down. Outside this range WW, there is no concentration or no change due to saturation. That is, according to the display window width WW, places where the pixel values are large become saturated and whitish, and on the contrary, places where the pixel values are small tend to be darkest. In addition, PMAX represents the maximum value of pixels, and CMAX represents the maximum value of transform coefficients.

P≤(WL-WW/2) C＝0

(WL-WW/2)≤P≤(WL+WW/2) C=CMAX/WW·P ( 1)

(WL+WW/2)≤P C=CMAX

Wherein, in the example shown in FIG. 3 , linear transformation is performed on the interval from (WL-WW/2) to (WL+WW/2), and arbitrary nonlinear transformation may be performed as needed. Alternatively, the pixel value P may be directly used as the transform coefficient C.

FIG. 4 shows a list (hereinafter referred to as LUT) used for creating a hybrid image. The LUT mentioned in this embodiment refers to the correspondence table between the above-mentioned transformation coefficient and each component of the displayed color (such as R component, G component, and B component). The conversion coefficient C applied to the pixels in the area below (WL-WW/2) is the darkest color (lower end color) at one end of the gradient color scale. In the following, the darkest colors among R, G, and B used for color display are represented as R1, G1, and B1. However, when assigning to each step of the above-mentioned gradient color scale by changing only the hue in the color tone, it may not always be possible to assign the darkest color to the darkest color part. However, in such a case, it is called the darkest color (lower end color) for convenience.

On the other hand, the value of the transformation coefficient C is suitable for the transformation coefficient C of the pixels in the area of (WL+WW/2) or more, and the other end of the gradient color scale is the brightest brightest color. Hereinafter, the brightest colors among R, G, and B used for color representation are expressed as Rh, Gh, and Bh. However, when assigning to each step of the above-mentioned gradient color scale by changing only the hue in the color tone as described above, it may not always be possible to assign the brightest color to the brightest color part. However, in such a case, it is called the brightest color (upper end color) for convenience.

The components R(c), G(c), and B(c) of the R, G, and B components of the LUT in a certain transform coefficient C, for example, can refer to the LUT for functional images shown in FIG. 5, and then use the following formula ( 2) to determine. Among them, FIG. 5 is an example of LUTs for each color of R, G, and B, and it is common that the RGB tables assigned to each functional image have different initial values and slopes. A color system for displaying biological function information is defined by a combination of the darkest colors R1, G1, and B1 as initial values of RGB.

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; Rh</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; Rl</span>}}{\mathrm{<span\; class="notranslate">\; CMAX</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Rl</span>}$

$\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; Gh</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; Gl</span>}}{\mathrm{<span\; class="notranslate">\; CMAX</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Gl</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; Bh</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; Bl</span>}}{\mathrm{<span\; class="notranslate">\; CMAX</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; Bl</span>}$

In the example shown in FIG. 5 , the component values from the darkest color to the brightest color are connected linearly, but they may be connected arbitrarily nonlinearly if necessary. If there are M functional images, it is preferable to set M lists corresponding to each functional image 1, functional image 2, ..., functional image M, that is, LUT1, LUT2, ..., LUTM. However, it is not limited thereto, and the same LUT can be used between a plurality of functional images.

(2) Creation of mixed function image

The processing of the display pixels in the portion where a plurality of functional images overlap will be described. Here, when each component of R, G, and B that dominates the display color of a certain pixel (i, j) in the blend function image is expressed as RF(i, j), GF(i, j), BF(i, j ), they can be determined by the following formula 3.

$\mathrm{<span\; class="notranslate">\; RF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}$

$\mathrm{<span\; class="notranslate">\; GF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; BF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}$

Here, Wk represents the weight for synthesizing a plurality of functional images, and Ck(i, j) represents the transformation coefficient of the functional image k in the pixel (i, j). In addition, Rk(Ck(i, j)), Gk(Ck(i, j)), and Bk(Ck(i, j)) represent R, G, and B defined by the LUTk of the transformation coefficient Ck(i, j). The value of each component of is calculated by inputting the transformation coefficient Ck(i, j) of each pixel into the transformation coefficient C of Equation 2. However, as described above, the number k of the types of functional images is particularly an integer of 1 to M here.

