CN100502791C - Ultrasonic diagnostic device - Google Patents

Abstract

The present invention provides an ultrasonic diagnostic apparatus comprising: a displacement measuring unit (109) for transmitting ultrasonic waves from a probe (101) to a subject (100), receiving a reflected echo signal corresponding to the transmission of the ultrasonic waves, and measuring the displacement of the living tissue of the subject (100) based on the transmitted echo signal; an elastic image forming unit for obtaining a deformation amount or elastic modulus from the displacement and forming an elastic image; and a display unit (107) for displaying the elastic image; wherein the apparatus comprises a scoring mechanism for specifying the deformation state or elastic state based on information of the elastic image output from the elastic image forming unit. Thus, an ultrasonic diagnostic apparatus is provided that can provide a certain degree of assistance in diagnosis by giving and displaying a score as a diagnostic indicator.

Description

Ultrasonic diagnostic device

technical field

The present invention relates to an ultrasonic diagnostic device capable of displaying a diagnostic site in a subject as an elastic image showing the hardness or softness of living tissue by using ultrasonic waves.

Background technique

Conventionally, an ordinary ultrasonic diagnostic apparatus has the following mechanisms: an ultrasonic transmission and reception control mechanism for controlling ultrasonic transmission and reception; an ultrasonic transmission and reception mechanism for transmitting and receiving ultrasonic waves to a subject; A tomographic means for periodically obtaining tomographic image data of a subject including moving tissues, and an image display means for displaying time-series tomographic image data obtained by the tomographic means. In addition, the structure of the living tissue inside the subject is displayed, for example, as a B-mode tomographic image.

In contrast, recently, it has been proposed that an external force is artificially applied from the body surface of the subject using a pressure device or a probe to compress the internal tissue of the living body, and that ultrasonic waves of two adjacent frames (continuous two frames) in time series are used. The correlation calculation of the received signal is used to obtain the displacement at each point, and then the displacement is measured by spatial differentiation, and the deformation data is visualized; and then the stress distribution and deformation data caused by the external force are proposed. A method of visualizing elastic modulus data such as Young's modulus of living tissue. Based on the elastic image based on the deformation and elastic modulus data (hereinafter referred to as elastic frame data), the hardness or softness of living tissue can be measured and displayed as an elastic image.

As an ultrasonic diagnostic apparatus, there are those described in Patent Document 1 or Patent Document 2, and the like.

Patent Document 1: JP-A-5-317313 Gazette

Patent Document 2: JP-A-2000-060853 Gazette

However, in imaging the elastic modulus data of living tissue by such a conventional ultrasonic diagnostic apparatus, the degree of hardness is only recognized for an actual disease, and no diagnostic index corresponding to the disease is given.

Contents of the invention

The present invention has been made in view of the above-mentioned viewpoints, and an object of the present invention is to provide an ultrasonic diagnostic apparatus capable of giving an index useful for diagnosing a disease site to a certain extent.

In order to achieve the above object, the ultrasonic diagnostic apparatus of the present invention includes: a displacement measurement unit that emits ultrasonic waves from the probe to the subject, receives reflected echo signals corresponding to the emission of the ultrasonic waves, and measures displacement based on the reflected echo signals. Displacement of the living tissue of the subject; an elastic image constructing unit that obtains a deformation amount or an elastic modulus from the displacement to construct an elastic image; a tomographic image constructing unit that constructs the subject based on the reflected echo signal a tomographic image of living tissue; and a display unit for displaying the above-mentioned elastic image; wherein, a scoring mechanism is provided, and the above-mentioned elastic image outputted from the above-mentioned elastic image forming unit is correspondingly assigned by the scoring mechanism according to a score indicating a deformation state or an elastic state. The deformation state or elastic state is set according to the distribution state of the hard area of the elastic image relative to the hypoechoic area of the tomographic image.

Therefore, by applying elastic images, tomographic images, and elastic images, giving and displaying scores as diagnostic indicators, it is possible to help diagnosis to a certain extent and make identification possible.

Description of drawings

FIG. 1 is a block diagram showing an embodiment of an ultrasonic diagnostic apparatus according to the present invention.

FIG. 2 is a diagram showing an example of a method of attaching a pressure measuring unit (pressure sensor) to an ultrasonic probe and measuring a pressure between a probe of the probe and a subject.

FIG. 3 is a diagram showing an example of scoring processing performed by the score input means in FIG. 1 .

FIG. 4 is a diagram showing an example of display on an image display when score processing is performed using a score input mechanism.

Fig. 5 is a diagram showing scoring based on stress signals.

Fig. 6 is a diagram showing scoring based on changes in regions of an elasticity image due to compression.

Fig. 7 is a diagram showing scoring based on tone patterns of elastic images.

Fig. 8 is a diagram showing another example of scoring processing performed by the scoring input means in Fig. 1 .

FIG. 9 is a diagram showing a different embodiment of scoring processing performed by the scoring input mechanism of FIG. 1 .

FIG. 10 is a flowchart showing an example of automatic scoring processing using an area extraction application.

FIG. 11 is a diagram schematically showing an example of the operation of the automatic scoring process.

