JP2011092677A - Medical image diagnostic apparatus and method using liver angiographic image, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To understand a time series variation in signal values of a region of interest in each time phase in detail and effectively based on liver angiographic images obtained at a plurality of time phases in a contrast examination using a contrast agent that reflects a liver blood flow and liver function in an image. <P>SOLUTION: A region-of-interest setting part 32 sets a region of interest in the liver positionally corresponding to each of liver angiopgraphic images between each time phase. A local image generation part 34 generates local sectional images representing an area of interest for each time phase based on each of liver angiographic images in each time phase. A display control part 36 causes the generated local sectional image for each time phase to be displayed in a comparative readable mode. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a medical image diagnostic apparatus and method for performing image diagnosis based on a liver contrast image obtained by using a contrast agent in which blood flow and liver function in the liver are reflected in an image, and a program.

  During liver ultrasonography with contrast media, significant liver tumors such as primary hepatocellular carcinoma, hepatocellular carcinoma, have been detected after rapid infusion of contrast media compared to surrounding normal tissue and benign tumors. Focusing on detection by early containment and brightening, look for and identify pixels or voxel areas where this fast containment and brightening of contrast agent occurs from images acquired in time series after contrast agent injection, There is known an ultrasonic diagnostic system that highlights the positions of these pixels or voxels in a parameter image representing the distribution of contrast agent arrival times in the liver (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).

  On the other hand, as a device for reconstructing and displaying a two-dimensional cross-sectional image from three-dimensional image data, a device that displays an image of three orthogonal cross sections of axial, sagittal, and coronal is well known (Patent Document 3).

  An apparatus for comparative interpretation of a plurality of series of medical images, which generates and displays cross-sectional images at the same cross-sectional position viewed from the same direction based on medical image data of each series. Is known (Patent Document 4).

  Furthermore, by capturing images of the same part such as the heart continuously in one imaging, a plurality of volume data with different imaging times are acquired, and a representative image of each volume data is generated and displayed in a reduced size. An apparatus is also known in which a marker indicating the shooting time of the volume data is superimposed on each displayed representative image (Patent Document 5).

By the way, as a liver-specific contrast agent for MRI, a substance containing paramagnetic sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient (hereinafter referred to as an EOB contrast agent) has been developed. Sodium gadoxetate has a basic skeleton similar to meglumine gadventetate (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 (eg, cysts, metastatic liver cancer, most hepatocellular carcinomas), especially small tumors of 1 cm or less, and to distinguish benign and malignant liver tumors. (Non-Patent Documents 1 to 3)
In the contrast examination of the liver using the EOB contrast agent, the site where the contrast effect appears depends on the elapsed time after administration of the contrast agent. Specifically, in the arterial phase taken at the timing 20 to 35 seconds after contrast medium administration, an abundant region of blood vessels in the liver is strongly imaged by the EOB contrast agent flowing into the liver from the hepatic artery. When the equilibrium phase is 3 minutes after administration of the contrast agent and the hepatocyte contrast phase is 20 minutes after administration, the EOB contrast agent is taken into the normal hepatocytes, so the normal hepatocyte portion is strongly imaged. The concentration of the EOB contrast agent at the site of abundantly imaged blood vessels decreases.

Therefore, it is considered that in liver image diagnosis using an EOB contrast agent, it is important to make a comprehensive judgment based on images obtained at a plurality of time phases after contrast agent administration. For example, if there is a bloody liver tumor in the liver parenchyma, that portion is strongly contrasted in the arterial phase and becomes a high-signal region in the image. On the other hand, in the equilibrium phase and hepatocyte contrast phase, the contrast agent concentration in the hepatic hepatic tumor portion decreases, and since this tumor portion is not a normal part in the liver parenchyma, EOB contrast agent is not taken up, It becomes a low signal area in the image. In other words, in the case of a polycytic liver tumor, the arterial phase is imaged as a high-signal region reflecting blood flow, and the equilibrium phase or hepatocyte contrast phase is imaged as a low-signal region reflecting abnormal liver function. As described above, it is possible to detect and differentiate with high accuracy by analyzing the change of the signal value in each phase in time series. (Non-Patent Documents 2 and 3)
Further, the EOB contrast agent is distributed in the blood vessel and intercellular space after intravenous administration, and imaging of the hepatocyte contrast phase becomes possible 20 minutes after administration, and the signal enhancement effect lasts for at least 2 hours, and Eventually excreted in urine and bile. Therefore, it is possible not only to diagnose by blood flow evaluation 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)

Japanese translation of PCT publication No. 2008-531082 JP 2006-334404 A JP 2004-105256 A JP 2007-282656 A JP 2009-148422 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 Yuji Kato, “New MRI Contrast Agent for Hepatocellular Carcinoma Screening”, [online], February 22, 2008, Nikkei Medical Online, [Search July 10, 2009], Internet <URL: http: // medical.nikkeibp.co.jp/leaf/all/search/cancer/news/200802/505562.html>

  In order to efficiently perform liver image diagnosis using an EOB contrast agent, it is required to accurately grasp time-series changes in signal values of a region of interest in a plurality of imaging time phases.

  On the other hand, since the parameter images described in Patent Documents 1 and 2 are images that are compressed in the time axis direction, the time-series changes in the signal value of the region of interest are grasped in detail. It is difficult. Also, in actual image diagnosis, it is not realistic to make a decision only from the parameter image, and the decision is made by returning to the original image. Display in a mode suitable for improvement is required.

  Therefore, when considering the adoption of the above-described prior art relating to the display of medical images, for example, the image display device described in Patent Document 3 is applied, and the entire screen is divided into small areas corresponding to the number of imaging time phases to be displayed. By displaying an image of three orthogonal cross sections in one imaging time phase in a small area, it is conceivable that images of three orthogonal cross sections are displayed side by side for a plurality of time phases on the screen. Due to this limitation, the size of each cross-sectional image in each imaging time phase becomes small, and it is difficult to grasp in detail the time-series change in the signal value of the region of interest. In addition, since the displayed cross-sectional image represents the entire subject in the cross-section, it is necessary to specify the region of interest in each cross-sectional image, and efficiently grasp time-series changes in the signal value of the region of interest. In that respect, it cannot be said that it has sufficient functions.

  Further, in the medical image display device described in Patent Document 4, if each series of medical images corresponds to a medical image at each imaging time phase associated with administration of the EOB contrast agent, a plurality of imaging time phase cross-sectional images are arranged in parallel. Although the displayed cross-sectional image is an image representing the entire subject in the cross-section, if the number of series to be displayed, that is, the number of imaging time phases is increased, each cross-sectional image is displayed. Therefore, it is difficult to grasp in detail the time-series change of the signal value of the region of interest as described above. There are also issues with efficient specific points of interest.

  Furthermore, in the apparatus described in Patent Document 5, although medical images at each imaging time phase are displayed as a list, a region of interest does not always exist in the representative image displayed at each imaging time, and is displayed. Since the representative image is also reduced, it is difficult to grasp in detail the time-series change in the signal value of the region of interest.

  The present invention has been made in view of the above circumstances, based on liver contrast images acquired in a plurality of time phases in a contrast examination using a contrast agent in which blood flow and liver function in the liver are reflected in the image, It is an object of the present invention to provide a medical image diagnostic apparatus and method, and a program that make it possible to grasp in detail and efficiently a time-series change in a signal value of a region of interest in each time phase.

