JP2011067594A - Medical image diagnostic apparatus and method using liver function angiographic image, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To perform image diagnosis in an easier and more appropriate manner by focusing on functional levels and segments of a liver. <P>SOLUTION: A segment function level calculation unit 33 obtains, from liver function angiographic images V<SB>tn</SB>obtained using a contrast agent that produces a contrast effect according to a functional level of a liver of diagnostic target, evaluations FS1, FS2, ... representing functional levels of a plurality of liver segments RS1<SB>tn</SB>, RS2<SB>tn</SB>, ... in liver regions RL<SB>tn</SB>, a display image generation unit 34 generates an image Img based on the evaluation values, and the image Img so generated is displayed on a display unit 35. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a medical image diagnostic apparatus and method using a contrast image representing a functional level of a liver, and a program.

  Various devices for performing image diagnosis using an image of the liver after administration of a contrast agent have been proposed.

  For example, during ultrasonography of the liver after contrast medium injection, significant liver tumors such as primary liver cancer, hepatocellular carcinoma are rapidly injected with contrast medium compared to surrounding normal tissue and benign tumors. Focusing on the fact that it will be detected by later containment and brightening, look for and identify the pixels or voxel areas where this fast containment and brightening of the contrast agent occur from the images acquired in time series after contrast agent injection An ultrasonic diagnostic system that highlights the positions of these pixels or voxels in a parameter image representing the distribution of the arrival time of a contrast medium in the liver is known (Patent Document 1).

  Also, as a perfusion CT application for the liver, etc., a predetermined color is assigned to each range of perfusion parameter values such as blood flow volume and time to peak enhancement, and color-coded display is performed for each perfusion parameter value in the image. Has been proposed (Patent Document 2).

  Also, in blood flow imaging with an ultrasound diagnostic device using a contrast agent, the hepatic artery and portal vein are identified and displayed by color-coding multiple blood flows with different contrast agent arrival times and blood concentration characteristics Has also been proposed (Patent Document 3).

  Furthermore, binarization segmentation and area expansion methods are performed on the X-ray CT image of the liver after contrast medium administration to extract the portal vein, liver parenchyma, and tumor part, and the position of the extracted portal vein core line The portal vein that feeds the tumor is identified by identifying which portal vein domain the extracted tumor belongs to, based on the blood vessel diameter, etc., and the liver parenchyma that the identified portal vein controls There has also been proposed an apparatus that identifies a cut region and displays it in a hue different from other regions (Patent Document 4).

On the other hand, as a liver-specific contrast agent for MRI, an agent containing paramagnetic sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient has been developed (hereinafter referred to as an EOB contrast agent). Gadoxetate sodium has a basic skeleton similar to meglumine (Gd-DTPA), a conventional non-specific extracellular fluid contrast agent, with a benzene ring and a lipophilic ethoxybenzyl group (EOB) added. Since it has a structure, it has the property of increasing its fat-solubility to enhance cell membrane permeability and to be selectively taken up into hepatocytes having normal functions. As a result, hepatocyte function that has not been realized with conventional contrast agents due to the contrast on the image of normal hepatocytes (high signal value) and impaired or lost hepatocytes (low signal value) It is expected that it will be possible to detect tumors (for example, cysts, metastatic liver cancer, most hepatocellular carcinomas), particularly small tumors, and distinguish benign and malignant liver tumors. In addition, the EOB contrast agent is distributed in the blood vessel and the cell gap after intravenous administration, and it becomes possible to take a contrast image of hepatocytes 20 minutes after administration, and the signal enhancement effect lasts for at least 2 hours, and Finally, it is excreted in urine and bile. Therefore, it is possible not only to diagnose blood flow using an image of blood flow dynamics from immediately after contrast medium administration to excretion, but also to evaluate hepatocyte function in the hepatocyte contrast phase. (Non-Patent Documents 1 to 3)
Then, using the dynamic MRI image using the EOB contrast agent, the time-series change (Time Intensity Curve: TIC) of the contrast agent concentration in the abdominal aorta and the liver parenchyma is obtained, and these time-series changes are determined by the deconvolution method. It has also been proposed to evaluate liver function by analyzing the above (Non-patent Document 3).

Japanese translation of PCT publication No. 2008-531082 JP 2006-334404 A JP 2002-238901 A JP 2003-033349 A

Hiroyuki Nakagawa, “Overview of Hepatocyte Specific MRI Contrast Agent“ EOB / Primobist Injection Syringe ”” [online], July 24, 2008, Gifu Prefectural Radiologists Association, Seino Branch, [July 2009 Search 10 days], Internet <URL: http://plaza.umin.ac.jp/~GifuART/sibu/seino/2008_seino_HP/pdf/dai1kai_syouroku_nakagawa.pdf> Satoshi Saito, “New Contrast Agent: Possibility of Sodium Gadoxetate (Education Lecture, Theme B: MR“ Rethinking Upper Abdominal MR Examination ”), Radiation Imaging Section Journal, October 1, 2008, No. 51 pp.30-33 Hun-Kyu Ryeom, et al., “Quantitative Evaluation of Liver Function with MRI Using Gd-EOB-DTPA”, Korean Journal of Radiology, Korea, The Korean Radiological Society, December 31, 2004, Vol. 5 (4) , pp. 231-239

  In liver diagnosis and surgery, it is common to use the area in the liver classified according to the dominant region of the hepatic artery, portal vein, and hepatic vein as a unit. As a specific example of such a classification method, Quinault sub-zone classification is known. Therefore, when the target region for excision is determined by liver image diagnosis, it is necessary to determine the liver area as a unit. At that time, it is required to determine which area is the target of resection by paying attention to the function level of each area, that is, to what extent each area in the liver functions normally.

  On the other hand, in the technique described in Patent Document 1, a contrast agent (microbubble) that is taken up by hepatocytes is used, but this technique is intended for detection of liver tumors, It does not focus on functional evaluation. In addition, the techniques described in Patent Documents 1 to 3 do not focus on an area in the liver. On the other hand, although the technique described in Patent Document 4 specifies a region in the liver, it does not focus on evaluating the function of the liver. Furthermore, the technique described in Non-Patent Document 3 is to evaluate the function of hepatocytes using an EOB contrast agent, but does not focus on an area in the liver. Therefore, it cannot be said that these conventional techniques can accurately meet the above-described needs in liver image diagnosis.

  The present invention has been made in view of the above circumstances, and focuses on the functional level of the liver and the area in the liver, and a medical image diagnostic apparatus and method that can perform image diagnosis more easily and appropriately, and The purpose is to provide a program.

