JP4843432B2 - Ultrasonic diagnostic equipment - Google Patents

Description

  The present invention relates to an ultrasonic diagnostic apparatus, and more particularly to an ultrasonic diagnostic apparatus that obtains a measurement amount that reflects the hardness of a target tissue.

  It has been found that changes in tissue hardness often occur when a living body becomes pathological. For example, it is known that a tumor tissue changes in mechanical characteristics as compared with surrounding tissues, and depending on the size of the tumor, it can be confirmed by palpation. However, since these tumors do not necessarily have a large difference in echo intensity (the magnitude of the echo signal) from surrounding tissues, it may be difficult to detect the tumor with a conventional B-mode image or the like. Thus, since the echo intensity and hardness of the tissue generally do not correlate (or the correlation is very small), it is difficult to image the tissue hardness.

  Against this background, there are attempts to image tissue hardness. For example, in Patent Document 1, an external force is applied from the body surface of a subject with a pressurizing device or an ultrasonic probe, and the correlation calculation of ultrasonic signals between two adjacent frames that change in time series in that state is disclosed. The strain is measured by obtaining the displacement at each point, further measuring the strain by spatially differentiating the displacement, and displaying the hardness and softness of the tissue by imaging the strain data.

JP 2005-334196 A

  In the method described in Patent Document 1, the tissue in the subject is pressed from the outside (body surface) to displace the tissue. Therefore, in order to obtain a good measurement result, it is necessary to perform good pressurization. For example, when pressurizing (compressing) tissue, it is desirable to minimize the displacement in the orthogonal direction and the azimuth direction of the cross section in which ultrasonic waves are transmitted and received. Therefore, when the inspector manually pressurizes, for example, it is desirable to feed back the pressurization state to the inspector. For example, it is desirable to inform the inspector whether or not the measurement result is good depending on the state of pressurization.

  The present invention has been made in such a background, and the purpose of the present invention is to determine whether the measurement state at the time of obtaining the measurement amount is good or not in the ultrasonic diagnostic device for obtaining the measurement amount reflecting the hardness of the target tissue. It is to provide a technique for determining.

  In order to achieve the above object, an ultrasonic diagnostic apparatus according to a preferred aspect of the present invention transmits and receives ultrasonic signals before and after pressurization of a target tissue by transmitting and receiving ultrasonic waves in a space including the target tissue. Measure the amount of displacement in the target tissue by comparing the wave part and the echo signal before and after pressurization based on correlation calculation, and reflect the hardness of the target tissue from the measured amount of displacement in the target tissue A measurement amount calculation unit for determining the amount, a measurement state determination unit for determining the quality of the measurement state when determining the measurement amount based on the parameter α that is an evaluation criterion of the calculation result of the correlation calculation, and a measurement amount determination unit A parameter setting unit that sets the optimum value of the parameter α from the value of the parameter β, based on optimization information indicating a correspondence relationship between the value of the parameter β reflecting the measurement condition and the optimum value of the parameter α according to the parameter β; Have And features.

  According to the above configuration, the quality of the measurement state is determined based on the parameter α that is an evaluation criterion for the calculation result of the correlation calculation. Further, according to the above configuration, since the optimum value of the parameter α is set from the value of the parameter β reflecting the measurement condition, the parameter α can be easily set. Further, for example, the user sets the value of the parameter β according to the measurement object, the measurement environment, etc., and the optimum value of the parameter α corresponding to the value is obtained, so that the user's request is reflected in the setting of the parameter α. It becomes possible.

  In a preferred aspect, the measurement state determination unit determines whether the measurement state is good or bad by comparing a correlation coefficient obtained by correlation calculation with a parameter α that is a threshold value of the correlation coefficient. In a preferred aspect, the optimization information indicates a correspondence relationship between the value of the parameter β that is a weight variable that determines the ratio between the speed of measurement and the accuracy of the measurement and the optimum value of the parameter α that is a threshold value of the correlation coefficient. It is characterized by being information.