  The medical diagnostic imaging apparatus according to the present invention provides two or more attention time phases among liver contrast images acquired in a plurality of time phases in a contrast examination using a contrast agent in which blood flow and liver function in the liver are reflected in the image. Region-of-interest setting means for setting a region of interest in the liver corresponding to each of the time phases of interest for each of the images, and the region of interest based on each of the liver contrast-enhanced images of the phase of interest A local image generating unit that generates a local image representing a region for each time phase of interest and a display control unit that displays the generated local image for each time phase of interest in a manner capable of comparative interpretation are provided. And

  The medical image diagnostic method of the present invention provides two or more attention time phases among liver contrast images acquired at a plurality of time phases in a contrast examination using a contrast agent in which blood flow and liver function in the liver are reflected in the image. Setting the region of interest in the liver corresponding to each of the time phases of interest for each of the images, and representing the region of interest based on each of the contrast-enhanced liver images of the phase of interest A step of generating a local image for each time phase of interest and a step of displaying the generated local image for each time phase of interest in a manner that allows comparative interpretation are performed.

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

  In the present invention, the liver contrast image is obtained by a contrast examination using a contrast agent in which the blood flow and liver function in the liver are reflected in the image. The reflected image is displayed.

  Moreover, it is preferable that the contrast agent used in the contrast examination reflects the blood flow in the liver and the liver function in the image at different timings. A specific example is an MRI contrast agent containing sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient.

  Further, all of a plurality of time phases in which the liver contrast image is acquired in this contrast examination may be set as the attention time phase, or two or more attention time phases may be selected from the plurality of time phases. As a specific selection method, a method of selecting a time phase of interest according to an elapsed time after administration of a contrast agent can be considered. For example, when a time phase of interest is selected at a predetermined time interval after contrast medium administration, or when a predetermined time (for example, 20 seconds, 70 seconds, 3 minutes, 20 minutes) has elapsed after contrast medium administration Can be selected as the time phase of interest. At this time, the arterial phase, which is the time when the contrast medium flows into the blood vessels in the liver from the hepatic artery, and the hepatocyte contrast phase, which is the time when the contrast medium is taken into normal hepatocytes, are included in the attention time phase. It is preferable to do so.

  “Set the region of interest in the liver corresponding to each time phase of interest” does not set a separate region of interest for the liver contrast image in each time phase of interest, but the same region of interest. Is set for the liver contrast image of each time phase of interest. Therefore, the set regions of interest correspond in position between the respective time phases of interest. Specifically, after setting the region of interest in a liver contrast image of a certain time phase of interest by manual setting by the user, automatic setting by image analysis, or a combination of both, the liver in another time phase of interest In the contrast image, it is conceivable to set a region having the same coordinate value as the region of interest set previously as the region of interest. In this case, considering the displacement of the region of interest between time phases due to body movement, breathing, etc., the size of the local image includes a range larger than the maximum possible displacement of the region of interest between the time phases of interest It is preferable to make it. Alternatively, in consideration of such displacement at the stage of setting the region of interest, after setting the region of interest in the liver contrast image of a certain time phase of interest, based on the content features of the liver contrast image of each time phase of interest The region of interest corresponding to the previously set region of interest may be set in the liver contrast image at another time phase of interest. Specifically, by performing linear and / or non-linear alignment with respect to the liver contrast image of each time phase of interest, the correspondence relationship between the coordinate values of the corresponding positions between the time phases is obtained. It is conceivable that the region of interest is set in the liver contrast image of another time phase of interest by converting the coordinate value of the region of interest set in (2) based on the corresponding relationship. Further, the positioning itself may be performed manually.

  Since the local image representing the region of interest in each time phase of interest is an image for observing the time-series change of the region of interest in detail, it may not be subjected to reduction processing, pixel thinning processing, or the like. preferable. Further, when the liver contrast image of each time phase is a three-dimensional image and the region of interest is a three-dimensional region, a local image obtained by viewing the region of interest from a plurality of directions may be generated for each time phase of interest. Specifically, it is conceivable to generate, for each time phase of interest, a cross-sectional image of three axial, sagittal, and coronal orthogonal cross sections that pass through the region of interest.

  Specific examples of modes that allow comparative interpretation when displaying local images for each time phase of interest include a mode in which local images for each time phase of interest are displayed side-by-side in one screen, or a local image for each time phase of interest. For example, the display mode may be switched and displayed in sequence order.

  Further, in the present invention, based on the liver contrast images acquired at a plurality of time phases, an entire image representing an area including at least the entire liver and including the region of interest is generated, and the local image and the entire image are generated. May be displayed simultaneously. Also for this whole image, when the liver contrast image of each time phase is a three-dimensional image and the region of interest is a three-dimensional region, the whole image viewed from a plurality of directions may be generated. At this time, the local image and the entire image are preferably images viewed from the same direction. Specifically, based on a liver contrast image of a certain time phase of interest, it is conceivable to generate a cross-sectional image of three orthogonal cross sections of axial, sagittal, and coronal that pass through the region of interest. At this time, the time phase of interest of the liver contrast image that is the basis for generating the entire image may be selectable by a user operation. The whole image may be a pseudo three-dimensional image obtained by performing volume rendering processing or the like on a three-dimensional liver contrast image at a certain time phase of interest. In addition, a functional image representing the liver function may be used as the entire image, or an image obtained by superimposing the morphological image such as the cross-sectional image and the functional image may be used as the entire image. The functional image is, for example, a liver function that represents a liver function in at least a part of a plurality of small regions obtained by extracting a liver region from a liver contrast image and dividing the liver region. An evaluation value can be calculated and generated based on the liver contrast image and the liver function evaluation value. Here, the small area obtained by dividing the inside of the liver area may be an area of one pixel (voxel), an area of about several pixels, or a liver area such as a Quino's liver area. The area may be a size that is divided into several pieces.

  Further, the entire image may be an image in which the position of the region of interest can be specified like the cross-sectional image of the three orthogonal cross sections described above, and a marker representing the region of interest may be superimposed on the entire image. Furthermore, it is assumed that the marker can be moved by the user using the input means, the position of the marker after the movement is detected, and based on the detected marker position, the liver contrast image that is the basis of the whole image A region of interest in the image may be set, and a corresponding region of interest may be set for each of the liver contrast images in the time phase of interest other than the liver contrast image in which the region of interest is set. Furthermore, a marker may be superimposed and displayed on the region of interest in the local image.

  Alternatively, the liver function evaluation value in the region of interest may be calculated for each time phase of interest, and the time-series change in the liver function evaluation value between the time phases in the region of interest may be visualized in a form such as a graph.

  According to the present invention, for each of the two or more liver contrast images of the time phase of interest acquired in the contrast examination using the contrast agent in which the blood flow and liver function in the liver are reflected in the image, The region of interest in the liver corresponding to the position is set in step, and a local image representing the region of interest is generated for each attention time phase based on each of the liver contrast images of the attention time phase. Since the local images are displayed in a manner that allows comparative interpretation, the displayed local images can be easily compared and interpreted for each time phase of interest, and the time-series changes in the signal value of the region of interest in each time phase of interest are displayed. It becomes possible to grasp in detail and efficiently.

  That is, the signal value of the region of interest between a plurality of time phases that could not be realized by the conventional technique for displaying the parameter image described in Patent Documents 1 and 2 by interpreting the displayed local image for each time phase of interest. It is realized by the present invention to grasp the time-series change in detail. Further, in the present invention, since only the region of interest that is a part of the entire image can be displayed, the region of interest in the reduced entire image is interpreted as in the prior art described in Patent Documents 3 to 5. Instead, if the screen size is the same as that of the prior art, a local image that is not reduced as much as the entire image can be interpreted, so that the region of interest in each time phase of interest can be observed in detail. Furthermore, since only the region of interest is displayed in the present invention, it is not necessary to specify the region of interest from the entire image as in the conventional techniques described in Patent Documents 3 to 5, and the interpretation efficiency is improved.