The area displayed with the gradient color scale can be the whole image or a part of the image. When the gradient color scale is used to display only a part of the image, that is, a specific area, it can be set according to at least one of threshold, range, and ROI through the console 4 . One or more of such thresholds, ranges, and ROIs can be set for each functional image (processing 1).

When a certain pixel (i, j) in a certain functional image k just represents the function of the living body, that is, for example, when the pixel (i, j) is within the ROI, or if the pixel value is determined by the threshold value of the relative functional image k Within the range, according to the LUTk shown in Figure 4 above, each component Rk(Ck(i,j)), Gk(Ck(i,j)), Bk(Ck(i,j)) is determined, if the pixel value or pixel is not within any range of threshold, range, and ROI as described above, assign a specific value to each component Rk(Ck(i,j)), Gk(Ck(i,j)), Bk(Ck(i , j)) to display in a specific color that does not interfere with the display of the function of interest (processing 2).

By performing processing 1 and processing 2 on all pixels, only the above-mentioned set range can be displayed with a gradient color scale in a certain functional image, and other ranges can be displayed with specific colors. Processing 1 and processing 2 can be performed on all functional images. It can also be performed on only some functional images.

Synthesis of each functional image after processing 1 and 2 will be described. The functional image thus synthesized is hereinafter referred to as a mixed functional image, and data of the mixed functional image is obtained in each pixel. Determine RF(i, j), GF(i, j), and BF(i, j) from each pixel according to Equation 3. At this time, the pixel displayed with a specific color is calculated by setting the weight Wk of the functional image to 0 of. If the data RF(i,j), GF(i,j), and BF(i,j) of all pixels are obtained, drawing is performed according to the coordinates (i,j), so as to display the image. In this way, a hybrid function image can be created. Even when it is not necessary to combine all N functional images, the weights of functional images that do not need to be combined can be set to 0 and combined. Here, as the setting of the specific range, the threshold, the range, and the ROI are exemplified, but other parameters may be used for setting as necessary.

(3) Creation of projected hybrid functional images on cross-sectional images

Next, a method of creating a superimposed composite image of a cross-sectional image and a blended image (hereinafter, a projected blended functional image on a cross-sectional image) will be described. Here, if each component of the display color of a certain pixel (i, j) in the mixed function image projected on the cross-sectional image is RTF(i, j), GTF(i, j), BTF(i, j) , can be determined by Equation 4 as shown below, that is, the color components RF(i,j), GF(i,j), BF(i,j) of each pixel of the mixed function image obtained according to Equation 3 ) and the coefficient of variation CC(i, j) of the cross-sectional image in the pixel (i, j), the transformation coefficient CC(P) obtained corresponding to the multiple cross-sectional images identified by the symbol t using the list table LUTT Each color component value RT(CC(i,j)), GT(CC(i,j)), BT(CC(i,j)), weights WB and WT of each mixed functional image and cross-sectional image.

$\mathrm{<span\; class="notranslate">\; RTF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; RF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; RT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CC</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; WT</span>}}{\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; WT</span>}}$

$\mathrm{<span\; class="notranslate">\; GTF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; GF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; GT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CC</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; WT</span>}}{\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; WT</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; BTF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; BF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; BT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CC</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; WT</span>}}{\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; WT</span>}}$

Cross-sectional images are generally displayed in grayscale, so the cross-sectional image list can be set as shown in FIG. 6, for example. In order to project a hybrid functional image onto a cross-sectional image, RTF(i,j), GTF(i,j), and BTF(i,j) are determined according to Eq. For pixels displayed in a specific color, the weight WB is set to 0 in FIG. 4 to determine RTF(i, j), GTF(i, j), and BTF(i, j). When all the pixels are drawn after such processing, the projection of the mixed function image on the cross-sectional image is completed.