  In a desirable aspect, the ultrasonic diagnostic apparatus includes an image forming unit that forms a measurement amount image reflecting the measurement amount when the measurement state determination unit determines that the measurement state is good, and a measurement amount image An image display ratio F (α) that is a ratio between the number of times of measurement and the number of times the measured amount image is displayed as an index corresponding to the speed of the measurement. As an index corresponding to the accuracy of measurement, the good image rate A (α), which is the ratio of the good image included in the displayed measurement amount image, is used, and the image display rate F (α) and the good image are used. An optimization processing unit for obtaining optimization information based on an evaluation function E (α, β) = βA (α) + (1−β) F (α) defined by the rate A (α) and the parameter β; It further has these.

  In a desirable mode, the optimization processing unit sets the value of the parameter α that maximizes the evaluation function E (α, β) when the value of the parameter β is fixed to the optimum value of the parameter α corresponding to the value of the parameter β. And by obtaining an optimum value of the parameter α for each of a plurality of values of the parameter β, optimization information indicating a correspondence relationship between the value of the parameter β and the optimum value of the parameter α is obtained. .

  According to the present invention, the quality of the measurement state can be determined on the basis of the parameter α that is an evaluation criterion for the calculation result of the correlation calculation. In addition, since the optimum value of the parameter α is set from the value of the parameter β reflecting the measurement conditions, for example, the parameter α can be easily set.

  DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, preferred embodiments of the invention will be described with reference to the drawings.

  FIG. 1 shows a preferred embodiment of an ultrasonic diagnostic apparatus according to the present invention, and FIG. 1 is a functional block diagram showing the overall configuration thereof.

  The probe 10 includes a plurality of vibration elements, and scans an ultrasonic beam in a three-dimensional space including a target tissue. That is, a scanning plane is formed by scanning an ultrasonic beam (a transmission beam and a reception beam) in a plane. Note that the ultrasonic beam may be scanned three-dimensionally (three-dimensionally) by scanning the ultrasonic beam while moving the scanning surface stepwise.

  The transmission / reception unit 12 functions as a transmission beamformer and a reception beamformer. That is, the transmission / reception unit 12 forms a transmission beam by supplying a transmission signal corresponding to the vibration element to each vibration element included in the probe 10, and phasing received signals obtained from the plurality of vibration elements. Addition processing is performed to form a reception beam. Thereby, an echo signal is acquired for each ultrasonic beam.

  In this embodiment, the amount of displacement in the target tissue is measured by comparing echo signals before and after pressurization of the target tissue, and further, the distortion that reflects the hardness of the target tissue from the measured amount of displacement in the target tissue Seeking the amount. Therefore, the principle of distortion amount measurement in the present embodiment will be described.

  FIG. 2 is a diagram for explaining the measurement principle of the distortion amount in the present embodiment. When a biological tissue including a tumor having a hardness different from that of the surrounding is pressurized from the outside, for example, when a body surface including the tissue is pressurized by a minute amount with the probe 10, as shown in FIG. In addition, before and after pressurization (before and after compression), the hard portion of the living tissue is hardly deformed compared to the surrounding tissue, and the soft portion is largely deformed.

  In this embodiment, the probe 10 is pressurized by a very small amount against the living body, and the echo signals before and after pressurization (RF echo signals) are compared based on the correlation calculation, so that the displacement amounts of a plurality of sites in the target tissue can be reduced. It is measured. That is, for a plurality of waveform portions (sample portions) included in the echo signal before pressurization, the waveform portions corresponding to the respective waveform portions before pressurization are specified from the echo signals after pressurization. At this time, the correspondence between the waveform portions before and after pressurization is specified by correlation calculation. That is, the waveform parts before and after pressurization with strong correlation (large correlation coefficient) are regarded as waveform parts corresponding to the same part. Thus, the waveform portions corresponding to the same site are specified from the echo signals before and after pressurization, and the displacement amounts of the plurality of sites in the target tissue are measured from the movement amounts of the plurality of waveform portions before and after pressurization.

