Source: https://patents.google.com/patent/JP2009240464A/en
Timestamp: 2020-02-17 20:17:45
Document Index: 498118272

Matched Legal Cases: ['art 11', 'art 12', 'art 11', 'art 12', 'art 5', 'art, 2', 'art, 3', 'art, 5', 'art, 6', 'art, 7']

JP2009240464A - Ultrasonic diagnostic apparatus - Google Patents
JP2009240464A
JP2009240464A JP2008089328A JP2008089328A JP2009240464A JP 2009240464 A JP2009240464 A JP 2009240464A JP 2008089328 A JP2008089328 A JP 2008089328A JP 2008089328 A JP2008089328 A JP 2008089328A JP 2009240464 A JP2009240464 A JP 2009240464A
JP2008089328A
JP5304986B2 (en
JP2009240464A5 (en
Takeshi Mitsutake
Akiko Tonomura
明子 外村
2008-03-31 Application filed by Hitachi Medical Corp, 株式会社日立メディコ filed Critical Hitachi Medical Corp
2008-03-31 Priority to JP2008089328A priority Critical patent/JP5304986B2/en
2009-10-22 Publication of JP2009240464A publication Critical patent/JP2009240464A/en
2011-05-12 Publication of JP2009240464A5 publication Critical patent/JP2009240464A5/ja
2013-10-02 Publication of JP5304986B2 publication Critical patent/JP5304986B2/en
2014-03-20 Priority claimed from US14/220,689 external-priority patent/US9301732B2/en
<P>PROBLEM TO BE SOLVED: To provide an ultrasonic diagnostic apparatus, wherein elastic images are classified using the elastic data and image information of the elastic images, for objectively evaluating the advance condition of a lesion. <P>SOLUTION: The ultrasonic diagnostic apparatus includes: an elastic information computing part for calculating the elastic data of the body tissue of an object using RF signal frame data from the inside of the object received by an ultrasonic transmission/reception means; an elastic image configuration part for generating the elastic images on the basis of a distortion amount and/or an elastic modulus calculated in the elastic information computing part; an elastic image evaluation part 11 for generating evaluation data for evaluating the characteristics of the body tissue on the basis of the elastic images; and an image classification part 12 for classifying the elastic images using at least one of the evaluation data generated by the elastic image evaluation part 11. The classified result of the classification part 12 is displayed at a display device through a switching display device 14. <P>COPYRIGHT: (C)2010,JPO&INPIT
The present invention relates to an ultrasonic diagnostic apparatus that obtains a tomographic image of a diagnostic region in a subject using ultrasonic waves, and in particular, distortion and / or elasticity of each point on an image from RF signal frame data arranged in time series. The present invention relates to an ultrasonic diagnostic apparatus capable of calculating a rate and displaying it as an elastic image indicating the hardness or softness of a living tissue.
A conventional general ultrasonic diagnostic apparatus includes an ultrasonic transmission / reception unit that transmits and receives ultrasonic waves to a subject, an ultrasonic transmission / reception control unit that controls ultrasonic transmission / reception, and a reflected echo signal from the ultrasonic reception unit. It comprises a tomographic scanning means for repeatedly obtaining tomographic image data in a subject including a moving tissue at a predetermined cycle, and an image display means for displaying time-series tomographic data obtained by the tomographic scanning means. The structure of the living tissue inside the subject is displayed as a B-mode image, for example.
On the other hand, in recent years, an ultrasonic diagnostic apparatus capable of acquiring and displaying elasticity data of a subject tissue has been developed (for example, Patent Document 1). In such an ultrasonic diagnostic apparatus, on the ultrasonic transmission / reception surface of the ultrasonic probe, an external force is applied from the body surface of the subject by a manual method, compresses the living tissue, and is adjacent in time series. The displacement at each point is obtained using the correlation calculation of the ultrasonic reception signals of two frames (two consecutive frames). Strain is measured by spatially differentiating the displacement, and the strain data is imaged. Further, the elastic modulus data represented by the Young's modulus of the living tissue is imaged from the stress distribution and strain data by the external force. The hardness and softness of the living tissue can be displayed by an elastic image based on such strain data and elastic modulus data (hereinafter referred to as elastic frame data).
