JP2006075590A - Apparatus, program and method for motion analysis in inside of living body - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To analyze a relative motion in the inside of a living body by moving a viewing point to an arbitrary place when a motion in the living body is analyzed by employing a tomographic image. <P>SOLUTION: The present invention is composed of: a tomographic image acquisition device 1 for acquiring a tomographic image of a subject targeted for observation; a motion analysis device 2 to which the tomographic image acquisition device 1 is connected, the analysis device 2 performing a motion analysis from the tomographic image obtained by the tomographic image acquisition device 1; and a display device 3 connected to the motion analysis device 2, the display device displaying data outputted by the motion analysis device 2. The motion analysis device 2 includes an image processing means 4 for processing the tomographic image obtained by the tomographic image acquisition device 1, a point specification means 5 for specifying a plurality of arbitrary points on the tomographic image after image processing by the image processing means 4, a viewing point conversion means 6 for converting a motion of a point serving as an observation point among the plurality of arbitrary points specified by the point specification means 5 to a motion obtained by defining a point other than the observation point as a viewing point, and a data output means 7 for outputting data obtained by the viewing point conversion means 6. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a living body internal motion analysis processing device, a living body internal motion analysis processing program, and a live body internal motion analysis processing method that analyze a motion inside a living body using a tomographic image.

  The ultrasonic diagnostic apparatus visualizes the inside of a living body as a tomographic image from the viewpoint of a probe (probe) placed on the surface of the living body as an observation target. For example, in the case of a circulatory system such as the heart and blood vessels and other organs with movement, the movement of living tissue constituting them is observed by a tomographic image to diagnose the function of those organs.

  By the way, in recent years, if the function of the heart or the like can be quantitatively evaluated by this ultrasonic diagnostic apparatus, it is expected that the accuracy of diagnosis is further improved. For example, Patent Document 1 captures a tomographic image of a subject, designates a part of the tomographic image to be traced by a mark, extracts a tomographic image corresponding to the part designated by the mark, and marks the movement of the tomographic image. A diagnostic imaging apparatus that tracks the above is proposed.

JP 2004-121834 A

  By the way, the movement of the living body local area is influenced not only by the movement of the adjacent living body local area but also by the movement of the remote living body local area. This is because the living body local parts are connected and related to each other and constitute the whole. The diagnostic imaging apparatus of the above-mentioned Patent Document 1 only tracks the movement of the designated part in the entire tomographic image of the subject, and eliminates the influence of the movement of the part that is close or remote from the designated part. Can not make the diagnosis.

  Therefore, in the present invention, when analyzing the internal movement of the living body using the tomographic image, the internal biological movement analysis processing apparatus capable of analyzing the relative internal movement of the living body by moving the viewpoint to an arbitrary place, An object of the present invention is to provide a living body internal motion analysis processing program and a biological internal motion analysis processing method.

  The living body internal motion analysis processing apparatus of the present invention includes a tomographic imaging unit that captures a tomographic image of a subject, a point specifying unit that specifies a plurality of arbitrary points on the tomographic image obtained by the tomographic imaging unit, Viewpoint conversion means for converting a movement of a point as an observation point among a plurality of points designated by the point designation means to a movement having a point other than the observation point as a viewpoint, and data obtained by the viewpoint conversion means Data output means for outputting.

  In addition, the internal body motion analysis processing program of the present invention specifies a plurality of arbitrary points on a tomographic image obtained by the tomographic means obtained by the computer connected with the tomographic means for taking a tomographic image of the subject. Point designating means, viewpoint conversion means for converting a movement of a point as an observation point among a plurality of points designated by the point designating means into a movement with a point other than the observation point as a viewpoint, and the viewpoint conversion It is for functioning as data output means for outputting data obtained by the means.

  Also, the living body internal motion analysis processing method of the present invention includes a tomographic step for capturing a tomographic image of a subject, and a point specifying step for specifying a plurality of arbitrary points on the tomographic image obtained by the tomographic imaging step; A viewpoint conversion step for converting a movement of a point as an observation point among a plurality of points specified in the point specification step into a movement having a point other than the observation point as a viewpoint, and the viewpoint conversion step And a data output step for outputting data.

  According to these inventions, an arbitrary observation point on a tomographic image of a subject is converted into a point movement based on an arbitrary viewpoint, and data is output. As a result, data of the movement of the observation point with the viewpoint shifted to an arbitrary place is obtained.

  Here, it is desirable that the data output is data of the locus of movement of the observation point after conversion. Thereby, the movement of the arbitrary observation point on the tomographic image of the subject is obtained as the data of the locus of the movement of the point based on the arbitrary viewpoint.

