Ultrasound diagnostic apparatus and image processing method

Abstract

The embodiment of the invention relates to an ultrasonic diagnostic apparatus and an image processing method. An ultrasonic diagnostic apparatus includes a Doppler processing unit and a super-analysis processing unit. The Doppler processing unit estimates first blood flow information by reducing a clutter component from tissue on the basis of a data sequence of reflected wave data collected in a plurality of frames in a time direction at one or more positions by ultrasonic transmission and reception.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the accompanying drawings relate to an ultrasonic diagnostic apparatus and an image processing method. Background Art

[0002] Ultrasonic diagnostic devices are widely used to observe and diagnose the blood flow of living organisms. Ultrasonic diagnostic devices generate and display blood flow information based on the reflected waves of ultrasound waves using the Doppler method based on the Doppler effect. Blood flow information generated and displayed by ultrasonic diagnostic devices includes color Doppler images, Doppler waveforms (Doppler spectra), and the like.

[0003] Color Doppler images are obtained by imaging using the color flow mapping (CFM) method. In the CFM method, ultrasound is transmitted and received multiple times on multiple scanning lines. In the CFM method, an MTI (Moving Target indicator) filter is applied to the data column at the same position, thereby suppressing the signal (clutter signal) from stationary tissue or slowly moving tissue, and extracting the signal from the blood flow (blood flow signal). In the CFM method, blood flow information such as the blood flow velocity, blood flow variance, and blood flow energy (power) is estimated based on the blood flow signal, and the distribution of the estimated result (blood flow information) is displayed as a Doppler image.

[0004] It is known that in a B-mode image or a Doppler image, the resolution is reduced due to a point spread function (PSF) determined by the wavelength of transmitted ultrasonic waves, the transmission and reception aperture width, and the like.

[0005] Although there are solutions such as increasing the frequency of transmitting ultrasonic waves, the frequency band of the probe is also limited, so the resolution of the image that can be obtained is limited.

[0006] The non-patent literature (Fast Super Resolution Ultrasound Imaging using the Erythrocytes, Jorgen Arendt Jensen, Mikkel Schou, Sofie Bech Andersen, et al., Proceedings Volume 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography; 120380E(2022)) describes a super-resolution technology for achieving a blood flow image with a resolution of approximately 1 / 5 of the wavelength of the transmitted ultrasound.

[0007] A plurality of normal Doppler images are acquired, and portions with high image values ​​are extracted and integrated, thereby generating a blood flow image with improved resolution. Non-Patent Document 1 only mentions super-resolution display of blood flow energy in grayscale. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a block diagram showing an example of the hardware configuration of the ultrasonic diagnostic apparatus according to the first embodiment.

[0009] Figure 2 This is a block diagram showing an example of functions of the reception signal processing unit according to the first embodiment.

[0010] Figure 3 This is a diagram showing an example of a process flow for acquiring a super-resolution blood flow image executed by the ultrasonic diagnostic apparatus according to the first embodiment.

[0011] Figure 4A These are various images of renal blood flow.

[0012] Figure 4B These are various images of renal blood flow.

[0013] Figure 4C These are various images of renal blood flow.

[0014] Figure 4D These are various images of renal blood flow.

[0015] Figure 5 This is a diagram showing an example of a process flow for acquiring a super-resolution blood flow image executed by the ultrasonic diagnostic apparatus according to the second embodiment.

[0016] Fig. 6A FIG. 4 is a diagram showing an example of a plurality of frames of blood flow images generated in step S430 in the second embodiment.

[0017] Figure 6B FIG. 1 is a diagram showing an example of image groups divided based on three features in the second embodiment.

[0018] Figure 6C : is a figure which shows an example of the image obtained by the process of step S460 in the second embodiment.

[0019] Fig.6D FIG. 1 is a diagram showing an example of a super-resolution blood flow image according to the second embodiment.

[0020] Figure 7 This is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image in the prior art.

[0021] Fig. 8A This is a diagram for explaining an example of a processing method in the related art.

[0022] Figure 8B This is a diagram for explaining an example of a processing method in the related art.

[0023] Figure 8C This is a diagram for explaining an example of a processing method in the related art.

[0024] Fig. 9 This is a diagram showing an example of Rayleigh distribution.

[0025] Fig. 10A This is a diagram for explaining an example of the problem of the conventional technology.

[0026] Fig. 10B This is a diagram for explaining an example of the problem of the conventional technology.

[0027] Fig. 10C This is a diagram for explaining an example of the problem of the conventional technology.

[0028] Fig.11A The figures show images of renal blood flow obtained by various methods.

