Medical image processor and medical image diagnosis apparatus

The present invention relates to a medical image processing device for processing a medical image and a medical image diagnostic device including the medical image processing device.

The medical image diagnostic device includes a medical image taking device for taking a medical image, a medical image processing device for processing the medical image, and the like. Examples of the medical imaging apparatus include an X-ray tomography apparatus (X-ray CT apparatus), a nuclear magnetic resonance apparatus (MRI apparatus), an ultrasonic diagnostic apparatus, and the like. The X-ray tomography apparatus is an apparatus that irradiates a subject with X-rays, detects the X-rays that have passed through the subject, and displays the inside of the subject (site of interest) as a CT image. The ultrasonic diagnostic apparatus is an apparatus that sends ultrasonic waves to a subject, receives the reflected waves (echo waves), and displays the inside (part of interest) of the subject as an ultrasonic image.

Various image processing techniques have been proposed as image processing techniques for processing the above-mentioned medical images. For example, a composite medical image diagnostic device that synthesizes and displays a CT image and an ultrasonic image of the same area of interest (site of interest) obtained by an X-ray CT device and an ultrasonic diagnostic device, and a specific magnetic resonance image. An image diagnosis support system has been proposed in which the corresponding feature part of the ultrasonic image is aligned with respect to the feature part and a magnetic resonance image is generated by superimposing the ultrasonic image (for example, Patent Document 1 and Patent Document). 2).

On the other hand, a technique for simulating a disease in a region of interest of a subject has also been proposed. For example, a technique that simulates the movement of the heart (wall movement) and the dynamics of blood in the heart chamber by modeling the shape of the heart and incorporating the mechanical parameters of the myocardium into the model, as well as the shape of the heart. A technique for simulating the excitatory propagation process and potential change by modeling and incorporating the electrical parameters of the myocardium into the model has been proposed. From the site model (for example, the heart model) obtained by such a simulation, some functional information of the site of interest can be obtained. Further, from the medical image (for example, a heart image) obtained by the medical imaging device, morphological information of the region of interest and other functional information can be obtained.<patcit num="1"><text>Japanese Unexamined Patent Publication No. 10-127623</text></patcit><patcit num="2"><text>Japanese Unexamined Patent Publication No. 2003-153877</text></patcit>

<p> However, since the medical image obtained by the medical imaging device and the part model obtained by the simulation are different images, it is difficult to recognize the relationship between the morphological information of the part of interest and its functional information, and a diagnostician such as a doctor. However, it is difficult to accurately grasp the condition of the region of interest of the subject.</p><p> The present invention has been made in view of the above, and is for medical use, which facilitates recognition of the relationship between the morphological information of the region of interest of the subject and the functional information thereof, and allows the diagnostician to accurately grasp the condition of the region of interest. It is to provide an image processing apparatus and a medical diagnostic imaging apparatus.</p>

<p> The first feature according to the embodiment of the present invention is that in a medical image processing apparatus, a medical image taken by the medical imaging apparatus and showing a region of interest of a subject and a disease of the region of interest are simulated. This is to provide a means for synthesizing a site model indicating a site of interest, which is a model for the purpose, to generate a medical image and a composite image of the site model.</p><p> A second feature according to the embodiment of the present invention is that the medical image diagnostic device includes a medical image capturing device for capturing a medical image and a medical image processing device according to the first feature described above.</p>

<p> According to the present invention, a medical image processing device and a medical image diagnostic device that facilitates recognition of the relationship between the morphological information of a region of interest of a subject and its functional information and enables a diagnostician to accurately grasp the status of the region of interest. Can be provided.</p>

An embodiment of the present invention will be described with reference to the drawings.

As shown in FIG. 1, the medical image diagnostic apparatus 1 according to the embodiment of the present invention includes a medical imaging apparatus 2 that captures a medical image showing a region of interest (for example, the heart) of a subject, and a medical imaging apparatus 2 that captures the image. A medical image database 3 for storing images, a model database 4 for storing a site model (for example, a heart model) for simulating a disease of a site of interest, and a composite process for generating a composite image of the medical image and the site model are performed. It is equipped with a medical image processing device 5. Each of these parts is connected by a network 6 such as LAN (Local Area Network).