  FIG. 2B shows an example of the measurement result of the displacement amount of the tissue site. FIG. 2B shows a displacement distribution in which the depth of the ultrasonic beam is shown on the vertical axis and the amount of displacement of the tissue site at each depth is shown on the horizontal axis. The displacement shown on the horizontal axis in FIG. 2B is the relative displacement before and after the compression of the tissue site, and the transmission / reception surface of the probe 10 (the contact surface between the body surface and the probe 10) is the reference for the displacement. (Relative displacement is zero).

  As shown in FIG. 2 (B), the hard portion of the living tissue is hardly compressed as compared with the surrounding tissue. Therefore, the displacement corresponding to the hard portion is substantially constant without depending on the depth. It has become. On the other hand, since the soft portion is relatively compressed, the amount of displacement corresponding to the soft portion changes greatly according to the depth. That is, the hardness of the tissue is reflected in the change (inclination) of the displacement amount in the depth direction.

  Therefore, in this embodiment, the strain distribution of the tissue is obtained by performing spatial differentiation on the displacement distribution shown in FIG. For example, the strain distribution is calculated by regarding the slope of the displacement distribution obtained by differentiating the displacement distribution shown in FIG. 2B in the depth direction (change of the displacement amount in the depth direction) as the tissue strain amount. To do. Then, for example, a color corresponding to the distortion amount is superimposed on each part in the tomographic image (B-mode image) of the living tissue, and the distribution of the distortion amount is displayed in color.

  A technique for imaging such a distribution of strain amounts is sometimes referred to as elasticity imaging. Therefore, in the following description, the distribution image of the strain amount is referred to as an elasticity image (elastic imaging).

  As described with reference to FIG. 2, in the present embodiment, the strain amount is obtained from the displacement of the tissue site caused by externally pressurizing the target tissue (living tissue) with the probe 10 or the like. Therefore, in order to obtain a good measurement result, it is necessary to perform good pressurization. For example, if the displacement in the target tissue accompanying pressurization is in the depth direction of the ultrasonic beam, the amount of displacement can be measured relatively accurately by comparing echo signals before and after pressurization. On the other hand, when the displacement in the target tissue due to pressurization deviates from the depth direction of the ultrasonic beam, particularly when it deviates from the scanning plane of the ultrasonic beam, the displacement amount is accurately measured from the echo signal. It becomes difficult.

  Therefore, in this embodiment, the compression evaluation unit (reference numeral 14 in FIG. 1) determines the quality of the measurement state by evaluating the compression state of the target tissue. The compression state is evaluated based on the calculation result of the correlation calculation executed when the amount of displacement of the tissue site before and after compression is obtained. The compression evaluation unit uses a correlation coefficient related to sample points in the region of interest (ROI) for elastic images in order to evaluate the compression state.

  FIG. 3 is a diagram for explaining the evaluation of the compressed state according to the present embodiment. FIG. 3 shows a state in which a region of interest (ROI) for an elastic image is superimposed on the B-mode image 42 and displayed. That is, the ROI 46 before compression and the ROI 44 after compression are displayed on the B-mode image 42. In addition, nine sample points are provided in the region of interest for the elastic image. That is, three sample points are provided for each of the ultrasonic beam depths A, B, and C.

  The compression evaluation unit (symbol 14 in FIG. 1) compares the echo signals before and after compression (before and after pressurization) based on the correlation calculation, thereby obtaining nine sample points after compression corresponding to the nine sample points before compression. Ask for. Note that since the echo signal corresponding to the two-dimensional image is obtained two-dimensionally, the correlation calculation is performed two-dimensionally in the image plane. For example, for one sample point out of nine sample points provided in the ROI 46 before compression, an image portion having a strong correlation with the sample point (maximum correlation coefficient) is a two-dimensional image after compression. Is searched two-dimensionally from the echo signal corresponding to.