  Alternatively, it is desirable that the data output is formed by connecting a plurality of converted observation points with line segments to form a multi-node link and outputting movement data of the multi-node link. As a result, the movement of each observation point whose viewpoint has been moved to an arbitrary location is obtained as movement data of a multi-node link in which these observation points are connected by line segments.

  At this time, it is desirable that the data output is output as movement data based on one of the multi-node links. Thereby, the movement of the multi-node link in which the observation points whose viewpoints are moved to arbitrary places are connected by line segments is obtained as movement data based on one multi-node link.

(1) Take a tomographic image of a subject, specify a plurality of arbitrary points on the obtained tomographic image, and set the movement of a point other than this observation point as an observation point among the specified plurality of points By converting the motion to a point of view and outputting the obtained data, motion data of the observation point with the view point moved to an arbitrary location can be obtained, and relative internal movements can be analyzed from this point of view. Therefore, it is possible to perform a diagnosis that excludes the influence of the movement of a living body local part that is close to or a remote living body local part of a designated biological local part inside the living body. In addition, by this viewpoint conversion, it is divided into the movement (passive movement) of a nearby living body local area or remote living body area which is an external force that moves the living body local area, and the movement of the specified living body local area (automatic), and the diagnosis is performed. It can be carried out.

(2) With the configuration that outputs the data of the movement locus of the observation point after conversion, the movement of the arbitrary observation point on the tomographic image of the subject is the data of the movement locus of the point with respect to the arbitrary viewpoint. Therefore, it is possible to analyze relative internal movements from the data of the trajectory.

(3) By connecting multiple observation points after conversion with line segments to form a multi-node link and outputting the movement data of this multi-node link, each observation point whose viewpoint has been moved to an arbitrary location Since movement is obtained as multi-node link movement data connecting these observation points with line segments, not only the movement of each observation point whose viewpoint is moved to an arbitrary location, but also the relative position of each observation point ( Angle) motion (for example, angular velocity, angular acceleration, angular strain, angular strain rate) can be analyzed.

(4) With the configuration that outputs the movement of the multi-node link as movement data based on one of the multi-node links, the multi-node link of each observation point whose viewpoint is moved to an arbitrary place is connected by a line segment. Since the movement is obtained as movement data based on one multi-node link, the movement of the relative position (angle) with respect to the reference position of each observation point can be analyzed. In addition, the center of movement and the axis of movement in the entire multi-node link can be obtained by comparing the differences in the area, moving direction, and moving speed of the multi-node link obtained by repeating the viewpoint conversion. It is possible to evaluate the movement by examining the deviation of the center of movement and the axis. Furthermore, by clarifying the center axis and center point of normal movement, these normal examples are stored in the apparatus as a template, called when diagnosing abnormal cases, and based on the center axis and center point of normal examples, Automatic diagnosis is possible by collating with abnormal cases.

  FIG. 1 is a block diagram showing a configuration of a living body internal motion analysis processing apparatus according to an embodiment of the present invention. In FIG. 1, a tomographic imaging apparatus 1 that captures a tomographic image of a subject to be observed and a tomographic imaging apparatus 1 are connected to the internal body motion analysis processing apparatus according to the embodiment of the present invention. The motion analysis device 2 that performs motion analysis processing from the tomogram obtained by 1 and the display device 3 that is connected to the motion analysis device 2 and displays data output by the motion analysis device 2.

  The tomographic imaging apparatus 1 according to the present embodiment transmits ultrasonic waves from a probe that is in close contact with the surface of the subject, captures the reflected ultrasonic waves, and electrically amplifies and converts the image to obtain a tomographic image of the subject. It is the well-known ultrasonic tomography apparatus which obtains. As the tomographic imaging apparatus 1, a magnetic resonance imaging (MRI) apparatus, an X-ray computed tomographic imaging (CT) apparatus, or the like can be used in addition to this ultrasonic tomographic imaging apparatus.

  The motion analysis apparatus 2 includes an image processing unit 4 that performs image processing on a tomographic image obtained by the tomographic imaging apparatus 1 and points that designate a plurality of arbitrary points on the tomographic image after image processing by the image processing unit 4. Designation means 5, viewpoint conversion means 6 for converting a movement of a point as an observation point among a plurality of points designated by the point designation means 5 into a movement with a point other than the observation point as a viewpoint, and the viewpoint And data output means 7 for outputting data obtained by the conversion means 6. The motion analysis device 2 is realized by operating a biological internal motion analysis processing program on a computer.