FIG. 12 is a diagram showing the timing at which elasticity images used for scoring are acquired.

Fig. 13 is a diagram showing an example of an RF signal frame data selection unit in Fig. 1 .

Fig. 14 is a diagram showing another embodiment of the RF signal frame data selection unit in Fig. 1 .

Detailed ways

Hereinafter, embodiments of the present invention will be described in detail based on the drawings. FIG. 1 is a block diagram showing an embodiment of an ultrasonic diagnostic apparatus according to the present invention. This ultrasonic diagnostic apparatus is an apparatus that obtains a tomographic image of a diagnostic site of the subject 100 using ultrasonic waves and displays an elastic image indicating the hardness or softness of living tissue.

As shown in FIG. 1 , the ultrasonic diagnostic apparatus includes an ultrasonic probe 101, a transmitting circuit 102, a receiving circuit 103, a phasing and adding circuit 104, and a signal processing unit 105 as a tomographic image forming unit 118 for forming a tomographic image. The displacement measurement unit 109, the pressure measurement unit 110, the deformation and elastic modulus calculation unit 111, the elasticity data processing unit 112, and the color scan converter 113 serve as the elastic image construction unit 119 for constituting the elastic image, and are further provided with the configuration of the tomographic image. The black-and-white scan converter 106 for image conversion of the output signal of the part, the image display 107, the RF signal frame data selection part 108, the switching adder 114, the score input mechanism 115 for inputting the score, and the elastic image or the elastic image and the tomographic image. The scoring unit 116 that characterizes the state or elastic state is constituted.

An ultrasonic transceiver mechanism is constituted by an ultrasonic probe 101 , a transmitting circuit 102 , a receiving circuit 103 , a phasing and adding circuit 104 and a signal processing unit 105 . The ultrasonic transceiver mechanism is a mechanism that scans the body of the subject 100 with an ultrasonic beam in a certain direction using the ultrasonic probe 101 to obtain a single tomographic image. The ultrasonic probe 101 is a member formed by arranging a plurality of vibrators in a rectangular shape, and performs mechanical or electronic beam scanning to transmit and receive ultrasonic waves to the subject 100. Although not shown in the figure, an ultrasonic probe 101 is built in it. The source of the generator receives the vibrator that reflects the echo at the same time. Each transducer is generally formed to have a function of converting an input pulse wave or continuous wave transmission signal into an ultrasonic wave and transmitting it, and a function of receiving an ultrasonic wave reflected from the inside of the subject 100 and converting it into a received signal of an electrical signal for output. .

The transmitting circuit 102 generates a transmitting pulse for driving the ultrasonic probe 101 to generate ultrasonic waves, and sets the focal point of the emitted ultrasonic waves to a certain depth through a built-in transmitting phasing and adding circuit. The receiving circuit 103 amplifies the reflected echo signal received by the ultrasonic probe 101 with a predetermined gain. The amplified received signals corresponding to the number of transducers are input to the phasing and adding circuit 104 as independent received signals. The phasing and adding circuit 104 inputs the received signals amplified by the receiving circuit 103, controls their phases, and forms ultrasonic beams for one or more focusing points. The signal processing unit 105 inputs the received signal from the phasing and adding circuit 104 and performs various signal processing such as gain correction, logarithmic compression, wave detection, contour enhancement, and filter processing.

The black-and-white scan converter uses the reflected echo signal output from the signal processing unit 105 of the above-mentioned ultrasonic transceiver mechanism to obtain RF signal frame data in the subject 100 including moving tissues in ultrasonic cycles, and converts the RF signal frame data by the switching adder 114. The frame data is displayed on the image display 107 . Therefore, the black-and-white scan converter 106 includes a tomography mechanism for sequentially reading RF signal frame data at a television-like cycle, and a mechanism for performing system control, such as converting the reflected echo signal from the signal processing unit 105 into a digital signal. A/D converter, a plurality of frame savers for storing tomographic image data digitized by the A/D converter in time series, a controller for controlling their operations, and the like.

The image display 107 displays the time-series tomographic image data obtained by the black-and-white scan converter 106, that is, the B-mode tomographic image, and converts the image data output from the black-and-white scan converter 106 into an analog signal by switching the adder 114 A D/A converter and a color TV monitor that inputs an analog video signal from the D/A converter and displays it as an image.

In this embodiment, an RF signal frame data selection unit 108 and a displacement measurement unit 109 are branched from the output side of the phasing and adding circuit 104, and the tissue elastic modulus is obtained at the same time. measurement unit 110 . In the rear stage of the displacement measurement part 109 and the pressure measurement part 110, a deformation and elastic modulus calculation part 111, an elastic data processing part 112, and a color scan converter 113 are provided, and the color scan converter 113 and the black-and-white scan conversion The output side of the device 106 is provided with a switching adder 114 . In addition, the outputs of the monochrome scan converter 106 and the color scan converter 113 are introduced into the scoring unit 116 to perform scoring processing. The details of this scoring process will be described later.