When projecting a mixed functional image on a mixed image or a cross-sectional image, when it is desired to change the gradient color scale of a certain functional image k, according to the parameters input from the console 4, the above-mentioned method is used to change the list table LUTk corresponding to the functional image, In this way, Rk(Ck(i, j)), Gk(Ck(i, j)), and Bk(Ck(i, j)) in Equation 3 above can be changed. However, k is an integer from 1 to m.

In projecting a mixed functional image on a mixed image or a cross-sectional image, when changing the emphasis degree of information obtained from a certain functional image k, Wk in Equation 3 above can be changed according to a parameter input from the console 4 .

When projecting a hybrid functional image on a hybrid image or a cross-sectional image, if it is desired to change the region displayed with the gradient color scale, parameters defining the region, such as threshold, range, and ROI, can be input from the console 4 to change.

In projecting the hybrid functional image on the cross-sectional image, when changing the degree of emphasis of the hybrid functional image, WB or WT in Equation 4 above can be changed according to the parameters input from the console 4 .

7 and 8 show an example of applying an embodiment of the present invention to a cerebral blood flow function image created from a CT image. Fig. 7 is a sample image of a mixed functional image created based on these three kinds of functional information (cerebral blood flow, cerebral blood volume, and average transit time), which are abnormal regions 31 of cerebral blood flow, abnormal regions 32 of cerebral blood volume, average transit Composite image of temporal anomalous region 33 and other regions 39 . 8 is a sample image in which a mixed functional image is projected on a cross-sectional image created from a mixed functional image created from three types of functional information (cerebral blood flow, cerebral blood volume, and mean transit time) and a CT image 30 . In these sample images, the abnormal area 31 of cerebral blood flow is displayed with a gradient color scale of green, the abnormal area 32 of cerebral blood volume is displayed with a gradient color scale of blue, and the average Pass time anomaly area 33. These sample images not only display areas considered to be abnormal in various physiological parameters such as cerebral blood flow, cerebral blood volume, and average transit time on one image, but also show the severity of the abnormality through the shade of color matching or the degree of mixing of various colors. Degree, thus can understand the effect of the present invention. By projecting a mixed functional image on these mixed functional images or cross-sectional images, not one but different types of multiple existing functional images are selected and synthesized, so that a plurality of different mixed functional images or cross-sectional images can be created. Project the mixed function image. These multiple mixed function images or projected mixed function images on the cross-sectional images can be simultaneously displayed on the images.

To sum up, according to the first embodiment, pixels showing a value equal to or greater than the threshold in the functional image are presumed to be characteristic sites such as lesion sites and displayed. By setting multiple parameters of the functional image, the information for judgment can be increased, and by displaying these flags in different colors for each parameter, it is clear at a glance which parameter shows which abnormality, even if they are overlapped And the abnormality of multiple parameters in the displayed part can also be seen at a glance.

Furthermore, in the above-mentioned colored characteristic part, the density or color matching of the color can be changed according to the size of the pixel value, so the degree of abnormality can be judged.

In addition, since the transparency of these characteristic parts can be changed, it is possible to change to a screen state that can be easily recognized by the operator. Furthermore, by selecting the periphery of a characteristic part or a part particularly desired to be diagnosed on the image, only this part can be colored and displayed as an ROI as described above, and diagnosis will not be hindered by useless information.

In addition, as shown in FIG. 8 , by superimposing and displaying the cross-sectional image 40 , the positional relationship with the outside of the skull or the like can be easily grasped, making diagnosis easier. Functional images can be displayed side by side, overlapped, partially overlapped, etc., and can be used in accordance with real-time diagnosis or user's intention. In addition, the functional images mainly use CBF, CBV, and MTT, and their measured values can be confirmed on the images at the same time. In addition, by recording and creating the above-mentioned ROI, threshold value, or arrangement of the above-mentioned characteristic parts, the diagnosis under the same condition can be repeated at any time, so the comparison of functional images before and after surgery or treatment becomes easy, and at the same time, it is possible to make a difference by the operator. Measurement of the effects of inaccessible surgeries or treatments, etc.