  As described above, the present invention accurately grasps time-series changes in signal values of regions of interest in a plurality of imaging time phases in a contrast examination using a contrast agent in which liver blood flow and liver function are reflected in an image. This is in response to the desire to do so, and is extremely useful in diagnostic imaging of the liver using such a contrast agent.

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 process flow which implement | achieve the medical image diagnostic function using the liver contrast image in embodiment of this invention 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 when selecting the display target phase of the whole image The figure showing another example of the user interface at the time of selecting the phase of the display target of the whole image The chart showing the operation | movement transition of the medical image diagnostic system in embodiment of this invention The figure showing the example of image display in embodiment of this invention The figure showing the other example of image display in embodiment of this invention The block diagram which showed typically the structure of the modification of embodiment of this invention, and the flow of a process Graph showing the time-series change (Time Intensity Curve: TIC) of the liver function contrast image The figure showing the example of an image display in the modification of embodiment of this invention 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 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. A diagram showing an example in which a marker is superimposed on the region of interest of the local image in the axial image display region

Hereinafter, a medical image diagnostic system using a liver contrast image according to an embodiment of the present invention will be described with reference to the drawings. The medical image diagnostic system according to the embodiment of the present invention is a liver contrast image (V [1], V [2 in FIG. 2) acquired in a plurality of phases (time phases) in the above-described contrast examination using the EOB contrast agent. ],..., V [8]; hereinafter, may be collectively referred to as V [t], where t represents the phase), and represents the entire liver in the phase specified by the user _A cross-sectional image of an axial, coronal, and sagittal section through the region of interest in the liver specified by (WI _A [T], WI _C [T], WI _S [T] in FIG. 2; 2) and a local cross-sectional image (LI in FIG. 2) through the region of interest and the corresponding region of interest in other phases of the liver contrast image. _{A [1] · LI C [} 1] · LI S [1], LI A [2] · LI C [2] · LI S [2], ···; (Hereinafter sometimes collectively referred to as LI [t]) for each phase, and the entire cross-sectional image WI [T] and the local cross-sectional image LI [t] are displayed side by side (see FIG. 6). ).

  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 this embodiment, the modality 1 includes an MRI apparatus. In addition, this MRI apparatus is used before administration of the aforementioned EOB contrast agent containing sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient and, for example, 20 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes after administration. Dynamic imaging for acquiring three-dimensional liver contrast images (voxel data) V [1], V [2],..., V [8] of the abdomen (including the liver) after elapse of minutes, 20 minutes, and 21 minutes. It is possible. The elapsed time after administration of the EOB contrast agent is associated with the liver contrast image of each phase obtained by this imaging as supplementary information.

  The image storage server 2 is a computer that stores and manages, in an image database, the three-dimensional liver contrast image V [t] acquired by the modality 1 and the image data of the medical image generated by the image processing at the image processing workstation 3. Yes, it has a large-capacity external storage device and database management software (for example, ORDB (Object Relational Database) management software).

  The image processing workstation 3 performs image processing (including image analysis) on the three-dimensional liver contrast image V [t] of one or more phases acquired from the modality 1 or the image storage server 2 in response to a request from the interpreter. ) And display the generated image, and has a known hardware configuration such as a CPU, main storage device, auxiliary storage device, input / output interface, communication interface, input device, display device, data bus, etc. A known operation system or the like is installed. In particular, the input device is provided with a pointing device (such as a mouse) for inputting a request from an interpreter, and the main storage device has a three-dimensional liver contrast image V [t ] Has a capacity capable of storing at least a part thereof. The medical image diagnosis process of the present invention is implemented in the image processing workstation 3, 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 processing according to the 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 embodiment of the present invention includes an input device 31, a region of interest setting unit 32, an entire image generation unit 33, a local image generation unit 34, a marker control unit 35, and a display control unit 36. This is realized by the display device 37. Details of each processing unit and the like will be described below. In the following description, the three-dimensional liver contrast image is referred to as a liver contrast image.

The input device 31 performs an operation of selecting a phase T to be generated and displayed for the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T], and the displayed overall cross-sectional image WI _A [T], WI _C [T], WI _S [T] is for the user to perform an operation of designating the center position MP [T] of the region of interest. In this embodiment, the pointing device such as a mouse is used. The description will be made assuming that The input device 31 may be another device such as a keyboard.

The region-of-interest setting unit 32 includes regions of interest VOI [1], VOI [2],... In the liver contrast images V [1], V [2],. The position of VOI [8] is specified. Specifically, the region-of-interest setting unit 32 is based on the center position MP [T] of the region of interest in the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T]. In the contrast image V [T], a cubic region having the number of pixels (voxels) whose one side centered on the position MP [T] corresponds to a predetermined length is set as the region of interest VOI [T]. Here, the length of one side is a predetermined length in consideration of the maximum possible displacement of the liver due to breathing or body movement of the subject during the contrast examination. Next, a region having the same coordinate value as the region of interest VOI [T] is set as a region of interest for each of the liver contrast images in phases other than phase T.

Based on the phase T designated by the user, the whole image generation unit 33 receives the phase-T liver contrast image V [T] as an input and uses a known MPR (Multi-Planar Reconstruction) technique to perform the phase T region of interest. The whole cross-sectional images WI _A [T], WI _C [T], and WI _S [T] are reconstructed by the axial, coronal, and sagittal sections passing through the center position of VOI [T].

The local image generation unit 34 receives the liver contrast images V [1], V [2],..., V [8] of each phase as input, and the regions of interest VOI [1], VOI of each phase among them. [2],..., VOI [8] The image data representing the inside of the region of interest VOI [1], VOI [2],. 8] Local cross-sectional images of axial, coronal, and sagittal sections passing through the center of LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [ 2], ..., LI _A [8], LI _C [8], LI _S [8] are reconfigured.

The marker control unit 35 acquires position information of the region of interest VOI [T] of the phase T based on the phase T specified by the user, and the position of the region of interest is the entire cross-sectional image WI _A based on the axial coronal sagittal cross section. generating a _{[T] · WI C [T} ] · WI S marker to indicate in _{[T] M a [T]} · M C [T] · M S [T]. Specific embodiments of the marker will be described later.

The display control unit 36 includes the entire cross-sectional images WI _A [T], WI _C [T], WI _S [T], the markers M _A [T], M _C [T], M _S [T], and the local cross-sectional images LI. _A [1] ・ LI _C [1] ・ LI _S [1], LI _A [2] ・ LI _C [2] ・ LI _S [2], ..., LI _A [8] ・ LI _C [8] Control is performed to display LI _S [8] on the display device (display) 37 in a predetermined layout.