  The medical image diagnostic apparatus of the present invention is a liver function contrast image representing a function level obtained after a predetermined time has elapsed after a contrast agent that produces a contrast effect according to the function level of the liver is administered to the subject. From the above, there is provided area function level information acquisition means for acquiring area function level information representing the function level of at least one area in the liver, and area function level presentation means for presenting the acquired area function level information. Features.

  The medical image diagnostic method of the present invention provides a liver function contrast image representing a function level obtained after a predetermined time has elapsed after a contrast agent that produces a contrast effect according to the function level of the liver is administered to a subject. From the above, there is provided a step of acquiring area function level information representing a function level of at least one area in the liver, and a step of presenting the acquired area function level information.

  The medical image diagnosis program of the present invention is for causing a computer to execute the above method.

  “Liver function level” means the degree of normal functioning of the liver.

  The “contrast agent that produces a contrast effect according to the function level of the liver” may cause a contrast effect that directly represents the function level of the liver, or a contrast effect that indirectly represents the function level of the liver. It may be a thing. The former contrast agent may be such that a higher (or lower) liver function level causes a larger amount of contrast agent to be taken up by hepatocytes, or only when the liver function level is normal. It may be selective such that the contrast medium is taken up by hepatocytes, or if the liver function is normal, an amount of contrast medium within a predetermined range is taken up by the hepatocytes and the liver function is abnormal. In some cases, a contrast agent greater than or less than that range may be incorporated. As a result of using a contrast agent that produces such a contrast effect, the obtained liver function contrast image reflects the degree of normality of the function of the liver, particularly the liver cells, as the level of the pixel value. Specific examples of such a contrast agent include an EOB contrast agent containing sodium gadoxetate (Gd-EOB-DTPA), which is a contrast agent used in MRI, and a superparamagnetic iron oxide particle preparation (SPIO contrast agent). ), Asialosinch (99mTc-GSA), which is a contrast agent used in SPECT. On the other hand, the latter contrast agent includes, for example, a contrast agent that directly targets the analysis of blood flow in the liver, and indirectly evaluates liver function by using a parameter that represents the degree of blood flow retention. can do. Specific examples include iodine-based contrast agents used in CT.

  The “at least one zone in the liver” may be a plurality of zones, in which case zone function level information is preferably presented for each zone.

  As a mode of presenting the area function level information, it is preferable to present the difference in the function level of the area in a visually identifiable manner. For example, when presenting area function level information of a plurality of areas, it is conceivable to display by color according to the function level of each area. Moreover, you may show area function level information using character information, an icon, etc.

  The modality for acquiring the liver function contrast image depends on the type of contrast agent. For example, in the case of an EOB contrast agent or SPIO contrast agent, a three-dimensional image obtained by MRI or the like, and particularly an image representing a time-series change in a three-dimensional space obtained by dynamic MRI or the like is preferable. . In the case of asialocinch, the image is obtained by SPECT.

  Here, the liver function contrast image may appropriately represent the liver function but may not clearly represent the anatomical structure of the liver. Therefore, a morphological image that appropriately represents the anatomical structure of the liver was separately acquired, and the liver area in the morphological image was identified for the morphological image, and the morphological image in the liver function contrast image was identified. What is necessary is just to specify the area corresponding to an area. Here, the “area corresponding to the area specified from the morphological image in the liver function contrast image” is determined by performing alignment between the morphological image and the liver in the liver function contrast image and using the alignment result. It may be. A specific example of the morphological image is an image obtained by CT.

  The “area” may be a three-dimensional area, and it is preferable to display the area function level information three-dimensionally. Here, specific examples of “three-dimensional display” include generating and displaying a pseudo three-dimensional image by volume rendering or the like, or cross-sectional images viewed from a plurality of viewpoints (for example, cross-sectional images of three orthogonal cross-sections). ) Is generated and displayed. The functional level of the area may include a time-series change in the functional level of the three-dimensional area.

  The liver function contrast image may further include image information of a reference region other than the liver, and the function level of the section may be obtained as a relative relationship with respect to the function level of the reference region. Here, a specific example of the reference region is a spleen region.

  According to the present invention, an area function representing a function level of at least one area in the liver from a liver function contrast image obtained by using a contrast medium that produces a contrast effect according to the function level of the liver to be diagnosed. Since the level information is acquired and presented, it is possible to easily and appropriately diagnose the liver image using the liver function level and the area in the liver. For example, when determining the target area for excision surgery by image diagnosis of the liver, the functional level of the liver can be confirmed for each liver area, so it is easy and appropriate to determine which area is the target for excision. It becomes possible to do.

1 is a schematic configuration diagram of a medical image diagnostic system using a liver function contrast image in an embodiment of the present invention. The block diagram which showed typically the structure and the flow of a process which implement | achieve the medical image diagnostic function using the liver function contrast image in the 1st Embodiment of this invention Graph showing the time-series change (Time Intensity Curve: TIC) of the liver function contrast image The figure which represented typically the structural example of the display screen in embodiment of this invention. The figure showing an example of the user interface at the time of extracting a liver region A figure showing an example of the user interface when specifying the liver area The figure which shows an example of the volume rendering image which displayed the function evaluation value for every liver area by color coding The figure which shows the other example of the volume rendering image which displayed the function evaluation value for every liver area color-coded The block diagram which showed typically the structure and process flow which implement | achieve the medical image diagnostic function using the liver function contrast image in the 2nd Embodiment of this invention Block diagram showing details of liver region extraction unit The figure which shows an example of the reference point of a liver area | region, and its vicinity area | region The figure for demonstrating one method of extracting an object area | region by the area | region extraction part of FIG. Diagram for explaining one method of setting s-link and t-link values based on the position of seed point and reference point The figure for demonstrating one method of extracting an object area | region by the area | region extraction part of FIG.

  Hereinafter, a medical image diagnostic system using a liver function contrast image according to an embodiment of the present invention will be described with reference to the drawings.

  FIG. 1 is a hardware configuration diagram showing an outline of a medical image diagnostic system. As shown in the figure, in this system, a modality 1, an image storage server 2, and an image processing workstation 3 are connected via a network 9 in a communicable state.

In the first embodiment of the present invention, modality 1 includes an MRI apparatus. In addition, this MRI apparatus is used before administration of a contrast agent (EOB contrast agent) containing sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient and, for example, 20 seconds, 1 minute, 2 minutes, 5 minutes after administration. Three-dimensional medical images (voxel data) V _t0 , V _t1 ,..., V _t6 of the abdomen (including the liver) after 10 minutes and 20 minutes (hereinafter collectively referred to as a three-dimensional dynamic image V _tn ) It is possible to perform dynamic shooting to acquire Here, as described above, since the EOB contrast agent has a property of being selectively taken up by normal hepatocytes, the three-dimensional dynamic image V _tn obtained by such imaging is a liver function representing the liver function level. It corresponds to a contrast image.