The medical imaging device 2 is an imaging device that captures a medical image indicating a region of interest (site of interest) of a subject. As the medical imaging device 2, for example, an X-ray tomography device (X-ray CT device), an ultrasonic diagnostic device, or the like is used. When an X-ray tomography apparatus is used, the medical image is a CT image, and when an ultrasonic diagnostic apparatus is used, the medical image is an ultrasonic image (ultrasound echo image).

The medical image database 3 is a storage device that stores medical images (for example, volume data or ultrasonic images that are CT images of each part of a subject) obtained by the medical imaging device 2. Here, examples of each part of the subject include the heart, lungs, stomach, and the like. The CT image (volume data) and the ultrasonic image are obtained by the medical imaging device 2 and stored in the medical image database 3 via the network 6.

The model database 4 is a model for simulating a disease of a region of interest, and is a storage device that stores a simulation model showing the region of interest as a site model. If the site of interest is the heart, the shape of the heart is modeled and a heart model is created. The information of the heart model is stored in the model database 4. The information of this heart model includes, for example, set value information such as morphology, mechanical spring constant, resting membrane potential, and ion concentration.

Here, as the shape model of the heart, a cell model (a model in which tens of thousands of cells are arranged in the shape of the heart), a tetrahedron division model (a model in which the heart is expressed by combining various shapes of tetrahedra), and the like are used. Used. For example, by extracting the heart region from the anatomical image of the heart taken by the X-ray tomography apparatus and automatically performing cell division or tetrahedron division, the individual heart shape is expressed and has the individual heart shape. A heart model is used. Also, a heart model with a standard heart shape may be used instead of an individual heart shape.

As shown in FIG. 2, the medical image processing device 5 includes a control unit 11 such as a CPU (Central Processing Unit) that centrally controls each unit, and a memory such as a ROM (Read Only Memory) or a RAM (Random Access Memory). 12, a display unit 13 that displays various images such as medical images and site models, an operation unit 14 that accepts input operations from the operator, a storage unit 15 that stores various programs and various data, and an external device. It is provided with a communication unit 16 for communicating with the user and an image processing unit 17 for processing various images such as medical images and site models. Each of these parts is electrically connected by a bus line 18.

The control unit 11 controls each unit based on various programs, various data, and the like stored in the storage unit 15. In particular, the control unit 11 performs a series of data processing for calculating or processing data based on various programs and data, a composite process for generating a composite image of a medical image and a site model, a display process for displaying the image, and the like. To execute.

The memory 12 is a memory that stores a startup program or the like executed by the control unit 11, and is a memory that also functions as a work area of the control unit 11. The start program is read and executed by the control unit 11 when the medical image processing device 5 is started.

The display unit 13 is a display device that displays various images such as a two-dimensional image and a three-dimensional image in color. As the display unit 13, for example, a liquid crystal display, a CRT (Cathode Ray Tube) display, or the like is used.

The operation unit 14 is an input unit that is input-operated by the operator, and is an input unit that accepts various input operations such as starting image display, switching images, and changing settings. As the operation unit 14, for example, an input device such as a mouse or a keyboard is used.

The storage unit 15 is a storage device that stores various programs, data, and the like, and in particular, is a storage device that stores synthetic data D1 related to synthesis (alignment) between a medical image and a site model. As the storage unit 15, for example, a magnetic disk device, a semiconductor disk device (flash memory), or the like is used. The compositing data D1 is information necessary for compositing the medical image and the site model, for example, information such as the amount of expansion and contraction and the amount of deformation. The composite data D1 is obtained by the image processing unit 17, and is stored in the storage unit 15 via the bus line 18.

