JP2007222393A - Ultrasonic diagnostic apparatus and image processing method therefor, and image processing program therefor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To increase the accuracy of tracking (tracking arithmetic operation) using a feature point extracted from an image of ultrasonic waves. <P>SOLUTION: In the ultrasonic diagnostic apparatus 1: the feature point extraction part 33 of an arithmetic operation part 26 extracts the feature points to be candidates on the basis of two or more pieces of image data generated under different generation conditions stored in a data storage part 25; a trackable feature point setting part 34 sets a trackable feature point on the basis of an extracted result supplied from the feature point extraction part 33; and a tracking arithmetic operation part 35 performs the tracking arithmetic operation on the basis of the image data stored in the data storage part 25 by using the trackable feature point set by the trackable feature point setting part 34. A physical parameter calculation part 36 calculates various kinds of parameters in the diagnostic region of a subject on the basis of a tracking arithmetic operation result supplied from the tracking arithmetic operation part 35 and supplies the calculation result to the data storage part 25. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an ultrasonic diagnostic apparatus, an image processing method thereof, and an image processing program thereof, and in particular, can improve the accuracy of tracking (tracking calculation) using feature points extracted from an ultrasonic image. The present invention relates to an ultrasonic diagnostic apparatus, an image processing method thereof, and an image processing program thereof.

  Quantitative evaluation of local movements (contraction and dilatability) such as the heart is very important for knowing its function. Therefore, in recent years, a method of quantitatively analyzing the movement of the myocardium from an ultrasonic B-mode image or the like using a pattern matching method has been proposed.

  As a conventional technique, for example, a method for estimating a position vector from a peak position of a two-dimensional cross-correlation coefficient and an optical flow method using a density gradient of an image have been proposed. As the amount of information to be estimated and displayed in these two methods, a movement vector, a locus, a cross-correlation value, and the like are known. Recently, a method has been proposed in which a plurality of feature points (Tag) are automatically extracted on an ultrasonic image, and the extracted feature points are tracked (tracking calculation) (for example, Patent Document 1). reference).

According to the prior art and the method proposed in Patent Document 1, feature points are extracted based on an ultrasonic image as shown in FIG. 1, and the extracted feature points are shown as shown in FIG. Can be used to perform cross-correlation processing between frames (in the case of the example in FIG. 2, between frames of time phase 1 to time phase 5) to perform tracking (tracking calculation).
Japanese Patent Laid-Open No. 2003-250804

  However, in the conventional technique and the method proposed in Patent Document 1, when extracting the feature points, the feature points are distinguished from those based on a traceable structure and those based on a random speckle pattern caused by interference of a plurality of scatterers. Could not be extracted. Therefore, the extracted feature points include not only those due to structures but also those due to speckle patterns, and as a result, tracking (tracking calculation) is performed accurately using the extracted feature points. There was a problem that it was not possible.

  In addition, the extracted feature points differ depending on the ultrasonic image generation conditions (frequency, ultrasonic wave transmission / reception direction, etc.), so the tracking (tracking calculation) results differ depending on the ultrasonic image generation conditions. There was a problem that local movements such as could not be accurately and quantitatively evaluated.

  The present invention has been made in view of such a situation, and an ultrasonic diagnostic apparatus capable of improving the accuracy of tracking (tracking calculation) using feature points extracted from an ultrasonic image and the image thereof It is an object to provide a processing method and an image processing program thereof.

  In order to solve the above-described problem, the ultrasonic diagnostic apparatus of the present invention transmits ultrasonic waves by vibrating a plurality of ultrasonic transducers, and among the transmitted ultrasonic waves, a reflected wave reflected from a subject. Receiving image data generation means for generating a plurality of ultrasonic image data under different generation conditions, and a feature for extracting feature points based on the plurality of ultrasonic image data generated under different generation conditions by the image data generation means Set by a point extraction means, a traceable feature point setting means for setting a traceable feature point that is a traceable feature point based on the feature point extracted by the feature point extraction means, and a traceable feature point setting means A tracking calculation means for tracking the movement of the traceable feature point, and a physical parameter calculation means for calculating a physical parameter based on a tracking calculation result by the tracking calculation means. .

  The image data generation means generates first ultrasonic image data and second ultrasonic image data at least under the first generation condition and the second generation condition, respectively, and the feature point extraction means includes image data generation means. The first generation condition image data feature point is extracted from the first ultrasound image data generated by the step, and the traceable feature point setting means is a first ultrasound image of substantially simultaneous phase generated by the image data generation means. Using the first generation condition image data feature points extracted by the feature point extraction means based on the data and the second ultrasound image data, images of the first ultrasound image data and the second ultrasound image data A similarity determination unit for determining a similarity between the images, and the traceable feature point setting unit outputs an image by the inter-image similarity determination unit among the first generation condition image data feature points extracted by the feature point extraction unit. while Can be made to and sets the feature points is determined that the degree of similarity and traceable feature points.

  The traceable feature point setting means calculates a cross-correlation coefficient between images based on the first ultrasonic image data and the second ultrasonic image data generated by the image data generation means. Means for determining whether or not the cross-correlation coefficient calculated by the cross-correlation coefficient calculation means is greater than a predetermined reference value, and the mutual correlation coefficient calculated by the cross-correlation coefficient calculation means If it is determined that the correlation coefficient is greater than the predetermined reference value, it is determined that the degree of similarity is high, and if it is determined that the cross-correlation coefficient calculated by the cross-correlation coefficient calculating means is smaller than the predetermined reference value, It can be determined that the degree is low.

  The image data generation means generates first ultrasonic image data and second ultrasonic image data at least under the first generation condition and the second generation condition, respectively, and the feature point extraction means includes image data generation means. The first generation condition image data feature points are extracted from the first ultrasonic image data generated by the above, and the second generation condition image data feature points are extracted from the second ultrasonic image data generated by the image data generation means. The traceable feature point setting unit determines the similarity between the images based on a plurality of ultrasonic image data having different time phases among the first ultrasonic image data generated by the image data generation unit. Based on a plurality of ultrasonic image data having different time phases among the second ultrasonic image data generated by the first ultrasonic image data inter-image similarity determination unit and the image data generation unit. Second ultrasound image data inter-image similarity determination means for determining similarity, and the traceable feature point setting means includes a first generation condition image data feature point extracted by the feature point extraction means. The feature point determined by the similarity determination unit between the ultrasonic images of one image as having high similarity between images in the predetermined region is set as a traceable feature point, and the second generation extracted by the feature point extraction unit Among the feature image data feature points, a feature point determined by the second ultrasound image data image similarity determination unit to have a high similarity between images in a predetermined region is set as a traceable feature point. be able to.

  The traceable feature point setting unit calculates a cross-correlation coefficient between images based on a plurality of ultrasonic image data having different time phases among the first ultrasonic image data generated by the image data generation unit. Based on a plurality of ultrasonic image data having different time phases among the second ultrasonic image data generated by the first ultrasonic image data cross-correlation coefficient calculating means and the image data generating means. And a second ultrasonic image data cross-correlation coefficient calculating means for calculating the cross-correlation coefficient of the first ultrasonic image data. It is determined whether or not the cross-correlation coefficient calculated by the number calculation means is larger than a predetermined reference value, and the cross-correlation coefficient calculated by the first ultrasonic image data cross-correlation coefficient calculation means is a predetermined reference Larger than the value If it is determined that the degree of similarity is high, and it is determined that the cross-correlation coefficient calculated by the first ultrasonic image data cross-correlation coefficient calculating unit is smaller than a predetermined reference value, the degree of similarity is determined to be low. The second ultrasonic image data inter-image similarity determination means determines whether or not the cross-correlation coefficient calculated by the second ultrasonic image data cross-correlation coefficient calculation means is greater than a predetermined reference value. If it is determined that the cross-correlation coefficient calculated by the second ultrasonic image data cross-correlation coefficient calculating means is greater than a predetermined reference value, it is determined that the similarity is high, and the second ultrasonic image When it is determined that the cross-correlation coefficient calculated by the data cross-correlation coefficient calculation means is smaller than a predetermined reference value, it can be determined that the similarity is low.

  The image data generation means generates first ultrasonic image data and second ultrasonic image data under at least two different first generation conditions and second generation conditions, respectively, and the feature point extraction means A first generation condition image data feature point is extracted from the first ultrasonic image data generated by the data generation means, and a second generation condition image data feature is extracted from the second ultrasonic image data generated by the image data generation means. The point is extracted, and the traceable feature point setting unit determines whether or not the coordinates of the first generation condition image data feature point and the second generation condition image data feature point extracted by the feature point extraction unit substantially match. The feature point coordinate determination unit further includes a traceable feature point setting unit, wherein the coordinates of the first generation condition image data feature point and the second generation condition image data feature point substantially coincide with each other by the feature point coordinate determination unit. That the determined feature point may be to set the traceable feature points.

  The plurality of ultrasonic image data generated by the image data generation means differ in the frequency band of ultrasonic waves transmitted / received from the ultrasonic transducer or the direction of ultrasonic transmission / reception when generating ultrasonic image data. Can be.

  In order to solve the above-described problems, the image processing method of the ultrasonic diagnostic apparatus of the present invention transmits an ultrasonic wave by vibrating a plurality of ultrasonic transducers, and reflects the reflected ultrasonic wave from the subject. Image data generation step for generating a plurality of ultrasonic image data under different generation conditions in response to the reflected wave, and feature point extraction for extracting feature points based on the plurality of ultrasonic image data generated under different generation conditions A traceable feature point setting step that sets a traceable feature point that is a traceable feature point based on the extracted feature point, and a tracking calculation step that tracks and calculates the movement of the set traceable feature point And a physical parameter calculating step of calculating a physical parameter based on the tracking calculation result.

