JP5192921B2 - Ultrasonic diagnostic equipment - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device non-invasively and easily extracting the cartilage of a knee joint, and positioning the shapes of the cartilage extracted at different times. <P>SOLUTION: Volume data on the inside of the knee including the distal end of a femur are obtained by scanning the knee of a person largely bent as the person is seated on a chair, etc., with an ultrasonic beam by a mechanical three-dimensional probe 10. An image pre-processing part 22 and a cartilage extraction part 24 extract the edge on the obtained volume data to extract the tissue boundary, and extract the shape of the cartilage contour from the extracted tissue boundary based on the specification by a user, etc. The shape of the cartilage measured in the past is stored in a database 60, and a positioning part 62 positions the shape of the cartilage measured this time to the shape of the cartilage measured in the past stored in the database 60. At the time, the user is made to specify the loaded part of the cartilage and the longitudinal direction of the cartilage, etc., and the shape of the cartilage is positioned based on the specification. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to an ultrasonic diagnostic apparatus, and more particularly to an apparatus for diagnosis of knee cartilage.

  Knee osteoarthritis is a disease in which the knee joint is inflamed or deformed, causing pain due to factors such as wear and abrasion of the cartilage that acts as a cushion for the knee joint. Although osteoarthritis of the knee increases with age, preventive measures against osteoarthritis of the knee can be taken by accurately grasping the thickness and surface shape of the femoral cartilage before onset. With the expectation that the number of knee osteoarthritis patients will increase with the aging of the population, a simple diagnostic method is expected.

  Known methods for diagnosing osteoarthritis include a method of observing the condition of the cartilage surface by inserting an arthroscope (endoscope) into the knee, and a method of estimating from the degree of joint gap opening by X-ray examination. It has been. However, these diagnostic methods cannot measure the thickness of cartilage. In addition, the three-dimensional shape of cartilage cannot be extracted and presented.

  On the other hand, Patent Document 1 discloses a system for evaluating the thickness of the cartilage of the knee joint by inserting an intra-joint probe into the knee joint and transmitting and receiving ultrasonic waves. However, since this system is invasive, it cannot be said that it can be easily used for examination of a large number of subjects for health examination.

  An MRI (Magnetic Resonance Imaging) apparatus is a non-invasive diagnostic imaging apparatus that can image cartilage in distinction from bones, muscles, body fluids, etc. in principle, and can also measure the thickness of cartilage. However, MRI is expensive to use and takes a long time to measure, so it is not suitable for testing many subjects.

  Further, when the three-dimensional shape of cartilage obtained by measurement is compared with the three-dimensional shape of cartilage measured in the past for the same subject, it is necessary to appropriately align the shapes of each other. For example, Patent Document 2 discloses an apparatus that aligns three-dimensional diagnostic images obtained by MRI.

JP 2002-345821 A JP 2000-40145 A

  The present invention is characterized by providing a device capable of measuring the shape of cartilage of a knee joint in a non-invasive and simple manner and aligning the shapes of cartilages measured at intervals.

  The apparatus according to the present invention obtains volume data for a three-dimensional region including cartilage at the distal end of the femur inside the knee by scanning an ultrasonic beam from the front body surface of the bent knee. Means, extraction means for extracting a part corresponding to the cartilage from the volume data based on an echo level value of each voxel in the volume data, and a three-dimensional shape of the part corresponding to the cartilage extracted by the extraction means The extraction means from the storage means for storing information, the first cartilage shape represented by the three-dimensional shape information of the part corresponding to the past cartilage stored in the storage means, and the volume data acquired by the wave transmitting / receiving means The three-dimensional shape information of the part corresponding to the current cartilage extracted by the means or the tertiary of the part corresponding to the past cartilage stored in the storage means Comprising a second cartilage shape represented by the shape information, and alignment means for aligning, the.

  In this configuration, volume data including a cartilage load portion can be obtained by using a wave transmitting / receiving unit that scans an ultrasonic beam from the body surface on the front side of the bent knee, and corresponds to cartilage from the volume data. A part can be extracted. Then, the three-dimensional shapes of the portions corresponding to the cartilage extracted at a plurality of different time points can be aligned by the alignment means. As a result of alignment, for example, it is easy to specify the same point or range between cartilage shapes extracted at different points in time, and examine changes in cartilage thickness and other conditions at those points and ranges Become.

In the present invention , the positioning means accepts designation of a point corresponding to the cartilage load portion in each of the first cartilage shape and the second cartilage shape from the user, and the designated cartilage load portion matches. And aligning the first cartilage shape and the second cartilage shape.

  Although it is possible to obtain the best matching with brute force while variously shifting the first cartilage shape and the second cartilage shape as in normal pattern matching, the calculation amount is enormous. On the other hand, in this aspect, by allowing the user (for example, a diagnostician) to specify a relatively easy-to-understand cartilage load portion and using this as a reference for alignment, reasonable alignment can be achieved with a smaller amount of calculation. Can be realized.

In a further the invention, the alignment means, as the first cartilage shape and axial direction according to the shape of the cartilage in each of the second cartilage shape, through the cartilage load portion, corresponding to the cartilage The designation of the longitudinal direction or the short direction of the part is received from the user, and the first cartilage shape and the second cartilage shape are aligned so that the designated axial directions coincide.

  In this aspect, the axial direction is further specified for the first cartilage shape and the second cartilage shape, and the alignment is performed so that the axial directions coincide with each other, so that the calculation amount for the alignment can be further reduced. it can.

  In a further aspect, the alignment means is defined by the cartilage load portion, the axial direction, and the direction of the bone axis of the femur for each of the first cartilage shape and the second cartilage shape. A cross-sectional shape in a first plane is obtained, and the first cartilage shape and the second cartilage shape are matched by matching the cross-sectional shape with respect to the first cartilage shape and the cross-sectional shape with respect to the second cartilage shape. Align cartilage shape.

  In this aspect, by performing matching between cross-sectional shapes in the same cross section of the first cartilage shape and the second cartilage shape, the calculation amount can be further reduced as compared with matching between three-dimensional shapes.

  In a further aspect, the alignment means further includes a plane including a straight line extending from the cartilage load portion toward the bone axis of the femur for each of the first cartilage shape and the second cartilage shape. A cross-sectional shape on a second plane different from the first plane is obtained, and a cross-sectional shape on the second plane of the first cartilage shape and the second of the second cartilage shape are obtained. The first cartilage shape and the second cartilage shape are aligned by matching with the cross-sectional shape of the plane.

  In another aspect, the positioning means moves the first cartilage shape and the second cartilage shape along the bone axis according to the positional relationship between the wave transmitting / receiving means and the bone axis of the femur. Then, an image when the cartilage is viewed from the outside of the knee is generated and displayed on a display device, and a cartilage load portion is specified by the user on each of the images.

  According to this aspect, it becomes easy for the user to specify the cartilage load portion.

  According to the present invention, the shape of the cartilage of the knee joint can be measured easily and non-invasively, and the shapes of the cartilages measured over time can be aligned.

  In this embodiment, the cartilage shape information and thickness and other evaluation values of the distal end of the femur in the knee joint are acquired and calculated using ultrasound, and the cartilage shapes measured at intervals are aligned. By doing so, an apparatus that facilitates comparison between the two is provided.

  Before describing an apparatus configuration example of an embodiment, the internal structure of a knee joint (particularly human) targeted by the apparatus will be briefly described with reference to FIG. FIG. 1 is a schematic cross-sectional view of the knee joint of the right leg in a standing position when viewed from the front side of the body.

  As shown in FIG. 1, the knee joint includes a distal end portion of the femur 100, a proximal end portion of the tibia 120, and a patella 130. The surface of the distal end of the femur 100 is covered with cartilage 105, and the surface of the proximal end of the tibia 120 is covered with cartilage 125. A portion of the surface of the bone (for example, the femur 100) covered with cartilage is called a subchondral bone. A meniscus 110 is present between the cartilage 105 of the femur 100 and the cartilage 125 of the tibia. When viewed from the front of the standing body, the distal end of the femur 100 has a bifurcated projection (referred to as a medial condyle and a lateral condyle, respectively) as shown in the figure. The cartilage 105 is in contact with the cartilage 125 at the proximal end of the tibia 120 through holes formed in the inner and outer meniscus 110, respectively. Note that the knee joint portion described above is covered with the synovium and the joint capsule 140.

  The cartilage 105 at the distal end of the femur 100 covers the surface of the distal end widely, and a portion of the cartilage 125 that contacts the cartilage 125 at the proximal end of the tibia 120 in the standing position carries a load on the upper body. It is the part that receives strongly. This portion is called a cartilage load portion. The cartilage loading part is easily worn, and if the wear becomes significant, it causes knee osteoarthritis. For the diagnosis of knee osteoarthritis, the cartilage thickness of the cartilage loading part is an important judgment index.

  The thickness of the cartilage 105 at the distal end of the human femur 100 is as thin as about 2 to 3 mm for a healthy person. Therefore, in order to accurately measure the thickness by an ultrasonic diagnostic technique, it is desirable to irradiate the ultrasonic beam at an angle as close to perpendicular to the surface of the cartilage 105 as possible. However, in the standing position, the cartilage load portion at the distal end of the femur 100 is in contact with the cartilage 125 of the tibia 120, and in this state, let's apply an ultrasonic beam at an angle close to the surface of the cartilage load portion. In this case, it is necessary to apply an ultrasonic beam from the tibia side upward. However, it is impossible to apply an ultrasonic probe to such a position. Even if the ultrasonic probe can be arranged at such a position, since the cartilage becomes a shadow of the femur or tibia, the ultrasonic wave hardly reaches the cartilage and it is difficult to image the cartilage.

  On the other hand, when the knee is bent largely (for example, up to about 90 degrees) by sitting on a chair, for example, the load portion 108 of the cartilage 105 at the distal end of the femur 100 is detached from the tibia side, as shown in FIG. It faces the front side of the kneecap (the load portion 108 shown is that of the medial condyle, and the load portion of the lateral condyle is not shown). Therefore, if the probe is applied from the front side of the kneecap, the ultrasonic beam can be applied at an angle close to perpendicular to the surface of the load portion 108. Therefore, in this embodiment, in a state where the knee is greatly bent by sitting on a chair or the like, echoes of a three-dimensional region inside the knee including the load part of the cartilage at the distal end of the femur are acquired by an ultrasonic probe, The three-dimensional shape of the cartilage is obtained based on the echo signal.

  FIG. 3 shows an example of a functional configuration of the ultrasonic diagnostic apparatus according to the embodiment. In this example, the mechanical three-dimensional probe 10 is used as an ultrasonic probe for acquiring an echo of a three-dimensional region inside the knee. The mechanical three-dimensional probe 10 includes a transducer array 12 in which vibration elements are arranged one-dimensionally and a mechanical scanning mechanism 14.

  An ultrasonic beam is formed by the vibration array 12, and the ultrasonic beam is electronically scanned. As the electronic scanning method, electronic sector scanning, electronic linear scanning, and the like are known.

  The mechanical scanning mechanism 14 mechanically scans the transducer array 12 in a direction substantially perpendicular to the scanning surface of the array 12 for electronic scanning. A three-dimensional region is covered by a combination of electronic scanning by the transducer array 12 and mechanical scanning by the mechanical scanning mechanism 14. That is, tomographic image data of one electronic scanning surface can be obtained by one electronic scanning, and a collection of tomographic image data of a plurality of electronic scanning surfaces is obtained by performing electronic scanning for each scanning position of mechanical scanning. be able to. A collection of tomographic image data in the entire mechanical scanning range is one volume data for the scanning range of the mechanical three-dimensional probe 10.

  The electronic scanning shape of the transducer array 12 is not particularly limited. For example, a linear scanning transducer array 12 having a width (array length) enough to cover the lateral width of the cartilage 105 can be used. Further, a probe for convex scanning or concave (concave) scanning may be used.