[0029] Fig. 11B The figures show images of renal blood flow obtained by various methods.

[0030] Fig. 11C The figures show images of renal blood flow obtained by various methods.

[0031] Fig.11D The figures show images of renal blood flow obtained by various methods.

[0032] Fig.11E The figures show images of renal blood flow obtained by various methods.

[0033] Fig.11FThe figures show images of renal blood flow obtained by various methods.

[0034] Fig.11G The figures show images of renal blood flow obtained by various methods.

[0035] Fig.11H The figures show images of renal blood flow obtained by various methods.

[0036] Fig.12 It is used to illustrate Figure 5 FIG. 5 is a diagram of the processing after step S450. DETAILED DESCRIPTION

[0037] One of the problems to be solved by the embodiments disclosed in this specification and the accompanying drawings is to obtain information that is highly convenient for users. Among them, the problems to be solved by the embodiments disclosed in this specification and the accompanying drawings are not limited to the above-mentioned problems. It is also possible to identify the problems corresponding to the effects of the various structures shown in the embodiments described later as other problems.

[0038] Hereinafter, embodiments and variations of an ultrasonic diagnostic apparatus, an image processing apparatus, and an image processing method will be described in detail with reference to the accompanying drawings. Moreover, the ultrasonic diagnostic apparatus, the image processing apparatus, and the image processing method involved in the present application are not limited to the embodiments and variations shown below. In addition, the embodiments can be combined with other embodiments, variations, or prior arts within the scope that the processing contents do not conflict. Similarly, variations can be combined with embodiments, other variations, or prior arts within the scope that the processing contents do not conflict.

[0039] (First Embodiment)

[0040] The ultrasonic diagnostic apparatus according to the first embodiment described below includes a processing circuit. The processing circuit acquires a data column of reflected wave data collected in a plurality of frames in the time direction at one or more positions by ultrasonic transmission and reception. The processing circuit reduces the clutter components from the tissue based on the data column of the plurality of frames to estimate the first blood flow information. The processing circuit generates a scalar value image of the blood flow signal, i.e., the second blood flow information, based on the first blood flow information, detects the local maximum value of the second blood flow information, accumulates the local maximum value or the image with the local maximum value emphasized to generate a scalar value accumulation image, calculates the autocorrelation function of the first blood flow information in the frame direction at the position of the local maximum value, accumulates the autocorrelation function to generate an autocorrelation function accumulation image, generates the third blood flow information in which the blood flow is distinguished by color based on the scalar value accumulation image and the autocorrelation function accumulation image, and displays the third blood flow information on a display.

[0041] Figure 1 1 is a block diagram showing an example of the hardware structure of the ultrasonic diagnostic apparatus according to the first embodiment. The ultrasonic diagnostic apparatus includes an ultrasonic probe 102, a probe connection unit 103, a transmission circuit 104, a reception circuit 105, a reception signal processing unit 106, an image processing unit 107, a display device 108, and a system control unit 109. The ultrasonic diagnostic apparatus is a system for transmitting ultrasonic pulses from the ultrasonic probe 102 to the subject 100, receiving reflected ultrasonic waves reflected inside the subject 100, and generating image information (ultrasonic images) of the inside of the subject 100 using the received reflected ultrasonic waves. The ultrasonic images obtained by the ultrasonic diagnostic apparatus are used in various clinical examinations.

[0042] The ultrasonic probe 102 is an electronic scanning probe, and has a plurality of transducers 101 arranged in one or two dimensions at the front end of the ultrasonic probe 102. The transducer 101 is an electromechanical conversion element that performs mutual conversion between an electrical signal (voltage pulse signal) and an ultrasonic wave (sound wave). The ultrasonic probe 102 transmits ultrasonic waves to the subject 100 from the plurality of transducers 101, and receives reflected ultrasonic waves from the subject 100 from the plurality of transducers 101. The reflected ultrasonic waves reflect the difference in acoustic impedance in the subject 100. Furthermore, when the transmitted ultrasonic pulse is reflected on the surface of a moving blood flow, a heart wall, etc., the reflected wave signal is subjected to a frequency shift due to the Doppler effect, depending on the velocity component of the moving body relative to the direction in which the ultrasonic wave is transmitted.