  In order to solve the above-described problem, the image processing program of the ultrasonic diagnostic apparatus of the present invention transmits an ultrasonic wave by vibrating a plurality of ultrasonic transducers, and reflects the reflected ultrasonic wave from the subject. Image data generation step for generating a plurality of ultrasonic image data under different generation conditions in response to the reflected wave, and feature point extraction for extracting feature points based on the plurality of ultrasonic image data generated under different generation conditions A traceable feature point setting step that sets a traceable feature point that is a traceable feature point based on the extracted feature point, and a tracking calculation step that tracks and calculates the movement of the set traceable feature point The computer is caused to execute a physical parameter calculation step of calculating a physical parameter based on the tracking calculation result.

  In order to solve the above-described problem, the ultrasonic diagnostic apparatus of the present invention transmits ultrasonic waves by vibrating a plurality of ultrasonic transducers, and among the transmitted ultrasonic waves, a reflected wave reflected from a subject. Receiving image data generation means for generating a plurality of ultrasonic image data under different generation conditions, and a feature for extracting feature points based on the plurality of ultrasonic image data generated under different generation conditions by the image data generation means Based on the point calculation means, the tracking calculation means for tracking the movement of the feature point set by the feature point setting means, and the tracking calculation result by the tracking calculation means, the traceable feature point is set as the traceable feature point A traceable feature point setting unit that calculates the physical parameter based on a tracking calculation result of the traceable feature point set by the traceable feature point setting unit. And wherein the door.

  In order to solve the above-described problems, the image processing method of the ultrasonic diagnostic apparatus of the present invention transmits an ultrasonic wave by vibrating a plurality of ultrasonic transducers, and reflects the reflected ultrasonic wave from the subject. Image data generation step for generating a plurality of ultrasonic image data under different generation conditions in response to the reflected wave, and feature point extraction for extracting feature points based on the plurality of ultrasonic image data generated under different generation conditions A tracking calculation step for tracking and calculating a movement of the set feature point, a trackable feature point setting step for setting a traceable feature point that can be tracked based on the tracking calculation result, and a traceable feature And a physical parameter calculation step for calculating a physical parameter based on the tracking calculation result of the traceable feature point set by the processing of the point setting step.

  In order to solve the above-described problem, the image processing program of the ultrasonic diagnostic apparatus of the present invention transmits an ultrasonic wave by vibrating a plurality of ultrasonic transducers, and reflects the reflected ultrasonic wave from the subject. Image data generation step for generating a plurality of ultrasonic image data under different generation conditions in response to the reflected wave, and feature points for extracting feature points based on the plurality of ultrasonic image data generated under different generation conditions An extraction step, a tracking calculation step for tracking the movement of the set feature point, a trackable feature point setting step for setting a traceable feature point that is a traceable feature point based on the tracking calculation result, and a setting step And a physical parameter calculation step of calculating a physical parameter based on the tracking calculation result of the traceable feature point.

  In the ultrasonic diagnostic apparatus, the image processing method thereof, and the image processing program of the present invention, an ultrasonic wave is transmitted by vibrating a plurality of ultrasonic transducers, and the reflected ultrasonic wave is reflected from the subject. Multiple ultrasonic image data are generated under different generation conditions in response to reflected waves, feature points are extracted based on multiple ultrasonic image data generated under different generation conditions, and tracking is performed based on the extracted feature points A traceable feature point, which is a possible feature point, is set, the movement of the set traceable feature point is tracked, and a physical parameter is calculated based on the tracking calculation result.

  In the ultrasonic diagnostic apparatus, the image processing method thereof, and the image processing program of the present invention, an ultrasonic wave is transmitted by vibrating a plurality of ultrasonic transducers, and the reflected ultrasonic wave is reflected from the subject. Multiple ultrasonic image data is generated under different generation conditions in response to reflected waves, feature points are extracted based on multiple ultrasonic image data generated under different generation conditions, and the movement of the set feature points is tracked A traceable feature point that is calculated and is a traceable feature point based on the tracking calculation result is set, and a physical parameter is calculated based on the tracking calculation result of the set traceable feature point.

  According to the present invention, it is possible to improve the accuracy of tracking (tracking calculation) using feature points extracted from an ultrasonic image.

  Embodiments of the present invention will be described with reference to the drawings.

  FIG. 3 shows an internal configuration of the ultrasonic diagnostic apparatus 1 to which the present invention is applied.

  As shown in FIG. 3, the ultrasonic diagnostic apparatus 1 includes a main body 11, an ultrasonic probe 12 connected to the main body 11 via an electric cable, an input unit 13, and a display unit 14.

  As shown in FIG. 3, the main body 11 of the ultrasonic diagnostic apparatus 1 includes a control unit 21, a transmission unit 22, a reception unit 23, an image data generation unit 24, a data storage unit 25, a calculation unit 26, and a DSC (Digital Scan Converter) 27.

  The control unit 21 includes a CPU (Central Processing Unit), an MPU (Micro Processing Unit), and the like, and generates various control signals and supplies them to the respective units to control the driving of the ultrasonic diagnostic apparatus 1 as a whole.

  The transmission unit 22 includes a rate pulse generator, a transmission delay circuit, and a pulsar (none of which are shown). The rate pulse generator generates a rate pulse that determines the repetition period of the ultrasonic pulse incident on the inside of the subject. Generated and supplied to the transmission delay circuit. The transmission delay circuit is a delay circuit for setting the convergence distance and the deflection angle of the ultrasonic beam at the time of transmission, and based on the control signal supplied from the control unit 21, the focus of the ultrasonic beam at the time of transmission. A delay time is added to the rate pulse supplied from the rate pulse generator so that the position and deflection angle become a predetermined focal position and deflection angle, and the pulse is supplied to the pulser. Furthermore, the pulser is a drive circuit that generates a high-pressure pulse for driving the ultrasonic transducer, and generates a high-voltage pulse for driving the ultrasonic transducer based on the rate pulse supplied from the transmission delay circuit. Then, the generated high-pressure pulse is output to the ultrasonic probe 12.

  The receiving unit 23 includes a preamplifier, a reception delay circuit, and an adder (all not shown), and the preamplifier acquires a reception signal based on the reflected wave of the ultrasonic pulse transmitted from the ultrasonic probe 12 to the subject. Then, the acquired reception signal is amplified to a predetermined level, and the amplified reception signal is supplied to the reception delay circuit.

  Based on the control signal supplied from the control unit 21, the reception delay circuit delays the amplified reception signal supplied from the preamplifier corresponding to the difference in propagation time of ultrasonic waves from the focus position of each ultrasonic transducer. Give time and feed to adder. The adder adds the reception signals from the ultrasonic transducers supplied from the reception delay circuit, and supplies the added reception signal to the image data generation unit 24.

  The receiving unit 23 is provided with a plurality of systems (for example, four systems) of reception delay circuits for each ultrasonic transducer so that parallel reception can be performed at the time of reception. The unit 23 can simultaneously perform reception from a plurality of directions. For example, when two reception delay circuits are provided in one ultrasonic transducer, an ultrasonic beam is diffused and transmitted at the time of transmission, and from two directions shifted by, for example, ± Δα degrees with respect to the direction of the transmission beam at the time of reception Can be received simultaneously.

  The image data generation unit 24 includes a B mode processing unit 31 and a Doppler mode processing unit 32. The B mode processing unit 31 includes a logarithmic amplifier, an envelope detection circuit, and an A / D converter (all not shown), and performs the following processing based on a control signal supplied from the control unit 21.

  That is, the logarithmic amplifier of the B-mode processing unit 31 logarithmically amplifies the reception signal supplied from the reception unit 23 and supplies the logarithmically amplified reception signal to the envelope detection circuit. The envelope detection circuit is a circuit that removes ultrasonic frequency components and detects only the amplitude, detects the envelope of the received signal supplied from the logarithmic amplifier, and A / D converts the detected received signal Supply to the vessel. The A / D converter converts the received signal supplied to the envelope detection circuit shell into a digital signal, generates B-mode image data, and supplies the generated B-mode image data to the data storage unit 25.

  The Doppler mode processing unit 32 includes a reference signal generator, a π / 2 phase shifter, a mixer, an LPF (Low Pass Filter), an A / D converter, a Doppler signal storage circuit, an FFT (Fast Fourier Transform) analyzer, and an arithmetic unit. (None of them are shown), and mainly performs quadrature detection and FFT analysis. That is, the Doppler mode processing unit 32 acquires the received signal supplied from the receiving unit 23 and inputs the acquired received signal to the first input terminal of the mixer. On the other hand, the reference signal generator generates a reference signal having a frequency substantially equal to the center frequency of the input signal, and supplies the reference signal to the first input terminal of the mixer and the π / 2 phase shifter. The π / 2 phase shifter shifts the phase of the reference signal supplied from the reference signal generator by 90 degrees and supplies it to the second input terminal of the mixer.

  The mixer supplies the reception signal to the LPF, and the LPF removes the high-frequency component of the reception signal supplied from the mixer and supplies it to the A / D converter. The A / D converter converts the received signal supplied from the LPF into a digital signal and supplies it to the FFT analyzer. The FFT analyzer temporarily stores the digitized orthogonal component of the received signal in the Doppler signal storage circuit, performs FFT analysis, and supplies the result to the arithmetic unit. The computing unit calculates the center frequency, variance, and the like of the frequency spectrum of the Doppler signal supplied from the FFT analyzer, and supplies the calculated data to the data storage unit 25.