The image storage server 2 is a computer that stores and manages, in an image database, three-dimensional dynamic images V _tn acquired by the modality 1 and image data of medical images generated by image processing at the image processing workstation 3. A capacity external storage device and database management software (for example, ORDB (Object Relational Database) management software) are provided.

The image processing workstation 3 performs image processing (including image analysis) on the three-dimensional dynamic image V _tn acquired from the modality 1 or the image storage server 2 in response to a request from the interpreter, and generates a generated image. In particular, an input device such as a keyboard or a pointing device for inputting a request from an interpreter, and a main capacity having a capacity capable of storing at least a part of the acquired three-dimensional medical image three-dimensional dynamic image _Vtn. A storage device and a display for displaying the generated image are provided. The medical image diagnosis process of the present invention is implemented in this image processing workstation, and this process is realized by executing a program installed from a recording medium such as a CD-ROM. The program may be installed after being downloaded from a storage device of a server connected via a network such as the Internet.

  The storage format of image data and communication between devices via the network 9 are based on a protocol such as DICOM (Digital Imaging and Communications in Medicine).

  FIG. 2 is a block diagram showing a part related to the medical image diagnosis process according to the first embodiment of the present invention, among the functions of the image processing workstation 3. As shown in the figure, the medical image diagnosis processing according to the first embodiment of the present invention includes a liver region extraction unit 31, a liver region specification unit 32, a region function level calculation unit 33, a display image generation unit 34, and a display unit 35. It is realized by. Details of each processing unit and the like will be described below.

The liver region extraction unit 31 extracts the liver region RL _tn from the three-dimensional dynamic image V _tn using, for example, the method proposed by the present applicant in Japanese Patent Application No. 2008-50615.

Specifically, a user operates a pointing device or the like on a three-dimensional image in a certain time phase (for example, a three-dimensional image V _t6 of a hepatocyte contrast phase in which the contrast between the liver region and the surrounding region is large) While setting an arbitrary point in the liver region (hereinafter, this point is referred to as a user set point), using a discriminator obtained by machine learning, detecting the corner of the liver region contour as a reference point, Using each classifier (voxel) in a three-dimensional region (hereinafter referred to as a processing target region) having a size including the liver around the user set point, a discriminator acquired by machine learning is used. An evaluation value indicating whether the point is on the contour is calculated, each point on the outer periphery of the processing target region is determined in advance as a point in the background region outside the liver region, and the user set point and the reference point are in the liver region point On a previously determined if there further by using an evaluation value of each point of the processing target area, by applying a graph-cut method, extracting the liver region RL _t6 from the three-dimensional image V _t6 (a more detailed description Is the supplementary explanation at the end).

For other time-phase three-dimensional images (for example, V _t0 ,..., V _t5 ), liver regions RL _t0 ,..., RL _t5 are extracted from each three-dimensional image using the same method as described above. _{Alternatively} , a region in which coordinate values coincide with the liver region RL _t6 extracted from the three-dimensional image V _t6 may be set as liver regions RL _t0 ,..., RL _t5 in other time phases.

  For the extraction of the liver region, other known methods such as the method described in JP-A-2002-345807 may be used.

The liver section specifying unit 32 specifies a plurality of liver sections RS1 _tn , RS2 _tn ,... In the liver area RL _tn in each time phase. Specifically, the user operates a pointing device or the like to designate a plane or curved surface that is a boundary of a plurality of liver sections in the liver region (for example, liver region RL _t6 of the hepatocyte contrast phase), thereby A plurality of liver sections (eg, RS1 _t6 , RS2 _t6 ,...) Within the region are identified. The plurality of identified liver sections are three-dimensional regions. For other time phase liver regions (for example, RL _t0 ,..., RL _t5 ), liver segments RS1 _t0 , RS2 _t0 ,..., RS1 _t1 ,. _{RS2 t1, ···, RS1 t5,} RS2 t5, may be to identify ..., has been liver segments RS1 _t6 identified in liver region RL _t6, RS2 _t6, ... and coordinate values match area May be identified as the liver segment in each time phase.

  In order to identify the liver region, blood vessels in the liver region are extracted, and each point in the region other than the blood vessel in the liver region (liver parenchyma, etc.) is determined from that point using the Voronoi diagram. A method for identifying the control region of each blood vessel as a liver region by determining that it is controlled by a nearby blood vessel, that is, by identifying which blood vessel region other than the blood vessel in the liver region belongs to (Patent Document 4 mentioned above, R Beichel et al., “Liver segment approximation in CT data for surgical resection planning”, Medical Imaging 2004: Image Processing. Edited by Fitzpatrick, J. Michael; Sonka, Milan, 2004, Proceedings of the SPIE, Volume 5370, pp. 1435-1446, etc.) and other known methods can also be used.

Based on the three-dimensional dynamic image V _tn , the section function level calculation unit 33 calculates evaluation values FS1, FS2,... Representing liver function levels in the section for each liver section RS1 _tn , RS2 _tn . That is, the zone function level calculation unit 33 calculates the evaluation value FS1 based on the image information of the liver zones RS1 _t0 , RS1 _t1 ,..., RS1 _t6 , and the liver zones RS2 _t0 , RS2 _t1 ,. _An evaluation value FS2 is calculated based on the image information at _t6 , and so on, and an evaluation value for each liver segment is calculated.

  Here, the following values are mentioned as specific examples of evaluation values for each liver segment.

(1) Average pixel value For each liver area, the average of all pixel values in each time phase of each pixel in the area is calculated.
(2) Maximum average pixel value For each liver area, the average value of the pixel values of each pixel in the area is calculated for each time phase, and the maximum value of the calculated average values is obtained.
(3) Minimum average pixel value For each liver area, the average value of the pixel values of each pixel in the area is calculated for each time phase, and the minimum value of the calculated average values is obtained.
(4) Area under curve in time-series change of average pixel value For each liver section, the average value of the pixel value of each pixel in the section is calculated for each time phase, and as shown in FIG. Each time phase (time), the vertical axis is the average pixel value, and the area under the curve when graphing the time-series change of the average pixel value for each liver section (see the shaded area in FIG. 3).
(5) Peak time of average pixel value in time-series change of average pixel value Similar to (4), time when average pixel value peaks when time-series change of average pixel value is graphed for each liver section (See t _{peak in} FIG. 3).
In addition, the average pixel value, the maximum average pixel value, the minimum average pixel value, and the area under the curve of (1), (2), (3), and (4) are also calculated for the entire liver region. The ratio of values in the liver area may be used as the evaluation value. In addition, using the analysis method of Non-Patent Document 3, the time-series changes of the pixel values in each of the abdominal aorta and each liver segment were obtained, and these time-series changes were obtained by analyzing the deconvolution method. An evaluation value may be used. Furthermore, it is conceivable to divert evaluation values used in known perfusion analysis for CT images.