FIG. 3 shows a layout example of the screen 50 displayed on the display device 37. As shown in the figure, the display screen 50 is roughly composed of an axial image display area 51A, a coronal image display area 51C, a sagittal image display area 51S, and an arbitrary image display area 51X. The axial image display area 51A includes an entire image display area 52A for displaying an entire section image WI _A [T] based on an axial section, and local section images LI _A [1] and LI _A [2] based on an axial section for each phase. ,..., LI _A [8] and local image display areas 53A [1], 53A [2],..., 53A [8]. In the entire image display area 52A, the marker M _A [T] is displayed superimposed on the entire cross-sectional image WI _A [T]. Specifically, the marker M _A [T] is an intersection of a straight line 54AS representing a sagittal section passing through the center point of the region of interest VOI [T] in the axial section image, a straight line 54AC representing a coronal section, and a straight line representing both sections. (Intersection line of both cross sections), that is, a point 55A representing the center of the region of interest VOI [T]. As described above, in this embodiment, the center point of the region of interest coincides with the intersection of the three cross sections of the axial coronal sagittal. Similar to the axial image display area 51A, the coronal image display area 51C / sagittal image display area 51S also has an entire image display area 52C for displaying the entire cross-sectional image WI _C [T] / WI _S [T] by the coronal / sagittal cross section. / 52S and local cross-sectional images of coronal / sagittal sections of each phase LI _C [1], LI _C [2], ..., LI _C [8] / LI _S [1], LI _S [2], ・..., local image display area 53C for displaying the _{LI S [8] [1]} , 53C [2], ···, 53C [8] / 53S [1], 53S [2], ···, 53S [ 8]. The marker M _C [T] in the entire image display area 52C of the coronal image display area 51C is a straight line 54CS representing a sagittal section passing through the center point of the region of interest VOI [T] in the coronal section image, and a straight line representing an axial section. 54CA is displayed as a point 55C representing the center of the region of interest VOI [T], and the marker M _S [T] in the entire image display region 52S of the sagittal image display region 51S is the region of the region of interest VOI [T] in the sagittal cross-sectional image. A straight line 54S representing a coronal section passing through the center point.
C, a straight line 54SA representing an axial section, and a point 55S representing the center of the region of interest VOI [T]. The arbitrary image display area 51X is an area where an arbitrary image can be displayed, and a plurality of images can be displayed by further dividing the area.

  4A and 4B show a specific example of a user interface for selecting the phase T by the input device 31. FIG. As shown in FIG. 4A, an interface for receiving a phase selection window by separately displaying a phase selection window and moving the slide lever by operating the input device 31 (for example, dragging the slide lever using a mouse) As shown in FIG. 4B, a phase selection menu is displayed by an operation of the input device 31 (for example, a right click operation of the mouse), and a selection operation of the input device 31 (for example, the mouse pointer is moved to a desired phase and left An interface that accepts selection of a phase by a click operation) is conceivable. In addition, an operation of selecting a desired phase T display area from the local image display areas 53A [t], 53C [t], 53S [t] in FIG. An interface that accepts the selection of a phase by performing a left-click operation after moving to the phase display area is also conceivable.

  On the other hand, as a specific example of the operation of changing the position of the center point MP [T] of the region of interest VOI [T] by the input device 31, input is performed at a desired position in the entire image display regions 52A, 52C, 52S of FIG. It is conceivable to perform a designation operation using the device 31. For example, a mouse double-click operation at a desired position, a straight line 54AS, 54AC, 54CS, 54CA, 54SC, 54SA or a center point 55A, 55C, 55S as markers in the entire image display areas 52A, 52C, 52S is dragged An operation of moving to a desired position by an operation is mentioned.

  Next, the flow of liver image diagnosis using the medical image diagnosis system according to the embodiment of the present invention will be described. FIG. 5 is a chart showing the transition of the operation in the medical image diagnostic process according to the embodiment of the present invention.

  First, when an examination order for a liver contrast examination using an EOB contrast agent is received at the terminal of the ordering system in the MRI radiographing room, the subject subject to the examination order takes an image of the abdomen before administration of the EOB contrast agent. And a liver contrast image V [1] is generated. Thereafter, an EOB contrast agent is administered to the subject, and, for example, similar imaging is performed at each timing after 20 seconds, 1 minute, 2 minutes, 5 minutes, 10 minutes, 20 minutes, and 21 minutes after administration. The liver contrast image V [2],..., V [8] is generated. The generated liver contrast images V [1],..., V [8] are transferred to and stored in the image storage server 2 and are examined in the interpretation room or diagnostic room in which the image processing workstation 3 is installed. The order (interpretation order) is transferred.

  When this examination order is selected in the image processing workstation 3, the image processing workstation 3 acquires the liver contrast image V [T] to be interpreted from the image storage server 2 and analyzes the contents of the examination order. Then, an application program corresponding to the inspection order, that is, an EOB analysis application is started here.

When the EOB analysis application is started, first, a setting file for this application is read, and the default value T ₀ of the phase to be displayed of the entire cross-sectional image and the default value MP [of the center point of the region of interest VOI [T ₀ ] T ₀ ] Get ₀ .

The region-of-interest setting unit 32 specifies the position VOI [t] ₀ of the region of interest in the liver contrast image V [t] of each phase based on the default value MP [T ₀ ] ₀ of the center point. Then, based on the default value T ₀ of the phase to be displayed and the default value VOI [T ₀ ] ₀ of the region of interest in the phase T ₀ , the whole image generation unit 33 performs the liver contrast image V [T of the phase T _{0. 0} ] as an input, the entire cross-sectional image WI [T ₀ ] ₀ to be displayed first is reconstructed, and the marker control unit 35 generates a marker M [T ₀ ] ₀ representing the position VOI [T ₀ ] ₀ of the region of interest. . In addition, the local image generation unit 34 regenerates the local cross-sectional image LI [t] ₀ that is initially displayed based on the region of interest VOI [t] ₀ of each phase and receives the liver contrast image V [t] of each phase as an input. Constitute. The display control unit 36 causes the display device 37 to display the generated entire slice image WI [T ₀ ] ₀ , marker M [T ₀ ] ₀ , and local slice image LI [t] ₀ in the layout shown in FIG. # 1).

The user observes the entire cross-sectional image WI [T ₀ ] ₀ and the local cross-sectional image LI [t] ₀ displayed on the display device 37, and if a lesion candidate area suspected to be a lesion is not found in the image, the user needs to Thus, it is possible to perform operations for changing the phase (# 2) of the display target of the whole cross-sectional image and changing the position of the region of interest, that is, the cross-section position of the whole cross-sectional image or the local cross-sectional image (# 4).

Here, when the user performs an operation of changing the phase to be displayed from T ₀ to T ₁ on the user interface shown in FIG. 4 (# 2), the phase value T ₁ after the change and the phase T ₁ in the phase T ₁ are changed. Based on the region of interest VOI [T ₁ ] ₀ , the whole image generation unit 33 reconstructs the whole cross-sectional image WI [T ₁ ] ₀ with the liver contrast image V [T ₁ ] of the phase T ₁ as an input, and a marker The control unit 35 generates a marker M [T ₁ ] ₀ representing the position VOI [T ₁ ] ₀ of the region of interest, and the display control unit 36 displays the entire cross-sectional image WI [T ₁ ] ₀ and the marker M [T ₁ ] _0. Update the display to (# 3). At this time, since the position of the region of interest is not changed, the display of the local cross-sectional image LI [t] ₀ is not updated.

On the other hand, when the user performs an operation of moving the markers in the entire image display areas 52A, 52C, and 52S in FIG. 3 on the display screen with default values, the position of the center point of the region of interest VOI [T ₀ ] ₀ is determined. When moving from MP [T ₀ ] ₀ to MP [T ₀ ] ₁ (# 4), the region-of-interest setting unit 32 determines the liver contrast of each phase based on the position MP [T ₀ ] ₁ of the center point after the movement. The position VOI [t] ₁ of the region of interest in the image V [t] is specified (# 5). Then, based on the phase T _{0 to be} displayed and the region of interest VOI [T ₀ ] ₁ after movement in the phase T ₀ , the whole image generation unit 33 inputs the liver contrast image V [T ₀ ] of the phase T _0. Then, the entire cross-sectional image WI [T ₀ ] ₁ is reconstructed, and the marker control unit 35 generates a marker M [T ₀ ] ₁ representing the position VOI [T ₀ ] ₁ of the region of interest after movement. Further, the local image generation unit 34 reconstructs the local cross-sectional image LI [t] ₁ using the liver contrast image V [t] of each phase as an input based on the region of interest VOI [t] ₁ of each phase after movement. To do. The display control unit 36 causes the display device 37 to display the generated entire slice image WI [T ₀ ] ₁ , marker M [T ₀ ] ₁ , and local slice image LI [t] ₁ in the layout shown in FIG. 3 ( # 6). In this way, when the position of the region of interest is changed, all displays of the entire cross-sectional image WI [T ₀ ] ₁ , the marker M [T ₀ ] ₁ , and the local cross-sectional image LI [t] ₁ are updated.