  The data storage unit 25 acquires B-mode image data and Doppler mode image data supplied from the B-mode processing unit 31 and the Doppler mode processing unit 32, and stores the acquired B-mode image data and Doppler mode image data. Further, the data storage unit 25 supplies the stored B-mode image data and Doppler mode image data to the DSC 27 according to an instruction from the control unit 21 as necessary. Furthermore, the data storage unit 25 stores the calculation result calculated by the calculation unit 26 and supplies the stored calculation result to each unit of the main body 11 as necessary.

  The calculation unit 26 includes a feature point extraction unit 33, a traceable feature point setting unit 34, a tracking calculation unit 35, and a physical parameter calculation unit 36. The feature point extraction unit 33 includes a plurality of feature points stored in the data storage unit 25. Desired image data is read out from the image data, candidate feature points are extracted based on the read-out image data, and the extracted results are stored in the data storage unit 25, the traceable feature point setting unit 34, and the tracking. It supplies to the calculating part 35. The traceable feature point setting unit 34 sets a traceable feature point based on the extraction result supplied from the feature point extraction unit 33 and supplies the setting result to the data storage unit 25 and the tracking calculation unit 35.

  The tracking calculation unit 35 reads desired image data from the plurality of image data stored in the data storage unit 25, performs tracking calculation based on the read image data, and stores the tracking calculation result in the data storage To the unit 25 and the physical parameter calculator 36. The physical parameter 36 calculates various parameters in the diagnostic region of the subject based on the tracking calculation result supplied from the tracking calculation unit 35 and supplies the calculation result to the data storage unit 25.

  The DSC 27 acquires the B-mode image data and Doppler mode image data supplied from the data storage unit 25, and acquires the acquired B-mode image data and Doppler mode image data from the scanning line signal sequence of the ultrasonic scan in a video format. The signal is converted into a scanning line signal string and supplied to the display unit 14.

  The ultrasonic probe 12 is connected to the main body 11 via an electric cable, and is an ultrasonic transducer that transmits and receives ultrasonic waves by bringing the front surface into contact with the surface of the subject, and is arranged in a one-dimensional array. Alternatively, the ultrasonic transducers (not shown) arranged in a two-dimensional matrix array are provided at the tip portion. This ultrasonic transducer is an electroacoustic transducer as a piezoelectric transducer. The ultrasonic probe 12 converts an electric pulse input from the transmission unit 22 of the main body 11 into an ultrasonic pulse (transmission ultrasonic wave) during transmission, and converts a reflected wave reflected by the subject into an electric signal during reception. , Output to the main body 11.

  The input unit 13 is connected to the main body 11 via an electric cable, and has an input device such as a display panel, a keyboard, a trackball, and a mouse for inputting various user instructions on the operation panel. It is used for the user to input information, measurement parameters, physical parameters, template size, time phase of images used for image calculation, lattice spacing, and the like.

  The display unit 14 is connected to the DSC 27 of the main body 11 via a cable, and is provided with an unillustrated LCD (Liquid Crystal Display) and an unillustrated CRT (Cathode Ray Tube). The B-mode image data and Doppler mode image data from the DSC 27 converted from the video signal to the scanning line signal sequence of the video format are acquired, and the acquired B-mode image data and Doppler mode image data are not shown on the LCD or the like. Display on the CRT.

  Next, the different generation condition multiple image data generation processing in the ultrasonic diagnostic apparatus 1 of FIG. 3 will be described with reference to the flowchart of FIG.

  In step S <b> 1, the control unit 21 determines whether or not an instruction to start the different generation condition multiple image data generation process is made by the user operating the input unit 13, and the different generation condition multiple image data generation process is performed. Wait until it is determined that an instruction to start has been issued.

  When it is determined in step S1 that an instruction to start the different generation condition multiple image data generation process has been issued, the control unit 21 generates an ultrasonic transmission control signal in step S2, and generates the generated ultrasonic transmission control signal. The data is supplied to the transmission unit 22.

  In step S <b> 3, the control unit 21 generates a different generation condition ultrasonic reception control signal for generating ultrasonic image data under different generation conditions, and sends the generated different generation condition ultrasonic reception control signal to the reception unit 23. Supply. Here, the different generation condition ultrasonic wave reception control signal is obtained from two directions (left direction and right direction) shifted by, for example, ± Δα degrees with respect to the transmission beam direction θ at the time of reception using, for example, a parallel simultaneous reception method. A control signal for simultaneous reception is included.

  In step S4, the transmission unit 22 transmits an ultrasonic beam to the subject based on the ultrasonic transmission control signal supplied from the control unit 21. That is, the rate pulse unit of the transmission unit 22 generates a rate pulse that determines the pulse repetition period of the ultrasonic pulse incident on the inside of the subject based on the ultrasonic transmission control signal supplied from the control unit 21. Supply to the transmission delay circuit. Further, the transmission delay circuit is configured so that the focal position and the deflection angle of the ultrasonic beam at the time of transmission become the predetermined focal position and the deflection angle (θ1) based on the ultrasonic transmission control signal supplied from the control unit 21. The delay time is added to the rate pulse supplied from the rate pulse generator, and the pulse is supplied to the pulser. Further, the pulser generates a high-pressure pulse for driving the ultrasonic transducer based on the rate pulse supplied from the transmission delay circuit, and outputs the generated high-pressure pulse to the ultrasonic probe 12. The ultrasonic probe 12 converts the high-pressure pulse (electric pulse) input from the transmission unit 22 into an ultrasonic pulse, and transmits the converted ultrasonic pulse to the subject. A part of the ultrasonic wave transmitted into the subject is reflected at the boundary surface or tissue between organs in the subject having different acoustic impedances.

  In step S <b> 5, the ultrasonic probe 12 converts the reflected wave reflected by the subject into an electrical signal and outputs it to the main body 11. In step S6, the reception unit 23 amplifies the reception signal input from the ultrasonic probe 12 based on the different generation condition ultrasonic reception control signal supplied from the control unit 21, and adds a predetermined delay time. To the image data generation unit 24. That is, the preamplifier of the reception unit 23 acquires the reception signal input from the ultrasonic probe 12, amplifies the acquired reception signal to a predetermined level, and supplies the amplified reception signal to the reception delay circuit.

  The reception delay circuit of the reception unit 23 includes a plurality of systems, for example, two systems of reception delay circuits for one ultrasonic transducer. Based on the different generation condition ultrasonic reception control signal supplied from the control unit 21, Each amplified received signal supplied from the preamplifier has a strong reception directivity in a predetermined direction (θ1 + Δα) corresponding to a difference in ultrasonic propagation time from the focus position of each ultrasonic transducer. (Delay time for receiving) and a delay time corresponding to the difference between the propagation times of ultrasonic waves from the focus position of each ultrasonic transducer (reception with a strong reception directivity in a predetermined direction (θ1−Δα)). Delay time) to be supplied to the adder. The adder adds a plurality of reception signals of different generation conditions (left reception direction and right reception direction) from each ultrasonic transducer supplied from the reception delay circuit, and adds the plurality of reception signals of the different generation conditions Is supplied to the image data generation unit 24.

  In step S7, the control unit 21 generates different generation condition multiple image data generation control signals for generating a plurality of image data under different generation conditions, and generates the generated different generation condition multiple image data generation control signals as image data. It supplies to the production | generation part 24.

  In step S <b> 8, the B mode processing unit 31 and the Doppler mode processing unit 32 of the image data generation unit 24 are supplied from the reception unit 23 based on the different generation condition multiple image data generation control signal supplied from the control unit 21. Various processes are performed on a plurality of reception signals under different generation conditions to generate first generation condition B-mode image data and first generation condition Doppler mode image data in the θ1 + Δα direction, and second generation condition B in the θ1-Δα direction. Mode image data and second generation condition Doppler mode image data are generated, and the generated first generation condition B mode image data in the θ1 + Δα direction, first generation condition Doppler mode image data, and second generation condition B in the θ1−Δα direction are generated. The mode image data and the second generation condition Doppler mode image data are supplied to the data storage unit 25.

  In step S9, the data storage unit 25 generates the first generation condition B-mode image data and the first generation condition in the θ1 + Δα direction supplied from the B mode processing unit 31 and the Doppler mode processing unit 32 of the image data generation unit 24. The Doppler mode image data, the second generation condition B-mode image data in the θ1−Δα direction, and the second generation condition Doppler mode image data are acquired, and the generated first generation condition B mode image data in the θ1 + Δα direction and the first 1 generation condition Doppler mode image data, second generation condition B mode image data in the θ1-Δα direction, and second generation condition Doppler mode image data are stored.

  Next, the ultrasonic wave transmission / reception direction is sequentially updated by Δθ while changing to θ1 + (N−1) Δθ, and ultrasonic wave transmission / reception is performed in the same procedure as above by scanning in the N direction, and the inside of the subject is scanned in real time. To do. At this time, the control unit 21 switches θ1 + Δα + Δθ to θ1 + Δα + while sequentially switching the delay times of the transmission delay circuit of the transmission unit 22 and the reception delay circuit of the reception unit 23 corresponding to a predetermined ultrasonic transmission / reception direction by the control signal. (N-1) First generation condition B-mode image data and first generation condition Doppler mode image data in the Δθ direction are generated, and first generation in the θ1-Δα + Δθ to θ1-Δα + (N-1) Δθ direction is performed. Each of the condition B mode image data and the first generation condition Doppler mode image data is generated.

  The data storage unit 25 generates the first generation condition B-mode image data and the first generation condition Doppler mode image data in the θ1 + Δα direction from the θ1 + Δα + Δθ to θ1 + Δα + (N−1) Δθ directions. The condition B-mode image data and the first generation condition Doppler mode image data are stored as two-dimensional first generation condition B mode image data and first generation condition Doppler mode image data of time phase T1. On the other hand, the data storage unit 25 has already stored the generated second generation condition B-mode image data and second generation condition Doppler mode image data in the directions of θ1−Δα + Δθ to θ1−Δα + (N−1) Δθ. Along with the second generation condition B-mode image data and second generation condition Doppler mode image data in the θ1-Δα direction, it is stored as two-dimensional second generation condition B mode image data and second generation condition Doppler mode image data of time phase T1. To do.