  The display image generation unit 34 generates an image Img of the liver region based on the evaluation values FS1, FS2,. Specifically, the image Img is generated by performing known volume rendering on the three-dimensional image data in which all the pixels in each liver section of the liver region have the evaluation value of the liver section as the pixel value. (See FIGS. 7 and 8). At this time, by assigning a different color to each evaluation value range, a difference in function level for each liver segment is imaged in a visually identifiable manner.

  The display unit 35 is a display for displaying the image Img.

  Next, the flow of medical image diagnosis processing according to the first embodiment of the present invention will be described with reference to the block diagram of FIG. 2 and the screen examples of FIGS.

First, when a test order for a liver function test using an EOB contrast agent is received at the terminal of the ordering system in the MRI radiographing room, the subject subject to the test order can take an image of the abdomen before administration of the EOB contrast agent. A three-dimensional medical image V _t0 is generated. Thereafter, an EOB contrast agent is administered to the subject. For example, similar imaging is performed at each timing after 20 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes, and 20 minutes from the administration, and the three-dimensional Medical images V _t1 ,..., V _t6 are generated. The generated three-dimensional medical images V _t0 , V _t1 ,..., V _t6 , that is, the three-dimensional dynamic image V _tn are transferred to the image storage server 2 and stored, and the image processing workstation 3 is installed. The examination order (interpretation order) is transferred to the read interpretation room or diagnosis room.

When this inspection order is selected in the image processing workstation 3, the image processing workstation 3 acquires the interpretation target 3D dynamic image V _tn from the image storage server 2 and analyzes the contents of the inspection order. An application corresponding to the inspection order, that is, an EOB analysis application is started here. FIG. 4 schematically shows the configuration of the initial screen 50 of the EOB analysis application. As shown in the figure, the initial screen 50 includes an image display area 51A, a coronal image display area 51C, a sagittal image display area 51S, and another image display area 51X, and user operations. As a menu part for the above, a phase selection menu 52 for selecting a time phase (phase) of an image displayed in each display area 51A, 51C, 51S, a liver extraction menu 53 which is an interface for extracting a liver area, liver A liver area specifying menu 54 that is an interface for specifying an area, a parameter selection menu 55 for selecting the type of evaluation value (parameter) of the function level of the liver area, and a user interface for performing detailed setting of a display image are displayed. And a display setting button 56.

Here, by the initial setting of the application, based on the hepatocyte contrast phase V _t6 of the three-dimensional dynamic image V _{tn to be} interpreted, cross-sectional images by three cross sections orthogonal to each other at a predetermined position are reconstructed, and each is axial. It is assumed that images are displayed in the image display area 51A, the coronal image display area 51C, and the sagittal image display area 51S. The predetermined position is preferably an optimal position for designating a user set point at the time of extraction of the liver region, but the position of the cross section is moved according to the user operation, and the cross section is moved to the position after the movement. The display may be updated by reconstructing the image. Thereby, the user can designate a user set point on a desired cross-sectional image.

Next, the user performs an operation for extracting a liver region. Specifically, when the user presses the intra-region designation button 53a of the liver extraction menu 53 using a pointing device or the like, the liver region extraction unit 31 changes the shape of the cursor of the pointing device to a specific shape and prompts the user. Prompt for user setpoint specification. The user performs a click operation of the pointing device at the point P1 in the liver region of the axial cross-sectional image as shown in FIG. 5 to designate the point P1 as the user set point. Furthermore, when the user presses the execution button 53b of the liver extraction menu 53, the liver extraction process is performed by the liver region extraction unit 31, and the liver region RL _t6 is extracted from the three-dimensional image _Vt6 , as shown in FIG. As described above, the volume rendering image of the liver region in the hepatocyte contrast phase is displayed in the display region 51X. At this time, in the background, liver regions RL _t0 to RL _t5 in other time phases are also extracted.

Next, the user performs an operation for specifying the liver area. Specifically, when the user presses the boundary designation button 54a of the liver area specifying menu 54 using a pointing device or the like, the liver area specifying unit 32 changes the shape of the cursor of the pointing device to a specific shape and prompts the user. Encourage the designation of the boundary (line) of the liver area. For example, when the pointing device is dragged within the liver region of the volume rendering image as shown in FIG. 6, the line segments L1 and L2 are designated as the boundary surface of the liver area. Further, when the user presses the execution button 54b of the liver area specifying menu 54, the liver area specifying unit 32 performs the above-described liver area specifying process. In this example, three liver areas RL _tn in each time phase are included in the liver area RL _tn . Identify liver segments RS1 _tn , RS2 _tn , RS3 _tn .

  Further, when the user selects a desired parameter from the parameter selection menu 55, the zone function level calculation unit 33 performs an evaluation value (here, function level evaluation value) in each liver zone based on the selected parameter by the above-described calculation method. Then, FS1, FS2, and FS3) are calculated. Here, the area under the curve in the time series change of the average pixel value described above is selected and the ratio check box is checked, so the area under the curve of each liver area relative to the area under the curve calculated for the entire liver The area ratio is calculated.

  Then, the display image generation unit 34 generates the display image Img by performing the volume rendering described above using the calculated evaluation values FS1, FS2, and FS3. The generated image Img is displayed in the display area 51X. 7 and 8 are display examples of the image Img. FIG. 7 is an example of a volume rendering image when two liver areas are specified. On the other hand, FIG. 8 is an example of a volume rendering image when three liver sections are specified. The opacity setting is changed from the image example of FIG. By assigning colors to the blood vessels in the liver, both the color-coded display of each liver area and the display of the blood vessel portion are made compatible. Such a change in display setting can be performed by a detailed setting interface for a display image displayed when the user presses the display setting button 56.

As described above, according to the embodiment of the present invention, from the liver function contrast image V _tn obtained by using the EOB contrast agent that produces the contrast effect according to the function level of the liver to be diagnosed, the area function The level calculation unit 33 acquires evaluation values FS1, FS2,... Representing the functional levels of a plurality of liver sections RS1 _tn , RS2 _tn ,... In the liver region RL _tn , and the display image generation unit 34 evaluates them. Since the image Img based on the value is generated and the generated image Img is displayed on the display unit 35, an easy and appropriate liver image diagnosis using the function level of the liver and the area in the liver becomes possible. For example, when determining the target area for excision surgery by image diagnosis of the liver, the functional level of the liver can be confirmed for each liver area, so it is easy and appropriate to determine which area is the target for excision. It becomes possible to do.