  The image processing unit 4 appropriately performs image processing such as reforming, frame division, optimization, enhancement processing, and background processing on the tomographic image obtained by the tomographic imaging apparatus 1. When the image input from the tomographic imaging apparatus 1 is analog data, the image processing means 4 performs various image processing after converting the analog data into digital data. On the other hand, in the case of digital data, the image processing means 4 performs image processing as it is.

  The point designating unit 5 arbitrarily designates a point to be an observation point and a point to be a viewpoint on the tomographic image by using a keyboard, a pointing device or the like (not shown) provided in the motion analysis apparatus 2. Here, the point to be observed and the point to be viewed can be designated separately, or collectively designated and later selected as the observation point or viewpoint. FIG. 3A shows an example in which twelve points 11a, 11b, 11c, 11d, 11e, 11f, 11g, 11h, 11i, 11j, 11k, and 11l on a living body (ventricular septum myocardium) are designated as observation points. Yes.

  The viewpoint conversion means 6 converts the movement of a point to be an observation point among a plurality of points designated by the point designation means 5 into a movement having a point other than the observation point as a viewpoint. For example, when the point 11b shown in FIG. 4A is the viewpoint and the points 11a and 11c adjacent to the point 11b are the observation points, the viewpoint conversion means 6 changes the movement of the points (observation points) 11a and 11c to the point 11b. The movement is converted into a view point (see FIG. 4B). That is, the viewpoint conversion unit 6 fixes the movement of the viewpoint 11b and calculates the relative movement of the observation points 11a and 11c with respect to the viewpoint 11b.

  Here, the viewpoint conversion unit 6 tracks the movement of each point 11a to 11l designated by the point designation unit 5 when performing the above-described viewpoint conversion. This tracking is performed by automatic identification or manual identification. FIG. 3B shows how the points 11a to 11l are tracked. In the case of automatic identification, the viewpoint conversion means 6 tracks a point having a high degree of coincidence between the points 11a to 11l of the image of each frame based on the color, shading, shape, distribution and the like of the image (matching method (matching method )) For identification. Alternatively, the viewpoint conversion means 6 predicts the movement position from the trajectory immediately before each point 11a to 11l, and identifies each point 11a to 11l by searching around this predicted position. At this time, if the previous movement is stopped or slow and the corresponding point is not found at the predicted position, there is a possibility that an abrupt movement change has occurred. Note that it is possible to combine the identification based on the color, shading, shape, distribution, etc. of the image and the identification based on the predictive search, and to select the priority of the identification method when combining.

  As a result of automatic identification, “○” is displayed when the certainty is high, “?” Is displayed when the certainty is uncertain, and “X” is displayed when search is impossible, and the user is visually confirmed. In addition, for easy visual confirmation, each frame and trajectory of the image can be rendered translucent and displayed in layers (semi-transparent superimposed image display), or displayed using animation effects such as afterimages. It is. As a result, the user can perform confirmation / correction or switch to manual identification. Note that automatic identification is stopped when search is impossible. The translucent superimposed image used here is a display unit that makes it easy to discriminate the phase difference, and is also a shooting unit that keeps the shooting conditions constant during shooting. For example, if you want to compare before and after surgery, before and after medication, or when an arrhythmia attack and non-attack, etc., superimpose the previously taken image as a translucent image on the image to be newly examined and photographed. By using as a reference at the time of shooting, it is possible to take an image for more accurate comparison while keeping the shooting conditions before and after the same as much as possible.

  When an observation point that moves three-dimensionally is tracked on a two-dimensional plane, the observation point may disappear from the screen due to a movement in a direction perpendicular to the plane. In this case, it is not possible to cope only by increasing the number of frames per unit time (for example, one second) of the moving image. As a method for solving this, a relay type (transfer type) tracking method is adopted in which tracking is continued by setting a new observation point in the vicinity. As shown in FIG. 5, during tracking with point A being designated as an observation point among points A and B, point A does not appear at the predicted search point (×) in frame 3 and analysis is impossible. After confirming that there is no change in the positional relationship between the point B adjacent to A and the point A, the point B is designated instead of the point A and is tracked. As a result, it is possible to complete the operation analysis of the points A and B areas from the frames 1 to 5.

  The data output means 7 outputs the data obtained by the viewpoint conversion means 6. The data output means 7 can output raw data obtained by the viewpoint conversion means 6 and can output image data, analysis data, and the like generated from this data. As the output destination of the data output means 7, in addition to the display device 3, a printing device or a data recording medium can be selected.

  For example, as described above, the data output unit 7 shows the case where the movement of the points (observation points) 11a and 11c is converted into the movement with the point 11b as the viewpoint by the viewpoint conversion unit 6 as shown in the display example of FIG. 4B. Thus, the position of the point 11b as the viewpoint is fixed, and data that changes only the arrangement of the observation points 11a and 11c with respect to this point 11b is output.

