JP5638641B2 - Ultrasonic diagnostic equipment - Google Patents

Description

  The present invention relates to an ultrasonic diagnostic apparatus, and more specifically, to a technique for improving discrimination in diagnosis by providing a user with an image showing the degree of variation in displacement of each part of a living body with respect to a compression force applied to a subject. .

  The ultrasonic diagnostic apparatus applies an ultrasonic probe to the surface of the subject, transmits ultrasonic waves from the probe to the subject, receives ultrasonic reflected waves from the inside of the subject, and receives the received signal. Based on the reflected echo signal, the biological information of each part of the subject is displayed as an image such as a tomographic image for diagnosis.

  In particular, recently, as described in Patent Documents 1 and 2, etc., a plurality of frame data composed of RF signals is applied while applying a compression force to a subject by a technique or a mechanical method and changing the compression force. Is obtained in time series, the displacement of each part of the living body is obtained based on a pair of frame data having different acquisition times, and the distortion of each part of the living body is obtained by spatially differentiating the obtained displacement to obtain the hardness or softness of each part of the living body. An elastic image relating to the height is generated and displayed. Further, it has been proposed to obtain a stress distribution inside the living body caused by the compression, and to obtain and display an elastic modulus image by obtaining an elastic modulus such as Young's modulus of each part of the living body from the obtained stress distribution and strain. These elastic images or elastic modulus images are displayed with different hues or gradations of luminance depending on the degree of hardness and softness of the living tissue. Thus, the examiner can distinguish normal tissues, cancer cells, tumors, and the like by observing the elastic image or elastic modulus image measured while adjusting the compression force.

  By the way, it is known that the hardness of the tissue of each part of the living body changes depending on the magnitude of the compression force. For example, the measured values of displacement and distortion are qualitative relative values. Therefore, since the hue of the elastic image differs depending on how the compression force is applied, the discrimination may differ depending on the experience and skill level of the examiner. In this respect, the measured value of the elastic modulus is a quantitative value obtained by taking into account the stress distribution inside the living body, so it is objective or universal regardless of individual differences such as the examiner's experience and skill level. Differentiation is possible.

JP-A-5-317313 JP 2000-60853 A

  However, in order to obtain the elastic modulus, it is necessary to calculate the stress inside the living body by calculating the compression force, but there is room for improvement in obtaining the stress inside the living body accurately and in a short time. .

  On the other hand, in image diagnosis, not only the elasticity information of tissues such as cancer cells and tumors, but also the degree of variation in displacement between tissues such as tumors and surrounding tissues can improve the accuracy or reliability of tissue differentiation such as tumors. Can be considered.

  In addition, since cysts and the like are not cellular tissues, they cannot be identified by elastic information such as strain and elastic modulus. Therefore, if cysts and the like can be identified, it is considered that the accuracy and reliability of discrimination can be further improved.

  An object of the present invention is to provide information on the degree of variation in the displacement of each part of a living body with respect to a compression force, and to further improve the accuracy or reliability of tissue discrimination.

  The principle of the present invention for solving the above problems will be described. First, when examining the dynamic behavior of tissues in various parts of the body when a compression force is applied to the subject, the dynamic behavior is conspicuous depending on the strength of the bond between the tissue such as a tumor and the surrounding tissue. It has been found that a significant difference appears. For example, tissues that form cancer tumors or the like collectively move in the same manner because of their high cell density and high tissue binding. On the other hand, the tissue around the cancer tumor or the like does not necessarily have a high degree of tissue binding, and therefore the movement has a variation in the magnitude and direction of the movement compared to the cancer tumor or the like. On the other hand, in the case of a relatively soft tissue such as a fibroadenoma, the magnitude and direction of the movement are dispersed according to the position where the compression force is applied, and variations in the displacement appear. Further, in the case of a non-cellular tissue such as a cyst, since there is no tissue connection in the cyst, variations appear in the magnitude and direction of the displacement of the measurement point in the cyst. Therefore, the accuracy or reliability of tissue discrimination can be further improved by the degree of variation in the displacement of each part of the living body when a compressive force is applied to the subject.