  Hereinafter, as indicated by the arrow from step # 3 to # 2 or # 4 in FIG. 5 or the arrow from step # 6 to # 2 or # 4, the user displays the entire cross-sectional image as necessary. Operations for changing the target phase (# 2) and changing the position of the region of interest (# 4) can be repeated, and accordingly, the displayed image and marker display are updated accordingly ( # 3, # 5, # 6). For example, when the user finds a lesion candidate region in the displayed image, the entire cross-sectional image of the phase suitable for observing the lesion candidate region and its peripheral region is displayed as necessary. Then, the phase change operation (# 2) of the entire cross-sectional image is performed to update the entire cross-sectional image and marker display (# 3), and the center of the candidate lesion area is the center of the region of interest (each Perform the marker movement operation (# 4) so that it becomes the intersection of the cross-sections (# 5), change the region of interest in each phase (# 5), and update the display of the whole cross-sectional image, marker, and local cross-sectional image (# 6 ). FIG. 6 is a screen example in which the entire cross-sectional image WI [8] of phase 8 is displayed and the local cross-sectional image LI [t] of the lesion candidate region in the image is displayed. As shown in the figure, the functional level of the hepatocytes in the lesion candidate region and the surrounding region is shown in the whole cross-sectional image WI by the contrast effect on normal hepatocytes in the late phase of contrast enhancement (phase 8: hepatocyte contrast phase) with the EOB contrast agent. [8] can be observed, and the time-series changes of the lesion candidate area associated with the change from the angiographic effect in the initial contrast (arterial phase) of the EOB contrast agent to the normal hepatocyte contrast in the late phase can be observed locally. It can be observed from the cross-sectional image LI [t].

As described above, according to the above-described embodiment of the present invention, the region-of-interest setting unit 32 acquires the liver contrast image acquired in the contrast examination using the EOB contrast agent in which the blood flow and liver function in the liver are reflected in the image. Regions of interest in the liver VOI [1], VOI [2],... Corresponding to each of V [1], V [2],. ., VOI [8] is set, and the local image generation unit 34 determines the region of interest VOI based on each of the liver contrast images V [1], V [2],. [1], VOI [2], ..., VOI [8] Local cross-sectional images LI _A [1] ・ LI _C [1] ・ LI _S [1], LI _A [2] ・ LI _C [2 ] · LI _S [2], ..., LI _A [8] · LI _C [8] · LI _S [8] are generated for each phase, and the display control unit 36 generates the local cross section for each phase. Images LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [2], ..., LI _A [8], LI _C [ 8] ・ LI _S [8] are displayed side by side in one screen LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [2], ..., LI _A [ 8] ・ LI _C [8] ・ LI _S [8] can be easily compared and interpreted, and the signal values of the regions of interest VOI [1], VOI [2], ..., VOI [8] in each phase It becomes possible to grasp the time-series changes in detail and efficiently. That is, the above-described embodiment of the present invention responds to a request for accurately grasping time-series changes in signal values of regions of interest in a plurality of phases in a contrast examination using an EOB contrast agent. It is extremely useful in the diagnostic imaging of the liver.

In addition, the entire image generation unit 33 displays the entire cross-sectional images WI _A [T], WI _C [T], which represent the region including the entire liver and the region of interest VOI [T] in the phase T to be displayed. WI _S [T] is generated, and the display control unit 36 performs overall cross-sectional images WI _A [T], WI _C [T], WI _S [T] and local cross-sectional images LI _A [1] and LI _C [1]. -LI _S [1], LI _A [2]-LI _C [2]-LI _S [2], ..., LI _A [8]-LI _C [8]-Displayed simultaneously with LI _S [8] Because of this, it is possible to observe the time-series changes of the region of interest VOI [1], VOI [2], ..., VOI [8] and the surrounding region at the same time, making more detailed diagnosis Is possible.

At that time, the marker control unit 35 uses the markers M _A [T] · M _C [T] representing the region of interest VOI [T] in the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T]. ] ・ M _S [T] is superimposed and displayed, so the position VOI [T] of the region of interest in the whole cross-sectional images WI _A [T], WI _C [T], WI _S [T] can be more easily It becomes possible to grasp and the interpretation efficiency improves.

The markers M _A [T], M _C [T], and M _S [T] can be moved by the user using the input device 31, and the marker M _A [T] The position MP [T] of M _C [T] · M _S [T] is detected, and the region-of-interest setting unit 32 determines the entire cross-sectional image WI _A [T] based on the detected position MP [T] of the marker. , WI _C [T], WI _S [T], the region of interest VOI [T] in the liver contrast image V [T] is set, and the liver contrast image V in which the region of interest VOI [T] is set If the region of interest VOI [t] is set for each of the liver contrast images V [t] of phases other than [T] (here, t indicates each phase other than T), the movement of the marker The regions of interest VOI [1], VOI [2],..., VOI [8] can be moved according to the operation, and the entire image generation unit 33 moves the region of interest VOI [T] after the movement in phase T. The whole cross-sectional images WI _A [T], WI _C [T], and WI _S [T] representing the The location image generation unit 34 in each phase shows local cross-sectional images LI _A [1] and LI _C [1] representing the regions of interest VOI [1], VOI [2],..., VOI [8] after movement. LI _S [1], LI _A [2], LI _C [2], LI _S [2], ..., LI _A [8], LI _C [8], LI _S [8] The display control unit 36 can display each generated image. That is, the regions of interest VOI [1], VOI [2],..., VOI [8] are linked to the movement operation of the markers M _A [T], M _C [T], and M _S [T] by the user. Whole cross-sectional images WI _A [T], WI _C [T], WI _S [T] representing the region of interest VOI [1], VOI [2],. Cross-sectional image LI _A [1] ・ LI _C [1] ・ LI _S [1], LI _A [2] ・ LI _C [2] ・ LI _S [2], ..., LI _A [8] ・ LI _C [8] ・ The display can be switched to LI _S [8]. Therefore, the user sets the region of interest VOI [T] in each part of the image while observing the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T], and the local cross-section at that position. Images LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [2], ..., LI _A [8], LI _C [ 8] ・ LI _S [8] is displayed, so it becomes possible to observe the time series change of the signal value in the part where the region of interest VOI [T] is set at the same time, and more detailed and flexible observation. It can be done efficiently.