  Further, the data output means 7 can output the movement locus data of the observation point after conversion obtained by the viewpoint conversion means 6. For example, when the two points (ventricular septal side aortic annulus 21 and left atrial aortic annulus 22) of the living body (left ventricular outflow tract 20) are designated by the point designation means 5, as shown in FIG. If the data output means 7 outputs the data of the movement locus of the two points 21 and 22 without performing the viewpoint conversion by the viewpoint conversion means 6, it is displayed on the display device 3 as shown in FIG. The expansion period is indicated by a black circle.) In the example of FIG. 7, the two points 21 and 22 are moving clockwise.

  However, the viewpoint conversion means 6 performs viewpoint conversion by the viewpoint conversion means 6 with the point 22 of the left atrial side aortic annulus as the viewpoint and the point 21 of the ventricular septal side aortic annulus as the observation point, and the data output means 7 When the data of the locus of movement of the observation point 21 after conversion is output by the above, it is displayed on the display device 3 as shown in FIG. In the example of FIG. 8, the X axis and the Y axis are displayed with the point 22 of the left atrial side aortic annulus as the viewpoint as the origin.

When evaluating the movement based on FIG. 8 output by the data output means 7, it is necessary to analyze the movement by dividing it into two components. The movement is composed of own movement (automatic) and movement by external force (passive movement). Alternatively, it can be paraphrased as local motion and overall motion, or local motion and peripheral motion. In addition, as an index for evaluating and comparing the movement of automatic, it can be expressed by the concept of the following automatic ability and other ability.
Automatic ability = automatic / (automatic + passive)
Passive ability = Passive / (Automatic + Passive)
Note that the degree of automatic and passive movement is represented by numerical values such as the area, distance, and speed of the moving region.

In FIG. 8, when it is determined that the point 22 is a part having no automatic function, the locus M ₂₂ of the point 22 shown in FIG. It will represent. On the other hand, when it is determined that the trajectory M _21-22 of the point 21 shown in FIG. 8 is the movement of the point 21 by the automatic function, the trajectory M of the point 21 shown in FIG. 8 from the trajectory M _{21 of the} point 21 shown in FIG. _The trajectory M _{21- (21-22)} obtained by subtracting _21-22 indicates a passive movement caused by an external force acting on the point 21.

As a result, the movement at the point 21 can be divided into automatic and other movements, and by calculating the ratio M _21-22 / M _{21 of the} locus (for example, the ratio of the movement distance and the movement area), It is possible to evaluate local movements based on the concept (for example, there is no automatic ability when 0%, 1: 1 automatic ability and other ability when 50%, only automatic ability when 100%, etc.) . It is also possible to compare the automatic ability and the passive ability in each part. For example, the comparison of the automatic ability at the points 21 and 22 is expressed by the locus ratio M _22-21 / M ₂₁ to 0, and the comparison of the other dynamics is the locus ratio M _{21- (21-22)} / M _21. It is expressed as the pair M ₂₂ / M ₂₂ .

  Further, the data output means 7 can connect a plurality of observation points with line segments to form a multi-node link, and can output data on the movement of this multi-node link (see FIG. 3C). For example, as shown in FIG. 11A, three points (ventricular septal side aortic annulus 21, left atrial aortic annulus 22, left ventricular outflow tract) are indicated by the point specifying means 5 as shown in FIG. 11A. And the observation points 21, 22, and 23 are connected by line segments to form multi-node links 24 and 25, and the movement data of the multi-node links 24 and 25 are output. Thereby, the movement of the multi-node links 24 and 25 constituted by the observation points 21, 22, and 23 is displayed on the display device 3 as shown in FIG. 11B.

  Further, the data output means 7 can output the formed movement of the multi-node link as movement data based on one of the multi-node links by the viewpoint conversion means 6 (see FIG. 3D). For example, the viewpoint conversion means 6 converts the viewpoint 21 of FIG. 11B as the viewpoint 21 by the viewpoint conversion means 6, and multi-node links 24 and 25 composed of the observation points 22 and 23 and the viewpoint 21 after the conversion are shown in FIG. As shown in the figure, the viewpoint 21 is set as the origin and translated in the X-axis and Y-axis directions and displayed. Alternatively, the motion data of the multi-node links 24 and 25 is rotated so that the viewpoint 21 is the origin and the viewpoint 21 and the observation point 22 are parallel to the X axis. As a result, as shown in FIG. 12B, the display device 3 has an angle formed by the multi-node link 24 (aortic valve annulus) and a point 23 (multi-node link 25) at the front of the left ventricular outflow passage. Is displayed. Further, the data output means 7 can output this angle as graph data as shown in FIG. In FIG. 13, the angle data is indicated by a black line, and the gray line indicates the interpolation function.