The present onset Ming, transmitting means for repeatedly scanning an ultrasound beam to the object, receiving means for receiving a reflected echo signal of the ultrasonic wave from the subject, the reflected echo signals corresponding to the scanning surface of the ultrasonic beam A frame data acquisition unit for acquiring frame data, a measurement unit for obtaining elasticity information correlated with elasticity at a plurality of measurement points on the scanning plane based on the frame data, and a variation representing a degree of variation in elasticity information at each measurement point Image generation means for generating an image, and display means for displaying a variation image. The degree of variation indicates the elasticity information of each measurement point and the elasticity information of a plurality of measurement points included in the local region surrounding the measurement point. It is a statistical feature value regarding the variation of elasticity information at each measurement point obtained by statistical processing based on the population, and the variation image is Characterized in that the total feature quantity is a feature quantity distribution images imaged in association with each measurement point.

According to the present invention, because it provides information about the variation degree of the displacement of the living body on various units in each compression force, Ru can further improve the accuracy or reliability of the tissue differentiation.

It is a block block diagram of the ultrasonic diagnostic apparatus of one Embodiment of this invention. It is a block block diagram of the displacement variation evaluation part of one Embodiment which concerns on the characteristic part of this invention. It is an external view which shows embodiment of a probe. It is a figure which shows the concept of displacement frame data. It is a figure explaining the displacement variation image by this invention, and the distribution image of the statistical feature-value. It is a figure explaining the local area | region used for a process of displacement variation data. It is a figure which shows the example of a display state which displayed the displacement variation feature-value image, the tomogram, and the elasticity modulus image side by side. It is a figure which shows the example of a display state which displayed the displacement variation feature-value image on the tomographic image.

  Hereinafter, the present invention will be described based on embodiments. FIG. 1 shows a block diagram of an ultrasonic diagnostic apparatus according to an embodiment of the present invention, and FIG. 2 shows a block diagram of a displacement variation evaluation unit according to an embodiment of the present invention. As shown in FIG. 1, an ultrasonic probe 2 used in contact with a subject 1 is formed by arranging a plurality of transducers that transmit and receive ultrasonic waves to and from the subject 1. Yes. The probe 2 is driven by ultrasonic pulses supplied from the transmission circuit 3. The transmission / reception control circuit 4 controls the transmission timing of ultrasonic pulses for driving the plurality of transducers of the probe 2 to form an ultrasonic beam toward the focal point set in the subject 1. ing. The transmission / reception control circuit 4 is configured to electronically scan the ultrasonic beam in the direction in which the transducers of the probe 2 are arranged.

  On the other hand, the probe 2 receives a reflected echo signal generated from the subject 1 and outputs it to the receiving circuit 5. In accordance with the timing signal input from the transmission / reception control circuit 4, the reception circuit 5 takes in the reflected echo signal and performs reception processing such as amplification. The reflected echo signal received and processed by the receiving circuit 5 is added in the phasing and adding circuit 6 by matching the phases of the reflected echo signals received by the plurality of transducers. A reflected echo signal (hereinafter referred to as an RF (high frequency) signal) phased and added in the phasing and adding circuit 6 is input to the signal processing unit 7 to perform gain correction, log compression, detection, contour enhancement, filter processing, and the like. Signal processing is performed. Note that the RF signal generated in the phasing addition circuit 6 may be a complex demodulated I and Q signal.

  The RF signal processed by the signal processing unit 7 is guided to the black and white scan converter 8 where it is converted into a digital signal and also converted into two-dimensional tomographic image data corresponding to the scanning surface of the ultrasonic beam. The signal processing unit 7 and the monochrome scan converter 8 constitute a tomographic image reconstruction unit. The tomographic image data output from the black and white scan converter 8 is supplied to the image display 10 via the switching addition unit 9 so that the tomographic image is displayed.

  On the other hand, the RF signal output from the phasing addition circuit 6 is guided to the RF signal frame data acquisition unit 11. The RF signal frame data acquisition unit 11 acquires a plurality of frames of RF signal groups corresponding to the scanning plane of the ultrasonic beam as frame data and stores them in a memory or the like. The displacement measurement unit 12 sequentially captures a plurality of pairs of frame data with different acquisition times stored in the RF signal frame data acquisition unit 11, and based on the acquired pair of frame data, a plurality of measurement points on the scanning plane of the ultrasonic beam. The displacement vector is obtained and output to the strain (elastic modulus) calculation unit 13 as displacement frame data. The strain (elastic modulus) calculation unit 13 obtains strains at a plurality of measurement points on the scanning plane based on the input displacement frame data, and outputs the strain to the elasticity data processing unit 14 as elasticity frame data. The elastic data processing unit 14 performs various images such as smoothing processing in the coordinate plane, contrast optimization processing, and smoothing processing in the time axis direction between frames on the elastic frame data input from the strain (elastic modulus) calculation unit 13. It is processed and sent to the color scan converter 15. A process for generating elastic frame data based on displacement data is described in Japanese Patent Application Laid-Open No. 2004-261198.