The above-described technique for obtaining elastic images is expected to be applied not only to the diagnosis of mass lesions such as cancer but also to the diagnosis of diffuse diseases. In a diffuse disease, when a local sclerotic tissue such as a nodule is scattered in the surrounding soft tissue, an elastic image obtained by applying the above technique also exhibits a mottled pattern reflecting the non-uniform structure. For example, when the disease progresses from hepatitis to cirrhosis and fibrosis progresses, the nodule spreads into the liver parenchyma, and the mottled pattern of the elastic image becomes complicated. The examiner can evaluate the progress of the disease based on the state of the mottled pattern.
JP-A-5-317313
In the above-described ultrasonic diagnostic apparatus capable of displaying an elasticity image, the examiner visually observes the displayed elasticity image and evaluates the progress of the disease. Therefore, a method for objectively evaluating the progress of a disease from image information of an elastic image is desired.
Therefore, the present invention provides an ultrasonic diagnostic apparatus capable of presenting objective evaluation information based on an elasticity image, specifically using elasticity data and image information of the elasticity image. It is an object of the present invention to provide an ultrasonic diagnostic apparatus capable of classifying elastic images and presenting information that evaluates the progress of disease.
An ultrasonic diagnostic apparatus of the present invention that solves the above problems includes an ultrasonic transmission / reception unit that transmits / receives ultrasonic waves in a subject, and a tomogram based on RF signal frame data received from the subject by the ultrasonic transmission / reception unit. Based on the tomographic image construction means for generating an image, the elasticity information calculation means for calculating the elasticity data of the biological tissue of the subject using the RF signal frame data, and the elasticity data calculated by the elasticity information calculation means Evaluation data generation for generating evaluation data for evaluating the structure of the living tissue based on the elasticity image, comprising elasticity image construction means for generating an elasticity image and display means for displaying the tomographic image and / or the elasticity image And classifying means for classifying the elasticity image using the evaluation data generated by the evaluation data generating means and displaying the classification result on the display means. It is characterized by that.
The tissue analysis tool of the present invention is an analysis tool for analyzing lesion information of the tissue of the subject using RF signal frame data acquired from within the subject, and includes two or more RFs obtained in time series. Displacement data calculation means for calculating displacement data using signal frame data, and elasticity data calculation for calculating elasticity data consisting of strain and / or elastic modulus at each point of the cross section of the object to be examined using the displacement data Means, elasticity image generating means for generating elasticity image data of the object cross section from the elasticity data, analyzing the elasticity data and elasticity image, and including a histogram of elasticity data, statistical processing data, and complexity of the diseased tissue Evaluation data generating means for generating a plurality of evaluation data, evaluation data selecting means for selecting at least one of the plurality of evaluation data, and the evaluation And classifying means for classifying the elastic image into any of a plurality of groups using the evaluation data selected by the data selection means.
The ultrasonic diagnostic apparatus of the present invention includes means for automatically classifying a subject tissue using evaluation data generated from elasticity data or elasticity images of the subject, so that the disease progress status objectively, etc. Can be evaluated. In particular, multiple evaluation data, including histograms of subject elasticity data, statistical processing data, and complexity of diseased tissue, are used as evaluation data, and evaluation data highly correlated with the group to be classified are automatically selected. By providing the means to do so, objectivity and accuracy can be improved.
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. FIG. 1 is a block diagram showing an embodiment of an ultrasonic diagnostic apparatus according to the present invention.
As shown in the figure, this ultrasonic diagnostic apparatus includes an ultrasonic transmission / reception control unit 1, a transmission unit 2, an ultrasonic probe 3, a reception unit 4, and a phasing addition unit 5 as main components. A signal processing unit 6, a black and white scan converter 7, an RF signal frame data selection unit 8, a displacement measurement unit 9, an elastic data calculation unit 10, an elastic image evaluation unit 11, an image classification unit 12, and a color A scan converter 13, a switching adder 14, an image display 15, a control unit 16, and an input device 17 such as a keyboard are provided. This ultrasonic diagnostic apparatus is appropriately operated by an external operator via the input device 17 and the control unit 16.