  Next, in the time phase T2 to the time phase TM, the image data generation unit 24 generates the first generation condition B-mode image data and the first generation condition Doppler mode image data by the same procedure, and the second generation condition B mode. Image data and second generation condition Doppler mode image data are generated. The data storage unit 25 stores the first generation condition B-mode image data and the first generation condition Doppler mode image data of the generated T2 to time phase TM, and the generated T2 to the second phase TM of the time phase TM. Generation condition B-mode image data and second generation condition Doppler mode image data are stored. Note that the M first generation condition B-mode image data and the first generation condition Doppler mode image data, or the second generation condition B-mode image data and the second generation condition Doppler mode image data are defined as 1 It shall be obtained during the heartbeat period. Of course, image data for a period of one heartbeat or more may be generated and stored.

  As described above, in the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, it is possible to generate a plurality of image data under different generation conditions (generation conditions with different ultrasonic transmission / reception directions). In the different generation condition multiple image data generation processing described with reference to the flowchart of FIG. 4, the first generation condition B mode image data and the first generation condition B mode image data are generated based on the reception signal received from the left direction using the parallel simultaneous reception method. The first generation condition Doppler mode image data is generated, and the second generation condition B mode image data and the second generation condition Doppler mode image data are generated based on the received signal received from the right direction. It is not limited to the method, and it is only necessary to be able to generate a plurality of images under different generation conditions (for example, the frequency band of transmission / reception ultrasonic waves and the transmission / reception direction).

  For example, in the case where the received data is processed by the image data generation unit, the image data generation unit 24 extracts the high frequency band image data and the low frequency band image data by cutting them into high frequency and low frequency bands, respectively. May be. In addition, the reception signal received from the left direction using the parallel simultaneous reception method is cut into a high frequency band to generate image data in the left reception direction and the high frequency band, and the reception signal received from the right direction is converted to a low frequency band. The image data in the right receiving direction and in the low frequency band may be generated by cutting into bands. Of course, without using the parallel simultaneous reception method, for example, the high frequency band ultrasonic wave and the low frequency band ultrasonic wave are transmitted and received in the same place twice in succession, and the received signal is used to reduce the high frequency band image data and the low frequency band image data. You may make it produce | generate each image data of a frequency band.

  In addition, since multiple image data generated under different generation conditions have different image generation conditions, random speckle patterns due to interference of multiple scatterers occur in different states, and the speckle pattern image changes. To do. On the other hand, for a structure that can be tracked, the structure image is maintained in the time direction even if the image generation conditions are different, so the image of the structure does not change.

  With reference to the flowchart of FIG. 5, the physical parameter calculation process in the ultrasonic diagnostic apparatus 1 of FIG. 3 will be described. This physical parameter calculation process is performed by, for example, one heart rate (time phase T1 to time phase TM) of the heart from the B-mode image data stored in the data storage unit 25 when the user operates the input unit 13. ) To select M image data.

  In step S11, the feature point extraction unit 33 of the calculation unit 26 follows the instruction of the control unit 21, and the first generation condition B-mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25. Is read. At the same time, the control unit 21 among the first generation condition B mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25, the first generation condition B mode image of the time phase T1. Data A1 is displayed on the display unit 14. The display unit 14 displays the first generation condition B-mode image data A1 of the time phase T1 stored in the data storage unit 25 on the display unit 14 in accordance with an instruction from the control unit 21.

  In step S12, the feature point extraction unit 33, based on the read first generation condition B mode image data A1 of the time phase T1, the first generation condition that is a feature point of the first generation condition B mode image data A1. Extract B-mode image data feature points. The extraction of the first generation condition B-mode image data feature points is performed by, for example, a corner detection method.

  FIG. 6 shows the first generation condition B-mode image data A1 displayed on the display unit 14.

As shown in FIG. 6, the first generation condition B-mode image data A1 is preliminarily arranged with a grid having a predetermined interval, and the first generation condition B-mode image data feature points are generally arranged at unequal intervals. . When the first generation condition B-mode image data A1 is displayed on the display unit 14, the user operates the input unit 13 to display a desired region of interest for performing tracking calculation and physical parameter calculation as shown in FIG. Set up a template that In the case of the example in FIG. 6, the first generation condition B-mode image data feature points a ₁ , a ₂ , a ₃ , a ₄ , a ₅ , and a ₆ are arranged in the template set by the user. .

  The feature point extraction unit 33 supplies the feature point extraction result to the traceable feature point extraction unit 34.

  In step S13, the traceable feature point setting unit 34 reads out the second generation condition B-mode image data B1 to BM of the time phase T1 to the time phase TM stored in the data storage unit 25. In step S14, the traceable feature point setting unit 34 acquires the feature point extraction result supplied from the feature point extraction unit 33, the acquired feature point collection result, and the read first time phase T1. Based on the generation condition B-mode image data A1 and the second generation condition B-mode image data B1 of the time phase T1, the first generation condition B-mode image data feature points in the set template are used for inter-frame (time phase T1 The cross-correlation coefficient between the first generation condition B-mode image data A1 and the second generation condition B-mode image data B1) of time phase T1 is calculated. That is, based on two image data generated under different generation conditions, using the feature points extracted based on the image data generated under one generation condition, the mutual phase relationship between the frames of the two image data Calculate the number. The calculated cross-correlation coefficient is a physical quantity having a value from 0 to 1.

Specifically, as shown in FIG. 7, first, it generates first, as first generation condition is one feature point in the template set by the user B-mode image data feature point a ₁ is centered A template for the condition B mode image data feature point a ₁ (dotted line template in FIG. 7) is newly set to a predetermined size. Then, between the newly set first generation condition B-mode image data feature point a ₁ template, the first generation condition B-mode image data A1 of the time phase T1 of the second generation condition B-mode image data B1 frame Cross-correlation processing is performed at The maximum value of the plurality of cross-correlation coefficient calculated in the course of this cross-correlation process, is used as the cross-correlation coefficient in the first generation condition B-mode image data feature point a _1.

In the case of the example in FIG. 8, the cross-correlation coefficient at the first generation condition B-mode image data feature point a ₁ is “0.5”. The cross-correlation coefficient at the first generation condition B-mode image data feature points a ₂ , a ₃ , a ₄ , a ₅ , and a ₆ is calculated in the same procedure. In the case of the example in FIG. 8, the cross-correlation coefficients at the first generation condition B-mode image data feature points a ₂ , a ₃ , a ₄ , a ₅ , and a ₆ are “0.2” and “0.7”. , “0.6”, “0.8”, and “0.4”.

  In step S15, the traceable feature point extraction unit 34 determines whether or not the calculated cross-correlation coefficient is larger than a predetermined reference value set in advance.

When it is determined that the cross-correlation coefficient calculated in step S15 is greater than a predetermined reference value set in advance, the traceable feature point setting unit 34 determines that the similarity is high in step S16. Here, the “similarity” is a value indicating the height of whether or not there is no change between images. In other words, “high similarity” means that the height of whether or not there is a change between images is high (the portion where there is no change between images by maintaining the structure in the time direction). "Similarity is low" means that the height of whether or not there is no change between the images is low (the structure is not maintained in the time direction, so the images are not This indicates that there is a change. In the case of the example of FIG. 8, when the predetermined reference value set in advance is “0.6”, the cross-correlation coefficient calculated in the first generation condition B-mode image data feature point a ₃ is “0.7. Therefore, it is determined that the first generation condition B-mode image data feature point a ₃ has a high similarity (a portion that does not change between images by maintaining the structure in the time direction). The As a result, it is possible to distinguish between a traceable structure and a random speckle pattern caused by interference of a plurality of scatterers.

When it is determined that the cross-correlation coefficient calculated in step S15 is not larger than a predetermined reference value set in advance, the traceable feature point setting unit 34 determines that the similarity is low in step S17. In the case of the example of FIG. 8, when the predetermined reference value set in advance is “0.6”, the cross-correlation coefficient calculated for the first generation condition B-mode image data feature point a ₁ is “0.5”. Therefore, it is determined that the similarity is low at the first generation condition B-mode image data feature point a ₁ .

  In step S <b> 18, the traceable feature point setting unit 34 determines whether similarity determination processing has been performed for all the calculated cross-correlation coefficients. If it is determined that the similarity determination process is not performed for all the cross-correlation coefficients calculated in step S18, the process returns to step S15, and the processes after step S15 are repeated. Thereby, the similarity can be determined for all feature points in the template set by the user.

  When it is determined that the similarity determination processing has been performed for all the cross-correlation coefficients calculated in step S18, the traceable feature point setting unit 34 can track the feature points determined to have high similarity in step S19. Set as a feature point and set a traceable feature point flag F to 1. On the other hand, the traceable feature point setting unit 34 does not set the feature point determined to have low similarity as the traceable feature point, and sets the traceable feature point flag F to 0.

  FIG. 9 shows the correspondence between the feature point number, cross-correlation coefficient, similarity, and traceable feature point flag of the first generation condition B-mode image data feature point.

  In the first to fourth columns of the table of FIG. 9, “feature point number”, “cross-correlation coefficient”, “similarity”, and “traceable feature point flag” are described, respectively. , Identification number of first generation condition B-mode image data feature point in preset template, cross-correlation coefficient value in first generation condition B-mode image data feature point in preset template, between images In FIG. 4, a value indicating the height of whether or not there is no change and a flag value indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 are shown.