  The color scan converter 15 converts the elastic frame data output from the elastic data processing unit 14 to generate a color elastic image and displays it on the image display 10 via the switching addition unit 9. That is, the color scan converter 15 gives hue information such as red, green, and blue to the elastic image based on a range of preset upper and lower elastic limits. For example, an area where the distortion of the elastic frame data is measured is converted into a red code, and an area where the distortion is measured is converted into a blue code. In place of the color scan converter 15, a black and white scan converter can be used. In this case, the distribution of the distortion can be expressed by brightening the brightness of the area where the distortion is large and darkening the brightness of the area where the distortion is small.

  Further, the switching adder 9 inputs black and white tomographic image data output from the black and white scan converter 8 and color elastic image data output from the color scan converter 15 and switches either image to either one of them. It has a function of displaying, a function of adding and synthesizing one of the two images in a semi-transparent manner and displaying the images on the image display 10 and a function of displaying both the images side by side. Further, the cine memory unit 18 stores the image data output from the switching addition unit 9 in the memory, and calls the past image data and displays it on the image display 10 in accordance with a command from the device control interface 17. Yes. Furthermore, the selected image data can be transferred to a recording medium such as an MO.

  Next, the displacement variation evaluation unit 16 which is an embodiment of the characteristic part of the present invention will be described. As shown in FIG. 2, the displacement variation evaluation unit 16 captures and stores the displacement frame data output from the displacement measurement unit 12, and the displacement variation data from the displacement frame data stored in the memory unit 21. A displacement variation data calculation unit 22 for obtaining the statistical feature amount and an image construction unit 23 for imaging the distribution of the displacement variation obtained by the displacement variation data calculation unit 22 are configured. The image construction unit 23 outputs the constructed tissue displacement distribution image data to the image display 10 via the switching addition unit 9 and displays it. In addition, a control command is input from the device control interface unit 17 to the displacement variation evaluation unit 16.

  Next, the basic operation of this embodiment will be described. First, while changing the pressure in the subject 1 by the probe 2, the subject 1 is scanned with an ultrasonic beam, and reflected echo signals from the scanning surface are continuously received from the phasing addition circuit 6. An RF signal is output. Based on the RF signal, the signal processing unit 7 and the monochrome scan converter 8 reconstruct a tomographic image on the scanning plane and display it on the image display 10.

  On the other hand, the RF signal frame data acquisition unit 11 captures the RF signal and repeatedly acquires the RF signal frame data in synchronization with the frame rate in the process in which the compression force applied to the subject 1 changes, and the built-in frame memory Save them in chronological order. Then, a plurality of pairs of RF signal frame data are successively selected and output to the displacement measuring unit 12 with a pair of RF signal frame data having different acquisition times as a unit. The displacement measuring unit 12 performs one-dimensional or two-dimensional correlation processing on the selected pair of RF signal frame data, and measures the displacement (vector having a size and direction) of each measurement point on the scanning plane to obtain the displacement frame data. Generate. As a method for detecting the displacement vector, for example, a block matching method or a gradient method described in JP-A-5-317313 is known. In the block matching method, an image is divided into blocks of N × N pixels, for example, a block closest to the target block in the current frame is searched from the previous frame, and based on this, the displacement of the measurement point is determined. Ask. Further, the displacement can be calculated by calculating the autocorrelation in the same region of the pair of RF signal frame data.