The displacement measurement unit 109 performs unary correlation or binary correlation processing based on a set of RF signal frame data selected by the RF signal frame data selection unit 108, and measures the displacement or motion vector (displacement vector) of each point on the tomographic image. orientation and size). As a detection method of the motion vector, there are, for example, the block matching method and the gradient method described in Patent Document 1. FIG. The block matching method divides an image into blocks composed of, for example, N×N pixels, searches for the block most similar to the block in the current frame from the previous frame, and performs prediction symbolization with reference to the block.

The pressure measurement unit 110 measures or estimates the pressure in the body cavity of the diagnostic site of the subject 100 . The method adopted by this ultrasonic diagnostic device is to use the ultrasonic probe 101 arranged on the detector probe 1011 and perform ultrasonic transmission and reception under the control of the control part 200, and simultaneously pass the pressurizer (not shown in the figure) arranged on the detector probe 1011 As shown), pressurization or decompression is performed, and a stress distribution is applied to the body cavity of the diagnosis site of the subject 100 . In this method, in order to measure how much pressure is applied between the probe head 1011 and the subject 100, for example, as shown in FIG. The side surface of the detector head 1011 measures the pressure between the detector head 1011 and the subject 100 at any time, and sends the measured pressure value to the deformation and elastic modulus calculation unit 111 . However, in FIG. 2 , the pressurizer for pressurizing and depressurizing the probe head 1011 is omitted.

The deformation and elastic modulus calculation unit 111 calculates the deformation or elastic modulus of each point on the tomographic image based on the displacement (displacement) and pressure respectively output from the displacement measurement unit 109 and the pressure measurement unit 110, and generates Numerical data (elasticity frame data) of deformation or elastic modulus is output to the elastic data processing unit 112 . The deformation calculation performed by the deformation and elastic modulus calculation unit 111 is calculated and obtained by spatial differentiation of the displacement, for example, without requiring pressure data. In addition, Young's modulus, which is one of the modulus of elasticity, is calculated by dividing the change in pressure by the change in movement amount.

The color scan converter 113 is provided with a color tone information conversion mechanism for inputting as elastic frame data output from the elastic data processing unit 112, a command output from the control unit 200 of the ultrasonic diagnostic apparatus, or an input from the elastic frame data. The upper limit value and the lower limit value of the gradation selection range in the elastic frame data output from the data processing unit 112 are given color tone information such as red, green, and blue from the elastic frame data as elastic image data. This color tone information conversion mechanism operates as follows. For example, in the elastic frame data output from the elastic data processing unit 112, the area with a large measurement distortion is converted into a red code in the elastic image data, and vice versa. Regions of are transformed into blue codes within the elastic image data. In addition, the color scan converter 113 may also be constituted by the black-and-white scan converter 106 described above. In this case, the region where the measured strain is large has a bright gradation in the elastic image data, and conversely, the region where the measured strain is small has a dark gradation in the elastic image data.

The switching adder 114 becomes a mechanism that inputs the black-and-white tomographic image data from the black-and-white scan converter 106 and the color elastic image data from the color scan converter 113, and adds or switches the two images; Elastic image data, or the method of additively combining and outputting two image data is switched. Also, as described in Patent Document 2, for example, in a two-screen display, a black and white tomographic image and a color or black and white elastic image by a black and white scan converter can be simultaneously displayed. The image data output from switching adder 114 is output to image display 107 .

Next, scoring in this embodiment will be described.

3 is a diagram showing a first embodiment of scoring processing performed by the scoring unit 116 in FIG. 1 , and FIG. picture. 3(B) to (F) are diagrams showing an example of the color elastic image data output from the color scan converter 113 , and each symptom is displayed schematically. These elastic image data are actually displayed in color corresponding to the hardness. For example, a soft part (a part with a large deformation) is displayed in red, a hard part (a part with a small deformation) in blue, and an intermediate color in green are displayed as continuous color changes. Wherein, in Fig. 3, because such colors cannot be used for display, the situation is shown with a grid, the soft area is displayed with a coarse grid, and the hard area is displayed with a fine grid. In the case of the B-mode tomographic image in FIG. 3(A), the central elliptical portion 61 is a diseased site such as a breast tumor, and is called a hypoechoic area. In addition, the region corresponding to the same coordinate region as the hypoechoic region 61 in the elastic image is shown by an elliptical dotted line in FIGS. 3(B) to (F). Regarding the hypoechoic region 61 , according to how the elastic image data is displayed, the elastic image data scoring input mechanism 115 is operated to perform scoring processing to classify the elastic images on a scale of 1 to 5 points. Each score 1 to 5 is based on the following criteria, and the observer judges whether or not the criteria are satisfied, and performs scoring processing. Next, the criteria for each of the scores 1 to 5 will be described.