  The movement has a center and axis of movement, and in the human body, the joints and spinal column of the limbs become the center and axis of movement, and movement during walking and sports is established. In motion analysis, it is necessary to obtain the motion center and motion axis and perform motion analysis on the basis of these motion centers and motion axes. Further, the center point and the axis of the motion also exist in the heart motion, and it is possible to detect and evaluate the center point and the axis by the viewpoint conversion by the viewpoint conversion means 6.

  FIG. 9A shows an example in which a multi-node link is formed by connecting a plurality of observation points in a part of the ventricular septal myocardium with line segments in the end diastole and the end systole, and movement data of the multi-node link is output. FIG. 9B is a diagram showing an example in which the region having the phase difference in FIG. 9A is displayed in gray. FIG. 10A is a diagram showing an image after point-of-view conversion with the point 11b in FIG. 9A as a viewpoint, and FIG. 10B is a diagram showing an example in which a region having a phase difference in FIG. 10A is displayed in gray.

  As can be seen from these figures, by performing viewpoint conversion, the region d having a phase difference displayed in gray in FIG. 9B is reduced in FIG. 10B. Further, as a result of sequentially performing viewpoint conversion using the respective points 11a to 11l as viewpoints and comparing the areas of the respective areas d, the area of the area d is the smallest and the next smallest when the viewpoint is at the point 11b. In the case of the point 11f, it is determined that the motion axis of the myocardium surrounded by the line segment is on the line segment 11m connecting the points 11b and 11f, and the motion center is closer to the point 11b on this line segment. it is conceivable that. Further, it can be seen that the portion on the right side of the line segment 11m connecting the lines 11b and 11f moves upward, and the left portion moves downward. By such a method, it is possible to determine the motion center and motion axis. In addition to the comparison of the individual areas of the region b when the viewpoint conversion is performed, the comparison of the overlapping areas of the end diastole and the end systole indicated by the region s, and the movement vector of each point indicated by the white arrow It is possible to determine the motion center and motion axis even by comparing the sum of the two.

  Further, the data output means 7 can display the moving direction and moving speed (distance) of each point and multi-node link in color. When color display is performed, considering a color blind person's use, a color scheme that refrains from using red and green is selected, and a barrier-free color corresponding to the color vision is used. For example, as shown in FIG. 14A, the moving direction is displayed from 0 to 180 degrees with an orange to yellow gradation 26, and 180 to 360 degrees is displayed with a blue to light blue gradation 27, and the hue decreases as the moving speed decreases. Can be displayed in color at the same time with different hues and shades. Thereby, it becomes possible to visually recognize the whole image of movement easily. FIG. 14B shows the movement direction and the movement speed at the same time by the color indicated by the color palette shown in FIG. 14A when the viewpoint is the myocardial point 11b surrounded by the line segment composed of the observation points 11a to 11l. An example is shown.

  The in-vivo operation analysis process by the in-vivo operation analysis processing device having the above configuration will be described with reference to the flowchart of FIG.

(S101) The tomographic imaging apparatus 1 obtains a tomographic image inside a living body as a subject.
(S102) The image processing means 4 performs image processing on the tomographic image obtained by the tomographic imaging apparatus 1 as necessary.
(S103) The point designating unit 5 designates a plurality of observation points and arbitrary points as viewpoints on the tomographic image after image processing (point 21 on the ventricular septal side aortic annulus in FIG. 11A, left atrial side) (See point 22 on the aortic valve annulus and point 23 on the left ventricular outflow tract.)
(S104) The viewpoint conversion means 6 converts the movement of a point as an observation point among a plurality of points designated by the point designation means 5 into a movement with a point other than the observation point as a viewpoint (for example, FIG. 11A, the point 22 of the left atrial side aortic valve annulus and the point 23 of the left ventricular outflow tract are the observation points, and the movement of these observation points is the movement from the point 21 of the ventricular septal side aortic annulus Converted to.)
(S105) The data output means 7 outputs the data obtained by the viewpoint conversion means 6. It is also possible to input the data output by the data output means 7 to the viewpoint conversion means 6 again, and perform viewpoint conversion of each observation point with another point as the viewpoint.