  The displacement frame data obtained by the displacement measurement unit 12 is input to the strain (elastic modulus) calculation unit 13, and the distortion at each measurement point is calculated and output to the elasticity data processing unit 14 as strain data (elastic frame data). The The distortion calculation is calculated by spatially differentiating the displacement as is well known. In addition, the strain (elastic modulus) calculation unit 13 can have a function of calculating the elastic modulus at each measurement point based on the obtained strain. However, when obtaining the elastic modulus, stress data at each measurement point is required. Therefore, when calculating | requiring an elasticity modulus, it is necessary to provide the pressure measurement part 19 shown with the broken line in FIG. For example, as shown in FIG. 3A, the pressure gauge side portion 19 is provided with a plurality of pressure sensors 32 on the surface of a compression plate 31 provided around the ultrasonic transmission / reception surface of the probe 2. Based on the 32 detection signals, the stress at the measurement point inside the subject 1 is calculated. Also, as shown in FIG. 3B, a reference deformable body 33 is provided on the surface of the compression plate 31, and the stress at the measurement point inside the subject 1 is calculated based on the deformation of the reference deformable body 33. You can also Thus, the elastic modulus (for example, Young's modulus Ym) of each measurement point on the scanning plane is calculated from the stress at the measurement point obtained by the pressure measurement unit 19 and the displacement frame data obtained by the displacement measurement unit 12. The result is output to the elasticity data processing unit 14. The elastic data processing unit 14 generates elastic image data or elastic modulus image data based on the strain or elastic modulus, and displays the elastic image or elastic modulus image on the image display 10 with the color scan converter 15 and the switching addition unit 9. .

Next, a detailed configuration of the displacement variation evaluation unit 16 that is a characteristic part of the present embodiment will be described together with operations. First, the displacement frame data obtained by the displacement measurement unit 12 is the same as the conventional displacement frame data used in the strain (elastic modulus) calculation unit 13, and as shown in FIGS. 4 (A) and 4 (B), It is represented by a two-dimensional displacement vector (Xi, j, Yi, j) at the measurement point. That is, the element data groups of the image vertical axis direction (y direction) component and the image horizontal axis direction (x direction) component are respectively
Xi, j (t) (i = 1,2,3, ..., N, j = 1,2,3, ... M)
Yi, j (t) (i = 1,2,3, ..., N, j = 1,2,3, ... M)
Represent as The subscript i indicates the coordinate in the horizontal axis direction, and j indicates the coordinate in the vertical axis direction. The displacement variation evaluation unit 16 calculates displacement variation data, which is a feature of the present invention, based on the displacement frame data of FIG. 4 stored in the memory unit 21, and generates a displacement variation image based on the displacement variation data. Thus, it is displayed on the image display 10. Hereinafter, an example of the displacement variation data will be described.

  As shown in FIG. 5, the dynamic behavior of the tissue of each part of the living body when a compressive force in a certain direction is applied to the subject 1 is (A) a very hard solid tissue (for example, a cancer tumor), (B There are significant differences depending on, for example, soft solid tissue (eg, fibroadenoma) or (C) fluid cyst tissue (eg, cyst). For example, as shown in (A), tissues forming a cancer tumor or the like collectively move in the same manner because the density of cells is high and the degree of tissue binding is high. That is, the tissue formed by a cancer tumor or the like moves in substantially the same direction as the compression direction. On the other hand, tissues around cancer tumors and the like do not necessarily have a high degree of tissue binding, and therefore have a variation in the magnitude and direction of movement compared to cancer tumors and the like. By evaluating such a difference in the dynamic behavior of the tissue as the degree of variation in displacement, the presence of a cancer tumor or the like can be identified, or the size of a cancer tumor or the like can be identified. On the other hand, in the case of a relatively soft tissue such as a fibroadenoma as shown in FIG. Appears, and the inclination increases as the distance from the center of compression increases. In this case, a fibroadenoma etc. can be identified. Furthermore, as shown in (C), in the case of a non-cellular tissue such as a cyst, since there is no tissue connection in the vesicle, the movement of each part is completely free. The distribution has a noticeable variation.

  Therefore, in the first embodiment, the displacement variation data calculation unit 22 directly associates the graphic indicating the magnitude and direction of displacement at each measurement point with the position of each measurement point, as shown by the arrows in FIG. Displacement variation data is calculated, and displacement variation image data is generated in the image construction unit 23 based on the displacement variation data. By the way, since the displacement correlates with the magnitude of the compression force, if the displacement variation image indicating the magnitude and direction of the displacement is displayed as it is, the same tissue may be mistaken as a different tissue.