  For example, in the example shown in FIG. 4, the mechanical scanning mechanism 14 performs an arc (concave) scanning up and down along the kneecap from the thigh side to the shin side with respect to the knee bent while sitting on a chair or the like. FIG. 4 is a view of the subject's knee as viewed from the side. The array direction of the transducer array 12 is, for example, a direction perpendicular to the paper surface of FIG. The mechanical scanning direction of the mechanical scanning mechanism 14 is a direction in which the mechanical scanning mechanism 14 rotates up and down as shown by arrows in the figure, with the rotation shaft 42 positioned at the distal portion of the femur 100 as the center. The rotation shafts 42 may be provided on both sides of the kneecap. Arms 44 extending from the respective rotating shafts 42 are attached to both side surfaces of the transducer unit 40 that accommodates the transducer array 12, and the transducer unit 40 can be moved in the direction of the arrow by a drive mechanism (not shown). it can. A flexible stand-off 46 in which an acoustic coupling agent such as water is sealed is provided on the transducer array 12 side of the transducer unit 40. At the time of measurement, one surface of the standoff 46 is in close contact with the shape of the kneecap, and the transducer array 12 moves in the direction of the arrow along the other surface. Although not shown, the vibrator unit 40, the rotating shaft 42, the arm 44, the standoff 46, the drive mechanism of the vibrator unit 40, and the like can be housed in a housing. The housing is formed with a recess for accommodating the knee, and a standoff 46 is provided in the recess. Then, when the housing is placed on the knee and the kneecap is housed in the recess, the standoff 46 and the housing structure in the vicinity thereof come into contact with the upper portion of the thigh 200, the kneecap, the shin, and the like. Thereby, the rotation shaft 42 is positioned and fixed in a predetermined range of the distal portion of the femur 120. The scanning of the mechanical scanning mechanism 14 may be automatic scanning using a motor or the like, or may be a method in which the transducer unit 40 is manually moved along an arcuate guide. The mechanical scanning mechanism 14 includes an encoder that obtains the rotational position of the transducer unit 40 (that is, the angle of the electronic scanning surface). Since the angle of the electronic scanning plane of the transducer array 12 is known from the output of this encoder, the three-dimensional position of each point on the scanning plane can be obtained.

  Note that the scanning mechanism illustrated in FIG. 4 is merely an example. For example, it is not limited to the above-described examples for mechanical arc scanning. Further, a mechanism for moving the casing up and down and / or back and forth may be provided, and the rotation shaft 42 may be positioned with respect to the knee by this mechanism. Further, instead of arc scanning, the transducer unit 40 (the arrangement direction of the transducers is perpendicular to the paper surface) may be linearly scanned along the front surface of the bent knee as shown in FIG. Of course, the shape of mechanical scanning is not limited to arc or linear. In the arc scanning, an ultrasonic beam can be applied to a large range of the cartilage 100 that is curved in a substantially arc shape from a direction close to vertical.

  In the above example, the transducer unit 40 arranged so as to match the element arrangement direction in the horizontal direction of the body is mechanically arc-scanned or linear-scanned along the vertical direction of the body. This is only an example. Instead of this, as shown in FIG. 6, the transducer unit 40 having the vertical direction of the body as the element arrangement direction may be mechanically scanned in the horizontal direction of the body. Although FIG. 6 shows linear scanning, the present invention is not limited to this, and arc scanning or other scanning shapes may be used.

  Returning to the description of FIG. 3, the transmission / reception unit 16 drives and controls the transducer array 12 and the mechanical scanning mechanism 14 to realize transmission / reception of ultrasonic beams, electronic scanning, and mechanical scanning. The transmission / reception unit 16 includes a transmission unit function and a reception unit function. The transmission unit functions as a transmission beam former. That is, a plurality of transmission signals are supplied from the transmission unit to the plurality of vibration elements of the transducer array 12. Thereby, an ultrasonic beam pulse is emitted from the transducer array 12 into the living body. The reflected wave from the living body is received by the transducer array 12. Thereby, a plurality of reception signals are output from the plurality of vibration elements. Those received signals are input to the receiving unit of the transmitting / receiving unit 16. The receiving unit functions as a receiving beam former. That is, the phasing addition process is applied to a plurality of received signals. The receiving unit performs various signal processing such as logarithmic compression processing and filter processing. The reception signal that has undergone such processing is input to the coordinate conversion unit 18. The received signal represents an echo level value at each point in the subject.

  The coordinate converter 18 coordinates the input received signal (echo signal) to a predetermined common coordinate system for display, image processing, storage, etc., for example, a three-dimensional Cartesian coordinate system (XYZ coordinate system). Perform conversion processing. In other words, the received signal includes information on the echo intensity at each point in the subject. In this case, each point is in the probe coordinate system defined by the scanning shape of the electronic scanning and mechanical scanning of the probe 10. is there. For example, when the electronic linear scanning transducer array 12 is mechanically arc-scanned as in the example of FIG. 4, the points in the subject are the rotation angle θ of the mechanical arc scanning, the scanning position x in the electronic linear scanning, and It is expressed in a coordinate system consisting of a distance (depth) d from the probe 10. The electronic scanning position x and the distance d can be obtained from the transmission / reception unit 16, and the mechanical scanning position (rotation angle θ) can be obtained from an encoder provided in the mechanical scanning mechanism 14. Thus, the reception signal output from the transmission / reception unit 16 represents volume data in the probe coordinate system. The coordinate conversion unit 18 converts the volume data from the probe coordinate system to a common coordinate system for display or the like. In addition, the coordinate conversion unit 18 obtains data of points having no received signal data (echo level value) among points (voxels) in the common coordinate system by interpolating data of points around the point. Since coordinate transformation and interpolation in a medical three-dimensional image are well-known techniques, further explanation is omitted.

  The received signal that has undergone coordinate transformation by the coordinate transformation unit 18 is written into the three-dimensional data memory 20. The three-dimensional data memory 20 stores an echo level value of each point (voxel) in a common coordinate system for display or the like. That is, the volume data after coordinate conversion is stored in the three-dimensional data memory 20.

  The image forming unit 28 generates an image to be displayed on the display unit 30 from the volume data stored in the three-dimensional data memory 20. For example, the image forming unit 28 renders volume data from a designated viewpoint, thereby generating a three-dimensional image inside the subject viewed from the viewpoint. Further, the image forming unit 28 may have a function of generating an image of one or more specified cross sections (slices) in the volume data. Further, the image forming unit 28 may have a function of arranging a plurality of these three-dimensional images and cross-sectional images on one screen.

  The image forming unit 28 has a function of generating a three-dimensional image or a cross-sectional image of cartilage based on the three-dimensional shape information of the cartilage at the distal end of the femur extracted by the cartilage extracting unit 24 described later. Further, a function of synthesizing the generated three-dimensional image or cross-sectional image of cartilage with the three-dimensional image or cross-sectional image of the entire scanning range may be provided. In this synthesis, the cartilage image may be emphasized from other parts of the scanning range. For example, the color of the cartilage image may be different from the color of other parts of the scanning range.

  Further, the image forming unit 28 has a function of combining quantification data (for example, cartilage thickness) of cartilage calculated by a quantification processing unit 34, which will be described later, with a display image in the form of numerical values, for example.

  In addition, although not essential, the image forming unit 28 uses a color flow mapping image (two-dimensional blood flow image), color, and the like from information obtained by other functions (for example, a Doppler image generation function) provided in the ultrasonic diagnostic apparatus. A function of forming various images such as a tissue image (tissue motion display image) and a power Doppler image may be provided. Moreover, you may provide the function which synthesize | combines and displays these various images with the above-mentioned three-dimensional image and the image of a cartilage.

  The image forming unit 28 is configured by, for example, a DSC (digital scan converter). An image generated by the image forming unit 28 is displayed on the display unit 30.

  Further, a configuration for cartilage extraction will be described with reference to FIG.

  The image preprocessing unit 22 performs preprocessing for making an image suitable for cartilage extraction on the volume data in the three-dimensional data memory 20 or the slice data of the cross section in the volume data. The preprocessing performed by the image preprocessing unit 22 is, for example, processing including smoothing for noise reduction, edge enhancement for clarifying the cartilage boundary, or both. Hereinafter, as an example, an example of preprocessing using the shape feature of cartilage will be described with reference to FIGS. In this example, volume data is decomposed into a plurality of slice slice data arranged in one direction (for example, X = constant slice taken at predetermined intervals in the X-axis direction in the XYZ coordinate system) and slice data. It is an example in the case of performing preprocessing for each.

  FIG. 7 is a diagram schematically illustrating a tomographic image inside the knee obtained by scanning with an ultrasonic beam. Such a cross-sectional image is, for example, a display of slice data of one cross section extracted from the volume data in the three-dimensional data memory 20. In the cross-sectional image example of FIG. 7, the inside of the femur 300, the inside of the cartilage 305, and the inside of the stand-off 360 in which the acoustic coupling agent is sealed are almost acoustically homogeneous, so that the ultrasonic waves are almost reflected. Instead, the image is dark on the ultrasonic image. A tissue such as the muscle 350 surrounding the knee joint becomes a mottled image with relatively high luminance due to reflection by the fine structure of the tissue. Since the acoustic impedance difference between the tissue such as the muscle 350 and the cartilage 305 is large, the boundary between them, that is, the surface of the cartilage has high brightness. Similarly, the boundary between the cartilage 305 and the femur 300 (subchondral bone) also has high brightness.

  In the schematic diagram of FIG. 7, there are fine irregularities at the boundary between the cartilage 305 and the muscle 350, and dark portions and bright portions are mixed in various proportions within the lattice of one pixel. This represents that the luminance value of the pixel of the part fluctuates greatly for each pixel.

  In this example, a smoothing process that emphasizes the shape feature of cartilage is performed on such an ultrasonic tomographic image. The cartilage (especially that of the distal end of the femur) has a thickness of about 2 to 3 mm and is basically smooth and sticks to the surface of the distal end of the femur. Therefore, in this example, instead of using the values of pixels (voxels) in all directions around the target pixel for smoothing, the boundary surface between the cartilage and other tissues (the boundary line in the case of a tomographic image), that is, the outline of the cartilage A smoothing method using only pixels in the direction along the line is used.

  In this method, the image preprocessing unit 22 extracts the luminance value of each pixel on a straight line of a certain length passing through the target pixel when obtaining a smoothed value of the target pixel in the slice data. Calculate the variance.

  For example, in the example shown in FIG. 8, the fixed length is 9 pixels. Each grid shown in FIG. 8 represents a pixel. That is, in the example of FIG. 8, the luminance values of nine pixels (indicated by hatching in the drawing) crossed by the line segment 420 passing through the pixel of interest 410 among the group of pixels 400 arranged in a matrix forming the slice data. (Echo level values) are taken out and their variance values are calculated. The variance value may be calculated by the following formula, for example.

Variance = 1 / n × Σ (Li−Lm) ²

  Here, Li is the luminance value of the i-th pixel (i is an integer from 1 to n) of the n pixels on the line segment 420, and Lm is the average value of the luminance values of these n pixels. Σ is the sum from i = 1 to i = n. Although n = 9 in the example of FIG. 8, it is not limited to 9 pixels. One of the n pixels is the target pixel 410 as the center, and half of the remaining (n−1) pixels exist on both sides of the target pixel 410. The length of the line segment (that is, the number of reference pixels) for obtaining the variance may be determined in consideration of the resolution of slice data (or volume data).