  As described above, the in-vivo motion analysis processing apparatus according to the present embodiment captures a tomographic image of a subject, designates a plurality of arbitrary points on the obtained tomographic image, and selects a plurality of designated points. By converting the movement of the observation point to a movement with a point other than the observation point as the viewpoint, and outputting the obtained data, the movement data of the observation point with the viewpoint moved to an arbitrary location is obtained. It is done. This makes it possible to analyze relative internal movements from any point of view, eliminating the effects of movements of nearby or remote living body localities on the specified living body local site inside the living body. It is possible to make a diagnosis.

  In addition, in the living body internal motion analysis processing apparatus according to the present embodiment, it is possible to output data of the movement locus of the observation point after conversion. As a result, the movement of an arbitrary observation point on the tomographic image of the subject can be obtained as the data of the movement of the point with respect to the arbitrary viewpoint. It becomes possible to analyze.

  In the living body internal motion analysis processing device according to the present embodiment, it is possible to connect a plurality of converted observation points with line segments to form a multi-node link, and to output movement data of this multi-node link. is there. As a result, the movement of each observation point whose viewpoint is moved to an arbitrary location is obtained as movement data of a multi-node link obtained by connecting these observation points with line segments. It is possible to analyze not only the movement of the observation point but also the movement of the relative position (angle) of each observation point.

  In the living body internal motion analysis processing apparatus according to the present embodiment, it is possible to output the movement of the multi-node link as movement data based on one of the multi-node links. As a result, the movement of the multi-node link that connects the observation points whose viewpoints are moved to arbitrary locations connected by line segments is obtained as movement data based on one multi-node link, so the reference position of each observation point It is possible to analyze the movement of the relative position (angle) with respect to.

  Moreover, the living body internal motion analysis processing apparatus according to the present embodiment can be applied to a color image obtained by the color Doppler method. In this case, color discrimination is performed on the color image of each frame of the aortic valve regurgitation (from the early diastole 30 to the end diastole 33), and the regurgitation area of the aortic regurgitation is identified for each frame. At the same time, the ventricular septal aortic valve annulus 21, the left atrial aortic annulus 22 and the backflow outlet 34 are designated as points, and their movements are superimposed (see FIG. 15). Then, the viewpoint conversion means 6 displays the movement of the point 34 of the backflow outlet with the point 21 of the ventricular septal side aortic annulus as the origin and the line segment connecting the two points 21 and 22 as an axis (see FIG. 16). .) This reveals changes in the area, direction, and positional relationship of the aortic valve regurgitation area with the aortic annulus during diastole, enabling more detailed and accurate quantification and qualitative evaluation of aortic regurgitation. It becomes.

  In the living body internal motion analysis processing apparatus according to the present embodiment, the movement of each point is automatically tracked by the matching method (matching method) when performing viewpoint conversion as described above, but the movement per unit time (between frames) is It is difficult to track a very large object only with the highest matching point using this matching method. In this case, conversely, it is necessary to track the point with the lowest degree of coincidence (inconsistency). Alternatively, identification tracking (phase difference tracking method) according to the degree of coincidence (inconsistency) is necessary. For example, the movement of the valve is very fast compared to the myocardium, and it is even more difficult to track the pathological movement of the valve due to ruptured chords and valve prolapse.

  As a solution, frame images are displayed in a semi-transparent manner, phase differences between frames are detected, areas to be tracked are specified according to the degree of this phase, and attributes between the areas (group attribute, permutation, position) Give information and track. Further, the number of frames is increased, and search tracking is performed centering on the moving region predicted from the tracking result. For example, FIGS. 17A and 17B show a continuous frame of the aortic valve portion 40, but as shown in these drawings, the moment from the closing of the aortic valve 41 to the opening is captured in the continuous frame, It moves very fast compared to other areas. Therefore, it is difficult to track the aortic valve portion 40 between these frames simply by searching for a region having a high degree of coincidence according to the coincidence degree method. In this case, it is necessary to track using a non-matching degree opposite to the matching degree.

  FIG. 17C is a translucent superimposed image of FIGS. 17A and 17B. As is clear from this figure, the aortic valve portion 40 is largely displaced from its original position by the opening. FIG. 17D shows an area having a large phase difference identified from the translucent superimposed screen of FIG. 17C. In FIG. 17D, a region having a large phase difference is displayed as a dark gray region. FIG. 17E shows areas (phases indicated by dark gray) d1 to d8 having a larger phase difference than the semi-transparent superimposed screen of FIG. 17C, and areas (overlapped) areas (areas indicated by light gray) s1, s2 having no phase difference. This is a divided display. In FIG. 17E, the aortic right coronary apex before opening corresponds to regions d1, s1, and d7, the aortic right coronary apex after opening corresponds to regions d8, s1, and d2, and the aortic valve non-coronary apex before opening is a region. d4, s2, d3, the aortic valve no-coronate after opening corresponds to regions d5, s2, d3. Therefore, the movement of the aortic valve 41 shown in FIGS. 17A and 17B is performed by adding (associating) attribute information between the regions that each region is a corresponding region having no change in the connected permutation. Can be tracked.