The communication unit 16 is a device that communicates with an external device via a network 6 such as a LAN or the Internet. A LAN card, a modem, or the like is used as the communication unit 16. Examples of the external device include a medical imaging device 2, a medical image database 3, a model database 4, and the like.

As shown in FIG. 3, the image processing unit 17 includes the time phase confirmation means 17a, the medical image information acquisition means 17b, the model information acquisition means 17c, the singular point extraction means 17d and 17e, and the contour extraction of the inner wall and the outer wall. The means 17f and 17g, the singular point fitting means 17h between the image and the model, the contour fitting means 17i between the image and the model, and the image and model positioning means 17j are provided. The image processing unit 17 is composed of software, hardware (circuits), or both of them. From here, the case where the region of interest is the heart will be described. That is, the medical image is the heart image (heart morphology image) G1 and the site model is the heart model M1 (see FIG. 4).

The time phase confirmation means 17a is a means for confirming the time phases in the electrocardiogram of the cardiac image G1 and the heart model M1 and adjusting the time phases so that the time phases are the same. By adjusting to the same time phase, it becomes possible to accurately match the shapes of the heart image G1 and the heart model M1. As the time phase, for example, a specific diastole phase, particularly the end diastole, is used. When the shape model of the heart is constructed in the end-diastolic phase, the heart image G1 and the heart model M1 are in the same state (approximately stationary state) in the end-diastolic phase. Therefore, the end-diastolic cardiac image G1 and the cardiac model M1 When is used, it is possible to fit those shapes more accurately.

The medical image information acquisition means 17b is a means for acquiring a cardiac image G1 in a specific time phase (for example, terminal diastole) from the medical image database 3. Similarly, the model information acquisition means 17c is a means for acquiring the cardiac model M1 in a specific time phase (for example, the terminal diastole) from the model database 4. As the information of the heart model M1, for example, set value information such as morphology, mechanical spring constant, resting membrane potential, and ion concentration is acquired.

The medical image information acquisition means 17b acquires, for example, a heart image G1 as shown in FIGS. 4 and 5 from the medical image database 3. Here, in FIG. 5, the heart image G1 of FIG. 4 is shown two-dimensionally. Further, the model information acquisition means 17c acquires, for example, the heart model M1 as shown in FIGS. 4 and 6 from the model database 4. Here, FIG. 6 shows the heart model M1 of FIG. 4 two-dimensionally.

The singular point extraction means 17d is a means for selecting and extracting a singular point from the cardiac image G1. Similarly, the singularity extraction means 17e is a means for selecting and extracting singularities from the heart model M1. Examples of this singularity include the apex of the heart, the position of the valve, the aorta, the vena cava, the coronary artery, the interventricular septum, the atrial septum, and the like. The singularity is automatically extracted on the heart image G1 and the singularity is preset on the heart model M1 and can be obtained from the above-mentioned set value information.

As shown in FIG. 5, the singularity extraction means 17d extracts, for example, a plurality of singularities A1 to A4 in the cardiac image G1. Further, as shown in FIG. 6, the singularity extraction means 17e can obtain, for example, a plurality of singularities B1 to B4 in the heart model M1. Here, the singular points A1 to A4 and B1 to B4 are represented by three-dimensional coordinates (x, y, z). The singular point A2 and the singular point B2 are the origins, respectively.

The inner wall and outer wall contour extraction means 17f is a means for extracting the contours of the inner and outer walls of the heart from the heart image G1. Similarly, the inner and outer wall contour extraction means 17g is a means for extracting the contours of the inner and outer walls of the heart from the heart model M1. On the heart image G1, those contours are automatically extracted, and on the heart model M1, those contours are preset and obtained from the above-mentioned set value information.

The singular point fitting means 17h between the image and the model is a means for fitting the singular points A1 to A4 and B1 to B4 between the heart image G1 and the heart model M1. At this time, the singular points A1 to A4 of the heart image G1 may be moved, or the singular points B1 to B4 of the heart model M1 may be moved. This is preset according to the input operation of the operator with respect to the operation unit 14. With this fitting means, movement information of singular points A1 to A4 and B1 to B4 (for example, a conversion coefficient indicating the amount of rotation, the amount of expansion and contraction, etc.) can be obtained. The movement information of the singular points A1 to A4 and B1 to B4 is stored in the storage unit 15 as the synthesis data D1.