  Hereinafter, modifications to the above embodiment will be described.

In the above embodiment, the position of the liver region may be shifted between each time phase due to body movement, breathing, etc. of the subject, so the liver region extracted in one time phase or the specified liver area If the coordinates are simply applied to other time phases, there is a possibility that an accurate liver region or liver area cannot be specified in other time phases. Therefore, a known rigid or non-rigid registration (for example, Rueckert D Sonoda LI, Hayes C, et al., “Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images”) is applied to the three-dimensional dynamic image V _tn . , IEEE Transactions on Medical Imaging, 1999, Vol.18, No.8, pp.712-721) and imaging region recognition processing for axially discontinuous images (for example, see JP-A-2009-095644) In addition, the liver region is aligned by parallel movement / rotation / deformation / enlargement / reduction, etc. between three-dimensional images of different time phases, and the movement direction / movement amount of each pixel between the time phases must be obtained in advance. Is preferred. In this case, by converting the coordinate value of the liver region or the specified liver area extracted in one time phase using the moving direction and moving amount of each pixel between the other time phases. It becomes possible to accurately identify the liver region and the liver area in other time phases.

  In the above embodiment, a plurality of liver sections are specified, and the function evaluation value for each section is imaged. However, only one liver section is specified, the function evaluation value of the section is calculated, and the evaluation value The liver area is displayed in the color according to the value of the liver, and then the other liver area is identified, the function evaluation value of the area is calculated, and the liver area is displayed in the color according to the value of the evaluation value. The function evaluation value may be displayed by sequentially specifying one area at a time.

  In the above embodiment, an example in which the ratio to the entire liver region is used when calculating the evaluation value of the function level is shown. However, the ratio to the spleen region in which uptake of the EOB contrast agent occurs is used. It is also possible (see FIG. 8). In the case of an image by MRI, the absolute value of the signal is meaningless, and the relative relationship with other regions is significant. Thus, by calculating the function evaluation value using another organ as a reference region, More appropriate function evaluation becomes possible. In this case, the spleen region can be extracted by applying the same method as the liver region extracting method described above.

  In the above embodiment, since the display image Img is a pseudo three-dimensional image using volume rendering, the function evaluation for each area is performed from various angles by changing the position of the viewpoint and the direction of the line of sight. However, depending on the performance of the image processing workstation, the display image Img may be a two-dimensional projection image. Further, instead of displaying the function evaluation value by color or in addition to the color display, the actual evaluation value may be displayed by characters as shown in FIG. 8, or an icon or the like may be displayed instead of characters. May be used.

  The EOB contrast agent used in the above embodiment is an example of a contrast agent that produces a contrast effect according to the functional level of the liver, and other contrast agents that produce the same action, that is, normal liver functional level. It may be another contrast agent capable of acquiring a liver function contrast image in which the height of the pixel value is reflected. For example, a SPIO contrast agent may be used.

The second embodiment of the present invention uses a liver function contrast image in which the liver function is appropriately expressed but the liver anatomy is not clearly represented and a morphological image representing the liver anatomy. This is a case where medical image diagnosis processing is performed. In the second embodiment, the modality 1 includes a SPECT device and a CT device in the hardware configuration of FIG. The SPECT apparatus is a three-dimensional medical image V2 _t1 , V2 _t2 , ... (hereinafter collectively referred to as a three-dimensional dynamic image V2 _tn ) of the thoracoabdominal region (including the heart and liver) after a predetermined time has elapsed since the administration of _asialosinci. It is possible to perform dynamic shooting to acquire the image. This three-dimensional dynamic image V2 _tn corresponds to a liver function contrast image representing the function level of the liver, but unlike the first embodiment, the anatomical structure of the liver is not clearly represented. Therefore, in the present embodiment, a three-dimensional morphological image V3 representing the anatomical structure of the liver is further acquired by a CT apparatus.

  FIG. 9 is a block diagram showing a part related to the medical image diagnosis process according to the second embodiment of the present invention, among the functions of the image processing workstation 3. As shown in the figure, the medical image diagnosis processing according to the second embodiment of the present invention includes a liver region extraction unit 31, a liver region specifying unit 32, an alignment processing unit 36, a region function level calculation unit 33, and a display image generation. This is realized by the unit 34 and the display unit 35.

  Here, the liver region extracting unit 31 and the liver region specifying unit 32 are configured from the three-dimensional morphological image V3 in the same manner as in the first embodiment, respectively, the liver region RL3, a plurality of liver regions R3S1, R3S2,. Get.

The alignment processing unit 36 aligns the liver region between the three-dimensional dynamic image V2 _tn and the three-dimensional morphological image V3 using the non-rigid registration described above, and moves each pixel between the images. Outputs direction / movement amount DR.

The zone function level calculation unit 33 uses the coordinate values of the liver zones R3S1, R3S2,... In the three-dimensional morphological image V3 specified by the liver zone specification unit 32 for each pixel obtained by the alignment processing unit 36. By converting using the moving direction and moving amount DR, the corresponding positions in the three-dimensional dynamic image V2 _{tn of} the liver sections R3S1, R3S2,... (Hereinafter referred to as the liver sections R2S1, R2S2 in the three-dimensional dynamic image V2 _tn ) ,...) Is specified, and evaluation values F2S1, F2S2,... Representing the liver function level in the specified three-dimensional dynamic image V2 _{tn for} each liver area R2S1, R2S2,. To do.

Display image generating unit 34, a three-dimensional form each liver segment in the image V3 R3S1, R3S2, all pixels in ... are three-dimensional dynamic images V2 corresponding liver segments in _tn R2S1, R2S2, ··· The image Img ₂ is generated by performing known volume rendering on the three-dimensional image data having the evaluation values F2S1, F2S2,.

The generated image Img ₂ is displayed on a display which is the display unit 35.

As described above, in the second embodiment of the present invention, for the three-dimensional morphological image V3 representing the anatomical structure of the liver, the liver areas R3S1, R3S2,. Corresponding to the liver areas R3S1, R3S2, ... identified from the three-dimensional morphological image V3 in the three-dimensional dynamic image V2 _{tn that} is identified and the liver function is contrasted using the movement direction and movement amount DR of each pixel Since the liver areas R2S1, R2S2,... Are specified, even if the anatomical structure of the liver is not clearly represented in the three-dimensional dynamic image V2 _tn , the three-dimensional morphological image V3 By supplementing the information of the anatomical structure of the liver, it is possible to obtain evaluation values F2S1, F2S2,... Representing the functional levels of the liver sections R2S1, R2S2,... From the three-dimensional dynamic image V2 _tn. Become.