  FIG. 17F is a translucent superimposed screen of the right coronal apex of the aorta in the regions d1, s1, and d7 and the regions d8, s1, and d2 subjected to the association processing. In this figure, if the aortic right coronal apex A of the regions d1, s1, and d7 is designated, the region d7 corresponds to the region d2 due to the relationship between the regions, and the tip A of the region d7 is to the right of the region d2. It is easily identified that it corresponds to the tip B point. As a result, it is found that the aortic right coronary tip moves from point A to point B as the aortic valve 41 is opened. Note that the rightmost end C of the aortic valve non-coronary apex portion of the regions d5, s2, and d3 is never traced by the association processing. Furthermore, even when tracking is performed when the number of frames per time is increased, it is predicted that the movement is in the region of the trajectory AB. Therefore, by narrowing the search range to the trajectory AB region, It can be easily tracked.

  In addition, after determining the start point and end point as described above, the backward search method that predicts the passing point and the forward search that predicts and searches the movement position on the next frame from the motion determined from the analyzed consecutive frames Two predictive search methods can be used.

  By the way, when performing point-by-frame tracking for each frame, the recorded video has low image quality, there is a resolution problem due to the frequency of the ultrasound, and the myocardium is complicated in layers like the inside of the myocardium. Tracking becomes very difficult if the ultrasonic waves are scattered by the fibers and noise is generated, and a speckled pattern speckle pattern appears discontinuously for each frame. For such a difficult analysis, a multi-step multi-means combination method is necessary.

  For example, as shown in FIG. 18, moving images input from a VHS video tape generally have deteriorated image quality, and this analysis is very difficult. Furthermore, a discontinuous speckled image must be tracked inside the myocardium. Each frame is optimized to improve this poor image quality and remove noise. As an example, as shown in FIG. 19, the ventricular septum long-axis tomographic image of FIG. 18 is converted into a two-level image, and as an initial stage tracking, firstly, the end diastole (dark gray area) and the end systole (light gray area) The movement in the direction indicated by the arrows is confirmed at three points L1, L2, and L3 inside the left ventricular side ventricle by the phase difference tracking method from the two-tone image semi-transparent superimposed screen.

  Next, in addition to the backward prediction search method, the arrangement permutation information of the points L1, L2, and L3, supplementary information that the three adjacent points move approximately, and the relay-type tracking method are added to track the three points. When appropriate, trace back to the pre-gradation image. Frames that are difficult to track are thinned out and then tracked again, using the relay tracking method, backward prediction search method, and forward prediction search method.

  FIG. 20 shows the result of tracing the point R1 of the ventricular septum right ventricle side endocardium and the point I1 inside the ventricular septum myocardium. FIG. 21 is a multi-node link display of the trajectories of three points R1, I1, and L2 on the ventricular septum right ventricle side, ventricular septal myocardium, and ventricular septum left ventricle side. FIG. 22 is a multi-link display of the trajectory when the viewpoint is converted with R1 in FIG. 21 as the origin and R1-L2 as the axis.

  The in-vivo operation analysis processing apparatus, in-vivo operation analysis processing program, and in-vivo operation analysis processing method of the present invention are useful as an apparatus, program, and method for analyzing internal operation using a tomographic image. In particular, it is suitable as an apparatus, program, and method capable of performing a diagnosis that eliminates the influence of the movement of a living body local part that is close to a remote living body part or a living body local part that is remote from a designated part of the living body local part inside the living body.