The signal processing unit 6 and the monochrome scan converter 7 constitute a tomographic image forming unit that generates a tomographic image, and create a tomographic image such as a B mode. The RF signal frame data selection unit 8, the displacement measurement unit 9, the elasticity data calculation unit 10, and the color scan converter 13 constitute an elasticity image configuration unit that generates an elasticity image, and the elasticity image evaluation unit 11 and the image classification unit 12 have elasticity. The image analysis unit is configured. This ultrasonic diagnostic apparatus has a function of automatically analyzing an elastic image and displaying the result by an elastic image analysis unit. In the present embodiment, the function of the elastic image analysis unit is realized by a program in a computer incorporated in the ultrasonic diagnostic apparatus. A computer that realizes the function of the elastic image analysis unit can be provided independently of the ultrasonic diagnostic apparatus.
The ultrasonic transmission / reception control unit 1 controls the transmission unit 2 and the reception unit 4, and controls the timing of transmitting the ultrasonic wave to the inspection target via the ultrasonic probe 3 and receiving the ultrasonic wave reflected from the inspection target. To do.
The ultrasonic probe 3 is formed by arranging a large number of transducers in a strip shape, and transmits and receives ultrasonic waves to a subject by performing beam scanning mechanically or electronically. Although not shown, the ultrasonic probe 3 incorporates a transducer that is a source of ultrasonic waves and receives reflected echoes. Each transducer converts the input pulse wave or continuous wave transmission signal into an ultrasonic wave and emits it, and receives the ultrasonic wave emitted from the inside of the subject and converts it into an electric signal. And has a function of outputting.
The ultrasound probe 3 constitutes a compression surface for applying a compression operation to the subject when receiving / transmitting the ultrasound. For this reason, a compression plate is attached to the ultrasonic transmission / reception surface of the ultrasonic probe 3, and this compression plate is brought into contact with the body surface of the subject, and the compression surface is manually moved up and down to be covered. Compress the specimen. This effectively gives a stress distribution in the body cavity of the diagnosis site of the subject. The compression plate may include a pressure sensor. It is also possible to use compression by heartbeat or arterial pulsation instead of manual compression, and in this case, the compression plate can be omitted.
The transmission unit 2 generates a transmission pulse for generating an ultrasonic wave by driving the ultrasonic probe 3, and has a convergence point of the ultrasonic wave transmitted by the built-in transmission phasing / adding unit. The depth is set.
The receiving unit 4 amplifies the reflected echo signal received by the ultrasonic probe 3 with a predetermined gain. A number of received signals corresponding to the number of amplified transducers are input to the phasing adder 5 as independent received signals. The phasing / adding unit 5 adjusts the phase of the received signal amplified by the receiving unit 4, adds them, and outputs RF signal frame data at a predetermined frame rate.
The signal processor 6 receives the received signal (RF signal frame data) from the phasing adder 5 and performs various signal processing such as gain correction, log correction, detection, contour enhancement, and filter processing.
These ultrasonic probe 3, ultrasonic transmission / reception control unit 1, transmission unit 2, reception unit 4, phasing addition unit 5 and signal processing unit 6 constitute ultrasonic transmission / reception means, and an ultrasonic transducer A tomographic image is obtained by scanning the ultrasonic beam in a certain direction in the body of the subject using 3.
The monochrome scan converter 7 has tomographic scanning means for reading out RF signal frame data output from the signal processing unit 6 of the above-described ultrasonic transmission / reception means at a television system cycle and means for controlling the system. ing. Specifically, an A / D converter that converts a reflected echo signal from the signal processing unit 6 into a digital signal, and a plurality of sheets that store tomographic image data digitized by the A / D converter in time series A frame memory and a controller for controlling these operations are included.
In this embodiment, the RF signal frame data selection unit 8, the displacement measurement unit 9, the elasticity data calculation unit 10, and the color scan converter 13 constituting the elasticity image configuration unit are branched from the output side of the phasing addition unit 5. A switching adder 14 is provided on the output side of the monochrome scan converter 7 and the color scan converter 13.