In the first row of FIG. 9, the “feature point number” is “a ₁ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₁ ”. It is shown that. “Cross-correlation coefficient” is “0.5”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.5”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the second row of FIG. 9, the “feature point number” is “a ₂ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₂ ”. It is shown that. “Cross-correlation coefficient” is “0.2”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.2”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the case of the third row in FIG. 9, the “feature point number” is “a ₃ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₃ ”. It is shown that. “Cross-correlation coefficient” is “0.7”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.7”. Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the case of the fourth line in FIG. 9, the “feature point number” is “a ₄ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₄ ”. It is shown that. “Cross-correlation coefficient” is “0.6”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.6” Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the case of the fifth row in FIG. 9, the “feature point number” is “a ₅ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₅ ”. It is shown that. “Cross-correlation coefficient” is “0.8”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.8”. Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the case of the sixth line in FIG. 9, the “feature point number” is “a ₆ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₆ ”. It is shown that. “Cross-correlation coefficient” is “0.4”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.4”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

  In the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, the first generation condition B mode that is a candidate for feature points that can be traced based on the first generation condition B mode image data generated by the first generation condition. Image data feature points are extracted, and the first generation condition B-mode image data of substantially the same phase generated under the first generation condition and the second generation condition using the extracted first generation condition B-mode image data feature points. And the second generation condition B-mode image data, the cross-correlation coefficient between frames is calculated, and traceable feature points are set based on the calculated cross-correlation coefficient. Among the first generation condition B-mode image data feature points that can be traceable feature points, the feature point is a structure whose image does not change depending on the frequency or the transmission / reception beam direction, and the image changes depending on the frequency or the transmission / reception beam direction. After having distinguished by certain speckle pattern can be set only as a feature point trackable by frequency and transmitting and receiving beams is no change in the image by the direction structure.

  The traceable feature point setting unit 34 supplies the set setting result to the tracking calculation unit 35.

  In step S20, the tracking calculation unit 35 obtains the setting result supplied from the traceable feature point setting unit 35, and uses the feature points that can be traced based on the traceable feature point flag F included in the obtained setting result. The first generation condition B-mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25 is read out, and the read time phases T1 to time phase are read out. Based on the first generation condition B mode image data A1 to AM of TM, the tracking calculation is performed using the traceable feature points set in the template.

Specifically, among the first generation condition B mode image data A1 to AM of the read time phases T1 to TM, the first generation condition B mode image data A1 and A2 of the time phases T1 and T2 are read out. Based on the traceable feature points (first generation condition B-mode image data feature points a ₃ , a ₄ , and a _{6 in} the example of FIG. 8) set in the template. Correlation processing is performed, and the displacement amount at the maximum value among the plurality of cross-correlation coefficients calculated in the course of the cross-correlation processing is measured at a measurement point in the template set by the user (in the case of the example in FIG. 6, Calculated as the amount of displacement of the myocardium at point P).

  Similarly, using the read first generation condition B mode image data A2 and A3, the measurement points set in the first generation condition B mode image data A1 (measurement point P in the case of FIG. 6) are set. A new template centering on the movement destination in the first generation condition B-mode image data A2 is set, cross-correlation processing is performed between the set template and the first generation condition B-mode image data A3, and the first generation is performed. The displacement amount of the measurement point in the condition B mode image data A3 is calculated. Further, by repeating the same procedure, the movement destination, that is, the displacement amount of the measurement points set in the first generation condition B mode image data A1 in the first generation condition B mode image data A3 to AM is calculated.

  The tracking calculation unit 35 supplies the tracking calculation result obtained by the tracking calculation to the physical parameter calculation unit 36.

  In step S21, the physical parameter calculation unit 36 acquires the tracking calculation result supplied from the tracking calculation unit 35, and calculates the physical parameter based on the acquired tracking calculation result. Specifically, the physical parameter calculation unit 36 uses physical parameters such as movement speed (first-order derivative with respect to displacement time) and movement acceleration (second-order derivative with respect to displacement time) based on the displacement amount included in the tracking calculation result. Is calculated.

  In step S <b> 22, the physical parameter calculation unit 36 supplies the calculated physical parameter calculation result to the data storage unit 25. In step S23, the data storage unit 25 acquires the physical parameter calculation result supplied from the physical parameter calculation unit 36, and stores the acquired physical parameter calculation result.

  In the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, after setting the traceable feature points, the tracking calculation is performed between the frames using only the traceable feature points. A tracking calculation result that is not affected by the speckle pattern can be acquired. Thereby, the accuracy of tracking (tracking calculation) using the feature points extracted from the ultrasound image can be improved.

  In the physical parameter calculation process described with reference to the flowchart of FIG. 5, the first generation condition B-mode image data feature that is a feature point candidate that can be traced based on image data generated under one generation condition. Although points are extracted, candidate feature points may be extracted based on two pieces of image data generated under different generation conditions. The physical parameter calculation process in this case is shown in the flowchart of FIG.

  With reference to the flowchart in FIG. 10, another physical parameter calculation process in the ultrasonic diagnostic apparatus 1 in FIG. 3 will be described. Note that the processing in steps S42 to S45 in FIG. 10 is the same as the processing in steps S20 to S23 in FIG.

  In step S31, the feature point extraction unit 33 of the calculation unit 26 follows the instructions of the control unit 21, and the first generation condition B-mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25. Is read. At the same time, the control unit 21 among the first generation condition B mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25, the first generation condition B mode image of the time phase T1. Data A1 is displayed on the display unit 14. The display unit 14 displays the first generation condition B-mode image data A1 of the time phase T1 stored in the data storage unit 25 on the display unit 14 in accordance with an instruction from the control unit 21.

  In step S32, the feature point extraction unit 33, based on the read first generation condition B mode image data A1 of time phase T1, the first generation condition that is a feature point of the first generation condition B mode image data A1. Extract B-mode image data feature points. The extraction of the first generation condition B-mode image data feature points is performed by, for example, a corner detection method.

  FIG. 11A shows the first generation condition B-mode image data A1 displayed on the display unit 14.

In the example of FIG. 11A, the first generation condition B-mode image data feature points a ₁ , a ₂ , a ₃ , a ₄ , a ₅ , and a ₆ are arranged in the template set by the user. Has been.

  The feature point extraction unit 33 supplies the feature point extraction result to the traceable feature point extraction unit 34.

  In step S33, the traceable feature point setting unit 34 acquires the feature point extraction result supplied from the feature point extraction unit 33, and the first generation of the acquired feature point extraction result and the read time phase T1. Based on the condition B-mode image data A1 and the first generation condition B-mode image data A1 of the time phase T2, the cross-correlation coefficient between frames using the first generation condition B-mode image data feature points in the set template Is calculated. The specific method for calculating the cross-correlation coefficient is the same as the method described with reference to FIG.

In the example of FIG. 12A, the cross-correlation coefficient at the first generation condition B-mode image data feature points a ₁ , a ₂ , a ₃ , a ₄ , a ₅ , and a ₆ is “0.7”. , “0.4”, “0.6”, “0.5”, “0.7”, and “0.3”.

  In step S34, the feature point extraction unit 33 of the calculation unit 26 follows the instructions of the control unit 21, and the second generation condition B-mode image data B1 to BM of the time phase T1 to the time phase TM stored in the data storage unit 25. Is read. At the same time, the control unit 21 among the second generation condition B mode image data B1 to BM of the time phase T1 to the time phase TM stored in the data storage unit 25, the second generation condition B mode image of the time phase T1. Data B1 is displayed on the display unit 14. The display unit 14 displays the second generation condition B mode image data B1 of the time phase T1 stored in the data storage unit 25 on the display unit 14 in accordance with an instruction from the control unit 21.

  In step S35, the feature point extraction unit 33, based on the read second generation condition B mode image data B1 of the time phase T1, the second generation condition that is a feature point of the second generation condition B mode image data B1. Extract B-mode image data feature points. The second generation condition B-mode image data feature points are extracted by, for example, a corner detection method.

  FIG. 11B shows the second generation condition B mode image data B1 displayed on the display unit 14.

In the example of FIG. 11B, the second generation condition B-mode image data feature points b ₁ , b ₂ , b ₃ , b ₄ , b _5, and b ₆ are arranged in the template set by the user. Has been.

  The feature point extraction unit 33 supplies the feature point extraction result to the traceable feature point extraction unit 34.

  In step S36, the traceable feature point setting unit 34 acquires the feature point extraction result supplied from the feature point extraction unit 33, and the second generation of the acquired feature point extraction result and the read time phase T1. Based on the condition B-mode image data B1 and the second generation condition B-mode image data B2 of the time phase T2, the cross-correlation coefficient between frames using the first generation condition B-mode image data feature points in the set template Is calculated. The specific method for calculating the cross-correlation coefficient is the same as the method described with reference to FIG.

In the example of FIG. 12B, the cross-correlation coefficient at the second generation condition B-mode image data feature points b ₁ , b ₂ , b ₃ , b ₄ , b _5, and b ₆ is “0.2”. , “0.8”, “0.5”, “0.1”, “0.6”, and “0.9”.

  In step S <b> 37, the traceable feature point setting unit 34 is a feature point having a higher cross-correlation coefficient among the extracted first generation condition B-mode image data feature points and second generation condition B-mode image data feature points. It is determined whether or not there is.

If it is determined that the cross-correlation coefficient is an upper feature point among the first generation condition B-mode image data feature points and the second generation condition B-mode image data feature points extracted in step S37, the traceable feature In step S38, the point setting unit 34 determines that the similarity is high. In the example of FIG. 12A, the first generation condition B-mode image data feature points a ₁ , a ₃ , and a ₅ are the upper three of the six first generation condition B-mode image data feature points. The first generation condition B-mode image data feature points a ₁ , a ₃ , and a ₅ are determined to have high similarity. Similarly, in the example of FIG. 12B, the second generation condition B-mode image data feature points b ₂ , b ₅ , and b ₆ are the top three of the six second generation condition B-mode image data feature points. Therefore, it is determined that the second generation condition B-mode image data feature points b ₂ , b ₅ , and b ₆ have high similarity.