Furthermore, the difference images before and after treatment can also be displayed

Example 2

Example 2 also utilizes the configuration shown in FIG. 1 as in Example 1. The constituent elements are as described in Embodiment 1, and therefore their descriptions are omitted. In Embodiment 2, the image processing device 2 is, for example, a computer, and is equipped with a program for controlling the data acquisition mechanism 1, a program for creating cross-sectional images such as image reconstruction, a program for analyzing and drawing biometric information, and a program for creating cross-sectional images. A program for compositing images. Here, each of the above-mentioned programs may be installed in one computer, or may be installed in a plurality of computers for each type of calculation.

FIG. 9 is a flow chart showing the procedure from data acquisition to display of a synthesized image by the program of the image diagnostic apparatus of this embodiment. The processing by this embodiment will be described according to this flowchart. In step 301, the acquisition mechanism 1 (see FIG. 1 ) controlled by the control program mounted on the computer 2 acquires X-ray attenuation signals or echo signals emitted by magnetic resonance.

For example, if the collection device is a CT device, if the biofunction information to be analyzed is the perfusion information of the head, after injecting a contrast-enhancing substance such as an iodine-based contrast agent into the patient 5, dynamic imaging can be performed, thereby collecting and performing biopsy information. Data necessary for the analysis of functional information.

In step 302 , a cross-sectional image is created using an image reconstruction program or the like mounted on the computer 2 . Cross-sectional images are preferably any cross-section, coronal plane, and sagittal plane. In step 303, the cross-sectional image created in step 302 is displayed. In step 304, parameters representing the biological function information are calculated using an analysis program for the biological function information mounted on the computer 2 or the like. From the viewpoint of preventing a decrease in resolution, it is preferable to calculate the parameters for each pixel of the cross-sectional image, but when it is urgent to complete the calculation in a short time, such as when rapidly diagnosing biological function information, the image can be reduced Calculations can also be performed with multiple pixels. In step 305, the calculation result obtained in step 304 is plotted using a plotting program installed in the computer 2 or the like, thereby creating a functional image. In step 306 , the functional image created in step 305 is displayed on the display unit 4 . Wherein, in step 306, not only the functional image is displayed, but the functional image and the cross-sectional image may also be displayed together if necessary.

In step 307, as will be described later, a composite image is created using a composite image creation program installed in the computer 2 .

In step 308, the operator selects whether inter-image calculations such as difference calculations are necessary, and if not, proceeds to step 309. When it is desired to emphatically display a region or the like in which biological function information significantly changes in multiple examinations, it is preferable to perform inter-image calculations such as difference calculations.

In step 307, if it is selected that inter-image calculation is necessary, the operator selects whether quantitative value correction is necessary, and if not, proceeds to step 308. For example, in cerebral perfusion images, according to the imaging section, due to the existence of objects with low CT values across the imaging plane, the influence of the Partial Volume Effect (Partial Volume Effect) cannot be properly corrected. The Partial Volume Effect refers to the inability to accurately calculate In the cross-sectional images along this slice, especially the CT value of the portion with the same high CT value as the aorta, sometimes the quantitative value is too large. In this case, it is preferable to perform calculation between images after correcting the quantitative value. In step 308, when the quantitative value needs to be corrected, the quantitative value correction program installed in the computer 2 is used to correct the quantitative value. In step 308 , the calculated image is further created using the inter-image calculation program installed in the computer 2 . Wherein, the inter-image processing in step 308 is preferably any calculation such as difference calculation. In step 309, the condition of the area to be combined with the cross-sectional image is set for the calculated image or the functional image.

However, this step is not useful when the entire area of the calculated image or functional image is directly superimposed on the cross-sectional image and synthesized. When superimposing and synthesizing only a specific region of the calculated image or functional image on the cross-sectional image, specify the superimposed region by setting a threshold or ROI, or selecting only pixels satisfying an arbitrary conditional expression. For example, if the calculated image is a difference image of brain perfusion function images in multiple examinations, if you want to display only the region that shows a significant change in the right hemisphere, specify the ROI on the entire right hemisphere, and you can set only Pixels whose pixel value P satisfies the condition of Expression 5 below are superimposed on the cross-sectional image and synthesized.