By the singular point fitting means 17h between this image and the model, as shown in FIGS. 5 and 6, for example, the separation distance (distance between singular points) m1 to m5 between the singular points A1 to A4 of the cardiac image G1 and So that the separation distance between each singular point B1 to B4 (distance between singular points) n1 to n5 of the cardiac model M1 matches (m1 = n1, m2 = n2, m3 = n3, m4 = n4, m5 = n5) , Each singular point A1 to A4 of the heart image G1 or each singular point B1 to B4 of the heart model M1 is moved. At this time, each expansion / contraction amount is linearly distributed to each cell (linear distribution), that is, the expansion / contraction amount of each cell is linearly adjusted, and the movement amount of each cell (movement information of the singular point) is obtained. The movement amount of these cells is stored in the storage unit 15 as the synthesis data D1.

The contour fitting means 17i between the image and the model is a means for fitting the contour of the inner wall, the outer wall, or the like between the heart image G1 and the heart model M1. At this time, the contour of the heart image G1 may be moved, or the contour of the heart model M1 may be moved. This is preset according to the input operation of the operator with respect to the operation unit 14. With this fitting means, movement information of the contour (for example, a conversion coefficient indicating the amount of rotation, the amount of expansion and contraction, etc.) is obtained. The movement information of this contour is stored in the storage unit 15 as the synthesis data D1.

As shown in FIGS. 5 and 6, for example, while fixing the distances m1 to m5 and n1 to n5 between the singular points by the contour fitting means 17i between the image and the model (each singular point A1 to A4, B1 to). (While fixing B4), one midpoint a1 is selected from multiple midpoints (x marks in FIG. 5) on the inner wall of the cardiac image G1, and the midpoint b1 correlated with the selected midpoint a1 is the cardiac model. Selected from M1. Then, the midpoint a1 of the heart image G1 or the midpoint b1 of the heart model M1 so that the shape of the singularity A1-midpoint a1-singularity A4 and the shape of the singularity B1-midpoint b1-singularity B4 match. Is moved. The cell corresponding to the moved midpoint a1 or midpoint b1 is also moved. At this time, each expansion / contraction amount (as much as the cell moves) is linearly distributed to each cell (linear distribution), that is, the expansion / contraction amount of each cell is linearly adjusted, and the movement amount of each cell (contour movement information). Is required. The movement amount of these cells is stored in the storage unit 15 as the synthesis data D1. Such processing is performed for all midpoints, and is also performed on the outer wall of cardiac image G1.

The image and model alignment means 17j aligns the heart image G1 and the heart model M1 based on the synthesis data D1 stored in the storage unit 15 and synthesizes them, and then synthesizes the heart image G1 and the heart model. It is a means to generate a composite image G2 of M1. As a result, the composite image G2 thereof is obtained. The composite image G2 is displayed on the display unit 13, and is additionally stored on the storage unit 15 as needed.

By the alignment means 17j of this image and model, the alignment (size, shape, etc.) of the heart image G1 and the heart model M1 is performed by using the synthesis data D1, that is, the movement amount of each cell, as shown in FIG. Are combined and a cardiac image G1 and a cardiac model M1 are synthesized. As a result, the heart image G1 and the composite image G2 of the heart model M1 are obtained, and the composite image G2 is displayed on the display unit 13. At this time, the wall motion abnormal portion R1 and the wall thickness abnormal portion R2 are also displayed in different colors on the composite image G2.