1 point: As shown in Fig. 3(B), the hard region 62 based on the elastic image is not clearly identified inside the hypoechoic region 61

2 points: As shown in FIG. 3(C), a case in which the hard regions 63 and 64 of the elastic image avoid the central part of the hypoechoic region 61 and are partially recognized

3 points: As shown in FIG. 3(D), the hard region 65 on the elastic image does not reach the boundary (contour) of the hypoechoic region 61, but the case is recognized including the central part of the hypoechoic region 61

4 points: As shown in FIG. 3(E), the hard region 66 of the elastic image is equally distributed to the boundary (contour) of the hypoechoic region 61 and identified

5 points: As shown in FIG. 3(F), the hard region 67 based on the elastic image covers the entire hypoechoic region 61 and is recognized beyond the boundary (contour) of the hypoechoic region 61 and extends to the outer region. of cases

Here, the above-mentioned hard region can be identified as a region where the deformation smaller than a certain deformation threshold Ts is measured in the deformation image as an elastic image. For example, if the deformation percentage is set to 0 as the threshold Ts, then As the above-mentioned hard area, it was selected as an area (measurement point group) in which no compression was formed at all even by pressure from the body surface. However, when the pressing force is received at the time of scanning the diagnostic site, and the elastic modulus image is used as the elastic image, at this time, the area where the elastic modulus is measured that is greater than a certain elastic modulus threshold Ty can be regarded as a hard area. identify.

Such scoring (classification) can be used as an indicator for disease judgment and help in diagnosis. FIG. 4 is a diagram showing a display example of the image display 107 when score processing is performed using the score input means 115 . On the display screen 1071 of the image display 107, an image obtained by superimposing a semi-transparent color image of elastic image data representing the hardness distribution of the tissue on the B-mode tomographic image as shown in FIGS. 3(B) to (F) is displayed. On the top of the display screen 1071, there are provided a column 1072 for inputting scoring indicators, and a display area 1073 for displaying each scoring indicator. In this display area 1073 , the content of each score as described above is displayed in simple and clear writing, or the figures of FIGS. 3(B) to 3(F) are combined and displayed. The examiner inputs a score deemed appropriate after observing the display screen 1071 into the score indicator input field 1072 while comparing it with the score indicator 1073 . Their action is carried out through the scoring input mechanism 115 . Among them, the images displayed on the display screen 1071 may display B-mode tomographic images in parallel in other windows as shown in FIG. 3(A) . Therefore, visual resolution at the time of scoring processing can be improved. The score entered in the scoring index input field 1072 may be linked to the individual patient's report function included in the ultrasonic diagnostic apparatus, or may be input simultaneously in the scoring index input field included in the reporting function. In addition, in the display area 1073, model diagrams serving as scoring indicators such as those shown in FIGS. 3(B) to (F) may be written at the same time.

In the above-mentioned embodiment, the case where the examiner observes the image displayed on the display screen 1071 and performs scoring processing is described, and scoring (classification) is automatically performed by the scoring unit 116 using image processing. Hereinafter, automatic scoring processing by the scoring unit 116 will be described. Scoring unit 116 performs boundary detection using known boundary detection software in the color elastic image data (distorted image) output from color scan converter 113, and obtains hard partial regions 62 to 67 from the number of pixels and the like. The area of B.

In the second embodiment, it is indicated that scoring is performed using only elastic image data. For example, an elastic image serving as a reference image is obtained in advance as reference information, the number of pixels in the elastic image region is defined as the area B, and the score based on the area of the elastic image is set as follows.

1 point when X1<B

When X2<B≦X1, it is 2 points

When X3<B≦X2, it is 3 points

When X4<B≦X3, it is 4 points

When B<X4, it is 5 points

In addition, the area B set as described above is applied to the newly acquired elastic image data area and scored. In addition, if the elastic image is a series of connected images, set the minimum score to 2 or higher, and if it is an image connected by a smooth circle system, set the minimum score to 3 or higher, and you can also set the score according to the shape of the elastic image .

In addition, the scoring process using the property that stress signals are emitted on so-called elasticity images by compressing the subject is shown in FIG. 5 . 5( a ) and ( b ) are diagrams showing a state in which the subject 100 is pressed by the probe 101 . When compressing a malignant tumor, a stress signal 122 is displayed as shown in FIG. 5 . Figure 5(a) shows benign tumors.

In the scoring unit 116 , a score corresponding to the amplitude 123 of the stress signal 122 is set in advance. For example, the larger the amplitude, the more malignant the elastic image is, and the scores are set as shown in FIG. 4 . Furthermore, the elastic image 121 is scored by comparing the amplitude set corresponding to the scoring with the amplitude 123 of the stress signal 122 of the elastic image 121 obtained. Here, an example of scoring using the magnitude of the stress signal 122 is shown, but the area of the stress signal 122 may be used for scoring. This allows scoring of stress signals via elastography.

In addition, an example of performing scoring processing using the elasticity image 133 that changes when the subject is pressed is shown in FIG. 6 . In the case of a malignant tumor, for example, the blue display area 132 has a characteristic of gradually shrinking as the subject is gradually pressed. On the contrary, in the case of a benign tumor, the blue display area 132 gradually increases in size when the subject is gradually pressed. Therefore, using this characteristic, a score for a characteristic corresponding to the color change of the region produced by compression is set in the scoring section 116 as shown in FIG. 4 . In addition, scoring is performed based on the color of the elastic image that changes according to compression. In this way, scoring can be performed based on changes in the region 132 of the elastic image 133 based on compression.