It is a block diagram which shows the structure of the biological body internal motion analysis processing apparatus in embodiment of this invention. It is a flowchart of a biological body internal motion analysis process. It is a figure which shows the example which designated 12 points | pieces on a biological body (ventricular septum myocardium) as observation points. It is a figure which shows the mode of the tracking of each point. It is a figure which shows the output example of the data of the movement of a multi-node link. It is a figure which shows the example of an output of the motion data on the basis of one of the multi-node links. It is a figure which shows the example of viewpoint conversion. It is a figure which shows the example of viewpoint conversion. It is explanatory drawing of a relay type (transfer type) tracking method. It is a figure which shows the example of the point designation | designated by a point designation | designated means. It is a figure which shows the example of an output of the data of the locus | trajectory of two points | pieces. It is a figure which shows the example which performed viewpoint conversion by making the point of the left atrial side aortic annulus part into a viewpoint, and making the point of the ventricular septum side aortic annulus part into an observation point. In the end diastole and end systole, a diagram showing an example of connecting multiple observation points in a part of the ventricular septal myocardium with a line segment to form a multi-node link and outputting the movement data of this multi-node link is there. It is a figure which shows the example which displayed the area | region with a phase difference of FIG. 9A in gray. It is a figure which shows the image after carrying out viewpoint conversion by making the point 11b of FIG. 9A into a viewpoint. It is a figure which shows the example which displayed the area | region with a phase difference of FIG. 10A in gray. It is a figure which shows the example which designated 3 points | pieces of the biological body. It is a figure which shows the example of an output of the data of the movement of the multi-node link which connected each observation point and viewpoint before viewpoint conversion with the line segment. It is a figure which shows the example which translated and output to the X-axis and the Y-axis direction from the viewpoint as the origin. It is a figure which shows the example which rotated and output the motion data on the basis of one multi-node link. It is a figure which shows the example output by a graph. It is a figure which shows the example of the color palette at the time of carrying out the color display of the moving direction and moving speed (distance) of each point and multi-node link. It is a figure which shows the example which displayed the moving direction and the moving speed simultaneously with the color shown with the color palette of FIG. 14A. It is a figure which shows the example which designated 3 points | pieces and displayed the movement superimposed. It is a figure which shows the example of an output of the data of the motion of the backflow outlet after a viewpoint conversion. It is a figure which shows 1 frame image of an aortic valve part. It is a figure which shows 1 frame image of an aortic valve part. It is the translucent superimposed image of FIG. 17A and FIG. 17B. It is what identified the area | region with a large phase difference from the semi-transparent superimposition screen of FIG. 17C. A region with a large phase difference is displayed as a dark gray region. Based on the semi-transparent superimposed screen of FIG. 17C, the display is divided into large phase difference areas (areas shown in dark gray) d1 to d8 and no phase difference (overlapping) areas (areas shown in light gray) s1 and s2. It is a thing. It is a translucent superimposition screen of the aortic right coronal apex of regions d1, s1, and d7 and regions d8, s1, and d2 that have been subjected to association processing. It is a figure which shows the continuous frame of a ventricular septum long-axis tomogram. It is a figure which shows the 2 gradation image of a ventricular septum long-axis tomogram. It is a figure which shows a tracking result. It is a figure which shows the multinode link display image of the ventricular septum right ventricle side, the ventricular septum myocardium, and the ventricular septum left ventricle side three points. It is a figure which shows the multi-node link display image of the ventricular septum right ventricle side, the ventricular septum myocardium, and the ventricular septum left ventricle side three points after viewpoint conversion.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Tomographic imaging apparatus 2 Motion analysis apparatus 3 Display apparatus 4 Image processing means 5 Point designation means 6 View point conversion means 7 Data output means

Claims (6)

Tomographic imaging means for imaging a tomographic image of a subject;
Point designating means for designating a plurality of arbitrary points on the tomographic image obtained by the tomographic imaging means;
Viewpoint conversion means for converting a movement of a point as an observation point among a plurality of points designated by the point designation means into a movement having a point other than the observation point as a viewpoint;
A living body internal motion analysis processing apparatus having data output means for outputting data obtained by the viewpoint conversion means.   The in-vivo motion analysis processing apparatus according to claim 1, wherein the data output means outputs data of the movement locus of the observation point after the conversion.   2. The living body internal operation according to claim 1, wherein the data output means is configured to connect the plurality of observation points after the conversion with line segments to form a multi-node link, and to output movement data of the multi-node link. Analysis processing device.   The in-vivo motion analysis processing apparatus according to claim 3, wherein the data output means outputs the movement of the multi-node link as movement data based on one of the multi-node links. A computer to which tomographic imaging means for imaging a tomographic image of a subject is connected;
Point designating means for designating a plurality of arbitrary points on the tomographic image obtained by the tomographic imaging means;
Viewpoint conversion means for converting a movement of a point as an observation point among a plurality of points designated by the point designation means into a movement having a point other than the observation point as a viewpoint;
A biological internal motion analysis processing program for functioning as data output means for outputting data obtained by the viewpoint conversion means. A tomographic imaging step for imaging a tomographic image of the subject;
A point designating step for designating a plurality of arbitrary points on the tomographic image obtained by the tomographic imaging step;
A viewpoint conversion step of converting a movement of a point as an observation point among a plurality of points specified in the point specification step into a movement having a point other than the observation point as a viewpoint;
A biological internal motion analysis processing method including a data output step of outputting data obtained by the viewpoint conversion step.