  Then, for each of the nine sample points, corresponding points after compression are detected based on the correlation calculation, nine sample points after compression are obtained, and the nine sample points after compression are used as the vertexes of the lattice. ROI 44 is set. By the way, the compression (deformation) of the tissue can be visually grasped from the ROI 44 after compression.

  The compression evaluation unit evaluates the compression state based on the correlation calculation result (correlation coefficient) for the nine sample points. That is, the quality of the compressed state (pressurized state) is determined by comparing the correlation coefficient obtained for each sample point with the correlation coefficient threshold value α. For example, if all nine correlation coefficients related to nine sample points exceed the threshold α, it is determined that the compression state is good, and otherwise, it is determined that the compression state is not good. . Further, for example, the quality of the compression state may be determined based on the average value of the nine correlation coefficients regarding the nine sample points and the threshold value α.

  Note that the correlation coefficient is standardized to be “0” when the correlation is the weakest and “100 (percent)” when the correlation is the strongest, for example. In this case, the threshold α of the correlation coefficient is also set to a value between 0 and 100.

  Further, the compression evaluation unit may display the correlation coefficient regarding the nine sample points as an image. For example, you may display the magnitude | size of the correlation coefficient of each of nine sample points by a numerical value, a bar graph, etc. Alternatively, the average value of the correlation coefficient may be obtained for each depth A to C, and the average value of the correlation coefficient for each depth may be displayed as a numerical value or a bar graph.

  Returning to FIG. 1, when the compression evaluation unit 14 determines that the compression state is good, the displacement amount measurement unit 16 measures the displacement amount of the tissue site over the entire area within the ROI for elastic images. For example, a large number of sample points are set so as to fill the entire area in the ROI before compression (reference numeral 46 in FIG. 3), and the corresponding points after compression are obtained for each sample point by correlation calculation. Measure the displacement before and after compression. In this way, the displacement distribution of the tissue site is obtained over the entire area within the elastic image ROI.

  Incidentally, the displacement amount measurement unit 16 may measure the displacement amount of the tissue site two-dimensionally. For example, the displacement amount in the depth direction (distance direction) of the ultrasonic beam and the displacement amount in the scanning direction (azimuth direction) of the ultrasonic beam may be measured.

  The strain amount calculation unit 18 obtains a tissue strain amount distribution over the entire region within the elastic image ROI by performing spatial differential processing on the displacement distribution of the tissue site obtained by the displacement amount measurement unit 16.

  The elastic image forming unit 32 forms an elastic image based on the distribution of the strain amount of the tissue obtained by the strain amount calculation unit 18. That is, a tomographic image (B-mode image) of the target tissue is formed from the echo signal output from the transmission / reception unit 12, and each tissue is spread over the entire area within the elastic image ROI provided in the tomographic image. By superimposing colors corresponding to the strain amount of the part, an elastic image in which the strain amount distribution is displayed in color is formed.

  The formed elastic image is displayed on the image display unit 34. The displayed elastic image corresponds to, for example, an image in which the strain amount of each tissue site is displayed in color over the entire area in the ROI 46 before compression shown in FIG. Further, the image shown in FIG. 3, that is, an image in which the ROI before and after compression is superimposed on the B-mode image 42 and the elastic image formed in the elastic image forming unit 32 are displayed side by side to form a display image. Also good.

  As described above, in this embodiment, when the compression evaluation unit 14 determines that the compression state is good, the displacement amount measurement unit 16 measures the displacement amount of the tissue site, and the elastic image is generated based on the displacement amount. It is formed. On the other hand, if the compression evaluation unit 14 determines that the compression state is not good, an elastic image is not formed. Therefore, the user can know whether or not the compression state is good depending on whether or not the elastic image is displayed. That is, it is possible to know whether the pressure state by the probe 10 is good or bad.