If it is determined in step S37 that the cross-correlation coefficient is a higher-order feature point among the first generation condition B-mode image data feature points and the second generation condition B-mode image data feature points, the traceable feature points In step S39, the setting unit 34 determines that the similarity is low. In the example of FIG. 12A, the first generation condition B-mode image data feature points a ₁ , a ₃ , and a ₅ are the lower three of the six first generation condition B-mode image data feature points. The first generation condition B-mode image data feature points a ₂ , a ₄ , and a ₆ are determined to have low similarity. Similarly, in the example of FIG. 12B, the second generation condition B-mode image data feature points b ₁ , b ₃ , and b ₄ are the lower three of the six second generation condition B-mode image data feature points. Therefore, it is determined that the second generation condition B-mode image data feature points b ₁ , b ₃ , and b ₄ have low similarity.

  In step S40, the traceable feature point setting unit 34 determines whether or not similarity determination processing has been performed for all the calculated cross-correlation coefficients. If it is determined that the similarity determination process is not performed for all the cross-correlation coefficients calculated in step S40, the process returns to step S37, and the processes after step S37 are repeated. Thereby, the similarity can be determined for all feature points in the template set by the user.

  When it is determined that the similarity determination processing has been performed for all the cross-correlation coefficients calculated in step S40, the traceable feature point setting unit 34 can track the feature points determined to have high similarity in step S41. Set as a feature point and set a traceable feature point flag F to 1. On the other hand, the traceable feature point setting unit 34 does not set the feature point determined to have low similarity as the traceable feature point, and sets the traceable feature point flag F to 0.

  13A and 13B show the feature point number, cross-correlation coefficient, similarity, and traceable feature point of the first generation condition B-mode image data feature point or the second generation condition B-mode image data feature point. It shows the correspondence with the flag. Note that the “feature point number”, “cross-correlation coefficient”, “similarity”, and “traceable feature point flag” in the first to fourth columns of FIGS. 13A and 13B are: This is the same as the “feature point number”, “cross-correlation coefficient”, “similarity”, and “trackable feature point flag” in the first column to the fourth column in FIG. 9, and the description will be repeated. I will omit it.

In the case of the first row in FIG. 13A, the “feature point number” is “a ₁ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₁ ”. ". “Cross-correlation coefficient” is “0.7”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.7”. Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the second row of FIG. 13A, the “feature point number” is “a ₂ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₂ ”. ". “Cross-correlation coefficient” is “0.4”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.4”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the case of the third row in FIG. 13A, the “feature point number” is “a ₃ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₃ ”. ". “Cross-correlation coefficient” is “0.6”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.6” Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the case of the fourth line in FIG. 13A, the “feature point number” is “a ₄ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₄ ”. ". “Cross-correlation coefficient” is “0.5”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.5”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the case of the fifth line in FIG. 13A, the “feature point number” is “a ₅ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₅ ”. ". “Cross-correlation coefficient” is “0.7”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.7”. Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the sixth line in FIG. 13A, the “feature point number” is “a ₆ ”, and the identification number of the first generation condition B-mode image data feature point in the preset template is “a ₆ ”. ". “Cross-correlation coefficient” is “0.3”, indicating that the cross-correlation coefficient at the first generation condition B-mode image data feature point in the preset template is “0.3”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

Next, in the first row of FIG. 13B, the “feature point number” is “b ₁ ”, and the identification number of the second generation condition B-mode image data feature point in the preset template is “B ₁ ”. “Cross-correlation coefficient” is “0.2”, indicating that the cross-correlation coefficient at the second generation condition B-mode image data feature point in the preset template is “0.2”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the second row in FIG. 13B, the “feature point number” is “b ₂ ”, and the identification number of the second generation condition B-mode image data feature point in the preset template is “b ₂ ”. ". “Cross-correlation coefficient” is “0.8”, indicating that the cross-correlation coefficient at the second generation condition B-mode image data feature point in the preset template is “0.8”. Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the third row in FIG. 13B, the “feature point number” is “b ₃ ”, and the identification number of the second generation condition B-mode image data feature point in the preset template is “b ₃ ”. ". “Cross-correlation coefficient” is “0.5”, indicating that the cross-correlation coefficient at the second generation condition B-mode image data feature point in the preset template is “0.5”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the case of the fourth row in FIG. 13B, the “feature point number” is “b ₄ ”, and the identification number of the second generation condition B-mode image data feature point in the preset template is “b ₄ ”. ". “Cross-correlation coefficient” is “0.1”, indicating that the cross-correlation coefficient at the second generation condition B-mode image data feature point in the preset template is “0.1”. Yes. The “similarity” is “low”, and the value indicating the height of whether or not there is no change between images is “low” (that is, the image is not maintained because the structure is not maintained in the time direction). It is a part where there is a change between). The “trackable feature point flag” is “F = 0”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 0”. (That is, not a traceable feature point).

In the case of the fifth line in FIG. 13B, the “feature point number” is “b ₅ ”, and the identification number of the second generation condition B-mode image data feature point in the preset template is “b ₅ ”. ". “Cross-correlation coefficient” is “0.6”, indicating that the cross-correlation coefficient at the second generation condition B-mode image data feature point in the preset template is “0.6” Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

In the case of the sixth line in FIG. 13B, the “feature point number” is “b ₆ ”, and the identification number of the second generation condition B-mode image data feature point in the preset template is “b ₆ ”. ". “Cross-correlation coefficient” is “0.9”, indicating that the cross-correlation coefficient at the second generation condition B-mode image data feature point in the preset template is “0.9”. Yes. “Similarity” is “high”, and the value indicating the height of whether or not there is no change between images is “high” (that is, the structure is maintained in the time direction) This is a portion where there is no change between images). The “trackable feature point flag” is “F = 1”, and the value of the flag indicating whether or not the trackable feature point is set by the trackable feature point setting unit 34 is “F = 1”. (That is, it is a traceable feature point).

  In the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, based on the first generation condition B-mode image data generated by the first generation condition and the second generation condition B-mode image data generated by the second generation condition. The first generation condition B-mode image data feature point and the second generation condition B-mode image data feature point, which are candidate feature points that can be traced, are extracted, and the extracted first generation condition B-mode image data feature point and the first 2. Generate cross-correlation coefficients between frames based on the first generation condition B-mode image data or the second generation condition B-mode image data of two different time phases using the two generation condition B-mode image data feature points. Since the traceable feature points are set based on the calculated cross-correlation coefficient, the image does not change depending on the frequency and the transmission / reception beam direction among the extracted candidate feature points. A feature that can trace only structures that have no change in the image depending on the frequency and the direction of the transmitted and received beam after distinguishing between those that are due to the structure and a speckle pattern that changes in the image depending on the frequency and the direction of the transmitted and received beam Can be set as

  Thereafter, the processing after step S42 is performed, the tracking calculation is performed using the set traceable feature points, and the physical parameter is calculated based on the tracking calculation result.

  As a result, tracking calculation results that are not affected by random speckle patterns can be obtained, and as a result, the accuracy of tracking (tracking calculation) using feature points extracted from ultrasound images can be improved. Can do.

  The first generation condition B-mode image data feature points and the first generation condition B-mode image data, which are candidates for traceable feature points, based on the first generation condition B-mode image data and the second generation condition B-mode image data having different generation conditions, respectively. 2 generation condition B-mode image data feature points are extracted, and a common feature point (for example, spatial feature) is extracted from the extracted first generation condition B-mode image data feature points and second generation condition B-mode image data feature points. It is also possible to set a feature point whose position is substantially the same as a traceable feature point. The physical parameter calculation process in this case is shown in the flowchart of FIG.

  With reference to the flowchart of FIG. 14, another physical parameter calculation process in the ultrasonic diagnostic apparatus 1 of FIG. 3 will be described. Note that the processing in steps S51 to S54 and steps S59 to S62 in FIG. 18 is similar to the processing in steps S31, S32, S34, S35, and steps S41 to S45 in FIG. It will be omitted.

  In step S55, the traceable feature point setting unit 34 determines whether or not the coordinates of the extracted first generation condition B mode image data feature point and the second generation condition B mode image data feature point substantially match.

When it is determined that the coordinates of the first generation condition B-mode image data feature point extracted in step S55 and the second generation condition B-mode image data feature point substantially coincide with each other, the traceable feature point setting unit 34 in step S56, A feature point determined to have substantially the same coordinates is set as a traceable feature point, and a traceable feature point flag F is set to 1. In the case of the example in FIG. 15, the coordinates of the first generation condition B-mode image data feature points a _{1 to} a ₁₇ and the second generation condition B-mode image data feature points b _{1 to} b ₁₇ are substantially the same, First generation condition B-mode image data feature points a ₁ and a ₃ (first generation condition B-mode image data feature points b ₁ and b ₃ ) are set as traceable feature points.

When it is determined that the coordinates of the first generation condition B-mode image data feature point extracted in step S55 and the second generation condition B-mode image data feature point do not substantially match (when it is determined that the coordinates are shifted) In step S57, the traceable feature point setting unit 34 does not set the feature point determined that the coordinates are not substantially coincident (determined that the coordinates are shifted) as the traceable feature point, and can track the feature point flag. Set F to 0. In the case of the example of FIG. 15, in the case of the example of FIG. 15, feature points other than the first generation condition B mode image data feature points a _{1 to} a ₁₇ and the second generation condition B mode image data feature points b _{1 to} b _17. The coordinates do not match and are not set as traceable feature points.