In addition, the liver contrast images V [1], V [2],..., V [8] of each phase are three-dimensional images, and the regions of interest are three-dimensional regions VOI [1], VOI [2],. , VOI [8], and the entire image generation unit 33 generates the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T] viewed from a plurality of directions. Can be easily observed three-dimensionally, and more detailed diagnosis is possible. The local image generation unit 34 also has local cross-sectional images LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [ 2], ..., LI _A [8], LI _C [8], LI _S [8] are generated, so the regions of interest VOI [1], VOI [2], ..., VOI [8] Interpretation can be performed from a plurality of directions, and more detailed diagnosis is possible. For example, in a local cross-sectional image LI _A [1], LI _A [2], ..., LI _A [8], an area that looks like a circular lesion is a local cross-sectional image LI with a sagittal or coronal cross-section. _{If C} [1] ・ LI _S [1], LI _C [2] ・ LI _S [2], ..., LI _C [8] ・ LI _S [8] appear circular, the area is a lesion. On the other hand, if the local cross-sectional image of the sagittal cross-section or coronal cross-section looks tubular, the region is a blood vessel, and the local cross-section image of the axial cross-section has a cross-section of the blood vessel cut into a lesion. It can be determined that it just looked like this.

In the above embodiment, the local cross-sectional image LI represents an image representing a range larger than the maximum possible displacement of the regions of interest VOI [1], VOI [2],..., VOI [8] between phases. _A [1] ・ LI _C [1] ・ LI _S [1], LI _A [2] ・ LI _C [2] ・ LI _S [2], ..., LI _A [8] ・ LI _C [8]・ Because LI _S [8] is used, there is a problem that the region of interest deviates from the range of the local cross-sectional image in a certain phase due to the displacement of the region of interest between phases due to the breathing and body movement of the subject. It does not occur and the accuracy of observation of time-series changes in the region of interest is improved.

  Next, modifications to the above embodiment will be described.

In the above embodiment, the region-of-interest setting unit 32 sets a region in another phase having the same coordinate value as the region of interest VOI [T] specified by the user in the phase T to be displayed as the region of interest. However, 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 ", IEEE Transactions on Medical Imaging, 1999, Vol.18, No.8, pp.712-721), and imaging region recognition processing for axially disconnected images (for example, JP 2009-095644 A) , Etc.) are used to align the liver contrast images in different phases by parallel movement, rotation, deformation, enlargement, reduction, etc., and the movement direction and movement amount of each pixel between phases are obtained in advance. , Phase T to be displayed By converting the coordinate value of the specified region of interest VOI [T] using the movement direction / movement amount of each pixel to / from another phase, the coordinate value of the corresponding region of interest in the other phase May be requested. For alignment, for example, by allowing the local image shown in FIG. 6 to move in a direction parallel to the screen of the local image to be operated by a drag-and-drop operation or the like on each local image, You may enable it to perform manual correction with respect to the manual alignment of a user, and the automatic alignment result by the said method. As described above, in the case where the region of interest VOI [t] corresponding to the position is set for each of the plurality of phases of the liver contrast image V [t] based on the content feature of the image, The same anatomical position with respect to the liver contrast image V [t] of each phase without being affected by the displacement of the region of interest VOI [t] between phases due to the body movement or breathing of the subject. Since the region of interest VOI [t] can be set to the region of interest, the size of the region of interest (local cross-sectional image) has not been determined in consideration of the possible amount of displacement of the region of interest as in the above embodiment. However, the problem that the region of interest is not included in the local sectional image of a certain phase does not occur, and the accuracy of observation of the time-series change of the region of interest is improved.

  In the above-described embodiment, the marker control unit 35 superimposes and displays the marker representing the region of interest only in the entire cross-sectional image. However, the marker is also superimposed on the region of interest in the local image in each phase. You may make it display. FIG. 19 shows a display example in which a marker is attached to a local cross-sectional image. In the figure, only the axial image display area 51A is shown, but the markers may be displayed in the same manner for the local images in the coronal image display area 51C, the sagittal image display area 51S, and the arbitrary image display area 51X. Various forms of the marker can be adopted, and for example, an arrow or a text comment may be used.

  In the above embodiment, the local cross-sectional images of all phases are displayed side by side in one screen to facilitate comparative interpretation. However, the size of the display screen is reduced due to physical restrictions on the display size. When it is not possible to increase the size, when it is necessary to observe a local cross-sectional image representing a wider region of interest, or when it is necessary to enlarge and observe the region of interest, as illustrated in FIG. In the axial image display area 51A, the coronal image display area 51C, and the sagittal image display area 51S, an overall cross-sectional image of one phase of the axial coronal sagittal is displayed, and in the arbitrary image display area 51X, one phase. Only the local cross-sectional images of the axial coronal sagittal are displayed side by side, and the operation of the input device 31 (for example, the local cross-section image) Such as by performing a wheel operation of the mouse) after clicking one of may be a local cross-section images of each phase so as to time-sequentially switched and displayed. With such a display mode, it is possible to capture time-series changes in the region of interest as a moving image.

  In the above-described embodiment, the liver contrast images V [t] of all phases obtained by dynamic imaging using the modality 1 (MRI apparatus) are used as the generation / display targets of the entire cross-sectional image and the local cross-sectional image. In the configuration of FIG. 2, an attention phase selection unit that selects a generation / display target phase may be further provided. The target phase selection unit may be one that automatically selects based on a predetermined selection condition, or one that is manually selected by a user operation. As a specific example of the automatic selection, the acquisition timing of the phase to be generated / displayed (for example, the elapsed time since contrast medium administration) is prepared in advance in a setting file or the like as a selection condition, and the liver contrast obtained by modality 1 is used. A method for selecting a contrast-enhanced liver image in a phase that matches the above-described selection conditions with reference to the supplementary information of the acquisition timing associated with each of the images V [t]. As a specific example of manual selection, representative images reconstructed from each of the liver contrast images V [t] obtained by the modality 1 (for example, axial cross-sectional images at predetermined positions) and information on the acquisition timing of the images Can be displayed as a list, and the user can use the input device 31 to select a generation / display target phase. By making it possible to select a generation / display target phase in this way, it is possible to observe only an image of a phase necessary for diagnosis, which contributes to improvement of diagnosis efficiency.

  In the above embodiment, the entire cross-sectional image WI [T] is generated and displayed from the liver contrast image V [T] of one display target phase T selected by the user. Alternatively, a functional image in which the time axis is compressed may be generated and displayed as an entire cross-sectional image using a plurality of liver contrast images of a plurality of phases. FIG. 8 is a block diagram showing such a modified example, in which a liver region extracting unit 38 and a liver function analyzing unit 39 are added to the block diagram shown in FIG.

  The liver region extraction unit 38 uses, for example, a method proposed by the present applicant in Japanese Patent Application No. 2008-50615, and uses a plurality of phases of liver contrast images V [1], V [2],. Extract liver regions LV [1], LV [2], ..., LV [8] from each of [8].

  Specifically, for a liver contrast image in a certain phase (for example, a liver contrast image V [8] in a hepatocyte contrast phase in which the contrast between the liver region and the surrounding region is large), the user operates a pointing device or the like. In addition, an arbitrary point in the liver region is set (hereinafter, this point is referred to as a user set point), and a corner portion of the liver region is detected as a reference point using a discriminator acquired by machine learning. Using a discriminator acquired by machine learning for each point (voxel) in a three-dimensional region (hereinafter referred to as a processing target region) having a size that includes the liver around the user set point, An evaluation value indicating whether the point is on the contour of the object 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 By applying the graph cut method using the evaluation value of each point in the processing target region in advance, the liver region LV [8] is determined from the liver contrast image V [8]. Extract (see supplementary explanation at the end for more detailed explanation).

  For the liver contrast images V [1],..., V [7] in the other phases, liver regions LV [1],. ], Or a region whose coordinate value coincides with the liver region LV [8] extracted from the liver contrast image V [8] is defined as a liver region LV [1],. [7] may be used. In addition, as described above, the liver region LV [8] is coordinate-transformed based on the result of alignment between different phases using rigid body or non-rigid registration, or imaging region recognition processing, etc. [1], ..., LV [7] may be specified.