  As illustrated in FIG. 9, such a variance value calculation process is performed by a line segment 420 whose direction is different by a predetermined interval (for example, 5 degrees) in one round (that is, 0 degrees to 180 degrees in this case). -1, 420-2, 420-3,... What is necessary is just to determine the interval of an angle | corner suitably. The n pixels crossed by the line segment 420 in the direction of variance calculation target may be calculated each time, but the relative position of each pixel with respect to the target pixel is calculated in advance for each direction of calculation target and the storage device (for example, lead It may be stored in an only memory or a hard disk and specified from the relative position and the position of the pixel of interest for each direction to be calculated.

  When the variance value for the line segment in each direction centered on the target pixel is obtained in this way, the image preprocessing unit 22 identifies the direction in which the variance value is the maximum among these directions. The specified direction represents the normal direction of the boundary between the media such as cartilage, femur, muscle, and standoff (in other words, the contour of each media).

  For example, FIG. 10 is an example of the pixel of interest 410A located slightly above the boundary between the cartilage 305 and the muscle 350 (in other words, the outline of the cartilage). In this example, when the variance value is calculated as described above for each of the line segments 420a, 420b, and 420c in each direction centered on the pixel of interest 410A, the variance value for the line segment 420a is maximized. That is, a pixel that crosses one side of the center (line of interest 410A) of the line segments 420a to 420c in each direction belongs to the muscle 350 and is basically a bright pixel, whereas a pixel that crosses the other side has a cartilage 305. Dark pixels within are included. And since the number of these dark pixels becomes the maximum at the line segment 420a in the state where the line segment is deepest in the cartilage 305, the dispersion value is also maximized. The direction of the line segment 420a is close to the normal direction to the contour of the cartilage 305 in the vicinity.

  As described above, when the direction (line segment 420a) in which the above-described variance value is maximum for the target pixel 410A can be specified, the image preprocessing unit 22 exists in the vicinity of the target pixel 410A in the plane of the slice data. The smoothed value of the target pixel is calculated using only the pixels in the direction perpendicular to the direction among the pixels to be processed. For example, in the example shown in FIG. 11, the luminance value of the pixel of interest 410A is determined from the luminance values of nine pixels (indicated by dot hatching in the figure) that the line segment 450a perpendicular to the maximum dispersion direction crosses around the pixel of interest 410A. A smoothing value is calculated.

  The nine pixels that the line segment 450a perpendicular to the maximum dispersion direction crosses may be calculated each time, but each direction is calculated in advance and stored in a storage device such as a hard disk, and the stored data is read and used. You may make it do. For example, for each maximum variance direction, position information (for example, a relative position with respect to the target pixel 410A) of these nine reference pixels on the line segment 450a perpendicular to the maximum is stored in the storage device, and the maximum variance direction is specified. The absolute position of each reference pixel may be specified by obtaining the relative position of each reference pixel corresponding to the direction from the storage device and combining the relative position with the position of the target pixel. Although the number of reference pixels is nine, this is only an example. The number of reference pixels may be determined in consideration of the curvature of the cartilage surface, the resolution of slice data (or volume data), the speckle size (number of pixels) at that resolution, and the like.

  Since the line segment 420a is a line in a direction close to the normal line of the boundary between the cartilage 305 and the muscle 350 (that is, the outline of the cartilage), the line segment 450a perpendicular thereto is a line segment in a direction almost parallel to the boundary. Taking the average of neighboring pixels all around the pixel of interest 410A, the average result includes the dark pixels in the cartilage 305, but if the average of only the pixels along the contour direction of the cartilage 305, the average result Does not include dark pixels in the cartilage 305.

  The smoothing value may be, for example, a simple average of the luminance values of these nine pixels. As another example, for example, a weighted average obtained by giving a higher weight to a pixel closer to the pixel of interest 410A at the center may be used as the smoothing value. Further, a smoothed value may be obtained by multiplying the average value by a coefficient corresponding to the magnitude of the average value (simple average or weighted average) of the luminance values of these nine pixels. For example, the contrast between a dark part and a bright part can be improved by increasing the coefficient as the average value is higher (that is, higher brightness). In any case, in the example of FIG. 11, the nine pixels to be calculated are mostly pixels belonging to the muscle 350, so the smoothed value of the pixel of interest 410A is a high luminance value.

  In addition, when the target pixel 410B is located at the boundary between the muscle 350 and the cartilage 305 as shown in FIG. 12, the image pre-processing unit 22 has nine line segments 450d perpendicular to the line segment 420d having the maximum variance. The smoothed value of the target pixel 410B is obtained by averaging the pixels (indicated by dot hatching). In this case, the line segment 450d is close to the tangent line of the outline of the cartilage 305, and these nine pixels are pixels located at or near the outline of the cartilage 305.

  Although not shown, when the target pixel 410 is located inside the cartilage 305, the smoothing value for the target pixel 410 is in the direction of the contour of the cartilage 305 in the same way as in FIG. It is the average of the pixels inside the cartilage 305 along.

  Note that the smoothing process exemplified above is performed not only in the direction along the boundary between the cartilage 305 and the muscle 350 but also at the boundary between the muscle 350 and the standoff 360 (see FIG. 7) and between the muscle 350 and the femur 300. Smoothing is also performed in the direction along the boundary between large tissues such as the boundary and the boundary between the cartilage 305 and the femur 300.

  For each target pixel, the direction of maximum dispersion obtained by the above method is not necessarily close to the normal direction of the tissue boundary near the target pixel. It is considered to be close to the normal direction of the tissue boundary. In particular, since the surface of the cartilage 305 is smooth, regarding the boundary between the muscle 350 and the cartilage 305 and the boundary between the cartilage 305 and the femur 300 (subchondral bone), the direction of maximum dispersion is close to the normal direction of those boundaries.

  By the smoothing process along the direction of the tissue boundary described above, the contour of the cartilage 305 (see FIG. 10) in the raw slice data that is not necessarily smooth due to noise, speckle, etc., is smooth as shown in FIG. become.

  Simple smoothing using neighboring pixels all around the pixel of interest blurs the tissue boundary (especially the boundary between cartilage and other tissues), but in this example smoothing considering the direction of the tissue boundary, The blurring of the border can be suppressed. In this sense, the smoothing processing in this example can be said to be smoothing having the effect of maintaining or enhancing edges (for example, when a smoothing value is obtained by multiplying a coefficient corresponding to an average value).

  Smoothing the entire slice data while maintaining or enhancing the tissue boundary by using each pixel on the slice data as the target pixel and performing smoothing in consideration of the directionality of the tissue boundary as described above for each target pixel. can do. By such a smoothing process, a smooth and clear cartilage image can be obtained. In this smoothing process, the speckles inside the muscle 350 and the like are smoothed, so that the muscle 350 becomes a relatively uniform image with relatively high luminance.

  By performing the smoothing process in consideration of the directionality as described above on each slice data constituting the volume data, the entire volume data can be smoothed.

  Note that the smoothing in consideration of the directionality of the tissue boundary exemplified above is only an example. Instead, a combination of existing smoothing filter processing and edge enhancement filter processing may be used.

  In the above, description has been given focusing on smoothing and edge enhancement (or edge maintenance), but the image preprocessing unit 22 may perform other image processing in addition to such processing.

  In the above example, the volume data is decomposed into slice data, and preprocessing is performed for each slice data. However, the same preprocessing can be directly performed on the volume data. For example, in the case of a method for performing smoothing in consideration of the directionality of the tissue boundary, the processing for each slice data described above specifies the direction in which the dispersion of pixel values is maximum from each direction in the two-dimensional plane. In the processing for the volume data, the direction in which the dispersion of pixel values is maximized may be specified from the three-dimensional directions for the target pixel (voxel). Then, the smoothing value of the target pixel may be calculated based on the pixels (for example, by averaging the pixels) crossed by a plane perpendicular to the direction (for example, a disk centered on the target pixel).

  As described above, the preprocessing result of the image preprocessing unit 22 is passed to the cartilage extraction unit 24. When the image preprocessing unit 22 performs preprocessing in units of slice data, the preprocessing result may be passed to the cartilage extraction unit 24 in units of slice data. Further, the volume data obtained by collecting the preprocessing results for each slice data may be collectively delivered to the cartilage extraction unit 24. In the following, a case of passing and processing in units of slice data will be exemplified.

  In this example, the cartilage extraction unit 24 inverts and binarizes the slice data of the preprocessing result. In the inversion / binarization, for example, the value of each pixel of the image (slice data) is inverted, and the inversion result is compared with a predetermined threshold value to be binarized. In this case, the inversion process may be, for example, a process of subtracting the current pixel value from the maximum value that can be taken by the pixel (pixel value 255 if one pixel is 8 bits). The threshold value for binarization is a value that can distinguish between cartilage parts that become bright as a result of inversion and parts around cartilage that become dark as a result of inversion (such as muscles and the boundary between cartilage and femur). What is necessary is just to obtain by experiment. Instead of binarizing after inverting the pixel value, the pixel value may be inverted after binarizing.

  Of the inversion and binarization, binarization processing is for, for example, distinguishing an image into parts for each tissue and facilitating cartilage extraction. The inversion process is to make it easier for the user to intuitively understand that the cartilage portion is a solid tissue by brightening (that is, whitening) the cartilage portion that is dark on the ultrasound image. The inversion process is performed for consistency with edge extraction to be described later. That is, since the edge extraction filter exemplified later extracts the edge of the binary data H (high: “1”) region, the inversion process is performed to extract the cartilage edge. is there. Therefore, when an edge extraction filter that extracts an L (low: “0”) region of binary data is used or when it is not necessary to display an image of the binarization result, the inversion process is not performed. Also good.

  When the slice data illustrated in FIG. 7 is inverted and binarized after the preprocessing by the image preprocessing unit 22, an image as illustrated in FIG. 14 is obtained. In the image example of FIG. 14, the cartilage 305, the femur 300, etc. are displayed in white, and the muscle 350, etc. are displayed in black.

  Next, the cartilage extraction unit 24 applies an edge extraction process to the inverted and binarized slice data, thereby extracting tissue boundary edges in the slice data. The extracted edge includes the outline of the cartilage.

  In this edge extraction process, an edge extraction filter may be applied to the binarized slice data. As the edge extraction filter, for example, a known secondary differential filter such as a Laplacian filter, a Prewitt filter, or a Sobel filter may be used.

  When edge extraction processing is performed on the binarized slice data illustrated in FIG. 14, for example, an image as illustrated in FIG. 15 is obtained. In the binary image of the edge extraction result, the edge is black (value “0”), and other than the edge is white (value “1”). In FIG. 15, various boundaries including the cartilage contour 306 are black contour lines.

  The cartilage extraction unit 24 receives designation of a point (hereinafter referred to as a reference point R) inside the cartilage (or on the cartilage surface) from the user on the screen displaying the slice data. The image displayed on the screen when receiving the designation of the reference point R may be an image after inversion and binarization (see FIG. 14) or an image of the edge extraction result (see FIG. 15). The reference point R may be specified using a pointing device such as a mouse provided in the input unit 32. FIG. 16 shows the reference point R designated for the image of the edge extraction result.

  Next, the cartilage extraction unit 24 obtains a point on the cartilage contour 306 from the vicinity of the reference point R. In the example of FIG. 17, intersections A (upper side) and B (lower side) of the vertical line passing through the reference point R and the cartilage contour 306 are obtained (in FIG. 17, other than the cartilage contour 306 is avoided in order to avoid complexity. Edges are omitted.) In the example of FIG. 17, the black pixel first found in the process can be determined as the point A on the cartilage contour 306 while sequentially proceeding one pixel at a time starting from the reference point R. Similarly, the black pixel which is found first after proceeding downward from the reference point R is the point B.