In the above embodiment, the liver area specifying unit 32, the alignment processing unit 36, and the area function level calculating unit 33 perform the process of specifying the liver areas R2S1, R2S2,... In the three-dimensional dynamic image V2 _tn. The combination is equivalent to the area specifying means of the present invention.

Further, in the above embodiment, if the photographing conditions such as the photographing posture and the setting of the coordinate axis of the generated image are matched between the SPECT device and the CT device, the alignment processing by the alignment processing unit 36 is unnecessary, The coordinate values of the liver sections R3S1, R3S2,... Obtained from the three-dimensional morphological image V3 can be used as they are for the three-dimensional dynamic image V2 _tn .

  In addition, the technical scope of the present invention includes various modifications made to the system configuration, processing flow, module configuration, and the like in the above embodiments without departing from the spirit of the present invention. For example, in the above-described embodiment, each process shown in FIG. 2 has been described as being performed by one image processing workstation 3, but each process is distributed to a plurality of image processing workstations to perform cooperative processing. You may comprise.

  Moreover, said embodiment is an illustration to the last and all the above description should not be utilized in order to interpret the technical scope of this invention restrictively.

[Supplementary explanation: Details of the liver region extraction unit 31]
The details of the method for extracting a liver region will be described below with reference to the specification of Japanese Patent Application No. 2008-50615. For the sake of brevity, the method for extracting from a two-dimensional image will be described first, and then 3 A method of extracting from a dimensional image will be described.

  As shown in FIG. 10, the liver region extraction unit 31 extracts a liver region from the medical image P, and includes a reference point detection unit 110, a point setting unit 120, a region setting unit 130, a contour likelihood calculation unit 140, An area extraction unit 150 is provided.

  The reference point detection unit 110 is a pixel and a reference point that represent a reference point in a plurality of sample images that exist on the outline of the liver region and whose reference points that can be specified based on the pixel value distribution in the neighboring region are known. For each of the pixels representing points other than the above, the pixel value distribution in the neighboring region is machine-learned in advance, and the reference point in the medical image P is detected based on the machine learning result. And a detection unit 112.

  Here, the reference points are determined according to the type of the target area to be extracted from the input image, and the number thereof is not limited. In the present embodiment, for example, as shown in FIG. 11, one or both of point A and point B existing at an angular position in the outline of the liver region consisting of a generally smooth curve is used as a reference point. .

  The discriminator acquisition unit 111 prepares a plurality of sample images including a liver region as described in, for example, Japanese Patent Application Laid-Open No. 2006-139369, and includes pixels other than the pixels representing the reference points and the reference points in the sample images. For each pixel representing a point, the pixel value distribution in the neighborhood area is pre-machine-learned to determine whether each pixel in the sample image is a pixel indicating a reference point in the pixel value distribution in the neighborhood area of the pixel. The discriminator D for identifying based on this is acquired. The discriminator D acquired in this way can be applied when discriminating whether each pixel in an arbitrary medical image is a pixel indicating a reference point. When a liver region is extracted using two or more reference points (for example, reference points A and B), the classifier D is acquired for each reference point.

  Here, the neighboring area of each pixel is the reference point based on the pixel value distribution of the neighboring area such as the direction in which the pixel value changes in each pixel in the neighboring area and the magnitude of the change. It is desirable that the area has a size that can be identified. In addition, this neighborhood region may be a region centered on the pixel, or a region having the pixel at a position off the center.

FIG. 11 shows an example of the vicinity regions R _A and R _B of the reference points A and B. Here, the case where the vicinity area is a rectangular area is illustrated, but the vicinity area may be an area having various shapes such as a circle and an ellipse. Further, only the pixel value distribution of some pixels in the vicinity region may be used for the machine learning.

  In addition, for the process of obtaining the discriminator D, an adaboosting algorithm, a neural network, a support vector machine (SVM), or the like can be used.

  The detection unit 112 detects the reference points A and B in the medical image P by scanning the classifier D acquired by the classifier acquisition unit 111 on the medical image P. In addition, before the detection of the reference point by the detection unit 21, when the determination region T that is supposed to include the entire target region is set in the region setting unit 130 described later, of the entire medical image P, The reference point in the medical image P may be detected by scanning the discriminator D only over the set discrimination region T or a part of the region including the discrimination region T.

  The point setting unit 120 sets an arbitrary point C (seed point) in the liver region in the medical image P. For example, the point setting unit 120 uses a keyboard or a pointing device provided in the liver region extraction unit 31 to The position on the medical image P designated based on the input may be set as an arbitrary point, and a constant mass is set at each point in the liver region automatically detected by the conventional target region detection method. May be set as the arbitrary point.

In addition, when the reference points A and B are detected by the reference point detection unit 110 before the arbitrary point C is set by the point setting unit 120, the liver region and the anatomical points of the reference points A and B are detected. With an arbitrary positional relationship, an arbitrary point C (x _C , y _C ) is set using the reference point A (x _A , y _A ) and the reference point B (x _B , y _B ) according to the following equation (1). You may make it do.

  The arbitrary point C may be set at the approximate center of the liver region or may be set at a position deviated from the center of the liver region.

  The region determination unit 30 sets a determination region T that is supposed to include the entire liver region in the medical image P. For example, an operator using a keyboard or a pointing device provided in the liver region extraction unit 31 The region on the medical image P designated based on the input of the above may be set as the discrimination region T, or there may be a liver region centered on the point C set in the point setting unit 120 An area having a size larger than the size may be automatically set as the discrimination area T. As a result, the range of the region of interest from the entire medical image P can be limited, and subsequent processing can be speeded up.

  The discrimination region T can adopt various shapes such as a rectangle, a circle, and an ellipse as the peripheral shape.

  The contour likelihood calculation unit 140 calculates the contour likelihood of each pixel in the discrimination region T set by the region setting unit 130 based on the pixel value information of the neighboring pixels of the pixel, and the evaluation function acquisition unit 141 and a calculation unit 142.

  The evaluation function acquisition unit 141 prepares a plurality of sample images including a liver region, and pixel value information of neighboring pixels for each of a pixel representing a point on the contour and a pixel representing a point other than the contour in the sample image Is previously learned as a feature amount in the pixel, thereby obtaining an evaluation function F for evaluating whether each pixel in the sample image is a pixel indicating an outline based on the feature amount.

  Specifically, as described in, for example, Japanese Patent Laid-Open No. 2007-307358, pixel value information of neighboring pixels of each pixel, for example, an area of 5 pixels in the horizontal direction × 5 pixels in the vertical direction centering on the pixel An evaluation function combining a plurality of weak discriminators for determining whether or not the pixel is a pixel indicating an outline using a combination of pixel value values of a plurality of different pixels in the combination of all the weak discriminators F is sequentially generated until it can be evaluated with a desired performance whether each pixel in the sample image is a pixel indicating an outline.