The RF signal frame data selection unit 8 includes a frame memory that sequentially secures RF signal frame data sequentially output over time at the frame rate of the ultrasonic diagnostic apparatus from the phasing addition unit 5, and a plurality of frames secured in the past. One RF signal frame data is selected from the RF signal frame data, and is output to the displacement measuring unit 9 as one set with the RF signal frame data newly secured in the frame memory. A reference for selecting one from the past RF signal frame data is given by a command from the control unit 16. For example, the operator may specify a specific time before the start of the compression operation and select the RF signal frame data acquired at that time, or when the compression operation is automatically performed according to the pulsation, etc. It is also possible to automatically determine a state in which there is no pressure from the pulsation, and to select the RF signal frame data obtained in that state.
In addition, although the signal output from the phasing addition part 5 was described as RF signal frame data, this may be the signal in the form of I and Q signals obtained by complex demodulation of the RF signal, for example.
The displacement measuring unit 9 performs a one-dimensional or two-dimensional correlation process on the set of RF signal frame data output from the RF signal frame data selecting unit 8, and the displacement or movement vector ( The displacement direction and magnitude) are measured, and displacement frame data and correlation frame data are generated. As a method for detecting the movement vector, for example, there are a block matching method and a gradient method as described in Patent Document 1. The block matching method divides the image into blocks consisting of N × N pixels, for example, searches the previous frame for the block closest to the target block in the current frame, and refers to these to predictive coding Is to do.
The elastic data calculation unit 10 calculates the strain amount and elastic modulus at each measurement point on the tomographic image from the displacement frame data output from the displacement measuring unit 9, and numerical data of the strain amount or elastic modulus (elastic frame data) And output to the color scan converter 13. The strain amount is an index indicating the hardness of the tissue, and is expressed by Δd / D, where D is the initial thickness (without compression) of a predetermined region and Δd is the compression amount due to compression. For each point in the vertical direction, the displacement can be obtained by spatial differentiation. The elastic modulus is expressed by Young's modulus, rigidity modulus, bulk elastic modulus, etc. For example, the Young's modulus Ym is obtained by applying the stress (pressure) at each calculation point to the strain amount at each calculation point, as shown in Equation (1). Calculate by dividing by.
Ym (i, j) = pressure (i, j) / strain (i, j) (1)
In the formula, i and j represent the coordinates of the frame data, i, j = 1, 2, 3,.
In addition, the pressure applied to the body surface is measured directly with a pressure sensor on the contact surface between the body surface and the compression mechanism, or a method described in Japanese Patent Laid-Open No. 2005-66041. As described above, the deformation of the pressure measuring deformable body can be measured by a method of detecting by signal processing.
The elasticity data calculation unit 10 performs various image processing such as smoothing processing in the coordinate plane, contrast optimization processing, and smoothing processing in the time axis direction between frames on the calculated elasticity frame data, and after processing Elastic frame data may be output as a strain amount.
The color scan converter 13 reads the elastic frame data sent from the elastic data calculation unit 10 in a television system cycle, and gives a predetermined color and gradation according to the value of the elastic data to obtain image data. Displayed on the display 15 via 14.
An example of the configuration of the color scan converter 13 is shown in FIG. In the illustrated example, the color scan converter 13 includes a gradation circuit 131 and a hue conversion circuit 132.
The gradation circuit 131 gradations the elasticity frame data output from the elasticity data calculation unit 10. For gradation, for example, from the elastic data calculation unit 10 or the control unit 16, the range to be gradation and the upper limit value and the lower limit value of the elastic frame data are input, and the element data of the elastic frame data within the selected range is input. Depending on the magnitude of the value, the element data of the selection range is converted into a plurality of levels (for example, 255 levels) to generate elastic gradation frame data. A region to be gradationized, that is, a selection range is set in a region of interest (ROI) set by the control unit 16, and can be arbitrarily changed by an operator. Further, the upper limit value and the lower limit value of the gradation are output from the elasticity data calculation unit 10 or determined by a command from the control unit 16.