  In step S58, the traceable feature point setting unit 34 determines whether or not all feature points have been set. If it is determined in step S58 that all feature points have not been set, the process returns to step S55, and the processes after step S55 are repeated. As a result, it is possible to determine whether or not the coordinates of all the feature points in the template set by the user are substantially the same, and to set whether or not the feature points can be added.

  If it is determined in step S58 that all feature points have been set, the process proceeds to step S59. Thereafter, a tracking calculation is performed using the traceable feature points, and a physical parameter is calculated based on the tracking calculation result. The

  This distinguishes between images with structures that do not change the image due to frequency and beam direction and those with speckle patterns that change the image according to frequency and beam direction. Only a structure with no change can be set as a traceable feature point. Therefore, it is possible to obtain a tracking calculation result that is not affected by a random speckle pattern, and as a result, it is possible to improve the accuracy of tracking (tracking calculation) using feature points extracted from an ultrasonic image. it can.

  It should be noted that the tracking calculation is performed for each of the image data generated under different generation conditions, and there is no contradiction from the two tracking calculation results, that is, a result that is not statistically significantly deviated, and is statistically largely deviated. Such a result may be excluded. The physical parameter calculation process in this case is shown in the flowchart of FIG.

  With reference to the flowchart in FIG. 16, another physical parameter calculation process in the ultrasonic diagnostic apparatus 1 in FIG. 3 will be described.

  In step S71, the feature point extraction unit 33 of the calculation unit 26 follows the instructions of the control unit 21, and the first generation condition B-mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25. Is read. At the same time, the control unit 21 among the first generation condition B mode image data A1 to AM of the time phase T1 to the time phase TM stored in the data storage unit 25, the first generation condition B mode image of the time phase T1. Data A1 is displayed on the display unit 14. The display unit 14 displays the first generation condition B-mode image data A1 of the time phase T1 stored in the data storage unit 25 on the display unit 14 in accordance with an instruction from the control unit 21.

  In step S72, the feature point extraction unit 33, based on the read first generation condition B mode image data A1 of the time phase T1, the first generation condition that is the feature point of the first generation condition B mode image data A1. Extract B-mode image data feature points. The extraction of the first generation condition B-mode image data feature points is performed by, for example, a corner detection method. The specific extraction method is the same as the extraction method described with reference to FIG.

  The feature point extraction unit 33 supplies the feature point extraction result to the tracking calculation unit 35.

  In step S73, the tracking calculation unit 35 acquires the extraction result supplied from the feature point extraction unit 33, and the first generation condition B-mode image data of the time phase T1 to the time phase TM stored in the data storage unit 25. A1 to AM are read, and a tracking calculation is performed using the first generation condition B-mode image data feature points based on the read first generation condition B-mode image data A1 to AM of the time phases T1 to TM. . Note that the specific method is the same as the method described in the processing of step S20 in FIG.

  In step S <b> 74, the tracking calculation unit 35 supplies the first generation condition tracking result, which is a tracking calculation result using the first generation condition B-mode image data feature points, to the data storage unit 25. In step S75, the data storage unit 25 acquires the first generation condition tracking result supplied from the tracking calculation unit 33, and stores the acquired first generation condition tracking result.

  In step S76, the feature point extraction unit 33 of the calculation unit 26 follows the instructions of the control unit 21, and the second generation condition B-mode image data B1 to BM of the time phase T1 to the time phase TM stored in the data storage unit 25. Is read. At the same time, the control unit 21 among the second generation condition B mode image data B1 to BM of the time phase T1 to the time phase TM stored in the data storage unit 25, the second generation condition B mode image of the time phase T1. Data B1 is displayed on the display unit 14. The display unit 14 displays the second generation condition B mode image data B1 of the time phase T1 stored in the data storage unit 25 on the display unit 14 in accordance with an instruction from the control unit 21.

  In step S77, the feature point extraction unit 33 uses the second generation condition B-mode image data B1 of the second generation condition B-mode image data B1 based on the read second generation condition B-mode image data B1 of the time phase T1. Extract B-mode image data feature points. The second generation condition B-mode image data feature points are extracted by, for example, a corner detection method. The specific extraction method is the same as the extraction method described with reference to FIG.

  The feature point extraction unit 33 supplies the feature point extraction result to the tracking calculation unit 35.

  In step S78, the tracking calculation unit 35 acquires the extraction result supplied from the feature point extraction unit 33, and the second generation condition B-mode image data of time phase T1 to time phase TM stored in the data storage unit 25. B1 to BM are read out, and tracking calculation is performed using the second generation condition B-mode image data feature points based on the read second generation condition B-mode image data B1 to BM of the time phases T1 to TM. . Note that the specific method is the same as the method described in the processing of step S20 in FIG.

  In step S <b> 79, the tracking calculation unit 35 supplies the second generation condition tracking result, which is a tracking calculation result using the second generation condition B-mode image data feature points, to the data storage unit 25. In step S80, the data storage unit 25 acquires the first generation condition tracking result supplied from the tracking calculation unit 33, and stores the acquired second generation condition tracking result.

  In step S81, the traceable feature point setting unit 34 reads the first generation condition tracking result and the second generation condition tracking result stored in the data storage unit 25. In step S82, the traceable feature point setting unit 34 performs statistical processing based on the read first generation condition tracking result and second generation condition tracking result.

  In step S83, the traceable feature point setting unit 34 determines whether or not the feature point setting unit 34 is statistically significantly different. For example, it is determined that a statistically large deviation from the average value is statistically deviated.

  When it is determined in step S83 that it is not statistically significantly deviated, the traceable feature point setting unit 34 sets, in step S84, a feature point that is determined not to be statistically significantly deviated as a traceable feature point, The traceable feature point flag F is set to 1. Further, the traceable feature point setting unit 34 supplies the set setting result to the physical parameter calculation unit 36.

  If it is determined in step S83 that the feature point is statistically significantly different, the traceable feature point setting unit 34 does not set, in step S85, the feature point determined to be statistically significantly separated as a traceable feature point. The traceable feature point flag F is set to 0. Further, the traceable feature point setting unit 34 supplies the set setting result to the physical parameter calculation unit 36.

  In step S86, the traceable feature point setting unit 34 determines whether all feature points have been set. If it is determined in step S86 that all feature points have not been set, the process returns to step S83, and then the processes in and after step S83 are repeated. Thereby, it can be determined whether or not all the feature points in the template set by the user are statistically deviated, and whether or not the feature points can be added can be set.

  If it is determined in step 86 that all feature points have been set, the process proceeds to step S87, and the physical parameter calculation unit 36 acquires the setting result supplied from the traceable feature point setting unit 34, and has been acquired. Based on the setting result, it is determined whether or not the feature point is a traceable feature point, and the first generation condition tracking calculation result and the second generation condition tracking calculation result stored in the data storage unit 25 are read and read. Of the first generation condition tracking calculation result and the second generation condition tracking calculation result, the physical parameter is calculated based on the tracking calculation result for the traceable feature point.

  As a result, it is possible to obtain a tracking calculation result that is not affected by a random speckle pattern, and to calculate a physical parameter based on the tracking calculation result. Accordingly, it is possible to improve the accuracy of tracking (tracking calculation) using the feature points extracted from the ultrasonic image.

  In the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, a two-dimensional tomographic image is used. However, for example, a three-dimensional tomographic image may be used.

  Further, in the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, images generated under different generation conditions are displayed on the display unit 14, but are used for setting traceable feature points inside the apparatus. As long as it is displayed, and may not be displayed.

  Furthermore, in the ultrasonic diagnostic apparatus 1 shown in the embodiment of the present invention, the traceable feature points are set only in the region of interest in the template set by the user, but the image displayed on the display unit 14 Traceable feature points may be set for the whole.

  In the embodiment of the present invention, the steps of the flowchart show examples of processing performed in time series in the described order. However, even if they are not necessarily processed in time series, they are performed in parallel or individually. The process to be executed is also included.

The conceptual diagram explaining the conventional feature point extraction method. The conceptual diagram explaining the cross correlation process in the conventional tracking calculation. 1 is a block diagram showing an internal configuration of an ultrasonic diagnostic apparatus 1 to which the present invention is applied. The flowchart explaining the different production | generation condition multiple image data production | generation process in the ultrasonic diagnosing device of FIG. The flowchart explaining the physical parameter calculation process in the ultrasound diagnosing device of FIG. The figure which shows the example of a display of 1st production | generation condition B mode image data displayed on the display part of FIG. The figure explaining the calculation method of the cross correlation coefficient in step S14 of FIG. The figure explaining the example of the cross correlation coefficient in the 1st generation condition B mode image data feature point in a template. The figure explaining the example of the correspondence of a feature point number, a cross correlation coefficient, similarity, and a traceable feature point flag. The flowchart explaining the other physical parameter calculation process in the ultrasonic diagnosing device of FIG. The figure which shows the example of a display of 1st production | generation condition B mode image data and 2nd production | generation condition B mode image data displayed on the display part of FIG. The figure explaining the example of the cross correlation coefficient in the 1st generation condition B mode image data feature point in a template, and the cross correlation coefficient in the 2nd generation condition B mode image data feature point in a template. The figure explaining the example of the correspondence of a feature point number, a cross correlation coefficient, similarity, and a traceable feature point flag. The flowchart explaining the other physical parameter calculation process in the ultrasonic diagnosing device of FIG. The figure which shows the example of a display of 1st production | generation condition B mode image data and 2nd production | generation condition B mode image data displayed on the display part of FIG. The flowchart explaining the other physical parameter calculation process in the ultrasonic diagnosing device of FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Ultrasonic diagnostic apparatus 11 Main body 12 Ultrasonic probe 13 Input part 14 Display part 21 Control part 22 Transmission part 23 Reception part 24 Image data generation part 25 Data storage part 26 Calculation part 27 DSC
31 B mode processing unit 32 Doppler mode processing unit 33 feature point extraction unit 34 traceable feature point setting unit 35 tracking calculation unit 36 physical parameter calculation unit