P＝Mean+k·SD (5)

Wherein, in the above formula, Mean is the average value of all pixel values of the calculated image, SD is the standard deviation, and k is any real number.

Also, for example, when the computed image is a brain perfusion function image under multiple examinations, and it is desired to display changes over time in an abnormal region, only pixels whose pixel values are greater than or less than the threshold are superimposed on the cross-sectional image and synthesis.

In step 310 , a composite image is created using a composite image creation program installed in the computer 2 . The details of creating the composite image will be described later. In step 311 , the synthesized image is displayed on the display unit 4 . In step 311, not only the synthesized image is displayed, but also the synthesized image and images such as functional images, calculated images, and cross-sectional images can be simultaneously displayed if necessary. In addition, at this time, by displaying the number of pixels, average value, standard deviation, and histogram in the synthesized area set in step 309 , useful information can be provided through analysis of biological function information.

When the X-ray attenuation data or the magnetic susceptibility signal strength data have been collected, after reading the X-ray attenuation data or the magnetic susceptibility signal strength data from the storage mechanism 5 such as the hard disk built in or externally connected to the computer 2, after performing step 302 A step of. In addition, when the cross-sectional image has been created, it is built in or connected externally to the computer 2 . After the cross-sectional image is read from the storage device 5 such as a hard disk, the steps after step 303 are executed. When the functional image has been completed, after the functional image is read from the storage mechanism 5 such as a hard disk built in or externally connected to the computer 2, the functional image and tomographic image are also read as required, and then the steps after step 306 are executed. .

Next, step 308 will be described in detail about creation of a new diagnostic image obtained through calculation of a plurality of series of functional images obtained in multiple examinations. For example, when it is seen that the function information of the living body changes during multiple examinations, the functional images of multiple examinations can be differentiated. The types of calculations are not limited to difference calculations, and addition, multiplication, division, or any combination of four arithmetic operations can be performed according to the application. Calculations between images can be performed on all pixels per pixel.

In addition, as shown in FIG. 10 , as needed, for example, each area surrounded by an arbitrarily set ROI8 is calculated, and feature quantities such as the average value, median value, maximum value, and minimum value can also be used as calculations in the ROI area. object. This allows visual evaluation in a diagnostically easy size.

In addition, by introducing isolines to divide the functional image into several regions, calculations can be performed in each region. For example, in the case of a functional image of a brain perfusion image, it can be segmented into white matter, gray matter, vascular bed, etc. by introducing contour lines, and then can be segmented into anatomical segments such as thalamus, lentiform nucleus, and marginal zone by specifying ROI ( segment). Calculations between images are performed for each of such anatomical segments, which is also useful for evaluating changes in living body function information.

Next, with regard to step 308, a correction method of the quantitative value will be described. For example, in the cerebral perfusion image, based on the maximum value or the area under the curve of the time density curve in the superior sagittal sinus, the influence of the partial volume effect as described in Example 1 is corrected to obtain Quantitative stability. However, sometimes the superior sagittal sinus is not included in the imaging section through the imaging section. In such a case, the influence of the partial volume effect cannot be properly corrected, and the quantitative value becomes inaccurate. If the image can be judged to have been appropriately corrected for the partial volume effect in multiple examinations, the quantitative values in other examinations are corrected using the correction parameters (maximum value or area under the curve) in this examination.

As another correction method, there is a method of performing correction based on the average value in the healthy area. This method assumes that the biological function information in the healthy area of the same subject is stable regardless of the time of examination. The average value of the quantitative values of the healthy regions on the functional images in a certain examination is set to Mean1, and the average value of the quantitative values of the healthy regions on the functional images in the other examinations is set to Mean2. Here, the quantitative value can be corrected by shifting (shifting) the pixel value (quantitative value) of a certain functional image so that Mean1 and Mean2 match.