The contour fitting means 17i between the image and the model and the image and model positioning means 17j can generally be configured as follows. Before fitting, each singular point A1 to A4 of the cardiac image G1 is represented by the coordinate system on the cardiac image, and each singular point B1 to B4 of the cardiac model M1 is represented by the coordinate system of the cardiac model. First, the relationship between the two coordinate systems is obtained by using the coordinate values of the singular points A1 to A4 of the heart image G1 and the singular points B1 to B4 of the heart model M1 in their respective coordinate systems. Generally, their relationship is expressed as a = f (b). a is the coordinate value of the heart image, and b is the coordinate value of the heart model. f is a function that represents the mapping of the coordinates of both, and a typical example is a linear mapping by a linear function. Other examples include polynomial functions and spline functions. In these latter cases, fitting including deformation can be performed. In each case, the concrete shape of the function f is represented by a finite number of parameters. In the fitting process, the simultaneous equations obtained by substituting the coordinate values of the singular points A1 to A4 of the heart image G1 and the singular points B1 to B4 of the heart model M1 into a = f (b) are solved. , Determine the parameters of the function f. The obtained parameter set concretely represents the correspondence between the points of the coordinate system of the heart image G1 and the coordinate system of the heart model M1, and when this is determined, the correspondence of all the points can be obtained. It means that. Substituting the contour points a1, a2, ... Extracted from the cardiac image G1 into a = f (b), and obtaining b1, b2, ... Of the corresponding positions in these cardiac models M1. You can also. On the contrary, the contour points b1, b2, ... Of the heart model M1 are substituted into a = f (b), and the corresponding positions a1, a2, ... In these heart models M1 are obtained. You can also.

In such an image processing unit 17, in addition to electrocardiographic synchronization technology and respiratory motion removal technology between simulation and real-time echocardiography, technology for registering heart disease simulation and CT images, heart disease simulation model and real-time ultrasonic waves. MPR (Multi Planer Reformation) and VR (Volume Reformation) technology (virtual sonography technology) that cut out the corresponding cross section and corresponding area from the model are used.

Next, the composite display processing of such a medical image diagnostic apparatus 1, particularly the medical image processing apparatus 5, will be described with reference to FIGS. 7 and 8. The control unit 11 of the medical image processing device 5 performs a compositing process by the image processing unit 17, and executes a display process for displaying the composite image G2 after the compositing process. Here, as an example, a synthesis process is performed to match the heart image G1 with the heart model M1.

As shown in FIG. 7, the control unit 11 selects the heart image G1 and the heart model M1 having the same phase time by the image processing unit 17 (step S1), and each singular point A1 to A4 of the selected heart image G1 (step S1). Extract (see FIG. 5) (step S2) and extract each singularity B1 to B4 (see FIG. 6) of the selected cardiac model M1 (step S3).

Next, the control unit 11 calculates the separation distance (distance between singular points) m1 to m5 between the singular points A1 to A4 of the heart image G1 by the image processing unit 17 (step S4), and each singularity of the heart model M1. The separation distance between points B1 to B4 (distance between singular points) n1 to n5 is calculated (step S5).

Next, the control unit 11 uses the image processing unit 17 to make the singular points that are the origins in the heart image G1 so that the distances m1 to m5 and n1 to n5 between the singular points of the heart image G1 and the heart model M1 are the same. Move one of the singular points A1, A3, and A4 other than A2 (step S6), and distribute the expansion and contraction amount linearly to each cell according to the expansion and contraction direction and expansion and contraction amount at this time, that is, expansion and contraction of each cell. Adjust the amount linearly (step S7). After that, the control unit 11 stores the movement amount and the deformation amount (movement information of the singular points A1 to A4 and B1 to B4) of each cell in the storage unit 15 as the synthesis data D1 (step S8).

Further, the control unit 11 determines whether or not the positions of the singular points A1 to A4 and B1 to B4 completely match (step S9), and repeats steps S6 to S8 until those positions completely match. (NO in step S9). If it is determined that their positions are in perfect agreement within the set accuracy (eg, 1 mm) (YES in step S9), then the inner wall of cardiac image G1 is shown, as shown in FIG. Select (step S10), extract the contour of the selected inner wall, that is, trace the inner wall (step S11), and set multiple midpoints (see FIGS. 5 and 6) in the traced line segment (step S12). .. The midpoints at this time may be set at equal intervals or may be biased. The above-mentioned accuracy setting is preset according to the input operation of the operator to the operation unit 14.