  As described above, the compression evaluation unit 14 compares the correlation coefficient obtained for each sample point with the correlation coefficient threshold value α to determine whether the compression state is good or bad. Therefore, the determination result of the quality of the compressed state greatly depends on the value of the threshold value α. However, it is not always easy to set the optimum value of the threshold value α. For example, when the threshold value α is set to a relatively large value, it is difficult to display an elastic image although a good elastic image having a strong correlation before and after compression can be selected. On the other hand, when the threshold value α is set to a relatively small value, it is easy to display an elastic image, but there is a possibility that an elastic image having a weak correlation before and after compression may be displayed.

  Therefore, in the present embodiment, an optimization function for setting the optimum value of the threshold value α is generated in the optimization processing unit 20, and the optimum value of the threshold value α is set in the parameter setting unit 30 based on the optimization function. The

  The optimization processing unit 20 includes an image storage unit 22, an evaluation function generation unit 24, and an optimization function generation unit 26. The image storage unit 22 stores the elastic image (image data) formed by the elastic image forming unit 32. As will be described later, a plurality of elastic images are formed according to the values of the threshold value α and the weight variable β and stored in the image storage unit 22.

  The evaluation function generation unit 24 generates an evaluation function E (α, β) having a threshold value α and a weight variable β as variables. In generating the evaluation function, the evaluation function generating unit 24 first measures the number of compressions C (α), the number of elastic image display times D (α), and the number of good elastic images G (α).

  The number of compressions C (α) is the number of compressions (pressurizations) by the probe 10 performed when forming an elastic image. For example, the number of compressions is measured with a counter or the like when the probe 10 is compressed (pressurized). Alternatively, a configuration in which a user inputs a measurement result by a counter from an operation panel or the like may be used. Further, the user may visually measure the number of compression operations using the B-mode image.

  The elastic image display count D (α) is the number of times the elastic image is displayed. That is, since the elastic image is displayed only when the compression evaluation unit 14 determines that the compression state is good among the measurements performed for the number of compressions C (α), the elastic image is displayed as the displayed number of times. The display count D (α) is measured. Since the formed elastic image is stored in the image storage unit 22, the number of elastic images stored in the image storage unit 22 may be used as the elastic image display count D (α).

  The number of good elastic images G (α) is the number of good elastic images included in the displayed elastic image. That is, it is the number of elastic images determined as good images among the elastic images stored in the image storage unit 22. Whether or not the elastic image is good is determined based on the correlation coefficient of the entire image of each elastic image.

  When forming each elastic image, the displacement measuring unit 16 sets a large number of sample points so as to fill the entire area in the ROI before compression (reference numeral 46 in FIG. 3), and compresses each sample point. The later corresponding points are obtained by correlation calculation. Therefore, for example, the evaluation function generation unit 24 obtains an average value of correlation coefficients for all sample points, and when the average value of the correlation coefficients is, for example, 50 (percent) or more, the elasticity image is good. Judge that there is.

  Then, the evaluation function generation unit 24 generates an evaluation function E (α, β) based on the number of compressions C (α), the number of elastic image display times D (α), and the number of good elastic images G (α). The evaluation function E (α, β) is defined as E (α, β) = βA (α) + (1−β) F (α). Here, A (α) is a good image rate of the elastic image and is defined as A (α) = (G (α) / D (α)) × 100. F (α) is an elastic image display rate and is defined as F (α) = (D (α) / C (α)) × 100. Β is a weighting variable that determines measurement conditions, and is set to a value between 0 and 1.

  For the user, it is desirable that the good image rate A (α) and the elastic image display rate F (α) each have a large value. However, both the good image rate A (α) and the elastic image display rate F (α) are in a trade-off relationship. Therefore, in this embodiment, an evaluation function E (α, β) is generated by weighting the good image rate A (α) and the elastic image display rate F (α) using the weight variable β, and the evaluation function Attempts are made to set parameters so as to increase the value of E (α, β).