For example, when the elasticity data is a strain amount, the hue conversion circuit 132 converts the corresponding area in the elasticity image frame data into a red code for an area where the distortion is greatly measured in the elasticity gradation frame data, On the other hand, for the area where the distortion is measured to be small, the corresponding area in the elastic image frame data is converted into a blue code.
Note that the color scan converter 13 may be a black and white scan converter, and an area where a large distortion is measured brightens the luminance of the area in the elastic image data, and conversely, an area where a small distortion is measured is the elastic image data. You may make it make the brightness | luminance of this area | region dark.
The switching adder 14 inputs the monochrome tomographic image data from the monochrome scan converter 7 and the elastic image data output from the color scan converter 13, and adds or switches both images. Thereby, only one of black and white tomographic image data and color elastic image data can be output, or both image data can be added and combined and output. Further, for example, as described in Japanese Patent Application Laid-Open No. 2004-135929, a color tomographic image may be displayed in a translucent manner on a monochrome tomographic image.
The image display 15 displays time-series tomographic image data obtained by the black and white scan converter 7, that is, the B-mode tomographic image and the elastic image obtained by the color scan converter 13, via the switching adder 14. A D / A converter that converts the image data output from the monochrome scan converter 7 and / or the color scan converter 13 into an analog signal, and a color television that receives the analog video signal from the D / A converter and displays it as an image It consists of a monitor.
In addition to the above-described tomographic image and elasticity image, the image display 15 displays the analysis result by the elasticity image analysis unit.
When the signal processing unit 6 has a function of extracting a harmonic component of a received signal or a function of extracting a Doppler component, as a black and white tomogram, in addition to a general B-mode image, a harmonic of the received signal In some cases, a tissue harmonic tomogram or a tissue plastic image in which components are imaged is displayed.
The elastic image evaluation unit (evaluation data generation means) 11 and the image classification unit 12 constituting the elastic image analysis unit receive the elastic frame data output from the elastic data calculation unit 10, and objectively evaluate the elastic frame data.・ Classify. Details of the elastic image evaluation unit 11 and the image classification unit 12 will be described in detail with reference to FIGS.
As shown in FIG. 3, the elastic image evaluation unit 11 includes a histogram calculation unit 111, a statistical processing unit 112, and a drawing area evaluation unit 113. The elastic image evaluation unit 11 displays an elastic image drawn by the elastic frame data in the image classification unit 12. Evaluation data serving as an index for classification into any of a plurality of groups is calculated.
The histogram calculation unit 111 counts the number of strains or the number of appearances of the elastic modulus from the elastic frame data output from the elastic data calculation circuit 10, and creates histogram data. The histogram corresponds to the distribution of tissue having different hardness in the elastic image, and itself serves as an index for image classification, but in this embodiment, the digitized data used for automatic classification by the image classification unit 12 Therefore, the degree of distortion (skewness) and kurtosis of the histogram are calculated.
FIG. 4 shows an analysis example by the elastic image evaluation unit 11 using a phantom. FIGS. 4A to 4C are diagrams showing histograms of strain amounts obtained from elastic frame data measured by an ultrasonic diagnostic apparatus using a phantom, and FIG. 4A uses a phantom simulating a homogeneous tissue. 4B is an example using a phantom with scattered inclusions, and FIG. 4C is an example using a phantom with star-shaped inclusions. As shown in the figure, with a homogeneous phantom, the shape of the histogram is symmetric, but when there is an inclusion, the symmetry is poor. Such distortion degree and kurtosis of the histogram can be converted into evaluation data, for example, by the following equations (2) and (3).
In equations (2) and (3), n is the number of samples, x (with an overline) is the average, and σ is the standard deviation.
The statistical processing unit 112 calculates statistical processing data such as the average value and standard deviation of the above-described elastic frame data (strain or elastic modulus), and digitizes the strain amount or elastic modulus distribution. Elastic frame data is data consisting of elastic data (strain amount or elastic modulus) at each point of frame data coordinates (i, j), and the average value is obtained by dividing the total elastic data of each point by the number of points. can get.