An example of using Cyst pattern and performing scoring processing is shown in Figure 7. The capsule pattern is a pattern in which the shallower portion 141 of a specific region of the displayed elasticity image is colored blue and the deeper region 142 is colored red, and a paired pattern is displayed. Among them, it is known empirically that the region displayed by the cystic figure is a benign tumor. Therefore, in the scoring unit 116, the cystic pattern is separately set as a benign score, and if the cystic pattern is recognized on the elasticity image, so-called benign scoring is performed. In this way, such can also be scored based on the hue graph of the elastic image.

In the above-mentioned embodiment, the case where scoring processing is performed using only elastic image data has been described, but in the third embodiment, the automatic scoring process of elastic image data using tomographic image data as reference information is applied to that of Fig. 3 . etc. for explanation. First, the scoring unit 116 detects the hypoechoic region 61 of the disease site in the black-and-white tomographic image data (B-mode tomographic image) output from the black-and-white scan converter 106 using known boundary detection software. The area A of the detected region 61 is obtained from the number of pixels and the like. Then, the scoring unit 116 performs the same boundary detection on the color elastic image data (deformed image) output from the color scan converter 113, and obtains the internal measurement value of 61 in the hypoechoic region of the disease site from the number of pixels and the like. The area B of the regions 62-67 of the harder portion. Here, when a plurality of hard regions are scattered like the regions 63 and 64 , the sum of their areas is defined as the area B.

The scoring unit 116 performs scoring processing as follows based on the relationship between the areas A and B obtained respectively.

First, as the ratio of the above-mentioned areas A and B, B/A is assumed to be Z and calculation is performed. In addition, in the ultrasonic diagnostic apparatus, the threshold value as the area ratio is set as follows.

1 point threshold Th1 (eg 0.1)

Threshold Th2 of 2 points (eg 0.3)

Threshold of 3 points Th3 (eg 0.7)

Threshold Th4 of 4 points (eg 1.0)

Therefore, the following judgments are made:

1 point when Z<Th1

When Th1<Z≦Th2: 2 points

3 points when Th2<Z≦Th3

When Th3<Z≦Th4: 4 points

When Th4<Z is 5 points

In addition, in addition to the above-mentioned embodiments, for example, as shown in FIG. 8, in the central part of the hypoechoic region 61, a region of interest 61a having an area smaller than the hypoechoic region and having a certain ratio (for example, area A×0.6) is set, For this region of interest 61a, similar to the above, in addition to the above-mentioned scoring method, it is possible to evaluate to what extent the hard region occupies the elastic image. Therefore, in the evaluation of the score, not only the ratio of the hard region to the hypoechoic region is increased The area occupancy ratio of the region and the spatial distribution of the hard region inside and outside the hypoechoic region can be expected to improve the evaluation accuracy.

Furthermore, for example, as shown in FIG. 9, not only the above-mentioned central region of interest, but also a plurality of regions of interest 62b are set inside or outside the boundary based on the boundary of the hypoechoic region. For these multiple regions of interest In the same way as above, it is possible to evaluate how much the hard region in the elastic image occupies. In addition, for each of the plurality of regions of interest 62b, first evaluate the occupied area of the elastic image by the hard region. Whether the ratio exceeds a certain ratio can be included in several regions of interest that exceed the ratio in the evaluation of the score. In addition, boundary detection can be achieved by applying a well-known snake method, region growing method, or the like. When the boundary is hidden by a shadow or the like, it is possible to deal with it by setting an ROI or using interpolation based on surrounding boundary information.

In addition, in the above-mentioned embodiment, an example is shown in which scoring is performed using the occupancy ratio of the hard areas distributed in the hypoechoic area, but as another example, the elasticity measurement point group included in the hypoechoic area may be used as the parent Groups perform statistical processing and perform scoring based on the statistical characteristic quantities. For example, a method of applying the average value will be described below.

Let the total number of elements of the measurement point group in the hypoechoic region be N, and the strain or elastic modulus of each measurement point be Ei (i=1, 2, 3...N). Therefore, the strain or the average value Em of the elastic modulus of the measurement points built into the hypoechoic region is calculated as follows.

(Average value Em)=∑Ei(i=1, 2, 3...N)

In addition, in the ultrasonic diagnostic device in advance, with

1 point threshold Tm1

2 points threshold Tm2

3-point threshold Tm3

Threshold Tm4 of 4 points

Set the threshold value as the average value, and calculate the modulus of elasticity as a value reflecting elasticity,

Keep the size relationship of Tm1<Tm2<Tm3<Tm4 for setting, and make the following judgments,

When Z<Tm1 is 1 point

2 points when Tm1<Z≦Tm2

3 points when Tm2<Z≦Tm3

When Tm3<Z≦Tm4: 4 points

When Tm4<Z is 5 points

Also, when using deformation as a value reflecting elasticity,

Keep the size relationship of Tm1>Tm2>Tm3>Tm4 for setting, and make the following judgments,

1 point when Tm1<Z

2 points when Tm2<Z≦Tm1

3 points when Tm3<Z≦Tm2

When Tm4<Z≦Tm3: 4 points

When Z<Tm4 is 5 points

In the above description, the average value is used as an example, but the present invention is not limited thereto, and the statistical processing is performed using the elastic measurement point group included in the hypoechoic region as the parent group, and the statistical feature value is used as the basis. Grading is important.