  As the value of the weight variable β is increased, the weighting of the good image rate A (α) is increased, and the accuracy of measurement is emphasized. On the other hand, the smaller the value of the weight variable β, the greater the weight of the elastic image display rate F (α), and the appearance frequency of the elastic image is emphasized.

The optimization function generation unit 26 generates an optimization function for obtaining an optimal value of the threshold value α from the value of the weight variable β. The optimization function CC (β) is defined as CC (β) = arg max _n E (α, β). Note that arg max _x f (x) is a function that returns the value of x that maximizes the value of f (x). That is, the optimization function CC (β) is a function having the weight variable β as a variable, and outputs a value of the threshold α that maximizes the value of the evaluation function E (α, β) according to the value of β.

  FIG. 4 is a flowchart for explaining processing for obtaining an optimization function. The processing content of each step of the flowchart will be described.

  First, a lower limit value of the displacement amount due to pressurization is set (S401). For example, the lower limit value of the displacement amount of each depth (depth A to C shown in FIG. 3) in the elastic image ROI is set. For example, the lower limit value of the displacement amount is set to 1% of the thickness of the target tissue.

  Next, the correlation coefficient threshold value α is initially set to α = 10 (percent) (S402), and the pressure is applied by the probe a plurality of times while maintaining the set threshold value α. Are formed, and the formed elastic image is displayed (S403). As described above, the elasticity image may not be displayed depending on the comparison result between the correlation coefficient and the threshold value α. Further, the effectiveness of measurement may be determined based on the lower limit value of the displacement set in S401. For example, when the amount of displacement due to pressurization is smaller than the lower limit set in S401, the measurement by pressurization at that time may be invalidated.

  Next, it is confirmed whether or not the threshold value α is 60 or more (S404). If the threshold value α is not 60 or more, the value of the threshold value α is increased by 10 (S408), pressurization with the probe is performed a plurality of times, and an elastic image is displayed (S403). It is confirmed whether or not this is the case (S404). That is, by repeatedly executing the processing of S403 → S404 → S408, the elastic image is formed a plurality of times for each value of the threshold α = 10, 20, 30, 40, 50, 60.

  When the threshold value α is 60 or more, the elastic image forming process is terminated, and the good image rate A (α) and the elastic image rate F (α) are obtained for each value of the threshold value α (S405). Furthermore, a specific value of the evaluation function E (α, β) can be obtained by setting the value of the weight variable β (S406). Further, an optimization function CC (β) is obtained based on a specific value of the evaluation function E (α, β) (S407).

  FIG. 5 is a diagram for explaining a specific example of the evaluation function. FIG. 5 shows respective evaluation functions when the weight variables are set to β = 0, β = 0.25, β = 0.5, β = 0.75, and β = 1. Each of the five evaluation functions corresponding to the value of the weight variable β has the value of the threshold value α on the horizontal axis and the value of the evaluation function E (α, β) on the vertical axis.

  The threshold value α is set to α = 10, 20, 30, 40, 50, 60, for example, and the good image rate A (α) and the elastic image rate F (α) are obtained for each value of the threshold value α (FIG. 4). Of S405). The evaluation function E (α, β) is defined as E (α, β) = βA (α) + (1−β) F (α) as described above. Therefore, by determining the value of the weighting coefficient β, the value of the evaluation function E (α, β) is obtained from the good image rate A (α) and the elastic image rate F (α) for each value of the threshold value α. Can do.

  In the graph of β = 0 shown in FIG. 5, the weighting coefficient is set to β = 0, and the evaluation function is obtained from the good image rate A (α) and the elastic image rate F (α) obtained for each value of the threshold value α. A specific value of E (α, β) is obtained. Therefore, FIG. 5 shows a total of six evaluation function values corresponding to each value of threshold α = 10 to 60 shown on the horizontal axis. Note that a function curve of the evaluation function E (α, β) may be obtained from the values of these six points by using processing such as interpolation and least square method.