The drawing area evaluation unit 113 first binarizes the elastic frame data with the binarization threshold Bth to create detection frame data. FIGS. 4D to 4F are diagrams showing examples of images drawn by the detection frame data generated by the drawing area evaluation unit 113 using the elastic frame data of the phantom described above. In the figure, a region drawn in white is a region to be evaluated (for example, a region having a lesion). Next, in this binarized image, the number of areas drawn in white, the area, the complexity of the shape, and the like are calculated. Regarding the area, the ratio (area ratio) of the area of the region (drawing region) where the distortion amount is equal to or greater than the threshold in the binarized image to the other region area is calculated. The simplest shape is a circle, and the longer the perimeter, the more complicated the region of the same area. Therefore, the complexity can be defined by the following equation (4).
Complexity = (perimeter) 2 / area (4)
In this way, the extent and shape of the target strain amount or elastic modulus region (that is, a region having a strain amount or elastic modulus equal to or greater than the threshold Bth) is digitized, and evaluation data used in the image classification unit 12 described later is obtained. It is done.
The threshold value Bth can be arbitrarily set by the operator via the control unit 16, but a threshold value that maximizes the complexity is obtained so that a binarized image in which the rendering area is emphasized is obtained. It is also possible to use it.
In the elastic image evaluation unit 11, in addition to the average value and standard deviation of the elastic frame data described above, a feature amount using a co-occurrence matrix, which is a general method for statistically calculating texture, for example, homogeneity, heterogeneity, etc. , Contrast, angular second moment, entropy, and inverse difference moment can be used as evaluation data.
As described above, the various evaluation data calculated by the elastic image evaluation unit 11 serve as a classification index in the image classification unit. Further, the operator inputs the result of the test, for example, the result of the blood test, via the control unit 16. It is also possible to add to the evaluation index.
The image classification unit 12 uses at least one evaluation data among the histogram data, statistical processing data, and rendering area evaluation data calculated by the elastic image evaluation unit 11 described above, to generate at least two elastic images. As shown in FIG. 5, an evaluation data selection unit 121, a memory 122, and a multivariate analysis unit 123 are provided.
The memory 122 stores elasticity image data and evaluation data of each group whose diagnosis is confirmed by pathological diagnosis or the like. New object diagnostic information and evaluation data generated by the ultrasonic diagnostic apparatus are stored in the memory 122 as needed. An example of evaluation data stored in the memory is shown in FIG. In FIG. 6, the average value of the distortion amount, the area ratio, the complexity, and the distortion degree of the histogram of the elastic image data classified into each group are shown in a graph for each parameter. In the figure, the value indicated by r indicates the correlation coefficient between the parameter and the classification, and the larger r, the higher the correlation. The calculated correlation coefficient is also stored in the memory 122 for each parameter.
The evaluation data selection unit 121 selects parameters used for evaluation. In addition to the method of selecting by the operator via the control unit 16, the parameter selection method is an evaluation parameter having a high correlation when classifying the elastic images of each group stored in the memory 122 in the image classification unit 12. There is a way to choose. For example, by default, a predetermined number of parameters having a high correlation with the classification are selected in order from the top, and the selected parameters are displayed on the display 14. The operator arbitrarily deletes or adds the selected parameter, and determines the parameter used for classification.
The multivariate analysis unit 123 inputs the evaluation data of at least one parameter selected by the evaluation data selection unit 121, and the classification result, that is, the elastic image having the evaluation data belongs to which group among the plurality of groups. The result indicating whether or not
The multivariate analysis methods adopted by the multivariate analysis unit 123 include, for example, multiple regression analysis, discriminant analysis, principal component analysis, quantification method, factor analysis, cluster analysis, multidimensional scaling, neural network, etc. However, a classification method using a perceptron of a neural network will be described here.
FIG. 7 shows the structure of a simple perceptron as an example of a neural network. As shown, a simple perceptron consists of an input layer and an output layer consisting of a single unit (neuron). Evaluation data of the parameter selected by the evaluation data selection unit 121 is input to the input layer. For example, if four parameters are selected as parameters, the average value of the distortion amount, the standard deviation, the area ratio of the region (drawing region) where the distortion amount is greater than or equal to the threshold in the binarized image, and the complexity, the elasticity image evaluation The value of each parameter calculated by the unit 11, that is, evaluation data x1, x2, x3, x4 is input.