A specific example in which the scoring unit 116 automatically performs scoring (classification) using image processing will be described. First, as shown in FIG. 3 , the scoring unit 116 detects hypoechoic disease sites in the black-and-white tomographic image data (B-mode tomographic image) output from the black-and-white scan converter 106 by, for example, the region growing method, which is known region extraction software. The area A of the region 61 . The area A of the detected region is obtained from the number of pixels and the like. Then, in the color elastic image data (deformed image) output from the color scan converter 113, the scoring unit 116 evaluates the hard regions 62 to 67 in the region of the area A, which can be arbitrarily set by the device user. , and the number of pixels with a specified gray value or more is counted. Let this number of pixels be the area B. Based on the area A and area B (the number of pixels having a predetermined gradation value or more), the following scoring process is performed.

First, as the ratio of the areas (number of pixels) A and B, B/A is set to Z for calculation. In addition, in the ultrasonic diagnostic apparatus in advance, set

1 point threshold Th1 (eg 0.1)

Threshold Th2 of 2 points (eg 0.3)

Threshold of 3 points Th3 (eg 0.7)

Threshold Th4 of 4 points (eg 1.0)

as a threshold for the area ratio.

Therefore, at this time, when Z<0.7, follow the rules shown below to return to scoring.

1 point when Z<Th1

2 points when Th1<Z≦Th2

3 points when Th2≦Z≦Th3

Furthermore, when Th3≦Z (0.7≦Z), in the obtained elastic image data (deformed elastic image), in the same manner as above, in a known region extraction application program (for example, the region growing method), extract as hard Regions 65 to 67 are used to display regions with a predetermined grayscale or higher. At this time, the extraction region is not limited to the above-mentioned hypoechoic region 61 , and the region including the edge is also an extraction target.

Let the extracted area obtained here be C. As the ratio of the area A to the area C, the calculation is performed by setting C/A as Z'. The Z' value is returned to

4 points when Th3<Z’≦Th4

When Th4<Z’, it is 5 points.

In the above-described embodiment, up to 3 points, the elastic image data does not require region extraction processing, and scoring can be performed.

FIG. 10 is a flowchart showing an example of automatic scoring processing using an area extraction application. FIG. 11 is a diagram schematically showing an example of the operation of the automatic scoring process. Here, as a region extraction algorithm, a region growing (Region Growing) method will be described as an example.

In step S91 , in order to obtain a deformation elasticity image, after applying pressure by any method, the hypoechoic part to be extracted is selected using the drawn B-mode tomographic image. This selection process is performed by specifying an arbitrary position within the hypoechoic region 61 as a source point (described as SP in the figure) as shown in FIG. 11(A).

In step S92, the hypoechoic region 61 as shown in FIG. 11(B) is extracted based on the source point SP indicated in step S91 and a threshold value given by an arbitrary method. The region growing method refers to a method of extracting a region whose gradation value difference falls within a preset value (threshold value) from the region adjacent to the indicated source point SP. Therefore, the area of the extracted hypoechoic region 61 is counted by counting the number of pixels or the like. In FIG. 11(B), this area is described as SBW.

In step S93, in the deformation elasticity image drawn in the hypoechoic region, the number of pixels is used to represent the grayscale of the harder region compared with the grayscale value arbitrarily set by the user to represent the harder region. The area of the value is counted. In FIG. 11(C), this relatively hard region is described as Sb1.

In step S94, it is judged whether the area Sb1 of the hard region is smaller than the area SWB×0.7 of the hypoechoic region. As a result of the judgment, when the area Sb1 of the hard region is smaller than the area SWB×0.7 of the hypoechoic region (when SWB×0.7>Sb1), go to step S95. Then, in step S95, it returns to the value of 1 to 3 points corresponding to each value.

On the contrary, as a result of the judgment in step S94, when the area Sb1 of the harder region is more than the area SWB×0.7 of the hypoechoic region (SWB×0.7≦Sb1), the area Sb1 of the harder part may not be included in the hypoechoic region, So go to step S96.

In step S96, the region (area Sb1') is extracted again by the region growing method from the elastic image data. At this time, extraction is performed until a region including the surrounding region of the hypoechoic region of the B-mode tomographic image is extracted. In the region extraction process here, the source point SP indicated by the above-mentioned B-type tomographic image may be used as it is, or may be newly set separately. Let the area of the region extracted here be Sb1'. Then, in step S97, it returns to the value of 4 or 5 points corresponding to the ratio (SWB/Sb') of the area SBW of the hypoechoic region to the area Sb1'. Specifically, when Sb1'<SBW, return to 4 points, otherwise return to 5 points.

In this way, by performing region extraction using a known region extraction application program (eg, region growing method), scoring processing corresponding to the hardness distribution of the tissue can be automatically performed. Here, the above-mentioned threshold values are merely examples, and can be freely changed according to the judgment of the user.