Next, the control unit 11 selects one midpoint a1 from a plurality of midpoints by the image processing unit 17 (step S13), and in a state where each singular point A1 to A4 is fixed, the midpoint a1 is set. Move (step S14). At this time, for example, the control unit 11 has a shape (singular point A1-midpoint a1-singular point A4 shape) composed of two different singular points and a midpoint in the cardiac image G1, and the singular points and the middle of them. Move the midpoint a1 so that the shape consisting of two different singular points and the midpoint (the shape of the singular point B1-midpoint b1-singular point B4) in the heart model M1 corresponding to the point coincides. At this time, the cell or element (element constituting the model) corresponding to the midpoint a1 is also moved.

Next, the control unit 11 linearly distributes the expansion / contraction amount at this time to each cell, that is, linearly adjusts the expansion / contraction amount of each cell, and the movement amount of each cell (movement information of the contour of the inner wall) is the synthesis data. Save as D1 in the storage unit 15 (step S15). Determine whether the distance and direction between each singular point A1 to A4 and the midpoint have reached the required accuracy (step S16), until the distance and direction reach the required accuracy at all midpoints. , Repeat steps S13 to S15 (NO in step S16).

If it is determined that the distance and direction have reached the required accuracy (YES in step S16), the outer wall of the cardiac image G1 is selected (step S17) and the contour of the selected outer wall is extracted, that is, the outer wall is traced. Then (step S18), set multiple midpoints in the traced line segment (step S19). The midpoints at this time may be set at equal intervals or may be biased.

Next, the control unit 11 selects one midpoint from a plurality of midpoints by the image processing unit 17 (step S20), and moves the midpoint with the singular points A1 to A4 fixed (step S20). Step S21). At this time as well, for example, the control unit 11 has a shape composed of two different singular points and a midpoint in the heart image G1 and a different 2 in the heart model M1 corresponding to those singular points and the midpoint. Move the midpoint so that the shape consisting of the two singular points and the midpoint match.

Next, the control unit 11 linearly distributes the expansion / contraction amount at this time to each cell, that is, linearly adjusts the expansion / contraction amount of each cell, and the movement amount of each cell (movement information of the contour of the outer wall) is the synthesis data. Save as D1 in storage 15 (step S22). It is determined whether the distance and direction between each singular point and the midpoint have reached the required accuracy (step S23), and step S20 until the distance and direction at all midpoints reach the required accuracy. Repeat ~ S22 (NO in step S23).

If it is determined that the distance and direction have reached the required accuracy (YES in step S23), a cardiac image is used using the synthetic data D1 stored in the storage unit 15, that is, the amount of movement of each cell. Transform G1 to match the cardiac model M1 (step S24). As a result, a composite image G2 of the heart image G1 and the heart model M1 is obtained (see FIG. 4). The control unit 11 displays the composite image G2 on the display unit 13, and further stores the composite image G2 in the storage unit 15 as needed. At this time, the control unit 11 displays the wall motion abnormal portion R1 and the wall thickness abnormal portion R2 in different colors on the composite image G2.

By such a composite display process, the composite image G2 is displayed on the display unit 13 and visually recognized by a diagnostician such as a doctor. At this time, the heart image G1 and the heart model M1 are displayed as one composite image G2, which facilitates the recognition of the relationship between the morphological information of the subject's heart and its functional information. It is possible to accurately grasp the situation (state). Furthermore, since the information of the disease site for simulation on the heart model M1 is superimposed and displayed on the heart image G1, it is possible to give the diagnostician new qualitative diagnostic information for the heart disease which is the site of interest. it can. In particular, since it is possible to accurately grasp the patient's heart condition (morphology, function, etc.) without depending on the experience and skill level of the diagnostician, accurate diagnosis can be performed.