  The graphs other than β = 0 shown in FIG. 5 are the same as in the case of β = 0, and the value of the weighting coefficient β is set, and a total of 6 corresponding to each value of the threshold α = 10-60 shown on the horizontal axis is shown. It shows the value of the point evaluation function. Further, a function curve of the evaluation function E (α, β) may be obtained from the 6 points.

  FIG. 6 is a diagram for explaining a specific example of the optimization function. FIG. 6 shows an optimization function corresponding to the evaluation function shown in FIG. As described above, the optimization function CC (β) is a function having β as a variable, and the value of the threshold α that maximizes the value of the evaluation function E (α, β) according to the value of β. Is output.

  Considering a specific example of the evaluation function in FIG. 5, the value of the threshold value α at which the value of the evaluation function becomes maximum when β = 0 is α = 30, and when β = 0.25, The threshold value α at which the value is maximum is α = 30. Similarly, α = 30 when β = 0.5, α = 50 when β = 0.75, and α = 60 when β = 1.

  In the specific example of the evaluation function shown in FIG. 5, the optimization function of FIG. 6 shows the correspondence between the value of the weight variable β and the optimum value of the threshold value α. In FIG. 6, the horizontal axis indicates the value of the weight variable β, and the vertical axis indicates the optimum value of the threshold value α corresponding to each value of the weight variable β. As can be obtained from the evaluation function of FIG. 5, FIG. 6 shows the optimum value α = 30 when β = 0, the optimum value α = 30 when β = 0.25, and β = 0.5. Each correspondence relationship of the optimum value α = 50 when the optimum value α = 30 and β = 0.75, and the optimum value α = 60 when β = 1 is indicated by a point.

  FIG. 6 shows only the optimal values of a total of five points corresponding to the five values of the weight variable β = 0 to 1, but processing such as interpolation and least square method is performed from these five optimal values. May be used to obtain the function curve of the optimization function.

  Returning to FIG. 1, when an optimization function is obtained in the optimization processing unit 20, the parameter setting unit 30 sets an optimum value of the threshold value α corresponding to the value of the weighting coefficient β based on the optimization function. The value of the weighting factor β is set by the user, for example. That is, the user sets the value of the weight variable β to a relatively large value if importance is placed on the measurement accuracy as the measurement condition, and emphasizes the appearance frequency (speed of measurement) of the elastic image as the measurement condition. If there is, the value of the weight variable β is set to a relatively small value. The user sets the value of the weighting factor β from, for example, an operation panel (not shown).

  When the value of the weighting factor β is set, the parameter setting unit 30 sets the optimum value of the threshold value α corresponding to the value of β set from the optimization function. For example, in the case of the optimization function shown in FIG. 6, when the weighting coefficient β is set to β = 0.5, the optimal value of the threshold value α is set to α = 30 (percent). Then, the compression evaluation unit 14 evaluates the compression state by the probe 10 using the optimum value of the threshold value α set by the parameter setting unit 30. The evaluation of the compressed state is as described with reference to FIG.

  As described above, according to the embodiment of the present invention, the quality of the measurement state (the quality of the compression state) is determined based on the threshold value α that is the evaluation criterion of the calculation result of the correlation calculation. Further, since the optimum value of the threshold value α is set from the value of the weight variable β reflecting the measurement condition, the threshold value α can be easily set. Further, for example, the user can change the weight variable according to the measurement target, the measurement environment, or the like. By setting the value of β and obtaining the optimum value of the threshold value α according to the value, it becomes possible to reflect the user's request in the setting of the threshold value α.

  Note that, for example, a plurality of optimization functions may be used depending on the measurement target. The optimization function may be incorporated in the apparatus in advance when the apparatus is manufactured, or may be registered in the apparatus prior to measurement when the apparatus is used.

  As mentioned above, although preferred embodiment of this invention was described, embodiment mentioned above is only a mere illustration in all the points, and does not limit the scope of the present invention. The present invention includes various modifications without departing from the essence thereof.