Next, with regard to step 310, a method for creating a calculated image or a composite image of a functional image and a cross-sectional image (hereinafter referred to as projecting a mixed functional image on a cross-sectional image) will be described. In this embodiment, the number of gray levels of the color scale is set as M for illustration, and the number of gray levels is a positive integer, which is set to 8 bits (256 gray levels), 12 bits (4096 gray levels), and 16 bits (65536 gray levels). , 32 bits (4294967296 gray levels) and other arbitrary gray levels. The higher the number of gray levels, the richer the gray levels that can be displayed. Generally, in the color scale, there are hue, brightness, and chroma color scales, and the types are also various.

When forming a composite image, the coefficient of variation C is calculated based on the pixel value P of the calculated image or functional image, the display window level value WL, and the display window width WW. The coefficient of variation C is determined as shown in Equation 1 and FIG. 3 , for example, as in the first embodiment. In this example, the interval from WL-WW/2 to WL+WW/2 is converted linearly, but any nonlinear conversion may be performed as necessary. Alternatively, pixel values can be directly used as transform coefficients.

FIG. 4 shows a lookup table (LUT) for creating a composite image. The LUT described in this embodiment refers to a correspondence table between the above-mentioned coefficient of variation C and each component (R component, G component, B component) of the displayed color. The darkest pixel in the display window, that is, the R, G, and B components of the darkest color (lower end color) whose conversion coefficient value is below WL-WW/2 is represented by R1, G1, and B1, which will be suitable for the brightest pixel in the display window. The R, G, and B components of the brightest color of the pixel whose transformation coefficient value is above WL+WW/2 are represented by Rh, Gh, and Bh. At this time, the R, G, and B of the LUT in a certain variation coefficient C The components R(C), G(C), and B(C) are determined, for example, as shown in Formula 2 and FIG. 5 .

In the example shown in FIG. 5 , the component values from the darkest color (lower end color) to the brightest color (upper end color) are connected linearly, but they may be connected arbitrarily nonlinearly if necessary. Even when there are a plurality of parameters indicating the biological function information and there are a plurality of functional images corresponding to the plurality of parameters, a plurality of calculated images can be created. If there are M post-calculated images, functional images, or post-calculated images and functional images, it is preferable to set the post-calculated image 1 (or functional image 1), post-calculated image 2 (or functional image 2), ... post-calculated image The M lists corresponding to M (or functional image M) are LUT1, LUT2, ... LUTM. However, even if the same table is shared among a plurality of calculated images or functional images, there is no hindrance.

Next, the composite image display in step 311 will be described in detail. Each component of the display color of a certain pixel (i, j) in the synthesized image is defined as RTF (i, j), GTF (i, j), and BTF (i, j), and they are determined by Equation 4.

Here, WB represents the weight of the calculated image or functional image, WT represents the weight of the cross-sectional image, CC(i, j) represents the transformation coefficient of the cross-sectional image in the pixel (i, j), and the above-mentioned calculated image or functional image The calculation method of the transformation coefficient is determined in the same way.

In addition, RT(CC(i,j)), GT(CC(i,j)), and BT(CC(i,j)) indicate R, which are defined by the cross-sectional image table LUTT among the transformation coefficients CT(P). The value of each component of G and B. When the gray scale represents the cross-sectional image, the cross-sectional image list LUT can be set as shown in FIG. 6 , for example. When the cross-sectional image is displayed in color, the RGB chart shown in FIG. 5 is required. In addition, RF(i, j), GF(i, j), and BF(i, j) are parameters determined according to the ratio of each calculated image or functional image combined, and are determined by Equation 3.

Here, Wk represents the weight of the combined calculated image k or functional image k, and Ck(i, j) represents the transformation coefficient of the calculated image k or functional image k in the pixel (i, j). Rk(Ck(i,j)), Gk(Ck(i,j)), Bk(Ck(i,j)) represent the R, G, and B components specified by LUTk in the transformation coefficient Ck(i,j) value. Wherein, k is an integer from 1 to M, and M is consistent with the number of calculated images or functional images. However, when the number of calculated images or functional images is one, Equation 4 becomes Equation 6.