In addition, in the above-mentioned embodiment, the example in which the contour information of the hypoechoic region of the diseased part in the B-mode tomographic image is detected by the known boundary detection software is shown, but as another embodiment, the interface (mouse, track, etc.) of the ultrasonic device can be used. ball, etc.), the examiner inputs the contour of the diseased part on the displayed B-type tomographic image, and calculates the area A of the region 61 based on the contour information, compares it with the area B obtained by the above method, and automatically scores judge. The above-mentioned scoring is an example, and it is important to perform scoring using an elastic image including a deformed image. The numerical values used in the above-mentioned scoring are merely examples, and it is of course preferable to use various optimal values according to actual clinical cases. In addition, in the above description, the scoring method targeting the mammary gland region was used as an example, but in other regions, it is of course necessary to define the scoring method for cases corresponding to the region. In addition, the scoring methods based on the above embodiments are not only used individually for scoring, but also can be used in combination of multiple methods, and of course scoring can be performed once.

In the above-described embodiment, scoring processing is performed on the scoring unit 116 using the monochrome tomographic image data (B-mode tomographic image) output from the monochrome scan converter 106 and the color elastic image data (deformed image) output from the color scan converter 113 However, the same scoring process may be performed using the output from the signal processing unit 105, the output from the elasticity data processing unit 112, or the strain and elastic modulus calculation unit 111. In addition, in the above-mentioned embodiment, the pressure measuring unit is marked in FIG. 1 , but when the strain is obtained and the strain image is displayed, the pressure measuring unit may not be present.

FIG. 12 is a diagram showing the timing at which strain and modulus of elasticity are calculated by the strain and modulus of elasticity calculation unit 111 in the above embodiment. A state where the subject 100 is not pressed by the probe 101 is shown as 0, and a pressed limit is shown as MAX. The timing of acquiring the elasticity image refers to the timing of the lightly compressed state, the elasticity image of the state of compressing at about 3 to 20% of the compression limit is acquired, and the acquired elasticity image is scored.

Specifically, the pressure value of the pressure limit is detected in advance by the pressure sensor 1012 , and the pressure value is stored in the scoring unit 116 as the pressure MAX value. Using the stored pressure MAX value as a reference, the score input means 115 is used to set the pressure value of the elastic image acquired by compression. Wherein, the set pressure value can be arbitrarily set as, for example, a pressure value of 10% of the pressure MAX value. Then, the subject 100 is pressed to acquire an elasticity image at a time point when the set pressure value has been reached, and the acquired elasticity image is scored.

In the elastic image under strong compression of the subject 100, it may be difficult to distinguish between hard and soft areas. Therefore, scoring can be improved by scoring the elastic image acquired at the time of light compression as described above.

The operation of the RF signal frame data selection unit 108 in the above-mentioned embodiment will be described with reference to FIG. 13 . Fig. 13 is a diagram showing an example of an RF signal frame data selection unit in Fig. 1 . The RF signal frame data selection unit 108 can arbitrarily select the number of frames going back to the past (the number of frame intervals from the current frame data) as one piece of RF signal frame data used as a reference for displacement measurement. That is, the RF signal frame data selection unit 108 sequentially outputs the data from the phasing and adding circuit 104 over time at the frame rate of the ultrasonic diagnostic apparatus in the frame holder 1081 included in the RF signal frame data selection unit 108. RF frame data is saved. The RF signal frame data selection unit 108 sets the data stored at the current point in the frame holder 1081 as the RF signal frame data N. The RF signal frame data selection unit 108 selects one RF signal frame from the past RF signal frame data N-1, N-2, N-3...N-M according to the time according to the control command from the control unit 200 of the ultrasonic diagnostic apparatus The data is temporarily stored in the RF signal frame data selection circuit 1082 as RF signal frame data X. The RF signal frame data selection unit 108 outputs the latest RF signal frame data N stored in the frame holder 1081 and the RF signal frame data X stored in the RF signal frame data selection circuit 1082 to the displacement measurement unit 109 in parallel. .

That is, in the RF signal frame data selection unit 108, first, as the past RF signal frame data X constituting a set of RF signal frame data output to the displacement measurement unit 109, not only the current RF signal frame data X can be arbitrarily selected RF signal frame data N−1 temporally adjacent to frame data N may optionally select RF signal frame data N−M that have been separated by M frames (M=1, 2, 3 . . . ) as past RF signal frame data X. Wherein, the interval number M of frame intervals (M=1, 2, 3...) can be set and changed arbitrarily according to the interface of the ultrasonic device.

Fig. 14 is a diagram showing another embodiment of the RF signal frame data selection unit in Fig. 1 . RF signal frame data selection unit 108 in FIG. 14 stores RF signal frame data P acquired in a certain time phase P in the past in frame saver 1081 according to a control command from control unit 200 of the ultrasonic diagnostic apparatus. The RF signal frame data selection circuit 1082 does not update the RF signal frame data P stored in the frame holder 1081, but usually refers to the past RF signal frame data in an arbitrary time phase. Therefore, in the displacement measurement unit 109, a set of RF signal frame data consisting of the currently stored RF signal frame data N and RF signal frame data P can be taken in. Whether to use the function shown in Fig. 14, and when it is used, what time to acquire the RF signal frame data P, etc., can be switched, set and changed arbitrarily according to the interface of the ultrasonic diagnostic device.