  If the case where an edge exists inside the cartilage 305 is considered, the cartilage extraction unit 24 obtains the distance between the points A and B thus obtained, and the distance is the thickness of the cartilage (usually 2 to 3 mm). If the reference point R is too narrow, the points A and B may be obtained again after shifting the position of the reference point R by a predetermined pixel in the horizontal direction. In the comparison, the interval between the points AB may be compared with a predetermined threshold value (for example, 1.5 mm) based on the knowledge of the cartilage thickness.

  In the example of FIG. 17 (and FIG. 7, FIG. 14 to FIG. 16), the image of the thin cartilage 305 is positioned so as to extend substantially laterally in the screen. It can be regarded as a direction. If the subject of the mechanical three-dimensional probe 10 is brought into contact with a knee bent by a subject sitting on a chair or the like (for example, such a direction is displayed on the probe 10, The user may make contact according to the orientation or only contact the orientation according to the mechanism), and the coordinate system of the volume data (and slice data obtained therefrom) is known. The slice data can be automatically oriented as shown in FIGS.

  As described above, when the points A and B of the cartilage contour 306 above and below the reference point R are specified, the cartilage extraction unit 24 searches for a connected component of black pixels connected to the points A and B. For example, starting from points A and B, a black pixel is searched from pixels in the vicinity of 4 or 8 of the target pixel, and if a black pixel is found, the same search may be repeated using the black pixel as a new target pixel. A connected component of black pixels obtained by the search process as described above is a cartilage contour 306. If search processing is performed on the edge extraction result illustrated in FIG. 16, only the cartilage contour 306 can be extracted as shown in FIG.

  The above processing is repeated for each slice data in the volume data. A set of cartilage contours 306 extracted from each slice data represents a three-dimensional cartilage contour shape.

  In the above extraction processing, the user specifies the reference point inside or on the surface of the cartilage. However, it is troublesome to make such designation for each of a large number of slice data constituting the volume data. Therefore, if the user designates one reference point with one slice data, it is also preferable to automatically determine the reference point of the cartilage in the remaining slice data from the reference point. An example of the processing for that is shown below.

In this example, as shown in FIG. 19, when the user designates a reference point R ₀ in the cartilage on a certain slice data S ₀ , the cartilage extraction unit 24 uses the vertical line passing through the reference point R ₀ and the cartilage contour 306. The cartilage contour 306 on the slice data is extracted with the intersection points A ₀ and B ₀ as the starting points. Further, cartilage extract unit 24 obtains the coordinates of the midpoint C ₀ thereof points A ₀ and B _0. In the figure, as an example, the coordinate system of the volume data is XYZ, and each slice data is assumed to be Z = a constant surface. Therefore, the coordinates of the midpoint C ₀ are represented by (X, Y).

Next, as shown in FIG. 20, the cartilage extraction unit 24 extracts slice data S ₁ adjacent to the slice data S ₀ from the volume data, and is the same as the point C ₀ (X, Y) in the slice data S ₁ . ) A point having coordinates is set as a reference point R ₁ . Since the cartilage is gently curved three-dimensionally, the midpoint of the upper and lower contours of the cartilage in a certain slice data is considered to fall between the upper and lower contours of the cartilage in the adjacent slice data with a very high probability. Therefore, the same contour extraction process as described above can be performed using the reference point R ₁ . Further, cartilage extract unit 24, the center point C ₁ of intersection A ₁ and B ₁ between the vertical line and the cartilage contour 306 therethrough reference points R ₁ determined, the next slice data based on the coordinate its midpoint C ₁ A reference point R ₂ for S ₂ is determined. In this way, the process of determining the midpoint C of the points A and B of the cartilage contour 306 from the reference point R for each slice data and determining the reference point R in the adjacent slice data from the midpoint C is repeated in a chain manner. Thus, if one reference point is designated in the first slice data, the reference points for all remaining slice data can be automatically determined.

  Note that the point for obtaining the reference point in the next slice data may not be the exact midpoint C of the points A and B, but may be a point in the range close to the midpoint C. The reference point specified by the user may be biased to either the upper or lower end in the cartilage thickness direction, and in this case, the point corresponding to that point goes out of the cartilage in the adjacent slice data. there is a possibility. On the other hand, if the points A and B on the upper and lower contours of the cartilage are obtained based on the designated reference point and the reference point of the next slice data is obtained from the points on the upper and lower contours, The possibility of overflowing can be almost eliminated.

  Further, in such automatic determination processing of the reference point R, when searching for black pixels (contours) from the reference point R upward and downward with a certain slice data, even if searching to a range exceeding the thickness of the cartilage, it is black. If the pixel is not found, it may be determined that the end of the cartilage end in the arrangement direction of the slice data has been reached, and the processing may be stopped, and subsequent slice data may not be processed.

  The example of the cartilage contour extraction process has been described above. In the above, the cartilage contour is extracted for each slice data, but the method of this embodiment is not limited to this. For example, instead of 4 or 8 neighbors in a two-dimensional plane, the search for connected components in the three-dimensional volume data can be performed by using pixels in the vicinity of the top, bottom, front, back, left, and right in the three-dimensional space as the search window. The same can be done for this. Therefore, a three-dimensional cartilage contour can be directly extracted from the volume data.

  Another example of the extraction process performed by the cartilage extraction unit 24 is the following process. That is, in this process, the well-known labeling process is performed on the slice data after inversion and binarization, so that each of the white and black connected components 1, 2, 3, and 4 is obtained as shown in FIG. Extract. The label values 1, 2, 3, 4 and the like need only be held internally by the cartilage extraction unit 24 and need not necessarily be displayed on the screen. Then, the cartilage extraction unit 24 causes the user to designate a point corresponding to the cartilage with a mouse or the like on the binary image displayed on the screen, and extracts a connected component including the label value of the designated point as the cartilage 305.

  Now, in the extraction of the cartilage at the distal end of the femur, the shadow by the patella may have an adverse effect. That is, as shown in FIG. 22, in ultrasonic diagnosis inside the knee, a part of the cartilage 305 is located behind the patella 370 when viewed from the probe 10. Behind the bone, the echo data is small and unclear due to attenuation or scattering of the ultrasonic beam. Therefore, in the contour extraction, the cartilage 305 and the shadow 375 of the patella 370 are connected, and the extracted cartilage contour 306 includes an inaccurate portion 308 due to the shadow of the patella as illustrated in FIG. . Therefore, hereinafter, an example of processing performed by the cartilage extraction unit 24 in order to remove an inaccurate portion due to the influence of the patella will be described.

  An example of the procedure of this processing is shown in FIGS. In this procedure, the cartilage extraction unit 24 receives the designation of the reference point R on the slice data of the edge extraction result as illustrated in FIG. 26 as in the above example (S102). Then, a search is performed from the reference point R in the vertical direction to find a black pixel closest to the reference point. The black pixels found are points A and B on the upper and lower contour lines of the cartilage (S104). Here, the upper contour is the cartilage surface (that is, the boundary between the cartilage and the muscle and body fluid covering it), and the lower contour is the boundary between the cartilage and the subchondral bone of the femur.

  Next, the cartilage extraction unit 24 performs contour end point detection processing (S200) with the points A and B as starting points and the right and left directions from the starting points as traveling directions, respectively. In this process, by executing S200 for each of the four types of combinations of the two types of start points A and B and the two types of right and left in the traveling direction, each of the upper and lower contours of the cartilage Find the right and left points. An example of the detailed procedure of step S200 is shown in FIG.

In the procedure of FIG. 25, firstly, the starting point (point A or point B) as the target point P _i (S202), calculates the inclination of a tangent line angle theta _i through the point of interest P _i (S204). The tangential inclination angle θ _i may be obtained by a known method used in the field of numerical calculation. For example, the outline point of interest P _i belongs (called the target contour), determine the points on both sides of the target point P _i, the slope of a straight line connecting their neighboring points, and slope of the tangent line passing through the point of interest P _i The angle θ _i may be used. The use of both adjacent points is merely an example, and two points separated by a predetermined number of pixels on the left and right sides from the point of interest may be used instead. In the example of FIG. 27, the inclination angle θ _{i of the} tangent line is expressed as an angle with respect to the reference line extending rightward from the point of interest P _i , and the clockwise direction is a positive direction (however, this is only an example). .

Next cartilage extract unit 24 searches for a neighboring point P _{i + 1} in the traveling direction with respect to the point of interest P _i in interest contour (S206). In this search, for example, a reference window shown in FIG. 28 may be used. That is, when the traveling direction is the right direction, as shown in (a), upper and lower and right of the target point P _i, upper right, if there is a black pixel in the reference window of five pixels in the lower right, the black pixel What is necessary is just to set it as the adjacent point _{Pi + 1} . If the traveling direction is the left direction, so as in (b), however, upper and lower and left target point P _i, the upper left, may be used a reference window comprising a lower left 5 pixels. Priorities are set for the pixels in the reference window. When there are a plurality of black pixels in the reference window, the pixel having the highest priority among the black pixels is detected as the adjacent point P _{i + 1} .

Next, the cartilage extraction unit 24 determines whether or not the adjacent point P _{i + 1} is found in S206 (S208), and if found, calculates the inclination angle θ _{i + 1} of the tangent line passing through the adjacent point P _{i + 1.} (S210) (refer FIG. 27). Then, a difference between the tangent slope angle theta _i through the point of interest P _i and the gradient of the tangent angle theta _{i + 1} through the adjacent point P _{i + 1} (absolute value), and the difference, to a preset The threshold value Th is compared (S212). The threshold Th may be determined in advance, for example, 5 degrees or 10 degrees according to the curvature of the cartilage surface.

In the comparison, if the difference is less than the threshold value, the adjacent point P _{i + 1,} in part of the effects of the patella is determined that not the case, the neighboring point P _{i + 1} as the next target point P _i, the tangent slope angle theta _{i + 1} corresponding to the neighboring point P _{i + 1} and the inclination angle theta _i corresponding to the next target point P _i (S214), S206 and repeats the following process.

While repeating steps S206 to S214, when the adjacent point P _{i + 1} reaches an inaccurate portion (portion 308 in FIG. 23) due to the shadow of the patella (in contrast, the attention point P _i is on the outline of the cartilage), θ The difference between _i and θ _{i + 1} increases. Then, the determination result of step S212 is negative (No), and the process proceeds to step S216. In step S216, the cartilage extraction unit 24 stores the attention point P _i at that time as the end point of the cartilage contour in the traveling direction.

For example, as illustrated in FIG. 29, when advancing the process to the right from point A on the upper cartilage contour, step S216, the point A _R is stored as the rightmost point of the upper edge. Likewise the point B _R is stored as a point right end of the lower contour.

Further, in the processing loop of steps S206～S214, while going to move the target point P _i in the traveling direction, there is a case where the point of interest P _i will reach the edge of the cartilage contour. In this case, since the adjacent point P _{i + 1} in the traveling direction is not found even if it is searched in step S206, the process proceeds from step S208 to step S216, and the cartilage extracting unit 24 determines the point of interest P _i at that time as the traveling direction. Is stored as the end point of the cartilage contour. For example, in the cartilage contour 306 in FIG. 23, when the processing proceeds in the left direction from the upper and lower points A and B (see FIG. 28), both reach the same point at the left end of the cartilage contour 306, and that point is the end point. Memorize and finish the process.

  When the end points of the cartilage contour are obtained as described above, the cartilage extraction unit 24 connects the right end points and the left end points of the upper and lower contour lines of the cartilage with straight lines, respectively (S106, S108). Either of S106 and S108 may be executed first. When the upper and lower end points are the same point, it is not necessary to connect them by a straight line between them.

  When the above process is applied to the cartilage contour 306 including the inaccurate portion 308 illustrated in FIG. 23, the closed cartilage contour 306a from which the inaccurate portion 308 is removed as illustrated in FIG. Is required.