  Therefore, in order to make the magnitude and direction of the displacement quantitative, as shown in FIG. 6, the displacement of each measurement point is normalized with the average value of the displacements of a plurality of points included in the local region surrounding the measurement point. To obtain a displacement variation degree. In the example of FIG. 6, an area including 15 measurement points surrounding the measurement point (4, 4) is set as a local area, but the size and shape of the local area are not limited to this. Thus, by normalizing, the variation variation at each measurement point can be represented by a quantitative arrow, and therefore, the tissue variation can be objectively or universally observed by observing the displacement variation degree.

  In the first embodiment, the displacement variation degree at each measurement point is displayed as an arrow as it is or normalized based on the displacement. Thereby, the examiner can intuitively observe the dynamic behavior of the cancer tumor and the dynamic behavior of the surrounding tissue by observing the size and direction of the arrow. However, it may be cumbersome to determine the magnitude and direction of the tissue connectivity based on the size and direction of the arrow.

  Thus, the second embodiment is characterized in that the degree of displacement variation in the region of interest is obtained by local statistical processing. The displacement variation data calculation unit 22 sets a local region similar to that described with reference to FIG. 6 from the wide area distribution using the displacement frame data stored in the memory unit 21 as a basic distribution, and the local distribution of the displacement in the local region. Are extracted, and a statistical feature amount is calculated based on the displacement data of the local region. In the present embodiment, as described below, the standard deviation of the two-dimensional displacement vector in the local distribution is calculated as a statistical feature quantity.

As shown in FIG. 6, the coordinate of the measurement point of interest in the displacement frame data is, for example, (4, 4), and the measurement points around 3 × 5 centered on the coordinate of the measurement point (4, 4) Set a local region consisting of. Then, the 15-point displacement data group distributed in the local region is used as a population, and, for example, an average and a standard deviation are calculated as the statistical feature amounts by the following expressions. Here, 3 ≦ i ≦ 5 and 2 ≦ j ≦ 6.
(Average of X components) _4,4 = {Σ (displacement frame data Xi, j)} / 15
(Average of Y component) _4,4 = {Σ (displacement frame data Yi, j)} / 15
(Average) _4,4 = (Average X component) _4,4 + (Average of Y component) _4,4
{(Standard deviation) _4,4 } ²
= Sigma {(mean of X component) _4,4 - (displacement frame data Xi, j)} ^2/15
+ Sigma {(average of the Y component) _4,4 - (displacement frame data Yi, j)} ^2/15
Similarly, calculate (standard deviation) i, j and (average) i, j for the measurement point (i, j) of interest, and correspond to each element data Zi, j of the displacement variation feature value as follows: It is set up like this. Here, i = 1, 2, 3,..., N, j = 1, 2, 3,.
(Displacement variation feature data Zi, j) = (standard deviation) i, j / (average) i, j
In each element data Zi, j constituting the displacement variation feature amount data thus obtained, the degree of variation of the displacement vector with the element data group distributed in the local area set in the displacement frame data as a population is included. It becomes the reflected value.

  Then, the image constructing unit 23 constructs an image based on the displacement variation feature value data. In this case, since the displacement variation feature value data is standardized by a value obtained by dividing the standard deviation of the displacement distribution by an average, the range of values that the displacement variation feature value data can take is uniquely determined. Therefore, the displacement variation feature value can be appropriately imaged by associating the minimum value Vmin and the maximum value Vmax of the displacement variation feature value with the maximum value and the minimum value in the gradation of the displacement variation image data.

(Statistical evaluation image data Ii, j)
= 255 × {(Statistical evaluation numerical data Zi, j) -Vmin} / (Vmax-Vmin)
That is, each element data Ii, j of the displacement variation image data is configured as data having any value from 0 to 255 according to the size of the displacement variation feature amount data.

  For example, when Vmax = 1.0 and Vmin = 0.0, as shown in FIG. 7, the range of the luminance gradation (SD) is set, and the luminance intensity corresponds to the magnitude of the displacement variation feature amount. Displacement variation feature amount image data is constructed and displayed as a displacement variation feature amount image 43 on the image display 10 via the switching addition unit 9. In FIG. 7, reference numeral 41 is a tomographic image, reference numeral 42 is an elastic modulus image, and these images can be displayed side by side on a single screen under the control of the switching addition unit 9. Further, as shown in FIG. 8, a displacement variation feature value image can be displayed so as to be superimposed on the tomographic image.