When the interval between past and current RF signal frame data N, P constituting one set of RF signal frame data is limited to adjacent frames, the amount of pressurization or decompression applied in the time interval between RF signal frame data The amount of compression, sometimes not enough to draw the amount of compression or decompression suitable for drawing elastic image data (generally about 1%), wherein the frame data of the RF signal is obtained during a series of compression or decompression operations A group of multiple RF signal frame data. On the other hand, by configuring the RF signal frame data selection unit as shown in FIGS. 13 and 14 , the frame interval between past and current RF signal frame data can be sufficiently increased, and an elastic image based on the elastic frame data can be sufficiently drawn. This is particularly useful in ultrasonography where the rate of compression or decompression during a series of compression or decompression operations cannot be sufficiently increased due to physical limitations of the subject's physique.

Claims (19)

1. An ultrasonic diagnostic device, comprising: a displacement measurement unit that emits ultrasonic waves from the probe to the subject, receives reflected echo signals corresponding to the emitted ultrasonic waves, and measures displacement of the living tissue of the subject based on the reflected echo signals; an elastic image constructing unit that obtains a deformation amount or an elastic modulus from the displacement and constructs an elastic image; a tomographic image forming unit that forms a tomographic image of the living tissue of the subject based on the reflected echo signal; and a display unit that displays the elastic image and the tomographic image; The ultrasonic diagnostic apparatus is characterized in that it includes a scoring mechanism for classifying the elasticity image output from the elasticity image forming unit into corresponding scoring points based on a score indicating a deformation state or an elastic state. The deformation state or the elastic state is set according to the distribution state of the hard region of the elastic image relative to the hypoechoic region of the tomographic image. 2. The ultrasonic diagnostic device according to claim 1, characterized in that: the deformed state or the elastic state is set according to the area of the hard region of the elastic image, The area is determined by the number of pixels of the elastic image. 3. The ultrasonic diagnostic device as claimed in claim 2, characterized in that: When there are multiple elastic images, the area is the sum of the areas of the multiple elastic images. 4. The ultrasonic diagnostic device according to claim 1, characterized in that: An area setting means for setting the area in the tomographic image or the elastic image is provided. 5. The ultrasonic diagnostic device as claimed in claim 4, characterized in that: The area setting means sets a hypoechoic area on the tomographic image as a first area, and sets an area of a hard part on the elastic image as a second area, and the scoring means The ratio of the 2 area to the 1 area characterizes the deformed or elastic state. 6. The ultrasonic diagnostic device as claimed in claim 4, characterized in that: The region setting mechanism obtains the region through the region growing mechanism or the boundary detection mechanism, wherein the region growing mechanism or the boundary detection mechanism extracts the region through the gray scale of the tomographic image or the elastic image. 7. The ultrasonic diagnostic device according to claim 6, characterized in that: The region growing mechanism sets a position in the region as a source point, and extracts a region whose grayscale difference falls within a preset threshold value in a region adjacent to the set source point. 8. The ultrasonic diagnostic device as claimed in claim 4, characterized in that: The region setting means inputs a contour on the tomographic image or the elastic image, and obtains the region based on the contour information. 9. The ultrasonic diagnostic device according to claim 5, characterized in that: The scoring mechanism is to set a plurality of regions of interest in the hypoechoic region, and characterize the deformation state or elastic state according to the occupancy degree of hard regions in the plurality of regions of interest. 10. The ultrasonic diagnostic device according to claim 5, characterized in that: The scoring mechanism is to set a region of interest in the central part of the hypoechoic region, and characterize the deformation state or elastic state according to the occupancy degree of the hard region in the elastic image. 11. The ultrasonic diagnostic device according to claim 1, characterized in that: A pressure setting mechanism is provided for setting the pressure value for pressing the subject, and the deformed state or the elastic state is represented based on the elastic image when the object is compressed with the set pressure value. 12. The ultrasonic diagnostic device according to claim 1, characterized in that: The scoring mechanism classifies the deformation state or the elastic state into a plurality of stages according to the hard region. 13. The ultrasonic diagnostic device according to claim 1, characterized in that: An input mechanism is provided, a score input column is provided on the display unit, and a score is input into the score input column using the input mechanism. 14. The ultrasonic diagnostic device according to claim 1, characterized in that: A frame data selection unit that selects a set of frame data of the reflected echo signal output to the displacement measurement unit is provided, and the elasticity image is constructed based on the selected set of frame data. 15. The ultrasonic diagnostic device according to claim 14, characterized in that: The frame data selection unit arbitrarily selects the set of frame data, and constructs the elastic image based on the selected set of frame data. 16. The ultrasonic diagnostic device according to claim 14, characterized in that: The frame data selection unit selects frame data of a set of adjacent frames. 17. The ultrasonic diagnostic device according to claim 1, characterized in that: The scoring mechanism characterizes the deformation state or the elastic state according to the stress signal of the elastic image. 18. The ultrasonic diagnostic device according to claim 1, characterized in that: The scoring mechanism is to characterize the deformation state or the elastic state according to the change of the region of the elastic image caused by gradual compression. 19. The ultrasonic diagnostic device according to claim 1, characterized in that: The scoring mechanism is to characterize the deformation state or the elastic state according to the hue graph of the elastic image.

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