  The evaluation function F acquired in this way can be applied when evaluating whether each pixel in an arbitrary medical image is a pixel indicating the outline of the liver region.

  Also, a machine learning technique such as an adaboosting algorithm, a neural network, or a support vector machine (SVM) can be used for the process of obtaining the evaluation function F.

  Based on the feature amount of each pixel in the discrimination region T, the calculation unit 142 calculates, using the evaluation function F, the likelihood of the contour of each pixel, that is, an evaluation value as to whether or not the pixel represents a contour. Is.

  The region extraction unit 150 extracts a liver region from the discrimination region T using an arbitrary point C, a reference point S, and the contour likeness of each pixel. For example, Yuri Y. Boykov, Marie-Pierre Jolly, “Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in ND images”, Proceedings of “International Conference on Computer Vision”, Vancouver, Canada, July 2001 vol.I, p.105-112., US Pat. No. 6973212 When the discrimination region T is divided into a liver region and a background region by a graph cut method (Graph Cuts) described in the specification etc., the boundary between the liver region and the background region exists on the outline of the liver region. A liver region is extracted from the discrimination region T so as to pass through the reference points A and B without fail.

Specifically, as shown in FIG. 12, first, a node N _ij (i = 1, 2,..., J = 1, 2,...) Representing each pixel in the discrimination region T and labels that each pixel can take. (In this embodiment, nodes S and T representing liver regions or background regions), a link n-link connecting nodes of adjacent pixels, a node N _ij representing each pixel, and a node S representing a liver region A graph composed of a link s-link that connects, a node N _ij that represents each pixel, and a link t-link that connects a node T that represents the background region is created.

Here, in the n-link, there are four links from each node to four adjacent nodes for each node N _ij representing each pixel in the determination region, and each adjacent node has a mutual node. There are two links going to. Here, the four links from each node N _ij to the four adjacent nodes represent the probability that the pixel indicated by the node is a pixel in the same region as the four adjacent pixels. The likelihood is obtained based on the contour likeness of the pixel. Specifically, when the contour likelihood of the pixel indicated by the node N _ij is less than or equal to the set threshold value, a predetermined maximum value of the probability is set for each of those links, and the contour likelihood is set to the threshold value. In the case described above, the probability of a smaller value is set for each link as the contour likelihood increases. For example, when the maximum value of the probability is set to 1000, when the likelihood of the contour of the pixel indicated by the node N _ij is equal to or less than the set threshold value (zero), four lines from the node to four adjacent nodes are displayed. If a value of 1000 is set for the link and the contour likelihood is greater than or equal to the set threshold (zero), the value calculated by the following formula (1000-(the contour likelihood / maximum value of the contour likelihood) x 1000) is used. Can be set for each link. Here, the maximum value of the contour likelihood means the maximum value of all the contour likelihoods calculated for each pixel in the discrimination region T by the calculation unit 142.

Further, the s-link connecting the node N _ij representing each pixel and the node S representing the liver region represents the probability that each pixel is a pixel included in the liver region, and the node N _ij representing each pixel The t-link connecting the nodes T representing the background area represents the likelihood that each pixel is a pixel included in the background area, and the certainty indicates whether the pixel is either the liver area or the background area. When information indicating whether the pixel is a pixel to be shown has already been given, the information is set according to the given information.

Specifically, since the arbitrary point C is a pixel set in the liver region, as shown in FIG. 12, the s-link that connects the node N ₃₃ indicating the point C and the node S indicating the liver region. Set the probability of a large value to. In addition, the discrimination region T set to include the liver region on the basis of an arbitrary point set in the liver region usually includes the liver region and a background region existing around the liver region. Therefore, it is assumed that each pixel on the periphery of the discrimination region TT2 is a pixel indicating the background region, and nodes N ₁₁ , N ₁₂ ,..., N ₁₅ , N ₂₁ , N ₂₅ indicating those pixels are indicated. , N ₃₁ , and a node T representing the background area are set to a probability of a large value.

  Further, as shown in FIG. 13, the reference point between the reference point A and the point C among the entire line segments extending in the direction passing through the reference points A and B from the point C set in the point setting unit 120. Since each pixel located in the portion between B and point C can be determined to be a pixel existing inside the liver region, the node indicating each pixel and the node S representing the liver region are connected. The probability of a large value is set in s-link, and each pixel located in a portion extending from the reference point A to the opposite side of the point C and a portion extending from the reference point B to the opposite side of the point C is the liver. Since it can be determined that the pixel exists outside the region, the probability of a large value is set in the t-link that connects the node indicating each pixel and the node T indicating the liver region.

  Since the liver region and the background region are mutually exclusive regions, for example, as shown by a dotted line in FIG. 14, all the n-links, s-links, and t-links are cut off. By separating the node S from the node T, the discrimination region T is divided into a liver region and a background region, and the liver region is extracted. Here, optimal region division can be performed by performing cutting so that the total probability of all n-links, s-links, and t-links to be cut is minimized.

  In this example, the case of extracting the liver region using the graph cut method (Graph Cuts) is illustrated, but instead, for example, dynamic planning as described in JP 2007-307358 A The liver region may be extracted using other methods such as determining the contour of the liver region using a method.

  Next, an example of processing performed when a liver region is extracted from the medical image P with the above configuration will be described.

First, the discriminator D _A , B that can identify whether each pixel in any medical image acquired in advance by the discriminator acquisition unit 111 is a pixel indicating any of the reference points _A and B. _{Using B} , reference points A and B in the medical image P are detected. Next, the point setting unit 120 uses the reference point A (x _A , y _A ) and the reference point B (x _B , y _B ) in the target area in the medical image P according to the above equation (1). An arbitrary point C (x _C , y _C ) (seed point) is set in. Next, the region setting unit 130 sets a discrimination region T that is supposed to include the entire target region in the medical image P. Next, the determination unit 142 uses the evaluation function F that is acquired in advance by the evaluation function acquisition unit 141 to evaluate whether each pixel in an arbitrary medical image is a pixel indicating the outline of the liver region. The contour likeness of each pixel in T is calculated. Finally, the area extraction unit 150 determines that the outline of the liver area is the reference point A based on the set arbitrary point C and the calculated outline of each pixel by, for example, the graph cut method (Graph Cuts). The target region is extracted from the discrimination region T so as to pass through B, and the process is terminated.