  For example, as a property of a lesion suspected of being cancerous, for example, in the mammary gland region, the tissue of the “breast cancer” site is solid, the cancer cells are high in density and hard, and infiltrate the surrounding tissue, so that it can move Because of its low nature, the degree of displacement variation is extremely small. On the other hand, the tissue of “fibroadenoma” is fibrous (medium connectivity is medium) and has high mobility, so the degree of displacement variation is medium. Furthermore, since the tissue of the “cyst” is liquid, the fluidity is high and there is no connectivity between the tissues. Therefore, the dynamic behavior of the tissue with respect to compression is irregular, and the mobility is high, so the degree of displacement variation is high. Thus, by identifying the statistical characteristics of the local distribution of tissue elements, for example, as illustrated in FIG. 8, it is possible to perform tissue discrimination.

  Further, in contrast to the displacement variation images of the first embodiment shown in FIGS. 5A, 5B, and 5C, the same drawings (D), (E), and (F) of FIG. The displacement variation feature value image is shown. From the figure, it is possible to intuitively grasp the degree of displacement variation in the displacement variation feature value image than in the displacement variation image in which the displacement variation is indicated by an arrow.

(Modification 1)
In the above embodiment, the displacement is described as a vector quantity, but it may be one-dimensional displacement data. That is, for example, one-dimensional displacement frame data composed only of the Y component of the displacement vector may be input to the displacement variation evaluation unit 16 to construct a displacement variation image or feature amount image.

(Modification 2)
In the above-described embodiment, the displacement variation data is obtained based on the displacement data. However, the present invention is not limited to this, and strain data and elastic modulus data obtained by the strain (elastic modulus) calculation unit 13 in FIG. Is possible. Further, the velocity distribution and strain rate distribution constituted by the speed information and strain rate (strain rate) information calculated by the Doppler method and the tissue Doppler method according to the conventional method are input to the displacement variation evaluation unit 16 as the basic distribution and the displacement is performed. Data on the degree of variation can be obtained. In short, by using information correlating with elasticity as a basic distribution, it is possible to obtain data on the degree of displacement variation similar to that in each embodiment.

(Modification 3)
The elasticity frame data after image processing in the elasticity data processing unit 14 of FIG. 1 is input as a basic distribution to the displacement variation evaluation unit, and data of the degree of displacement variation can be obtained. Similarly, elasticity image data calculated by the color scan converter 15 can be input as a basic distribution to a displacement variation evaluation unit to obtain displacement variation data.

(Modification 4)
In the second embodiment, the standardized standard deviation is used as a statistical feature amount indicating the degree of variation in displacement. However, the present invention is not limited to this, and the general variance and the relationship between surrounding data elements are not limited. An amount obtained by calculating the sum of the differences may be used, and any amount may be used as long as the amount represents a variation degree of data elements in the local distribution.

(Modification 5)
In the first and second embodiments, an area including 3 × 5 measurement points centered on the target coordinate is set as the local area to be set in the basic distribution. However, the present invention is not limited to this, and the size and elements of the local area are set. This selection method may be arbitrary. For example, it may be selected as a circular region, for example, it is composed of only five points including the coordinate of interest and the surrounding four points. Further, the local region can be set, selected, and changed from the device control interface unit 17.

(Modification 6)
In the second embodiment, the image construction unit 23 constructs a 256-level (8-bit) gray-scale luminance intensity feature quantity distribution image that corresponds linearly according to the magnitude of the displacement variation feature quantity. The present invention is not limited to this, and the gradation resolution may be, for example, 512 steps or 128 steps. The relationship between the magnitude of the displacement variation feature amount and the brightness of the feature amount distribution image is not limited to linear, and the exponent Any appropriate relational expression such as a function or a quadratic function may be followed.

  Furthermore, it is possible to construct a feature amount distribution image by assigning hues according to the magnitude of the displacement variation feature amount according to a predetermined color map. Further, as shown in FIG. 8, a feature amount distribution image having a hue can be displayed by superimposing a tomographic image between the same coordinates in a translucent manner. These settings can be selected and changed by commands from the device control interface unit 17.