$\mathrm{<span\; class="notranslate">\; RTF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; RF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; RT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CC</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; WT</span>}}{\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; WT</span>}}$

$\mathrm{<span\; class="notranslate">\; GTF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; GF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; GT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CC</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; WT</span>}}{\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; WT</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; BTF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; BF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CF</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; BT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CC</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; WT</span>}}{\mathrm{<span\; class="notranslate">\; WB</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; WT</span>}}$

Here, Ck(i, j) represents the conversion coefficient of the calculated image or the functional image in the pixel (i, j). In order to make the calculated image or the composite image of the functional image and the cross-sectional image, when it is a pixel displayed with a gradient color scale, RTF (i, j), GTF (i, j), BTF ( i, j), a pixel displayed with a specific color sets the weight WB to 0 in Equation 4 or 6, and then determines RTF(i, j), GTF(i, j), BTF(i, j). This processing can be performed on all pixels, and then drawing is performed according to RTF(i, j), GTF(i, j), BTF(i, j).

The region (specific region) displayed with the gradient color scale can be determined based on a threshold value set in the console 4, an arbitrary conditional expression, or an ROI. One or more thresholds, arbitrary conditional expressions, or ROIs are set for each calculated image. The pixel value in a certain pixel (i, j) of a certain image k after calculation or functional image k, if within the range set by the threshold value or conditional expression for image k after calculation or functional image k and the pixel ( i, j) Within the range set by ROI, determine Rk(Ck(i,j)), Gk(Ck(i,j)), Bk(Ck(i,j)) according to the above-mentioned LUT setting method Each component of Rk(Ck(i,j)), Gk(Ck(i,j)), Bk(Ck(i,j)) is assigned an arbitrary specific value if it is out of range .

By performing the above processing on all pixels, only a specific area can be displayed with a gradient color scale in a certain calculated image or functional image. In addition, other regions are represented by specific colors such as black or gray in FIGS. 7 to 8 and FIGS. 11 to 18 . The above processing is performed on all calculated images or functional images. Even when it is not necessary to synthesize all the M calculated images or functional images, the weights of the calculated images or functional images that do not need to be synthesized can be set to 0 and then synthesized.

In the synthesized image, when changing the degree of emphasis of information obtained from a certain calculated image k or functional image k, Wk in Equation 5 can be changed according to a parameter input from the console 4 .

In the composite image, when it is desired to change the region displayed with the gradient color scale, the threshold value, the conditional expression, or the ROI can be changed according to the parameters input from the console 4 .

In the composite image, when changing the degree of emphasis of the calculated image, functional image, or cross-sectional image, WB or WB in Equation 4 or 6 is changed according to the parameters input from the console 4 .

11 to 18 show examples in which the present invention is applied from CT image creation to cerebral perfusion function map. FIGS. 11 to 13 are sample images for creating a composite image of a difference image of a mean transit time image MTT before and after treatment and a CT image of a patient with right internal carotid artery stenosis before and after treatment. In this sample image, the iridescent gradient color scale is used to display only the region where the mean transit time MTT changes significantly. In the region where the change is meaningful, the WB of formula 5 is set to 0.8, and the WT is set to 0.2. The area is synthesized by setting WB to 0 and WT to 1. In FIG. 13 , 34 represents a part with a relatively large change in MTT and is displayed in a warm color system, and 35 represents a region with a relatively small change in MTT and is displayed in a cool color system. Here, the colors are assigned according to the magnitude of the difference between FIG. 11 and FIG. 12 .

11 , 12 , and 14 to 16 are sample images for creating a composite image of a difference image of a mean transit time image MTT, a difference image of a cerebral blood volume image CBV, and a CT image of a patient with right internal carotid artery stenosis before and after treatment. In this sample image, the region where the cerebral blood volume image CBV significantly changes is displayed by the blue gradient color scale 36 , and the region where the mean transit time MTT significantly changes is displayed by the red gradient color scale 37 . Also, W1 (that is, the weight of the difference image of the cerebral blood volume image) in Equation 3 is set to 0.75, and W2 (that is, the weight of the difference image of the average transit time image) is set to 0.25, and then combined. Furthermore, WB in Formula 4 was set to 0.8 and WT was set to 0.2 in the region where the cerebral blood volume CBV or the mean transit time MTT changed significantly, and then synthesized.