In the output layer, according to the following equation (5), the sum u obtained by weighting the coupling load ωi to the input value xi is converted by a predetermined function f (u), and the value is output.
As the function f (u) used in the output layer, a threshold function or a linear function is used. For example, in the case of a threshold function, f (u) = 1 when u is larger than the threshold value h, f (u) = 0 when u is smaller, and the output value z is z = f (u). When the output is a linear function that linearly increases or decreases with respect to the input, the input value u becomes the output value as it is.
The result of f (u) = 1 means that the elastic image that created the evaluation data belongs to a specific group, and the result of f (u) = 0 indicates that the elastic image does not belong to a specific group. means. When a simple perceptron using a threshold function is used as the output layer conversion function, a plurality of simple perceptrons having different output layer threshold values are arranged in parallel and classified into a plurality of groups. When the input value u is an output value, threshold values are set in multiple stages according to the number of groups, and the output values are classified into a plurality of groups.
The perceptron is characterized in that the output signal is compared with a teacher signal (correct answer), and when it is different, the coupling weight ωi and the threshold value h are changed, that is, learning is performed. Specifically, when the difference between the teacher signal z * and the output value z is δ, as shown in the equation (6), correction is made so that δ 2 becomes the minimum coupling load ω i.
In the equation, ε is a learning coefficient. As the teacher signal, for example, a result (any one of the first to Nth groups) diagnosed by the pathological diagnosis for the evaluation target is used. Such perceptron learning can be performed using a plurality of elastic image data whose diagnosis has been determined in advance and evaluation data thereof. Moreover, it can be performed whenever a correct answer (confirmed diagnosis result) is obtained for a new classification result, thereby improving the accuracy of the result.
When the teacher signal is not input, the classification result is output using the latest connection weight ωi and the threshold value h.
The classification result by the multivariate analysis unit 123 is sent to the switching adder 14 and displayed on the image display 15 together with the tomographic image and the elasticity image. As a display method, any method such as displaying the classified group name, plotting on a graph with the group as the horizontal axis and the vertical axis as the output value (u) can be adopted.
According to the ultrasonic diagnostic apparatus of the present embodiment, using the elasticity data obtained from the RF frame data, evaluation data such as a histogram, statistical processing data, binarized frame data, and the like are created, and at least the evaluation data By selecting one and analyzing by multivariate analysis, it is possible to provide information that objectively indicates the progress of the disease.
In the above embodiment, the function of the image analysis unit including the image evaluation unit 11 and the image classification unit 12 has been described as an internal function of the ultrasonic diagnostic apparatus. However, these functions are independent of the ultrasonic diagnostic apparatus. It can also be constructed as an image analysis tool on a computer. In this case, it is possible to transfer the elastic frame data from the ultrasonic diagnostic apparatus to the image analysis tool and perform image diagnosis remotely.
For 61 cases of liver disease whose diagnosis was confirmed by pathological diagnosis, a liver region was selected from an elastic image obtained by an ultrasonic diagnostic apparatus, and image analysis according to the present invention was performed. In other words, the average value, standard deviation, and area ratio and complexity of the region rendered in the binarized image are calculated for the selected region, and these are used as evaluation data for no fibrosis (stage 0), portal vein Fibrous enlargement of the region (stage 1), formation of fibrous crosslinks (stage 2), formation of fibrous crosslinks with lobular strain (stage 3), and cirrhosis (stage 4) were classified.
The results are shown in FIG. In the illustrated graph, the horizontal axis indicates the stage, the vertical axis indicates the output (arbitrary unit) of the image classification unit, and the output value of the patient whose stage is determined by pathological diagnosis is plotted for each stage. A range indicated by an arrow indicates a range classified by the image classification unit. The correlation between the classification result and the stage was 0.71, and it was confirmed that the correlation was very good compared to the correlation between the individual parameters (FIG. 6).