  According to the above embodiment, an arbitrary point is set in the target area in the input image, and a determination area that is supposed to include the entire target area is set in the input image. When calculating the contour likeness of each pixel based on pixel value information of neighboring pixels of that pixel and extracting the target area from the input image based on the set arbitrary point and the calculated contour likeness of each pixel In addition, a pixel that represents a reference point and a reference in a plurality of sample images that exist on the contour of the target region of the same type as the target region and have known reference points that can be identified based on the pixel value distribution of the neighboring region For each pixel representing a point other than a point, the pixel value distribution in the neighboring area is machine-learned in advance, the reference point in the input image is detected based on the machine learning result, and the target area is further based on the detection result To do the extraction Therefore, even if there is a pixel value distribution such as a contour inside or outside the target area, the target must be surely passed through the reference point detected as a point existing on the correct contour of the target area. The contour of the region can be determined, and the extraction performance of the target region can be further improved.

  In the above-described embodiment, the case where the liver region extraction unit 31 of the present invention is applied to the one that extracts the target region from the two-dimensional input image has been described. However, the target region is extracted from the three-dimensional input image. It can also be applied to things.

  For example, when determining the contour of a liver region in a three-dimensional medical image, the same as when extracting a liver region from a two-dimensional medical image, the corner of the contour of the liver region consisting of a smooth surface as a whole A point existing in is used as a reference point.

  The discriminator acquisition unit 111 prepares a plurality of three-dimensional sample images including the liver region, and for each of the voxels representing the reference points and the voxels representing points other than the reference points in the sample images, By performing machine learning in advance on the pixel value distribution, a discriminator is obtained that identifies whether each voxel in the sample image is a voxel indicating a reference point based on the pixel value distribution in the region near the voxel. Here, the neighborhood area of each voxel is the reference point based on the pixel value distribution in the neighborhood area, such as the direction in which the pixel value in each voxel within the neighborhood area changes, the magnitude of the change, and the like. It is desirable that the region is a three-dimensional region that is large enough to be identified. The detection unit 112 detects a reference point in the medical image by scanning the classifier acquired by the classifier acquisition unit 111 on the three-dimensional medical image.

  Further, the point setting unit 120 sets an arbitrary point C in the three-dimensional coordinate system in the liver region of the three-dimensional medical image, and the region determining unit 30 includes the liver region in the three-dimensional medical image. A three-dimensional existence range that is supposed to include the whole of is set. Here, the existence range can adopt various shapes such as a hexahedron and a sphere as the peripheral shape.

  In addition, the evaluation function acquisition unit 141 prepares a plurality of three-dimensional sample images including a liver region, and for each of the voxels representing points on the contour and points other than the contour in the sample images, By performing machine learning in advance on the pixel value information of neighboring voxels, an evaluation function F that can evaluate whether each voxel in an arbitrary three-dimensional medical image is a voxel indicating the outline of the liver region is acquired. Here, as the pixel value information of neighboring voxels, for example, pixels in a plurality of different voxels in a cubic region of 5 voxels in the X-axis direction × 5 voxels in the Y-axis × 5 voxels in the Z-axis centering on the voxel. A combination of values can be used. Next, the calculation unit 142 uses the evaluation function F to calculate the likelihood of the outline of each voxel based on the feature amount of each voxel in the discrimination region T, that is, the evaluation value of whether the voxel is a voxel indicating the outline. To calculate.

  When the region extraction unit 150 divides the three-dimensional discrimination region T into a liver region and a background region by a three-dimensional graph cut method (Graph Cuts) described in, for example, US Pat. No. 6,732,212, The liver region is extracted from the discrimination region T so that the boundary between the liver region and the background region always passes through the reference point detected by the detection unit 112, and the process ends.

31 Liver region extraction unit 32 Liver segment specification unit 33 Zone function level calculation unit 34 Display image generation unit 35 Display unit 36 Position processing unit

Claims (16)

At least one area in the liver from a liver function contrast image representing the function level obtained after a predetermined time has elapsed after administering a contrast agent that produces a contrast effect according to the function level of the liver to the subject. Area function level information acquisition means for acquiring area function level information representing the function level of
A medical image diagnostic apparatus comprising: an area function level presenting unit for presenting acquired area function level information. The zone function level information acquisition means acquires the zone function level information in each of a plurality of zones in the liver,
2. The medical image diagnosis apparatus according to claim 1, wherein the area function level presenting unit presents the area function level information for each of the plurality of areas.   The medical image diagnostic apparatus according to claim 2, wherein the area function level presenting means presents a difference in function level for each area in a visually distinguishable manner. The liver function contrast image is a three-dimensional image or an image representing a time-series change in a three-dimensional space,
The medical image diagnostic apparatus according to claim 1, wherein the section is a three-dimensional region.   5. The medical image diagnosis apparatus according to claim 4, wherein the area function level presenting means presents area function level information three-dimensionally.   6. The medical image diagnostic apparatus according to claim 1, further comprising an area specifying unit that specifies an area in the liver.   The area specifying means specifies the area of the liver in the morphological image for the morphological image representing the anatomical structure of the liver, and corresponds to the area specified from the morphological image in the liver function contrast image. The medical image diagnostic apparatus according to claim 6, wherein an area is specified as an area in the liver. The liver function contrast image further includes image information of a region other than the liver,
The medical image diagnostic apparatus according to any one of claims 1 to 7, further comprising liver region extracting means for extracting a liver region from the liver function contrast image. The liver function contrast image further includes image information of a reference region other than the liver,
The medical image according to any one of claims 1 to 8, wherein the area function level information acquisition unit obtains the function level of the area as a relative relationship with respect to the function level of the reference area. Diagnostic device.   The medical image diagnostic apparatus according to claim 9, further comprising reference area extraction means for extracting the reference area.   The medical image diagnostic apparatus according to claim 9 or 10, wherein the reference region is a spleen region of the subject.   The medical image diagnostic apparatus according to claim 1, wherein the liver function contrast image is an image in which a degree of normality of a liver function is reflected as a pixel value.   The medical image diagnostic apparatus according to claim 1, wherein the liver function contrast image is an image acquired by MRI.   The medical diagnostic imaging apparatus according to claim 13, wherein the contrast agent contains sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient. At least one area in the liver from a liver function contrast image representing the function level obtained after a predetermined time has elapsed after administering a contrast agent that produces a contrast effect according to the function level of the liver to the subject. Obtaining area functional level information representing functional levels of
And a step of presenting the acquired area function level information. On the computer,
At least one area in the liver from a liver function contrast image representing the function level obtained after a predetermined time has elapsed after administering a contrast agent that produces a contrast effect according to the function level of the liver to the subject. Obtaining area functional level information representing functional levels of
And a step of presenting the acquired area function level information.