  24 and 25 described above are repeated for each slice data in the volume data. By combining the cartilage contour 306a for each slice data obtained in this way, the surface shape of the three-dimensional cartilage contour from which an inaccurate portion due to the influence of the patella is removed is obtained. Note that the automatic determination process for the reference point R described above can also be applied to this process. When the automatic determination process of the reference point R is applied, the slice data that does not include the cartilage contour 306 among the slice data included in the volume data is not subjected to the processes of FIGS. You can also.

  As described above, in the processing procedure of FIG. 24 and FIG. 25, while the change (difference) in the inclination angle of the tangent line between the target point and the adjacent point is slight, the adjacent point is on the cartilage contour 306. It is determined that there is (that is, not on the inaccurate portion 308). That is, in such a case, the adjacent points are smoothly continuous along the cartilage contour 306 with respect to the target points on the cartilage contour 306, and therefore the adjacent points are points on the cartilage contour 306. Judgment is made. On the other hand, when the change in the inclination angle of the tangent line between the target point and the adjacent point becomes steep (that is, the threshold Th or more), it is determined that the adjacent point has entered the inaccurate portion 308, It is determined that the cartilage contour 306 is accurate up to the attention point immediately before the adjacent point.

  Through the processing as described above, a contour portion that smoothly continues with respect to the points A and B on the cartilage contour 306 in the vicinity of the reference point R is extracted as an accurate cartilage contour from which the inaccurate portion 308 is removed. Is done.

  In the above example, the point at which the tangential direction of the contour changes sharply is determined as the end point of the cartilage contour 306, but the feature of interest for finding the end point is not limited to the tangential direction. A point at which the normal direction of the contour changes sharply may be determined as an end point. Further, a point where the contour differential coefficient changes abruptly may be determined as an end point. In any case, in this embodiment, the point at which the feature amount indicating the directionality of the contour changes sharply may be determined as the end point.

  In the above example, it is determined whether or not the directionality of the contour has changed sharply by comparing the difference in inclination angle in the tangential direction with a threshold value. However, the determination condition is not limited to this. .

  Next, a modified example of the process of removing the inaccurate portion 308 from the cartilage contour 306 will be described. In this example, a three-dimensional region of interest (hereinafter referred to as ROI, where ROI is an abbreviation of Region Of Interest) is set for cartilage, and a portion of the contour that deviates from the ROI is removed. With this method, it is difficult to completely remove the inaccurate portion 308, but a considerable portion can be removed, and the calculation processing can be speeded up. Hereinafter, an example of the ROI setting method in this modification will be described.

  In this example, the volume data (more preferably, the data after inversion / binarization) of the smoothed result in consideration of the directionality of the tissue boundary by the image preprocessing unit 22 is volume-rendered and displayed on the display unit 30. 3D display. Then, by allowing the user to operate the three-dimensional image display via the input unit 32 and selecting the viewpoint position and the line-of-sight direction, the cartilage 305 at the distal end of the femur is viewed from substantially the front as shown in FIG. Make sure the status is displayed. In the example of FIG. 31, for convenience, the three-dimensional depth of the cartilage 305 is displayed with contour lines, but the actual three-dimensional display is not limited to such a display. Further, in actual ultrasonic measurement, since there is a portion that becomes a shadow of the patella, the entire three-dimensional shape of the cartilage is not clearly visible as shown in FIG. 31, but in FIG. Let me show you.

  Next, in order to extract the medial condyle (the left side of the bifurcated in the figure) of the bifurcated portion of the cartilage 305, the point closest to the viewpoint on the medial condyle (this is roughly the cartilage loading portion) The user is allowed to designate a horizontal axis (XY axis) and a vertical axis (VW axis) through the input unit 32. The XY axis is the short axis of the medial condyle, and the VW axis is the long axis of the medial condyle. The medial condyle is taken up because the medial condyle is less covered by the patella (especially the load part of the medial condyle is generally the patella when the knee is largely bent as shown in FIGS. This is because it is suitable for measurement of cartilage.

  Next, the volume data is cut by a plane (VW plane 500) extending in the depth direction from the viewpoint through the VW axis, and a tomographic image of the cut plane is displayed on the display unit 30. FIG. 32 shows an example of such a tomographic image of the VW surface 500. In the drawing, only the outline of the cartilage 502 is shown and the surrounding tissue image is not shown in order to avoid complication, but the image of the surrounding tissue exists in the actual tomographic image. The user operates the input unit 32 (for example, a pointing device) while viewing such a tomographic image, and designates a curve 504 along the shape of the cartilage 502.

  Further, the volume data is cut by a plane (XY plane 510) passing through the XY axis and extending in the depth direction from the viewpoint, and a tomographic image of the cut plane is displayed on the display unit 30. FIG. 33 shows an example of such a tomographic image of the XY plane 510. In the figure, in order to avoid complexity, only the outline of the cartilage 512 is shown, and the image of the surrounding tissue is not shown. The user operates the input unit 32 while viewing such a tomographic image, and designates the ROI outline 514 that encloses the cartilage 512. The ROI outline 514 may be specified to be somewhat larger than the cartilage 512.

  The extraction ROI setting unit 34 (see FIG. 3) translates the contour line 514 designated as described above so that its center (that is, a point on the VW axis) passes through the curve 504, thereby obtaining a three-dimensional view. Set ROI. That is, as illustrated in FIG. 34, contour lines 514-1 to 514-5 are obtained by parallel movement on the respective surfaces 510-1 to 510-5 parallel to the XY plane, and the contour lines are collected. The enclosed inner region becomes a three-dimensional ROI.

  The cartilage extraction unit 24 cuts out only the inside of the three-dimensional ROI thus obtained from the original volume data after inversion / binarization or volume data after edge extraction (see FIG. 23). Thereby, a considerable portion of the inaccurate portion 308 (see FIG. 23) due to the influence of the patella can be removed.

  The example of the processing content of the cartilage extraction unit 24 has been described above. Returning to FIG. 3 again, the three-dimensional shape data of the cartilage obtained by the cartilage extraction unit 24 is stored in the three-dimensional data memory 26. The image forming unit 28 renders the data in the three-dimensional data memory 26 to generate a three-dimensional cartilage image and displays it on the display unit 30. The image forming unit 28 may generate and display an image obtained by synthesizing the rendered cartilage image with a three-dimensional image obtained by rendering the raw volume data of the entire knee in the three-dimensional data memory 20. The synthesis may be, for example, a process in which the cartilage image is displayed in a color different from the image of the entire inside of the knee, and both are superimposed. The image forming unit 28 may form an image representing the cartilage shape in the cross section designated by the user based on the cartilage shape data in the three-dimensional data memory 26 and display the image on the display unit 30.

  Next, a mechanism for quantitative evaluation of cartilage based on the extracted cartilage shape information will be described. For this quantitative evaluation, the ultrasonic diagnostic apparatus of this embodiment includes a quantification processing unit 36, a thickness calculation unit 38, and a measurement ROI setting unit 39.

  The quantification processing unit 36 performs calculation of quantitative evaluation information based on the thickness of the cartilage and control for the calculation. The thickness calculator 38 calculates the thickness of the cartilage at a position selected on the three-dimensional shape of the cartilage as basic information for obtaining quantitative evaluation information. An example of the thickness calculation procedure is shown in FIG.

  In the procedure of FIG. 35, the quantification processing unit 36 generates a three-dimensional image of cartilage based on the cartilage shape data in the three-dimensional data memory 26 and displays it on the display unit 30 (S300). If necessary, the user operates the pointing device or the like of the input unit 32 to change the direction of the three-dimensional image of the cartilage so that it can be easily seen. Then, the user designates a point or range for calculating the thickness on the three-dimensional outline (surface) of the cartilage, and the quantification processing unit 36 recognizes the designated point or range (S302). When the range is designated, the quantification processing unit 36 sets a plurality of points (pixels = voxels) on the cartilage contour included in the range as thickness calculation target points. In the following description, it is assumed that one target point A for thickness calculation is designated as illustrated in FIG. Although there are portions that cannot be seen such as a portion corresponding to the shadow of the patella in ultrasonic measurement, FIG. 36 also shows the entire shape of the cartilage 600 for the sake of convenience as in FIG.

  In the example of FIG. 36, a point corresponding to the cartilage load portion in the medial condyle 602 of the cartilage 600 at the distal end of the femur is designated as the calculation target point A. For example, when a three-dimensional image of cartilage is formed parallel to the femoral bone axis (the central axis of the femur extending in the longitudinal direction of the femur) with the direction of gaze as viewed from the outside of the knee The cartilage loading portion of the medial condyle is the highest point (that is, the closest point from the viewpoint) in the range of the medial condyle 602 in the three-dimensional image.

  Here, for example, when the subject sits on a chair, the direction in which the thigh extends in the horizontal plane can be regarded as the direction of the bone axis of the femur. Alternatively, by appropriately determining the angle of the seat surface of the chair, the direction of the bone axis of the femur can be in the horizontal plane. In this way, when the mechanical three-dimensional probe 10 is positioned with respect to the knee, the positional relationship between the coordinate system of the mechanical three-dimensional probe 10 and the bone axis direction of the femur can be known. Therefore, the image forming unit 28 uses the volume data obtained by the probe 10 or the shape of the cartilage extracted from the volume data in the sight line direction (for example, the cartilage at the distal end of the femur along the bone axis from the outside of the knee). Can be rendered as). If rendering is performed in this way, it is possible to automatically generate a three-dimensional image display of cartilage in which the point closest to the viewpoint as viewed from the viewpoint side is approximately the cartilage load portion as shown in FIG. On the display, the user can designate a point of the cartilage load portion (or a range having a certain size corresponding to the cartilage load portion).

  In addition, as described above, if a three-dimensional image display of cartilage in which the closest point when viewed from the viewpoint side is approximately the cartilage load portion can be automatically generated, the user can select the point or range of the cartilage load portion. Instead of specifying, it is also possible to automatically detect the closest point or range on the three-dimensional image as a cartilage load portion. Although automatic detection may be inferior to the accuracy specified by the user, it is useful for improving the processing speed.

  When the calculation target point A is designated, the thickness calculation unit 38 obtains the normal direction of the cartilage contour surface at the calculation target point A.

  In order to calculate the normal direction, for example, the thickness calculator 37 obtains three reference points B, C, and D from the vicinity of the point A in the surface of the cartilage contour (S304). As an example, the three reference points are shown in FIG. 37 (an enlarged view of the vicinity of the target point A in FIG. 36). Are preferably selected so as to be located in the three directions. Further, it is preferable that the distances from the target point A to the reference points B, C, D are substantially equal. The distance may be about one pixel (voxel), but if the number is set to several pixels (for example, about 5 or 6 pixels), the accuracy of specifying the normal direction can be improved later.

Next, the thickness calculation part 38 calculates | requires the equation of the plane E passing through those reference points B, C, and D (S306). In this step, the plane equation ax + by + cz + d = 0
By substituting (Bx, By, Bz), (Cx, Cy, Cz), (Dx, Dy, Dz) for the three-dimensional coordinates of the reference points B, C, D, respectively, a, b, c, d Three equations are obtained using as variables. Then, by solving the simultaneous equations composed of these three equations, the ratio of the variables a, b, c, and d can be obtained, and the equation of the plane E is obtained from the ratio. This plane E is substantially equal to the tangent plane of the cartilage contour at the calculation target point A.

  In this processing, the normal direction of the plane E is regarded as the normal direction of the contour surface at the calculation target point A. Since the normal vector of the plane ax + by + cz + d = 0 is expressed by (a, b, c), the normal vector can be specified by solving the simultaneous equations and knowing the ratio of the variables a, b, c, d.