(Modification 7)
The image construction unit 23 of the displacement variation evaluation unit 16 can perform signal processing such as temporal smoothing processing (persistence etc.) and spatial smoothing processing (smoothing etc.) on the feature quantity distribution image.

(Modification 8)
In the first and second embodiments, based on the two-dimensional displacement frame data calculated by the displacement measuring unit 12, the displacement variation evaluating unit 16 generates a displacement variation image or a feature amount distribution image in real time with tomographic image processing. The explanation is based on the assumption that this is done. However, the present invention is not limited to this. Even after the temporal displacement frame data is stored in the cine memory unit 18 shown in FIG. 1 and the measurement of the tomographic image is frozen by the control signal from the apparatus control interface unit 17. The displacement variation image or the feature amount distribution image can be constructed based on the past displacement frame data stored in the cine memory unit 18 and displayed on the image display 10.

(Modification 9)
In the embodiment of FIG. 1, the case where the probe 2 is brought into contact with the body surface of the subject 1 has been described. However, the present invention is not limited to this, and a transrectal probe, transesophageal probe, intraoperative probe are used. The present invention can be similarly applied to any ultrasonic probe such as a probe or an intravascular probe.

  As described above, in a conventional elastic imaging method using an ultrasonic diagnostic apparatus, a living tissue is compressed, a strain and an elastic modulus generated in each internal region are calculated, and the degree of the size is gradationized. Although the expressed elasticity image is constructed and displayed, it has been impossible to grasp the relationship between the region of interest and the surrounding tissue and how hard it is. According to each of the embodiments, it is possible to obtain the local combination degree of the tissue of the region of interest as a statistical feature quantity, generate a distribution image of the statistical feature quantity, and feed it back to the examiner. As a result, it is possible to objectively and quantitatively evaluate the connection relationship between the region focused on by the examiner and the surrounding tissue. For example, it is possible to provide new information for performing tissue differentiation, such as easy identification of tissue properties such as tissue integrity and cysticity.

  In particular, information such as the magnitude of displacement and strain is qualitative information that changes depending on the compression method, but even if they are used as basic information, local statistical features are normalized and evaluated. It is possible to evaluate the tissue property by a quantitative method independent of the compression method.

  In addition, depending on the examiner's subjectivity, such as the magnitude of the luminance (color tone) in the elastic image and how it changes, diagnosis results that differ depending on the examiner may be given. By avoiding this, an objective and universal diagnosis can be established, and a clinically useful ultrasonic diagnostic apparatus can be provided.

DESCRIPTION OF SYMBOLS 1 Subject 2 Probe 3 Transmission circuit 4 Transmission / reception control circuit 5 Reception circuit 6 Phased addition circuit 7 Signal processing part 8 Black-and-white scan converter 9 Switching addition part 10 Image display 11 RF signal frame data acquisition part 12 Displacement measurement part 16 Displacement variation evaluation unit 21 Memory unit 22 Displacement variation data calculation unit 23 Image construction unit

Claims (3)

Frame data comprising transmission means for repeatedly scanning an object with an ultrasonic beam, reception means for receiving an ultrasonic reflection echo signal from the object, and the reflection echo signal corresponding to the scanning surface of the ultrasonic beam Frame data acquisition means for acquiring, measurement means for obtaining any one elastic information of strain data, elastic modulus data, velocity distribution, strain distribution at a plurality of measurement points on the scanning plane based on the frame data; Statistical characteristics about the variation of the elasticity information at each measurement point by statistically processing the elasticity information of each measurement point as a population of the elasticity information of a plurality of measurement points included in the local region surrounding the measurement point Statistical processing means for obtaining a feature amount distribution image for generating a feature amount distribution image obtained by imaging the statistical feature amount in association with each measurement point And stage, becomes and a display means for displaying the feature quantity distribution image, the statistical characteristic amount, either the variance and standard deviation of the elasticity information of the plurality of measurement points included in the local region one of the average normalized Ru value der ultrasonic diagnostic apparatus of the elasticity information of the population.   The ultrasonic diagnostic apparatus according to claim 1, wherein the statistical feature amount is based on a size and direction of the elasticity information.   Image composing means for constructing a tomographic image of the subject based on the reflected echo signal corresponding to the scanning plane; and adding means for displaying the feature quantity distribution image on the tomographic image on the display means. The ultrasonic diagnostic apparatus according to claim 1, wherein:

Images