  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 function analysis unit 39 includes a plurality of phases of the liver contrast image V [1], V [2],..., V [8], and a liver region LV [1] in each phase image. Based on LV [8], the liver region is divided into small regions in units of predetermined pixels (voxels), and a liver function evaluation value LF representing the liver function level is calculated for each divided small region. For example, the small region may be a cubic region having a side length of about 3 to 5 pixels (voxels), but for each pixel (voxel), that is, the small region has a length of one side. Alternatively, the liver function evaluation value LF may be calculated as a one-pixel cubic region.

Here, specific examples of the liver function evaluation value LF include the following values.
(1) Average pixel value For each small region, an average of all pixel values in each phase of each pixel in the small region is calculated.
(2) Maximum average pixel value For each small region, the average value of the pixel values of each pixel in the small region is calculated for each phase, and the maximum value of the calculated average value is obtained.
(3) Minimum average pixel value For each small area, the average value of the pixel values of each pixel in the small area is calculated for each 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 small region, the average value of the pixel values of each pixel in the small region is calculated for each phase, and as shown in FIG. The area under the curve when each phase (time) and the vertical axis represent the average pixel value and the time-series change of the average pixel value for each small region is graphed (see the shaded area in FIG. 9).
(5) Peak time of average pixel value in time-series change of average pixel value Time when average pixel value peaks when time-series change of average pixel value is graphed for each small region as in (4) (See t _{peak in} FIG. 9).
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 small area may be used as the evaluation value. In addition, evaluation values obtained by obtaining time-series changes in pixel values in each of the abdominal aorta and each liver segment and analyzing these time-series changes by the deconvolution method may be used (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). Furthermore, it is conceivable to divert evaluation values used in known perfusion analysis for CT images. Furthermore, when calculating the liver function evaluation value, a ratio to another organ, for example, a spleen region where EOB contrast agent uptake occurs may be used (see FIG. 13). In the case of an image by MRI, the absolute value of the signal has no meaning and has a meaning in the relative relationship with other regions. Thus, by calculating the liver function evaluation value using another organ as a reference region in this way. Therefore, 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.

Moreover, in this modification, the whole image generation unit 33 includes a plurality of phases of the liver contrast images V [1], V [2],..., V [8], and the calculated liver function evaluation value LF. Based on the above, the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T] are generated. Specifically, an overall cross-sectional image (morphological cross-sectional image) with an axial, coronal, and sagittal cross-section passing through the center position of the region of interest VOI [T] in a predetermined display target phase T is generated, and the liver function in each small region Taking the evaluation value LF as a three-dimensional functional image, generating a cross-sectional image (functional cross-sectional image) with the axial, coronal, and sagittal cross-sections passing through the center position, and generating an image in which both the generated images are superimposed Can be considered. FIG. 10 shows an example in which the image generated in this way is displayed on the display device 37 by the display control unit 36. As shown in the figure, in the liver region where the liver function evaluation value LF is calculated, the functional cross-sectional image is displayed superimposed on the morphological cross-sectional image. In addition, for the liver function evaluation value that is a pixel value in the functional cross-sectional image, a different color is assigned to each range of the liver function evaluation value, so that a difference in liver function level in the liver region can be visually identified. Map image. LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [2], ..., LI _A [8] LI _C [8] and LI _S [8] are the same as in the above embodiment.

As described above, according to the present modification, the whole image generation unit 33 further uses the function evaluation value LF of the liver region obtained by the liver region extraction unit 38 and the liver function analysis unit 39 to cause the whole image generation unit 33 to perform the whole cross-sectional image WI _A [ Since T], WI _C [T], and WI _S [T] are generated, the functional image representing the liver function and the local morphological image representing the region of interest can be displayed at the same time, which contributes to improvement in diagnostic efficiency.

In the above modification, the liver function evaluation value LF is calculated by dividing the liver area into relatively small areas, but the liver is divided into relatively large areas, such as the Quino's liver area that divides the entire liver area into eight. May be. In liver image diagnosis and surgery, it is generally performed in units of areas in the liver classified according to the dominant regions of the hepatic artery, portal vein, and hepatic vein. When determining the target area for surgery, it is necessary to determine the liver area as a unit, so the whole cross-sectional image WI _A [T], WI _C [T], WI _S showing the functional level of each area [T] and local cross-sectional images LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI representing the region of interest that is a candidate lesion in the liver By displaying _S [2], ..., LI _A [8], LI _C [8], and LI _S [8] at the same time, it is possible to more accurately meet the needs of the medical field as described above.

  As a specific method of dividing the liver section, as shown in FIG. 11, the user operates a pointing device or the like to display an image representing one liver region (for example, a liver region in the hepatocyte contrast phase). There is a manual division method for specifying a plurality of liver sections in the liver region by designating planes or curved surfaces (line segments L1 and L2 in FIG. 11) that serve as boundaries between the plurality of liver sections in the volume rendering image). . Here, the plurality of identified liver sections are three-dimensional regions. For liver regions in other phases, the liver area in each phase may be specified using the same method as described above, or an area whose coordinate value matches the liver area specified in the liver region in one phase. It may be specified as a liver area in each other phase, or manually set based on the result of registration between different phases using the above-mentioned rigid body or non-rigid registration, or imaging region recognition processing, etc. By converting the coordinate values of the liver area, the position of the corresponding liver area in another phase may be specified. In order to identify the liver area, blood vessels in the liver region are extracted, and a region other than the blood vessel (liver parenchyma, etc.) in the liver region belongs to which blood vessel control region by using a Voronoi diagram. By identifying the dominant region of each blood vessel as a liver segment (JP 2003-033349, 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.), known automatic segmentation methods can also be used.

  Further, in the above-described embodiment, the arbitrary image display area 51X in FIG. 3 has an image obtained under other imaging conditions, for example, observation of the contrast of a T1-weighted image or a T2-weighted image before administration of a contrast agent, You may make it display these images based on the knowledge that it is useful in a discrimination. Specifically, dysplastic nodule has a high signal on T1-weighted images and low signal on T2-weighted images, and well-differentiated liver tumors have a slightly higher signal from equal signals and T2-weighted images on T1-weighted images. It is known that the signal is slightly lower than the equal signal, and the moderately differentiated liver tumor is known to have a low signal in the T1-weighted image and a high signal in the T2-weighted image, before administration of the EOB contrast agent displayed in the arbitrary image display area 51X. By observing the T1-weighted image and the T2-weighted image together with the liver contrast image after administration of the EOB contrast agent displayed in the other display areas 51A, 51C, 51S, a more accurate liver image diagnosis can be performed. It becomes possible.

Alternatively, an image generated by other image processing may be displayed in the arbitrary image display area 51X in FIG. 12 and 13 are specific examples. In both figures, after dividing the liver region into a plurality of liver sections by the above method and calculating the liver function evaluation value for each liver section, the calculated liver function evaluation value is calculated for all the pixels in each liver section. It is an image generated by capturing the evaluation value of the liver area as three-dimensional functional image data having pixel values and performing known volume rendering using the three-dimensional functional image data as an input. By assigning a different color for each range of evaluation values, a difference in function level for each liver segment is imaged in a visually identifiable manner. FIG. 12 is an example of a volume rendering image when two liver areas are specified. On the other hand, FIG. 13 is an example of a volume rendering image in the case where three liver areas 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. In FIG. 13, the actual function evaluation value is also displayed as text information. In this way, the entire cross-sectional images WI _A [T], WI _C [T], and WI _S [T] representing the form of the entire liver region as shown in FIG. 6 and the region of interest that is a lesion candidate in the liver are shown. LI _A [1], LI _C [1], LI _S [1], LI _A [2], LI _C [2], LI _S [2], ..., LI _A [8] ], LI _C [8], LI _S [8], and further displaying an image representing the function evaluation value for each liver area as shown in FIG. 12 or FIG. In addition to being able to observe time-series changes in the region of interest and the area around the region of interest at the same time, as described above, it is possible to simultaneously evaluate liver function in units of liver segments that are important for liver image diagnosis. This is very effective for improving the efficiency of liver image diagnosis.