  As the three-dimensional coordinates of the reference points B, C, and D used for obtaining the normal vector, the average value of the three-dimensional coordinates of a plurality of points (pixels) near the reference point among the points on the cartilage contour. May be used. For example, in the example of FIG. 38, as the three-dimensional coordinates of the point B indicated by the white circle, the three-dimensional coordinates of the point B itself, the three-dimensional coordinates of the points in the vicinity of the point B (black circles), The average value is used. By averaging the coordinates of the neighboring points of the reference point in this way, it is possible to cancel the high-frequency error component included in the extracted cartilage contour, and it is expected to improve the accuracy of the normal vector.

  Next, as shown in FIG. 39, the thickness calculator 38 obtains a straight line that passes through the calculation target point A and extends in the direction of the obtained normal vector (S308). This is the normal line of the cartilage contour passing through the target point A. As shown in FIG. 40, the thickness calculator 38 obtains one point where the normal intersects the three-dimensional contour surface of the cartilage, one for each of the upper and lower contour surfaces (S310). In the example of FIG. 40, in order to avoid complexity, a cross-sectional view when a three-dimensional cartilage contour is cut along a plane to which a normal passing through the point A belongs is schematically shown. As illustrated, one of the two intersections is the calculation target point A itself. The other intersection is defined as point F.

  The thickness calculation unit 38 calculates the length of the line segment connecting the two intersection points AF. This length is the thickness of the cartilage at the position of the calculation target point A (S312).

  In the above, the normal vector is obtained using the three reference points around the calculation target point A, but this is only an example. In addition to this, for example, an equation of the tangent plane of the cartilage contour at the calculation target point A is obtained from the coordinates of a plurality of points on the cartilage contour in the vicinity of the calculation target point A by a regression operation such as a least square method. A normal vector may be obtained from the coefficient of.

  In the above, the case where the calculation target point is set on the surface of the cartilage surface, that is, the boundary surface between the cartilage and the muscle or body fluid covering the cartilage is exemplified. On the other hand, the calculation target point can be set on the back surface of the cartilage, that is, the boundary surface between the cartilage and the distal end of the femur (subchondral bone). For this purpose, for example, the back side of the cartilage is displayed in the three-dimensional display of the cartilage, and the user can specify the calculation target point on the back side. At this time, for example, in a situation where the relationship between the coordinate system of the mechanical three-dimensional probe 10 and the bone axis direction of the femur is known, the volume data of the cartilage is obtained from the femur side along the bone axis of the femur. If rendering is performed in a state where the cartilage is viewed from the line of sight, the three-dimensional image of the cartilage can be displayed in a posture such that the innermost point when viewed from the viewpoint is the cartilage load portion. Therefore, the user can specify a point corresponding to the cartilage load portion on the back side of the cartilage by viewing this display. A point corresponding to the cartilage load portion may be automatically determined from the three-dimensional image from such a viewpoint. Alternatively, a point may be specified by the user on the cartilage surface side, a point where a line of sight line direction passing through the point intersects the cartilage back surface, and the point may be set as a measurement target point. The cartilage surface may be uneven due to wear due to friction with the cartilage of the opposing bone. In such a case, if a measurement target point is set in the uneven portion, there is a possibility that the normal direction at that point is considerably deviated from the correct thickness direction. On the other hand, the back surface of the cartilage sticks to the femur and does not receive such friction, so it maintains smoothness and there are few such problems.

  In general, since the cartilage surface (boundary between cartilage and muscle or body fluid) is often smooth enough, even if a measurement target point such as a cartilage load part is selected from the cartilage surface, there is no problem in accuracy. There are many cases.

  The method for calculating the cartilage thickness at a certain point has been described above. In addition, the quantification processing unit 36 receives designation of a measurement target range having a certain area on the three-dimensional image of cartilage, and obtains statistical information about the thickness of the cartilage at a plurality of points within the range. May be. An example of this processing is shown in FIG.

  In the procedure of FIG. 41, first, the quantification processing unit 36 displays a three-dimensional image of the cartilage contour on the display unit 30 (S320), and receives designation of a measurement target range (measurement ROI) from the user (S322). The user operates the pointing device or the like of the input unit 32 to specify the measurement ROI. Information on the designated measurement ROI is recognized by the measurement ROI setting unit 39 and transmitted to the quantification processing unit 36.

In the example of FIG. 42, the range including the cartilage load portion of the medial condyle 602 is designated as the measurement ROI 610. A large number of black circles displayed in the measurement ROI 610 schematically indicate points in the ROI (that is, pixels = voxels). FIG. 43 shows an example in which the entire medial condyle 602 is designated as the measurement ROI 610 (in the figure, the measurement target points included in the ROI are indicated by black circles).
The quantification processing unit 36 causes the thickness calculation unit 38 to calculate the thickness of the cartilage at each point in the designated measurement ROI (S324). Then, the quantification processing unit 36 statistically processes the cartilage thickness at each of these points (S326).

  The statistical process is a process for obtaining one or more kinds of statistical feature values such as an average value or variance or standard deviation of the thickness at each point. Also, statistical processing. As shown in FIG. 44, a process of generating a thickness distribution map image at each point may be performed. FIG. 44 schematically shows an enlarged image of the portion of the medial condyle 602. Of the points in the measurement ROI, the portion 620 in which the cartilage thickness at that point is equal to or less than the average value in the ROI, and the average value The portion 622 exceeding the range 622 is distinguished and displayed in a display form (for example, color or density). The distribution map image can be generated, for example, by setting the pixel value of each point in the three-dimensional space to a value corresponding to the cartilage thickness at that point. That is, this distribution map image is a three-dimensional image. In the example of FIG. 44, a two-stage distribution map image having a point where the cartilage thickness is equal to or less than the average value and a point exceeding the average value is generated, but a distribution map image having a larger number of stages can also be generated.

  Further, the quantification processing unit 36 may obtain the frequency distribution of the cartilage thickness at each point in the measurement ROI as shown in FIG.

  Further, the quantification processing unit 36 may obtain an evaluation value other than the evaluation value related to the thickness of the cartilage. For example, the volume of the cartilage can be obtained from the information of the three-dimensional shape of the cartilage held in the three-dimensional data memory 26. Moreover, the unevenness | corrugation degree of the cartilage surface can also be calculated | required. The degree of unevenness may be obtained, for example, by counting the number of voxels corresponding to the cartilage contour and normalizing the counted value per unit area. It can be said that the greater the number of voxels in the cartilage contour per unit area, the higher the degree of unevenness.

  The quantitative information obtained by the quantification processing unit 36 is synthesized by the image forming unit 28 with respect to the three-dimensional image inside the knee, the three-dimensional image of the cartilage, or a combination thereof, and displayed on the display unit 30. The For example, when the quantification processing unit 36 obtains a statistical value such as an average value of cartilage thickness in the measurement ROI, the image forming unit 28 displays numerical information of the average value on a screen displaying a three-dimensional image or the like. Superimpose. When the measurement ROI is set, the numerical information may be superimposed in or near the ROI. When the quantification processing unit 36 obtains the distribution map image of the cartilage thickness in the measurement ROI, the image forming unit 28 superimposes the distribution map image on the three-dimensional image inside the knee or the three-dimensional image of the cartilage. That's fine. The image forming unit 28 may display the frequency distribution of the cartilage thickness at each point in the measurement ROI on the display unit 30 side by side with the three-dimensional image or on a separate screen.

  Next, a description will be given of a support function for obtaining a temporal change in an evaluation value such as a thickness of cartilage in the ultrasonic diagnostic apparatus according to the embodiment.

  For example, in order to correctly obtain the change with time in the thickness of the cartilage at the same point, it is necessary to correctly align the cartilage shapes obtained at different time points.

  This alignment processing is performed by the alignment unit 62 in the quantification processing unit 36 in FIG. The database 60 is a database that accumulates past measurement results. For example, each time the subject's knee is measured, the cartilage shape information (contour shape or volume data) extracted by the cartilage extraction unit 24 is stored in the database 60 as the subject's ID (identification) information and measurement date and time. It is registered in association with. A diagnosis information management system for inputting ID information of a subject by a user (diagnostic) from the input unit 32, reading the ID card of the subject by a card reader included in the input unit 32, and managing medical record information and the like (Not shown). In addition to cartilage shape data, raw volume data (in the three-dimensional data memory 20) obtained by the mechanical three-dimensional probe 10 may be registered in the database 60. Further, the volume data of the preprocessing result of the image preprocessing unit 22 may be registered. Further, an evaluation value such as a thickness of the cartilage load portion calculated by the thickness calculation unit 38 or a statistical value of the thickness in the vicinity of the cartilage load portion may be registered. The database 60 also registers information indicating whether the registered cartilage shape or the like is the right foot or the left foot.

  Now, to align the two images, for example, when considering a planar image, rotate and translate one image, compare it with the other image for each pixel, and rotate so that the difference is minimized What is necessary is just to obtain | require an angle and a parallel displacement. In other words, if the width of the image is w and the height is h, one image is rotated by, for example, 1 degree from −180 degrees to +180 degrees, moved by 1 pixel in the x direction from −w to + w, and moved in the y direction. What is necessary is just to find the optimal rotation angle and amount of parallel movement each time it is moved pixel by pixel from -h to + h, and compared with the other image each time. However, this method takes an enormous amount of time to process. For example, if the image size is 512 × 512 pixels, 1024 × 1024 × 360 = 94,371,840 comparison operations are required. In the case of deep three-dimensional image data, the processing time becomes enormous.

  On the other hand, in the processing method exemplified below, the processing time is reduced by limiting the range of the comparison calculation to a range along the shape of the cartilage. This processing method is shown in FIG.

  FIG. 46 illustrates an example in which the cartilage shape of the subject newly measured this time is aligned with the previous measurement result of the cartilage shape for the same subject.

  In the procedure of FIG. 46, first, the inside of the subject's knee is measured by the mechanical three-dimensional probe 10, and the current shape measurement result of the subject's knee cartilage is obtained by the above-described processing (S400). The raw volume data inside the knee and the three-dimensional shape data of cartilage obtained at this time may be registered in the database 60 in association with the ID information of the subject.

  The ultrasonic diagnostic apparatus has a mode for comparing with the previous time. When the diagnostician instructs execution of the mode, the alignment unit 62 reads out cartilage shape data at the time of the previous measurement of the subject from the database 60 (S402). Here, the latest cartilage shape data registered in association with the ID information of the subject input at the beginning of the current measurement (excluding the current measurement result) may be searched from the database 60. If the right foot is measured this time, the previous right foot cartilage shape data is searched from the database 60.

  Next, the alignment unit 62 passes the measured cartilage shape data and the searched previous cartilage shape data to the image forming unit 28, and displays the three-dimensional images of the cartilage shape on one screen, for example. Display images arranged side by side are generated and displayed on the display unit 30. Note that instead of displaying the previous and current images side by side on one screen, they may be displayed one by one on the screen and subjected to investigations such as changing the viewpoint or specifying the axis each time.

  The diagnostician can change the viewpoint (or line-of-sight direction) at the time of rendering by operating a pointing device or the like included in the input unit 32 for each of the previous and current three-dimensional cartilage images displayed on the screen. . By changing the viewpoint, the previous and current cartilage three-dimensional images are oriented in the direction in which the cartilage load portion in the medial condyle of the cartilage comes to the front. At this time, the line-of-sight direction of the three-dimensional image is substantially equal to the direction of the bone axis of the femur, and is also substantially equal to the direction of the thickness of the cartilage at the cartilage load portion.

  If the relative positional relationship between the mechanical three-dimensional probe 10 and the subject's knee or thigh is mechanically restricted as in the above example, the coordinate system of the probe 10 and the bone axis direction of the femur The three-dimensional image display of the cartilage can be automatically generated so that the point on the most front side when viewed from the viewpoint side is substantially the cartilage load portion. In this case, the diagnostician does not need to change the viewpoint, and even if it is performed, it may be a slight change.