Claims (12)

Image data that transmits ultrasonic waves by oscillating multiple ultrasonic transducers and generates multiple ultrasonic image data under different generation conditions by receiving reflected waves reflected from the subject. Generating means;
Feature point extraction means for extracting feature points based on a plurality of ultrasonic image data generated under different generation conditions by the image data generation means;
A traceable feature point setting unit that sets a traceable feature point that is a traceable feature point based on the feature point extracted by the feature point extraction unit;
A tracking calculation means for tracking and calculating the movement of the traceable feature point set by the traceable feature point setting means;
An ultrasonic diagnostic apparatus comprising: physical parameter calculation means for calculating a physical parameter based on a tracking calculation result by the tracking calculation means. The image data generation means generates first ultrasonic image data and second ultrasonic image data at least under the first generation condition and the second generation condition, respectively.
The feature point extraction unit extracts a first generation condition image data feature point from the first ultrasonic image data generated by the image data generation unit,
The traceable feature point setting means includes:
The first generation condition image data feature extracted by the feature point extraction unit based on the first ultrasonic image data and the second ultrasonic image data of substantially the same phase generated by the image data generation unit Using a point, a similarity determination unit that determines a similarity between images of the first ultrasonic image data and the second ultrasonic image data;
The traceable feature point setting unit is a feature point that is determined by the inter-image similarity determination unit to have high similarity between images among the first generation condition image data feature points extracted by the feature point extraction unit. The ultrasonic diagnostic apparatus according to claim 1, wherein the feature point is set as a traceable feature point. The traceable feature point setting means includes:
Cross-correlation coefficient calculation means for calculating a cross-correlation coefficient between images based on the first ultrasonic image data and the second ultrasonic image data generated by the image data generation means;
The similarity determination unit determines whether or not the cross-correlation coefficient calculated by the cross-correlation coefficient calculation unit is greater than a predetermined reference value, and the mutual correlation coefficient calculated by the cross-correlation coefficient calculation unit When it is determined that the correlation coefficient is greater than a predetermined reference value, it is determined that the degree of similarity is high, and the cross-correlation coefficient calculated by the cross-correlation coefficient calculating unit is determined to be smaller than the predetermined reference value The ultrasonic diagnostic apparatus according to claim 2, wherein the similarity is determined to be low. The image data generation means generates first ultrasonic image data and second ultrasonic image data at least under a first generation condition and a second generation condition,
The feature point extraction unit extracts a first generation condition image data feature point from the first ultrasonic image data generated by the image data generation unit, and the second generation point generated by the image data generation unit. Extracting second generation condition image data feature points from the ultrasound image data,
The traceable feature point setting means includes:
A first ultrasonic image data image for determining similarity between images based on a plurality of ultrasonic image data having different time phases among the first ultrasonic image data generated by the image data generating means. Between similarity determination means,
Second ultrasonic image data image for determining similarity between images based on a plurality of ultrasonic image data having different time phases among the second ultrasonic image data generated by the image data generating means An inter-similarity determination means,
The traceable feature point setting unit is configured to determine, among the first generation condition image data feature points extracted by the feature point extraction unit, between images in a predetermined region by the first ultrasonic image data inter-image similarity determination unit. Feature points determined to have high similarity are set as traceable feature points, and among the second generation condition image data feature points extracted by the feature point extraction means, the second ultrasonic image data image The ultrasonic diagnostic apparatus according to claim 1, wherein the feature point determined by the similarity determination unit as having high similarity between images in a predetermined region is set as a traceable feature point. The traceable feature point setting means includes:
A first ultrasonic image for calculating a cross-correlation coefficient between images based on a plurality of ultrasonic image data having different time phases among the first ultrasonic image data generated by the image data generation means. Data cross-correlation coefficient calculating means;
A second ultrasonic image for calculating a cross-correlation coefficient between images based on a plurality of ultrasonic image data having different time phases among the second ultrasonic image data generated by the image data generating means. A data cross-correlation coefficient calculating means;
The first ultrasonic image data inter-image similarity determination unit determines whether or not the cross-correlation coefficient calculated by the first ultrasonic image data cross-correlation coefficient calculation unit is greater than a predetermined reference value. If it is determined that the cross-correlation coefficient calculated by the first ultrasonic image data cross-correlation coefficient calculation means is greater than a predetermined reference value, it is determined that the similarity is high, and the first ultrasonic wave When it is determined that the cross-correlation coefficient calculated by the image data cross-correlation coefficient calculation means is smaller than a predetermined reference value, it is determined that the similarity is low,
The second ultrasonic image data image similarity determination unit determines whether or not the cross-correlation coefficient calculated by the second ultrasonic image data cross-correlation coefficient calculation unit is larger than a predetermined reference value. When it is determined that the cross-correlation coefficient calculated by the second ultrasonic image data cross-correlation coefficient calculation means is greater than a predetermined reference value, it is determined that the similarity is high, and the second ultrasonic image 5. The ultrasonic diagnostic apparatus according to claim 4, wherein when the cross-correlation coefficient calculated by the image data cross-correlation coefficient calculating means is determined to be smaller than a predetermined reference value, the similarity is determined to be low. . The image data generation means generates first ultrasonic image data and second ultrasonic image data under at least two different first generation conditions and second generation conditions,
The feature point extraction unit extracts a first generation condition image data feature point from the first ultrasonic image data generated by the image data generation unit, and the second generation point generated by the image data generation unit. Extracting second generation condition image data feature points from the ultrasound image data,
The traceable feature point setting means includes:
A feature point coordinate determination unit that determines whether or not the coordinates of the first generation condition image data feature point extracted by the feature point extraction unit and the second generation condition image data feature point substantially match,
The traceable feature point setting unit can trace a feature point determined by the feature point coordinate determination unit to determine that the coordinates of the first generation condition image data feature point and the second generation condition image data feature point substantially coincide with each other. The ultrasonic diagnostic apparatus according to claim 1, wherein a point is set. The plurality of ultrasonic image data generated by the image data generating means has an ultrasonic frequency band transmitted or received from an ultrasonic transducer or an ultrasonic transmission / reception direction when generating ultrasonic image data. The ultrasonic diagnostic apparatus according to claim 1, which is different. Image data that transmits ultrasonic waves by oscillating multiple ultrasonic transducers and generates multiple ultrasonic image data under different generation conditions by receiving reflected waves reflected from the subject. Generation step;
A feature point extraction step for extracting feature points based on a plurality of ultrasonic image data generated under different generation conditions;
A traceable feature point setting step for setting a traceable feature point that is a traceable feature point based on the extracted feature point;
A tracking calculation step for tracking the movement of the set traceable feature points;
An image processing method for an ultrasonic diagnostic apparatus, comprising: a physical parameter calculation step for calculating a physical parameter based on a tracking calculation result. Image data that transmits ultrasonic waves by oscillating multiple ultrasonic transducers and generates multiple ultrasonic image data under different generation conditions by receiving reflected waves reflected from the subject. Generation step;
A feature point extraction step for extracting feature points based on a plurality of ultrasonic image data generated under different generation conditions;
A traceable feature point setting step for setting a traceable feature point that is a traceable feature point based on the extracted feature point;
A tracking calculation step for tracking the movement of the set traceable feature points;
An image processing program for an ultrasound diagnostic apparatus, which causes a computer to execute a physical parameter calculation step of calculating a physical parameter based on a tracking calculation result. Image data that transmits ultrasonic waves by oscillating multiple ultrasonic transducers and generates multiple ultrasonic image data under different generation conditions by receiving reflected waves reflected from the subject. Generating means;
Feature point extraction means for extracting feature points based on a plurality of ultrasonic image data generated under different generation conditions by the image data generation means;
Tracking calculation means for tracking and calculating the movement of the feature points set by the feature point setting means;
A traceable feature point setting unit that sets a traceable feature point that is a traceable feature point based on a tracking calculation result by the tracking calculation unit;
An ultrasonic diagnostic apparatus comprising: physical parameter calculation means for calculating a physical parameter based on a tracking calculation result of the traceable feature point set by the traceable feature point setting means. Image data that transmits ultrasonic waves by oscillating multiple ultrasonic transducers and generates multiple ultrasonic image data under different generation conditions by receiving reflected waves reflected from the subject. Generation step;
A feature point extraction step for extracting feature points based on a plurality of ultrasonic image data generated under different generation conditions;
A tracking calculation step for tracking and calculating the movement of the set feature points;
A traceable feature point setting step for setting a traceable feature point that is a traceable feature point based on the tracking calculation result;
An image processing method for an ultrasound diagnostic apparatus, comprising: a physical parameter calculation step for calculating a physical parameter based on a tracking calculation result of a set traceable feature point. Image data that transmits ultrasonic waves by oscillating multiple ultrasonic transducers and generates multiple ultrasonic image data under different generation conditions by receiving reflected waves reflected from the subject. Generation step;
A feature point extraction step for extracting feature points based on a plurality of ultrasonic image data generated under different generation conditions;
A tracking calculation step for tracking and calculating the movement of the set feature points;
A traceable feature point setting step for setting a traceable feature point that is a traceable feature point based on the tracking calculation result;
An image processing program for an ultrasonic diagnostic apparatus, which causes a computer to execute a physical parameter calculation step for calculating a physical parameter based on a tracking calculation result of set traceable feature points.

Images