Furthermore, by displaying an abnormal part such as a wall motion abnormal part R1 or a wall thickness abnormal part R2 on the composite image G2, the abnormal part is displayed on the composite image G2, and the abnormal part is immediately displayed in addition to the heart condition. And it can be grasped accurately. That is, a heart model M1 of Mr. A (individual) is created from a CT image without wall motion abnormalities, and abnormalities such as wall motion abnormalities are reproduced on the composite image G2 by a heart disease simulation using the heart model M1. Therefore, new qualitative diagnostic information for heart disease is given to doctors, etc., and diagnosticians such as doctors can easily recognize the relationship between the morphological information of the heart of the subject and its functional information. It can be carried out. Furthermore, since it becomes possible to easily perform various comparative studies and comparative diagnoses between the heart image G1 such as a CT image or an ultrasonic image and the heart disease simulation of the heart model M1, the patient's heart condition (morphology and morphology and diagnosis) can be easily performed. Functions, etc.) can be accurately grasped, and as a result, accurate diagnosis can be performed.

As described above, according to the embodiment of the present invention, a medical image (for example, a heart image G1) which is an image taken by the medical imaging apparatus 2 and shows a region of interest of a subject, and a disease of the region of interest. By synthesizing a site model (for example, heart model M1) that is a model for simulating the above and showing a site of interest, and generating a medical image and a composite image G2 of the site model, the medical image and the site model are combined into one. Since it is displayed as a composite image G2 and it becomes easy to recognize the relationship between the morphological information of the region of interest of the subject and its functional information, a diagnostician such as a doctor should accurately grasp the condition (state) of the subject's heart. Can be done. In addition, since it is possible to superimpose the disease site information for simulation on the site model on the medical image (diagnostic image) and display it as a composite image G2, new qualitative diagnostic information for the disease of the site of interest can be obtained. Can be given. As a result, a diagnostician such as a doctor can accurately grasp the condition of the region of interest of the subject.

In particular, when the region of interest is the heart, complex diagnostic information (image information) between the heart image G1 such as a CT image or an ultrasonic image and the heart model M1 of a heart disease simulation is displayed in comparison, that is, superimposed. Therefore, it is possible to provide a comprehensive diagnostic environment for heart disease. That is, it is possible to present qualitative diagnostic information regarding ischemic heart disease, angina, etc., for example, the activity of ion current of cardiomyocytes, the mechanical expansion and contraction (movement) of cardiomyocytes, and the like. This allows the diagnostician to obtain new qualitative diagnostic information for estimating the infarcted state or ischemic state of the myocardium. In addition, cardiac function can be observed in a wide visual field area that cannot be covered by ultrasonic images alone.

In addition, when synthesizing a medical image (for example, heart image G1) and a site model (for example, heart model M1), a plurality of singular points A1 to A4 of the medical image and a plurality of singular points B1 to B4 of the site model are used. Accuracy is achieved by aligning the region of interest in the medical image with the region of interest in the site model based on the multiple singular points A1 to A4 of the extracted medical image and the multiple singular points B1 to B4 of the site model. It is possible to generate a composite image G2 of a medical image and a site model well.

Further, in the case of alignment, medical use is based on a plurality of singular points A1 to A4 of the extracted medical image (for example, heart image G1) and a plurality of singular points B1 to B4 of the site model (for example, heart model M1). Match the size of the region of interest in the image and the region of interest in the site model, and based on the multiple singular points A1 to A4 of the extracted medical image and the multiple singular points B1 to B4 of the site model, the region of interest in the medical image. By matching the shape of the image with the site of interest of the site model, it is possible to generate a medical image and a composite image G2 of the site model with higher accuracy.