Figures 11 to 13 show the application examples when there is one calculated image or functional image, and Figures 11, 12, and Figures 14 to 16 show the application examples when there are two calculated images or functional images, which can also be applied to the calculated When there are three or more images or functional images. Through these sample images, it is possible to easily grasp the time-dependent change of regions or diseased regions where significant changes have occurred in the functional information of the living body, and it is also possible to reduce the uncertainty of ROI setting caused by the operator's subjectivity without damaging the inspection. original shape of the part. As described above, according to this embodiment, there is an effect that the uncertainty of ROI setting caused by the operator's subjectivity can be reduced, and the original shape of the inspection site can be easily grasped in the living body function information. Changes in areas of significant change or disease areas over time.

As described above, according to the present description, there is an effect that information obtained from a cross-sectional image and information obtained from a plurality of functional images can be obtained from one image, and the determination of the severity of an abnormal function of a living body becomes easy.

Claims (11)

1. an image display device comprises: the mechanism of collecting person under inspection's view data; Make the mechanism of cross-section image according to described view data; Calculate the mechanism of a plurality of live body function informations from described cross-section image; Each described live body function information is made the mechanism of function image; Described function image and described cross-section image are synthesized, make the mechanism of composograph; And, show to it is characterized in that the indication mechanism of described function image, described cross-section image and described composograph,

Make the mechanism of described composograph, make each described live body function information, come at least a portion zone of described each function image of conversion corresponding to different gradient colour codes, and each function image after the synthetic conversion,

The random color that other zones in the described function image do not contain with described gradient colour code shows or is shown as transparent.

2. image display device according to claim 1 is characterized in that,

Also comprise: perform calculations using among the four arithmetic operation at least one at the 1st o'clock at the 2nd o'clock between function image that engraved and the function image that engraved, make the mechanism of calculation back image, the described mechanism that makes composograph, described calculation back image and described cross-section image is synthetic, make composograph.

3. according to claim 1 or 2 described image display devices, it is characterized in that,

Described composograph can be by overlapping demonstration, show side by side or part any in showing is shown.

4. image display device according to claim 1 and 2 is characterized in that,

It is 0 that the mechanism that makes described function image makes the ratio of the described function image beyond the described part zone in the described function image.

5. image display device according to claim 1 and 2 is characterized in that,

Make the mechanism of described function image, can change the gradient colour code of distributing to described live body function information arbitrarily.

6. image display device according to claim 1 and 2 is characterized in that,

Make the mechanism of described composograph, possess each function image that to set arbitrarily in the described composograph and the ratio of described cross-section image.

7. image display device according to claim 1 and 2 is characterized in that,

Make the mechanism of described function image, according to the image data value of pixel cell within the prescribed limit or outside, decide the described part zone in the described function image.

8. image display device according to claim 1 and 2 is characterized in that,

Make the mechanism of described function image, any region of interest decision in the described function image is regional as the described part in the described function image.

9. image display device according to claim 1 and 2 is characterized in that,

Make the mechanism of described function image, will and be arranged in the window position of regulation and the value of window width as the pixel value of the value of each pixel of view data, corresponding with conversion coefficient, and be that described gradient colour code is determined on the basis with this conversion coefficient.

10. image display device according to claim 1 and 2 is characterized in that,

Make the mechanism of described function image, the gradient colour code that is assigned to described function image is decided by various catalogs, wherein said catalog be in each RGB, make as the pixel value of the value of each pixel of view data corresponding with conversion coefficient.

11. image display device according to claim 1 and 2 is characterized in that,

Described live body function information is with blood flow, and blood flow volume and mean transit time are at least a in the middle of the blood flow function information of representative.

Images