1 is a block diagram showing an overall configuration of an ultrasonic diagnostic apparatus according to the present invention. It is a figure for demonstrating the measurement principle of distortion amount. It is a figure for demonstrating evaluation of a compression state. It is a flowchart for demonstrating the process which calculates | requires an optimization function. It is a figure for demonstrating the specific example of an evaluation function. It is a figure for demonstrating the specific example of an optimization function.

Explanation of symbols

  14 compression evaluation unit, 16 displacement measurement unit, 18 strain calculation unit, 20 optimization processing unit, 30 parameter setting unit.

Claims (5)

A transmission / reception unit for acquiring echo signals before and after pressurization of the target tissue by transmitting / receiving ultrasonic waves in a space including the target tissue;
Measurement by measuring the amount of displacement in the target tissue by comparing the echo signals before and after pressurization based on correlation calculation, and obtaining the measured amount reflecting the hardness of the target tissue from the measured amount of displacement in the target tissue A quantity calculation unit;
A measurement state determination unit that determines the quality of the measurement state when determining the measurement amount based on the parameter α that is a threshold value of the correlation coefficient obtained by the correlation calculation;
Based on optimization information indicating the correspondence between the value of parameter β, which is a weighting variable that determines the ratio between the speed of measurement and the accuracy of measurement when determining the measurement amount, and the optimum value of parameter α corresponding to the parameter β a parameter setting unit for setting the optimum value of the parameter α from the value of β;
Having
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 1,
The measurement state determination unit determines the quality of the measurement state from a comparison between a correlation coefficient obtained by correlation calculation and a parameter α that is a threshold value of the correlation coefficient.
An ultrasonic diagnostic apparatus. A transmission / reception unit for acquiring echo signals before and after pressurization of the target tissue by transmitting / receiving ultrasonic waves in a space including the target tissue;
Measurement by measuring the amount of displacement in the target tissue by comparing the echo signals before and after pressurization based on correlation calculation, and obtaining the measured amount reflecting the hardness of the target tissue from the measured amount of displacement in the target tissue A quantity calculation unit;
A measurement state determination unit that determines the quality of the measurement state when determining the measurement amount, based on the parameter α that is an evaluation criterion of the calculation result of the correlation calculation;
Based on the optimization information indicating the correspondence between the parameter β value that reflects the measurement conditions for obtaining the measurement amount and the optimal value of the parameter α, the optimal value of the parameter α is set from the value of the parameter β. A parameter setting unit to
I have a,
The measurement state determination unit determines the quality of the measurement state from a comparison between a correlation coefficient obtained by correlation calculation and a parameter α that is a threshold value of the correlation coefficient ,
The optimization information is information indicating a correspondence relationship between the value of the parameter β that is a weighting variable that determines the ratio between the speed of measurement and the accuracy of the measurement and the optimum value of the parameter α that is a threshold value of the correlation coefficient.
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 3.
An image forming unit that forms a measurement amount image reflecting the measurement amount when the measurement state determination unit determines that the measurement state is good;
An image display unit for displaying the measurement amount image when the measurement amount image is formed;
As an index corresponding to the speed of measurement, an image display rate F (α) that is a ratio between the number of times of measurement and the number of times the measured amount image is displayed is used and displayed as an index corresponding to the accuracy of the measurement. An evaluation function E () defined by an image display rate F (α), a good image rate A (α), and a parameter β is used by using a good image rate A (α) that is a ratio of a good image included in a measurement amount image. α, β) = βA (α) + (1−β) F (α) based optimization processing unit for obtaining optimization information;
Further having
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 4,
The optimization processing unit sets the value of the parameter α that maximizes the evaluation function E (α, β) when the value of the parameter β is fixed as the optimum value of the parameter α corresponding to the value of the parameter β, and sets the parameter β By obtaining an optimum value of the parameter α for each of the plurality of values, optimization information indicating a correspondence relationship between the value of the parameter β and the optimum value of the parameter α is obtained.
An ultrasonic diagnostic apparatus.

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