  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.

  For example, the EOB contrast agent used in the above embodiment is an example of a contrast agent in which blood flow and liver function in the liver are reflected in the image, and other contrast agents that cause the same effect, that is, blood in the liver. Other contrast agents capable of acquiring a liver contrast image in which the flow and liver function are reflected as high and low pixel values may be used.

  In the above embodiment, the entire cross-sectional image is displayed simultaneously with the local cross-sectional image. However, only the local cross-sectional image may be displayed, or the local cross-sectional image and those shown in FIGS. You may make it display the functional image for every liver area simultaneously. These display modes may be displayed automatically or based on a user operation.

  Furthermore, in the above embodiment, the entire cross-sectional image and the local cross-sectional image are interlocked at the same cross-sectional position, but different cross-sectional positions may be used.

  For the region of interest, a time-series change in the liver function evaluation value may be visualized and separately displayed. As a specific aspect of visualization, there is a graph or the like representing a time-series change of the average pixel value in the region of interest in each phase as shown in FIG.

[Supplementary explanation: Details of the liver region extraction unit 38]
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. 14, the liver region extraction unit 38 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. 15, 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 the 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. 15 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 38, and the like. 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 38 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 the 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. 16, 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 an arbitrary point C is a pixel set in the liver region, as shown in FIG. 16, an s-link that connects a node N ₃₃ indicating the point C and a 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.

  In addition, as shown in FIG. 17, 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 by 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. 18, 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, 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 38 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 determination 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.

  Further, the evaluation function acquisition unit 141 prepares a plurality of three-dimensional sample images including the liver region, and in 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 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, 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.

DESCRIPTION OF SYMBOLS 31 Input device 32 Area of interest setting part 33 Whole image generation part 34 Local image generation part 35 Marker control part 36 Display control part 37 Display apparatus 38 Liver area extraction part 39 Liver function analysis part

Claims (21)

For each of the images of the two or more time phases of interest among the liver contrast images acquired in a plurality of time phases in the contrast examination using the contrast agent in which the blood flow and liver function in the liver are reflected in the image, A region-of-interest setting means for setting a region of interest in the liver corresponding in position between each time phase of interest;
Local image generation means for generating a local image representing the region of interest for each of the attention time phases based on each of the liver contrast images of the attention time phase;
A medical image diagnostic apparatus comprising: display control means for displaying the generated local image for each time phase of interest in a manner that allows comparative interpretation.   The medical image diagnostic apparatus according to claim 1, wherein the local image is an image that has not been reduced. The liver contrast image of each time phase is a three-dimensional image,
The region of interest is a three-dimensional region;
The medical image diagnostic apparatus according to claim 1, wherein the local image generation unit generates the local image obtained by viewing the region of interest from a plurality of directions for each time phase of interest. Based on the liver contrast images acquired at the plurality of time phases, further comprising a whole image generating means for generating a whole image representing a region including at least the whole liver and including the region of interest;
The medical image diagnostic apparatus according to any one of claims 1 to 3, wherein the display control unit displays the local image and the entire image simultaneously. The whole image is an image generated so that the position of the region of interest can be specified,
The medical image diagnostic apparatus according to claim 4, further comprising a marker control unit that superimposes and displays a marker representing the region of interest in the entire image. The marker can be moved by the user using the input means,
The region-of-interest setting means detects the position of the marker after the movement, sets the region of interest in the liver contrast image that is the basis of the whole image based on the detected position of the marker, 6. The medical image according to claim 5, wherein the region of interest is set for each of the liver contrast images in the time phase of interest other than the liver contrast image in which the region of interest is set. Diagnostic device.   The medical image diagnostic apparatus according to claim 5, wherein the marker control unit is configured to superimpose and display the marker also on the region of interest in the local image in each of the time phases of interest. The liver contrast image of each time phase is a three-dimensional image,
The region of interest is a three-dimensional region;
The medical image diagnostic apparatus according to any one of claims 4 to 7, wherein the whole image generation unit generates the whole image viewed from a plurality of directions.   9. The system according to claim 4, wherein the whole image generation unit generates the whole image based on the liver contrast image of the one time phase of interest selected by a user operation. The medical image diagnostic apparatus according to Item. Liver region extraction means for extracting a liver region from the liver contrast image;
Liver function analysis means for calculating a liver function evaluation value representing a liver function in the small region for at least some of the small regions obtained by dividing the liver region,
The medical image diagnosis according to any one of claims 4 to 8, wherein the whole image generation unit generates the whole image based on the liver contrast image and the liver function evaluation value. apparatus.   11. The attention time phase selection means for selecting the two or more attention time phases from a plurality of time phases at which liver contrast images were acquired in the contrast examination. The medical image diagnostic apparatus described in 1.   The medical image diagnostic apparatus according to claim 11, wherein the attention time phase selection unit selects the attention time phase according to an elapsed time after administration of the contrast agent.   The medical image diagnostic apparatus according to claim 11 or 12, wherein the time phase of interest includes an arterial phase and a hepatocyte contrast phase.   14. The region of interest according to claim 1, wherein the region-of-interest setting means sets the region of interest corresponding to the position based on the characteristic features of the liver contrast image at the time phase of interest. The medical image diagnostic apparatus according to Item.   The medical image according to any one of claims 1 to 13, wherein the local image is an image representing a range larger than a maximum possible displacement value of the region of interest between the attention time phases. Diagnostic device.   It further comprises means for calculating a liver function evaluation value representing a liver function in the region of interest for each time phase of interest, and visualizing a time-series change between the time phases of interest of the calculated liver function evaluation value. The medical image diagnostic apparatus according to claim 1, wherein:   The medical diagnostic imaging apparatus according to any one of claims 1 to 16, wherein the liver contrast image is an image in which blood flow and liver function in the liver are reflected as high and low pixel values.   18. The medical image diagnostic apparatus according to claim 1, wherein the contrast agent reflects blood flow and liver function in the liver at different timings. 18. The contrast agent comprises sodium gadoxetate (Gd-EOB-DTPA) as an active ingredient,
The medical image diagnostic apparatus according to claim 15 or 18, wherein the liver contrast image is an image acquired by MRI. For each of the images of the two or more time phases of interest among the liver contrast images acquired in a plurality of time phases in the contrast examination using the contrast agent in which the blood flow and liver function in the liver are reflected in the image, Setting a region of interest in the liver that corresponds positionally between each time phase of interest;
Generating a local image representing the region of interest for each time phase of interest based on each of the liver contrast images of the time phase of interest;
And a step of displaying the generated local image for each time phase of interest in a manner capable of comparative interpretation. On the computer,
For each of the images of the two or more time phases of interest among the liver contrast images acquired in a plurality of time phases in the contrast examination using the contrast agent in which the blood flow and liver function in the liver are reflected in the image, Setting a region of interest in the liver that corresponds positionally between each time phase of interest;
Generating a local image representing the region of interest for each time phase of interest based on each of the liver contrast images of the time phase of interest;
And a step of displaying the generated local image for each time phase of interest in a manner capable of comparative interpretation.