The figure which shows the whole structure of one Embodiment of the ultrasonic diagnosing device of this invention. The figure which shows the structure of the color scan converter of the ultrasonic diagnostic apparatus of FIG. The figure which shows the structure of the elasticity image evaluation part of the ultrasonic diagnosing device of FIG. The figure which shows the example of analysis by the elasticity image evaluation part The figure which shows the structure of the image classification part of the ultrasonic diagnosing device of FIG. The figure which shows the example of the evaluation data stored in the memory of an image classification part The figure which shows an example of an image classification part (multivariate analysis part) The figure which shows the classification result obtained in the Example
DESCRIPTION OF SYMBOLS 1 ... Ultrasonic transmission / reception control part, 2 ... Transmission part, 3 ... Probe, 4 ... Reception part, 5 ... Phased addition part, 6 ... Signal processing part, 7 ... Black and white scan converter, 8 ... RF frame data selection unit, 9 ... Displacement measurement unit, 10 ... Elastic data calculation unit, 11 ... Elastic image evaluation unit, 12 ... Image classification unit , 13 ... Color scan converter, 14 ... Switching adder, 15 ... Image display, 16 ... Control unit, 17 ... Input device, 111 ... Histogram calculation unit, 112 ... Statistical processing unit, 113... Detection region evaluation unit, 121... Evaluation data selection unit, 122.
Ultrasonic transmitting / receiving means for transmitting / receiving ultrasonic waves in the subject, tomographic image constructing means for generating a tomographic image based on RF signal frame data from within the subject received by the ultrasonic transmitting / receiving means, and the RF signal frame Elasticity information calculation means for calculating elasticity data of the biological tissue of the subject using data, elasticity image construction means for generating an elasticity image based on the elasticity data calculated by the elasticity information calculation means, and the tomographic image And / or an ultrasonic diagnostic apparatus comprising display means for displaying the elastic image,
Evaluation data generating means for generating evaluation data for evaluating the characteristics of the biological tissue based on the elastic image, and classifying the elastic image using the evaluation data generated by the evaluation data generating means, and displaying the classification result as the display means An ultrasonic diagnostic apparatus, further comprising classification means for displaying on the screen.
The ultrasonic diagnostic apparatus according to claim 1,
The evaluation data generated by the evaluation data generating means includes a histogram representing the frequency of elasticity data in the elasticity image, statistical data obtained by statistical processing of elasticity data, area evaluation data obtained by quantifying the form of areas having specific elasticity data, And at least one of the data derived therefrom,
The classification means comprises evaluation data selection means for selecting at least one of the evaluation data, and classifies the elastic images into two or more groups using the evaluation data selected by the evaluation data selection means. Ultrasonic diagnostic equipment.
The ultrasonic diagnostic apparatus according to claim 2,
The classification means includes storage means for storing, as a database, evaluation data of a plurality of elastic images classified in advance into two or more groups, and the evaluation data selection means includes evaluation data of elastic images accumulated in the database An ultrasonic diagnostic apparatus characterized by calculating correlation with a group and selecting evaluation data having high correlation.
The classification means includes at least an input layer and an output layer, receives a plurality of evaluation data as inputs, outputs a classification result as an output, and a coupling load and a threshold value from the input to the output are obtained from the output and known data. An ultrasonic diagnostic apparatus characterized by being changed so that a difference from a signal is minimized.
Using an RF signal frame data acquired from within a subject, an ultrasonic diagnostic apparatus including an analysis tool for analyzing lesion information of the tissue of the subject, wherein the analysis tool includes:
Displacement data calculating means for calculating displacement data using two or more RF signal frame data obtained in time series;
Elasticity data calculating means for calculating elasticity data comprising strain and / or elastic modulus at each point of the cross section of the subject to be examined using the displacement data;
An elasticity image generating means for generating an elasticity image of the cross section of the subject from the elasticity data;
Analysis data generating means for analyzing the elasticity data and the elasticity image, and generating a plurality of evaluation data including a histogram of the elasticity data, statistical processing data, and complexity of the diseased tissue;
Evaluation data selection means for selecting at least one of the plurality of evaluation data;
An ultrasonic diagnostic apparatus comprising: a classifying unit that classifies the elastic image into any of a plurality of groups using the evaluation data selected by the evaluation data selecting unit.
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