In addition, when the sizes are matched, the distance between each singular point m1 to m5 and the extracted site model (for example, heart) from a plurality of singular points A1 to A4 of the extracted medical image (for example, heart image G1). The separation distances n1 to n5 between the singular points were obtained from the plurality of singular points B1 to B4 of the model M1), and the separation distances m1 to m5 of each of the obtained medical images and the separation distances n1 to each of the obtained site models were obtained. By matching the size of the region of interest in the medical image and the region of interest in the site model so that they are the same as n5, it is possible to generate a composite image G2 of the medical image and site model by simple processing. Further, since it is possible to accurately match the sizes of the medical image and the site model, it is possible to obtain a highly accurate composite image G2 while preventing the processing time from becoming long.

When matching the shapes, the contour of the region of interest (for example, heart image G1) of the medical image and the contour of the region of interest of the site model (for example, heart model M1) are extracted, and a plurality of singular points of the extracted medical image are extracted. And, based on multiple singular points of the extracted site model, the attention site of the medical image and the attention of the site model so that the contour of the attention site of the extracted medical image and the contour of the attention site of the extracted site model are the same. By matching the shape with the part, it is possible to generate a composite image G2 of the medical image and the part model by simple processing, and further, it is possible to accurately match the shape of the medical image and the part model. Therefore, it is possible to obtain a highly accurate composite image G2 while preventing the processing time from becoming long.

Furthermore, by showing the abnormal part of the region of interest (for example, wall motion abnormal portion R1 or wall thickness abnormal portion R2) in the generated composite image G2, the abnormal portion is displayed in the composite image G2. Since it becomes possible to give new qualitative diagnostic information to the patient, the diagnostician can immediately and accurately grasp the abnormal part in addition to the cardiac condition, and further, the cardiac condition (morphology and function, etc.). ), The positional relationship of the abnormal part can be easily grasped.

(Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the gist thereof.

For example, in the above-described embodiment, when medical image information (heart image G1) or model information (heart model M1) is acquired, a specific diastole phase (end diastole) is used as the time phase. However, the present invention is not limited to this, and other time phases may be used, and medical image information and model information may be acquired for each time phase to generate a composite image G2. In this case, by arranging the generated composite images G2 in chronological order and continuously displaying them on the display unit 13, it is possible to intuitively recognize the time-series changes in the heart condition. The situation can be easily grasped.

Further, in the above-described embodiment, the medical imaging device 2 is configured by using an X-ray tomography device (X-ray CT device), an ultrasonic diagnostic device, or the like, but the present invention is not limited to this, for example. , Other imaging devices may be used, and further, the medical image processing device 5 may be incorporated into the medical imaging device 2 such as an X-ray tomography device or an ultrasonic diagnostic device to form the medical image diagnostic device 1. It may be.

<figref num="1">It is a block diagram which shows the schematic structure of the medical image diagnostic apparatus which concerns on one Embodiment of this invention.</figref><figref num="2">It is a block diagram which shows the schematic structure of the medical image processing apparatus included in the medical image diagnostic apparatus shown in FIG.</figref><figref num="3">It is a block diagram which shows the schematic structure of the image processing part included in the medical image processing apparatus shown in FIG.</figref><figref num="4">It is explanatory drawing for demonstrating the image composition performed by the image processing unit shown in FIG.</figref><figref num="5">It is a schematic diagram which shows the heart image in order to explain the image composition performed by the image processing unit shown in FIG.</figref><figref num="6">It is a schematic diagram which shows the heart model in order to explain the image composition performed by the image processing part shown in FIG.</figref><figref num="7">It is a flowchart which shows the flow of the synthesis processing performed by the medical image diagnostic apparatus shown in FIG. 1, in particular, the medical image processing apparatus shown in FIG.</figref><figref num="8">It is a flowchart which shows the flow of the synthesis processing performed by the medical image diagnostic apparatus shown in FIG. 1, in particular, the medical image processing apparatus shown in FIG.</figref>

Code description

1 Medical diagnostic imaging equipment 2 Medical imaging device 5 Medical image processing equipment A1 ~ A4 singularity B1 ~ B4 singularity G1 medical image (heart image) G2 composite image M1 site model (heart model) m1 ~ m5 separation distance (singular point separation distance) n1 ~ n5 Separation distance (singular point separation distance)