  When the orientations of the previous and current cartilage 3D images are adjusted in this manner, the alignment unit 62 then asks the diagnostician on the cartilage 3D images of the cartilage and the load on the medial condyle. The cartilage longitudinal axis passing through the part is designated (S404). For example, as shown in FIG. 36, the cartilage inner condyle 602 has a generally long shape when viewed from the direction in which the cartilage loading portion faces the front and the direction in which the medial condyle and the outer condyle are aligned is a horizontal direction. I am doing. In S404, the diagnostician designates the cartilage load portion using a pointing device or the like, and designates a line extending through the cartilage load portion in the longitudinal direction of the shape of the medial condyle as the longitudinal axis.

  FIG. 47 shows cartilage load portions 630a and 630b in the cartilage three-dimensional image (A) and the previous cartilage three-dimensional image (B) (in the drawing, the vicinity of the load portion of the medial condyle is enlarged). And the longitudinal axes 632a and 632b are schematically shown.

  The alignment unit 62 determines the intersection of the straight line extending in the line-of-sight direction of rendering and the front surface of the cartilage shape from the point designated as the cartilage load unit on the screen by the diagnostician, as the cartilage load unit in the three-dimensional space. Find as a point. Similarly, the direction of the cartilage longitudinal axis in the three-dimensional space is a direction extending in a direction specified on the screen in a plane perpendicular to the line-of-sight direction. The alignment unit 62 recognizes the axis in the direction passing through the point of the cartilage load portion in the obtained three-dimensional space as the cartilage longitudinal axis.

  In ultrasonic diagnosis, the entire medial condyle may not be extracted due to the influence of the shadow of the patella, but even in such a case, the diagnostician can determine the shape of the extracted part of the medial condyle or the lateral condyle. Considering the relationship with the patella surface (portion between the medial condyle and the lateral condyle), the original longitudinal axis direction can be estimated and designated.

  Further, the alignment unit 62 obtains, as a short axis of cartilage, an axis that is perpendicular to the longitudinal axis in a plane perpendicular to the line-of-sight direction and passes through the point of the cartilage load portion specified in the three-dimensional space. FIG. 47 shows short axes 634a and 634b in the current and previous three-dimensional cartilage images.

  In the above description, the diagnostician is allowed to specify the cartilage load portion and the longitudinal axis direction. Alternatively, the cartilage load portion and the short axis direction may be specified. In addition, the long axis and the short axis may be designated. In this case, for example, a point obtained by projecting the intersection of the two onto the front surface of the cartilage shape is the cartilage load portion.

  Next, the alignment unit 62 translates the current cartilage three-dimensional image so that the cartilage load portions match between the current and previous cartilage three-dimensional images, and this time so that the longitudinal axes match. The cartilage three-dimensional image is rotated (S406).

  Next, the alignment unit 62 cuts each of the previous and current cartilage three-dimensional shapes by a cross-sectional shape cut along a plane including straight lines that extend in the longitudinal axis and the line-of-sight direction through the designated cartilage load part, respectively ( (Referred to as the longitudinal cross section) (S408). For example, FIG. 48A illustrates the current longitudinal axis section 640a, and FIG. 48B illustrates the previous longitudinal axis section 640b.

  Next, the alignment unit 62 calculates a translation amount and a rotation angle for aligning the current longitudinal axis section with the previous longitudinal axis section, and translates and rotates the current three-dimensional cartilage shape according to the calculation result. Let (S410) (details will be described later).

  Next, the alignment unit 62 cuts each of the previous and current cartilage three-dimensional shapes by a cross-sectional shape cut along a plane including straight lines extending through the specified cartilage load portion and extending in the lateral axis and the line-of-sight direction, respectively ( (Referred to as a short-axis cross section) (S412). Then, a translation amount and a rotation angle for aligning the current short axis section with the previous short axis section are calculated, and the current cartilage three-dimensional shape is translated and rotated according to the calculation result. (S414) (details will be described later). Thus, the current cartilage 3D image is three-dimensionally aligned with the previous cartilage 3D image.

  Next, an example of a specific processing procedure for alignment (S410 and S414) for the longitudinal axis section and the short axis section will be described with reference to FIG. In the following description, the case of alignment of the longitudinal axis cross sections will be described as an example, but the short axis cross sections can be aligned by the same processing.

  In the procedure of FIG. 49, the alignment unit 62 first sets the reference point K on the convex outline (that is, the boundary between cartilage and muscle, etc.) of the longitudinal cross-sectional shape of the cartilage this time, and the previous longitudinal length. N inspection points K1, K2,..., Kn are set on the convex outline of the axial cross-sectional shape, for example, at equal intervals in the horizontal direction (S500). FIG. 50A illustrates the reference point K set on the surface of the current longitudinal section 640a, and FIG. 50B illustrates the inspection points K1, K2 set on the surface of the previous longitudinal section 640b. ,..., Kn are exemplified. Hereinafter, in order to simplify the description, the cross-sectional shape of the longitudinal axis of the cartilage this time is referred to as “cross-sectional shape X”, and the cross-sectional shape of the longitudinal axis of the previous cartilage is referred to as “cross-sectional shape Y”.

  As the reference point K, for example, a point on the convex outline corresponding to the midpoint between the right end and the left end of the cross-sectional shape X on the display screen may be selected. As another example, the point of the designated cartilage load portion may be used as the reference point K. The number n of inspection points may be determined according to the required alignment accuracy.

  Next, the alignment unit 62 initializes the counter j to 1 (S502), and translates the sectional shape X so that the reference point K of the sectional shape X coincides with the inspection point Kj of the sectional shape Y (S504). . FIG. 51 shows the positional relationship between the cross-sectional shapes X (640a) and Y (640b) after translation.

  In this state, the alignment unit 62 calculates the area of the overlapping portion of the cross-sectional shapes X and Y, and stores the calculated area in a memory or the like (S506). The area can be obtained, for example, by counting the number of pixels included in the overlapping portion. In FIG. 52, a portion 650 indicated by hatching is an example of an overlapping portion. Here, the calculated area is stored in association with the value of j (1 at this time) and the rotation angle of the cross-sectional shape X (0 degrees at this time). Instead of the value of j, a vector (representing translation) from the reference point K to the inspection point Kj may be stored in the memory.

  Next, the alignment unit 62 determines whether or not the cross-sectional shape X has been rotated 360 degrees (S508). If the rotation is not completed 360 degrees, the alignment unit 62 rotates around the reference point K in a predetermined direction (counterclockwise in the example of FIG. 52) with the reference point K aligned with the inspection point Kj. The cross-sectional shape X (640a) is rotated by a predetermined unit angle (for example, 1 degree) (S510). In S506 again, the area of the intersection of the cross-sectional shapes X and Y is calculated and stored in a memory or the like in association with the information indicating the translation vector and the rotation angle at that time.

  With the reference point K matched with a certain inspection point Kj, the processing of S506 to S510 is repeated. When the cross-sectional shape X is rotated 360 degrees, the determination result in S508 is affirmative (Yes). In this case, the alignment unit 62 determines whether or not the value of the counter j has reached n (total number of inspection points Kj) (S512). If not, j is incremented by 1 (S514), step The process returns to S504. That is, the same processing (S504 to S510) is repeated for the next inspection point Kj.

  If the processing of S504 to S514 is repeated until j reaches n, the determination result of S512 becomes affirmative. In this case, the alignment unit 62 obtains the maximum value of the area of the overlapping portion stored in the memory or the like. FIG. 53 illustrates a state in which the area of the overlapping portion of the cross-sectional shapes X (640a) and Y (640b) is maximized. The alignment unit 62 translates and rotates the current three-dimensional cartilage image by the translation amount (vector) and the rotation angle stored in association with the maximum value of the area (S516).

  With the above processing, alignment using the longitudinal axis cross-sectional shape is completed. In this alignment, the current three-dimensional cartilage image is translated and rotated around the short axis.

  Thereafter, the same processing (FIG. 49) is performed on the short-axis cross-sectional shape in S414, whereby the current cartilage 3D image is further translated and rotated around the longitudinal axis, and aligned with the previous cartilage 3D image. can do.

  In the procedure of FIG. 49, the inspection is performed for the case where the reference point of the current cross-sectional shape X is moved with respect to all the inspection points set over the entire area of the previous cross-sectional shape Y. It is also possible to narrow down the point distribution range and reduce the number of inspection points. For example, the inspection point may be set in a predetermined range in the vicinity of the cartilage load portion in the previous cross-sectional shape Y with the cartilage load portion of the current cross-sectional shape X as a reference point. By doing so, the number of calculations can be reduced.

  As described above, by aligning the current and previous three-dimensional cartilage images, it is possible to accurately compare the two. For example, after positioning, if a point of interest is specified on one of the previous or current cartilage three-dimensional images, the point corresponding to the point of interest can be identified on the other image. . If the cartilage thickness at each point of interest in the current and previous cartilage 3D images is calculated, the thickness of almost the same part in this time and the previous time can be obtained.

  Accordingly, the temporal change calculation unit 64 shown in FIG. 3 obtains, for example, the cartilage thickness at the same point designated by the diagnosis from the current and previous cartilage three-dimensional images (or cartilage contour shape), For example, an image for displaying the thicknesses side by side can be generated. It is also possible to calculate how much the thickness at the same point has changed from the previous time to this time (increase or decrease) and display the amount of change.

  In addition, if the alignment is performed, when the diagnostician designates a region of interest having a size (for example, a cartilage load portion and its vicinity) on one of the previous or current three-dimensional cartilage images, the other tertiary A region of interest corresponding to the original image is determined. Therefore, the temporal change calculation unit 64 can obtain statistical information (average, variance, distribution map, etc.) of cartilage thickness at a plurality of points in the same region of interest between the previous time and this time. In addition, the difference between the statistical information in the same region of interest in the previous time and the current time, the comparison result, and the like can also be obtained as a kind of analysis result indicating a change with time.

  Also, since the positional relationship between the raw volume data inside the knee obtained by ultrasonic measurement and the cartilage extraction result is known, only the same translation and rotation for positioning between the previous and current cartilage 3D images, If the current raw volume data is translated and rotated, the current volume data can be aligned with the previous volume data.

  In the above, the case where the previous three-dimensional cartilage image is used as a reference and the current one is aligned with the previous one has been described. However, the alignment may be performed based on either the previous or the current one. Further, for example, a reference XYZ coordinate system may be defined, and both the previous and current cartilage 3D images may be aligned with the reference coordinate system. In this case, for example, the cartilage load portion is the origin of the reference coordinate system, and the line of sight in the state where the cartilage load portion faces the front in the longitudinal axis, the short axis, and the three-dimensional image of the cartilage, for example, the X axis, the Y axis, Positioning (coordinate conversion) may be performed so as to coincide with the positive direction of the Z axis.

  In the above example, the newly measured cartilage shape is compared with the stored previous cartilage shape for comparison. However, as can be understood by those skilled in the art, the above alignment process is not limited to the alignment of the previous and current measurement results, but is generally applied to the alignment of measurement results at two different time points for the same subject. Is possible. For example, the diagnostician reads out cartilage shapes at two or more time points from the measurement result information of the same subject stored in the database 60 at a plurality of past times, and positions them mutually by the above processing. Can be combined.

  In the above example, the longitudinal axis direction (or the short axis direction) of the cartilage is specified by the user as a reference for aligning the cartilage shapes of the same subject at different points in time. However, instead of the long axis direction (or short axis direction), the user may designate another characteristic direction in the shape of the cartilage as the reference direction.

  In the example described above, as illustrated in FIG. 3, the image preprocessing unit 22 and the cartilage extraction unit 24 perform processing on the volume data after coordinate conversion in the three-dimensional data memory 20. However, this is only an example. Instead, the image preprocessing unit 22 and the cartilage extraction unit 24 may perform processing for each frame image obtained by electronic scanning. An example of the device configuration in this case is shown in FIG. In FIG. 54, the same components as those of FIG.

  In the example of FIG. 54, image data of one electronic scanning frame obtained by the transmission / reception unit 16 is accumulated in the frame memory 52. The image preprocessing unit 22 performs the above-described preprocessing on the image data in the frame memory 52, and the cartilage extraction unit 24 performs the above-described cartilage extraction processing on the preprocessing result. Then, the coordinate conversion unit 54 converts the shape of the cartilage contour for each electronic scanning frame extracted by the cartilage extraction unit 24 into a common coordinate system for display and storage, and stores the conversion result in the three-dimensional data memory 26. To do. The data of the electronic scanning frame in the frame memory 52 is coordinate-converted into a common coordinate system by the coordinate conversion unit 18, and the conversion result is stored in the three-dimensional data memory 20.

  In the above example, the user specifies a portion corresponding to cartilage (for example, the reference point R), and cartilage extraction is performed based on this, but this is not essential. For example, by appropriately positioning the rotation shaft 42 (see FIG. 4) with respect to the knee, the cartilage image can be positioned within a predetermined range in the volume data. By performing matching processing between such volume data (or slice data therein) (for example, after edge extraction is suitable) and a template image representing the shape and size of cartilage prepared in advance, the cartilage is roughly cartilage. An estimated portion can be obtained, and a certain point or region can be selected as the reference point R or the like. Such processing can be automated.

  In the above, the probe 10 that combines the electronic scanning transducer array 12 and the mechanical scanning mechanism 14 is used to obtain volume data in the knee. Instead, a probe that electronically performs two-dimensional scanning is used. It may be used.

  According to the embodiment described above, it is possible to extract the three-dimensional shape of the cartilage at the distal end of the femur using a non-invasive ultrasound probe and a low-cost apparatus compared to MRI or the like. . Then, by aligning the cartilage shapes obtained at different points in time for the same subject, it can be expected that the accuracy of comparison between the cartilage states (for example, thickness) at different points in time will be improved.

  Further, in the above embodiment, the user specifies the cartilage feature point and the feature direction such as the cartilage load portion and the longitudinal axis, and aligns the cartilage shapes at different time points so as to match the feature point and the feature direction. doing. As a result, highly accurate alignment can be realized with a relatively small amount of calculation processing.

  Further, in the above embodiment, by performing matching between the cross sections obtained by cutting the three-dimensional cartilage shape on the surface defined by the designated cartilage load portion and the longitudinal axis direction, the calculation amount is more than the matching of the three-dimensional shapes. Can be reduced.

  In the above embodiment, the cartilage load that is the site of interest in osteoarthritis of the knee is adjusted by adjusting the orientation of the extracted three-dimensional image of the cartilage according to the known positional relationship between the probe and the femoral bone axis. It is possible to generate a three-dimensional display of cartilage in a direction that facilitates finding the part. The user can easily specify a point or range corresponding to the cartilage load portion while viewing this display.

It is typical sectional drawing for demonstrating the structure of a knee joint. It is a figure for demonstrating the state of a knee joint when the knee of the state sitting on the chair is seen from the front side. It is a figure which shows an example of a function structure of the ultrasound diagnosing device of embodiment. It is a typical side view for demonstrating an example of the mechanical scanning system of a mechanical three-dimensional probe. It is a typical side view for demonstrating another example of the mechanical scanning system of a mechanical three-dimensional probe. It is a typical side view for demonstrating another example of the mechanical scanning system of a mechanical three-dimensional probe. It is a figure which illustrates typically the cross-sectional image (slice data) inside the knee obtained by scanning of an ultrasonic beam. It is a figure for demonstrating the reference range (line segment) at the time of calculating | requiring the pixel value dispersion | distribution of each direction around an attention pixel. It is a figure for demonstrating calculating | requiring the reference range (line segment) of pixel value dispersion | distribution about all the directions for every predetermined angle interval. It is a figure for demonstrating that pixel value dispersion | variation changes according to the direction of the line segment of a reference range. It is a figure which shows the example of the reference pixel group for smoothing value calculation along the direction perpendicular | vertical with respect to the direction of maximum dispersion | distribution. It is a figure which shows the example of the reference pixel group for smoothing value calculation about another attention pixel. It is a figure which shows typically the result of having performed the smoothing process which considered the direction of the boundary (contour) with respect to the slice data of FIG. It is a figure which shows typically the result of having performed inversion and a binarization process with respect to the slice data after a smoothing process. It is a figure which shows typically the edge extraction result with respect to the slice data after inversion and binarization. It is a figure which shows a mode that the user designated the reference point R inside a cartilage with respect to the edge extraction result. It is a figure for demonstrating the intersection of the vertical line which passes the reference | standard point R, and a cartilage outline. It is a figure which shows typically the cartilage outline extracted from slice data. It is a figure for demonstrating the example of the process for automatically determining the reference point on other slice data based on the reference point which the user designated on one slice data. It is a figure for demonstrating the example of the process for automatically determining the reference point on other slice data based on the reference point which the user designated on one slice data. It is a figure which shows the example of the result of having applied the labeling process with respect to the binarized slice data. It is a figure which shows typically the example of the tomographic image in which the shadow of the patella appeared. It is a figure which shows typically a mode that the extraction result of a cartilage contains the inaccurate part by the influence of the shadow of a patella. It is a flowchart which shows the whole procedure of the example of the process for removing the inaccurate part by the influence of a patella. It is a flowchart which shows the procedure of the example of the outline end point detection process in the process of FIG. It is a figure which shows a mode that the reference point R inside a cartilage was designated in the cartilage outline containing the inaccurate part by the influence of the shadow of a patella, and the points A and B on the cartilage outline of the upper and lower sides were specified. It is a figure for demonstrating the difference of the tangent angle in an attention point and an adjacent point. It is a figure which shows the example of the reference window at the time of searching the adjacent point on the cartilage outline with respect to an attention point. It is a figure which shows the example of the contour end point found when searching rightward from the points A and B on the cartilage contour above and below the reference point R. It is a figure which shows the example of the cartilage outline remove | excluded the inaccurate part by the influence of the shadow of a patella, which was produced | generated by connecting the upper and lower outline end points. It is a figure for demonstrating the axis designation in the ROI setting for cartilage extraction. It is a figure for demonstrating the curve designation | designated in the long-axis cross section in ROI setting. It is a figure for demonstrating ROI outline designation | designated in the short-axis cross section in ROI setting. It is a figure for demonstrating forming a three-dimensional ROI by moving a ROI outline along a curve. It is a flowchart which shows an example of the process sequence of a thickness calculation part. It is a figure for demonstrating designation | designated operation of the target point A of thickness calculation. 6 is a diagram for explaining an example of how to determine reference points B, C, and D for normal calculation corresponding to a target point A. FIG. It is a figure for demonstrating the process which reflects the three-dimensional coordinate of the reference point, and also reflects the three-dimensional coordinate of the some point of the circumference | surroundings. 4 is a diagram for explaining a normal line passing through a target point A. FIG. 4 is a diagram for explaining how to obtain the thickness of cartilage at a target point A. FIG. It is a flowchart which shows an example of the process sequence of a quantification process part. It is a figure which shows the example of the some point of the cartilage load part vicinity. It is a figure which illustrates the calculation object point of the thickness set over the wide range of the medial condyle of the cartilage of the femur distal end. It is a figure which shows typically the display image which superimposedly displayed the distribution of the thickness of the cartilage on the three-dimensional display of the cartilage. It is a figure which shows the example of the frequency distribution of the cartilage thickness in a target range. It is a flowchart which shows an example of the whole procedure of position alignment of the cartilage from which measurement date differs. It is a figure for demonstrating the setting of the longitudinal axis and short axis which pass through a cartilage load part on the three-dimensional image of the cartilage measured this time and last time. It is a figure which shows the example of the cross-sectional image in the cross section which passes along a longitudinal axis of the cartilage measured this time and last time. It is a flowchart which shows the example of the procedure of the alignment about the individual axis | shaft in the alignment of the cartilage from which a measurement date differs. It is a figure for demonstrating the reference point set to the cross-sectional image of the cartilage measured this time and last time, and each test | inspection point. It is a figure which shows the state which translated the reference point of the cross-sectional image of this cartilage to the position of the 1st test | inspection point of the cross-sectional image of the last cartilage. It is a figure for demonstrating the overlap part of the cross-sectional image of the cartilage measured this time and last time. It is a figure which illustrates the state where the overlap part of the cross-sectional image of the cartilage measured this time and last time became the maximum. It is a figure which shows the structural example of the ultrasonic diagnosing device in the case of performing cartilage extraction for every electronic scanning frame.

Explanation of symbols

  10 mechanical 3D probe, 12 transducer array, 14 mechanical scanning mechanism, 16 transmission / reception unit, 18 coordinate conversion unit, 20 3D data memory, 22 image preprocessing unit, 24 cartilage extraction unit, 26 3D data memory, 28 image Forming unit, 30 display unit, 32 input unit, 34 extraction ROI setting unit, 40 transducer unit, 42 rotation axis, 44 arm, 46 standoff, 100 femur.

Claims (4)

A wave transmitting / receiving means for acquiring volume data about a three-dimensional region including cartilage at the distal end of the femur inside the knee by scanning an ultrasonic beam from the body surface on the front side of the bent knee;
Based on the echo level value of each voxel in the volume data, extraction means for extracting a portion corresponding to the cartilage from the volume data;
Storage means for storing the three-dimensional shape information of the portion corresponding to the cartilage extracted by the extraction means;
The first cartilage shape represented by the three-dimensional shape information of the past corresponding to the cartilage accumulated in the accumulating means, and the current cartilage extracted by the extracting means from the volume data acquired by the wave transmitting / receiving means Positioning means for positioning the three-dimensional shape information of the part corresponding to the second cartilage shape represented by the three-dimensional shape information of the part corresponding to the past cartilage stored in the storage means,
With
The positioning means receives designation of a point corresponding to a cartilage load portion in each of the first cartilage shape and the second cartilage shape from a user, and the first cartilage load portion matches the first cartilage load portion. And aligning the second cartilage shape with the second cartilage shape,
In the first cartilage shape and the second cartilage shape, the positioning means has a longitudinal direction or a short direction of a portion corresponding to the cartilage passing through the cartilage load portion as an axial direction corresponding to the cartilage shape in each of the first cartilage shape and the second cartilage shape. accepting a designation of the hand direction from a user, the given axial aligning the first cartilage shape as the second cartilage shaped to match, the ultrasonic diagnostic apparatus you wherein a. The positioning means is a first plane defined by the cartilage load portion, the axial direction, and the direction of the bone axis of the femur for each of the first cartilage shape and the second cartilage shape. The first cartilage shape and the second cartilage shape are aligned by matching the cross-sectional shape of the first cartilage shape with the cross-sectional shape of the second cartilage shape. The ultrasonic diagnostic apparatus according to claim 1, wherein: The alignment means is a plane including a straight line extending from the cartilage load portion toward the bone axis of the femur for each of the first cartilage shape and the second cartilage shape, A cross-sectional shape on a second plane different from the first plane is obtained, and a cross-sectional shape on the second plane of the first cartilage shape and a cross-section on the second plane of the second cartilage shape The ultrasonic diagnostic apparatus according to claim 2 , wherein the first cartilage shape and the second cartilage shape are aligned by matching with a shape. The positioning means is configured to change the first cartilage shape and the second cartilage shape along the bone axis to the outside of the knee according to the arrangement relationship between the wave transmitting / receiving means and the bone axis of the femur. the cartilage is displayed on the display device to generate respective images when viewed in the direction of view, is specified for each user cartilage load portion on each of these images, the ultrasound according to claim 1, characterized in that from Diagnostic device.