[0043] The transmission circuit 104 is a transmission unit that outputs a pulse signal (driving signal) to the plurality of vibrators 101. The transmission circuit 104 applies a pulse signal with a time difference to the plurality of vibrators 101, thereby transmitting ultrasonic waves with different delay times from the plurality of vibrators 101. Thus, a transmission ultrasonic beam is formed. The transmission circuit 104 selectively changes the vibrator 101 to which the pulse signal is applied (that is, the vibrator 101 to be driven), or changes the delay time (application timing) of the pulse signal, thereby being able to control the direction and focus of the transmission ultrasonic beam. The direction and focus of the transmission ultrasonic beam are sequentially changed, thereby scanning (scanning) the observation area inside the subject 100. In addition, the transmission circuit 104 can change the delay time of the pulse signal to form a transmission ultrasonic beam as a plane wave (focused far away) or a diffuse wave (with respect to the plurality of vibrators 101, the focus point is the opposite direction of the ultrasonic transmission direction), or can use one vibrator 101 or a part of the plurality of vibrators 101 to form a transmission ultrasonic beam. The transmitting circuit 104 transmits a pulse signal of a predetermined driving waveform to the vibrator 101, thereby generating a transmission ultrasonic wave having a predetermined transmission waveform in the vibrator 101. The receiving circuit 105 is a receiving unit that inputs an electrical signal output from the vibrator 101 that receives the reflected ultrasonic wave as a reception signal. The reception signal is input to the reception signal processing unit 106. Furthermore, the receiving circuit 105 converts the analog reception signal (analog signal) input from the vibrator 101 into a digital reception signal (digital signal), and outputs the digital reception signal to the reception signal processing unit 106.

[0044] In this specification, analog signals output from the oscillator 101 and digital data obtained by sampling (digital conversion) the analog signals are not particularly distinguished, and both are referred to as received signals. However, in order to clearly indicate digital data according to the context, received signals are sometimes described as received data.

[0045] The operation of the transmission circuit 104 and the reception circuit 105, that is, the transmission and reception of ultrasound waves, is controlled by the system control unit 109. The system control unit 109 changes the voltage signal and forms the position of transmitting ultrasound waves, for example, based on the generation of B-mode images and blood flow images described later.

[0046] In the case of generating a B-mode image, the ultrasonic diagnostic device obtains the received signal of the reflected ultrasonic wave obtained by scanning the observation area, and uses the obtained received signal for image generation. In the case of generating a blood flow image, the ultrasonic diagnostic device performs multiple transmission and reception of ultrasonic waves on one or more scanning lines in the observation area, respectively, obtains multiple frames of received signals of reflected ultrasonic waves, and uses the obtained multiple frames of received signals for image generation, i.e., extracting blood flow information. The scanning for generating a blood flow image can be a method of performing multiple transmission and reception of ultrasonic waves on one scanning line and then performing the same transmission and reception on the next scanning line, or a method of performing multiple repeated transmission and reception on each scanning line. In addition, the generation of B-mode images and blood flow images can also be a method of reducing the number of scanning lines, such as transmitting a plane wave or a diffuse wave and transmitting ultrasonic waves in a large range of the observation area. In addition, the ultrasonic diagnostic device can also change the transmission angle of the plane wave or diffuse wave or the range of the observation area to be transmitted, perform multiple transmission and reception on a large range of the observation area, and add the received signals for use.

[0047] The reception signal processing unit 106 is an image generating unit that generates various images (various image data) based on the reception signal obtained from the ultrasonic probe 102. The image processing unit 107 performs image processing such as brightness adjustment, interpolation, and filtering on the image data generated by the reception signal processing unit 106. The display device 108 is a display unit for displaying image data and various information, and is composed of various displays such as a liquid crystal display and an organic EL display. The system control unit 109 is a control unit that controls the transmission circuit 104, the reception circuit 105, the reception signal processing unit 106, the image processing unit 107, and the display device 108.

[0048] (Structure of the receiving signal processing module)

[0049] Figure 2 1 is a block diagram showing an example of functions of the reception signal processing unit 106 according to the first embodiment. The reception signal processing unit 106 includes a reception signal storage unit 200, a phasing and adding processing unit 201, a signal storage unit 202, a B-mode processing unit 203, a displacement correction unit 204, a Doppler processing unit 205, and a super-resolution processing unit 206.

[0050] The received signal storage unit 200 stores the received signal output from the receiving circuit 105. In addition, depending on the device structure and the type of the received signal, the received signal storage unit 200 may not store the received signal, but the received signal processed by the phasing and adding processing unit 201 described later may be stored in the signal storage unit 202. In addition, the received signal storage unit 200 may be formed of a common module with the signal storage unit 202 described later, and store (storage) the received signal from the receiving circuit 105 and the received signal processed by the phasing and adding processing unit 201.

[0051] The phasing addition processing unit 201 performs phasing addition processing and quadrature detection processing on the received signal obtained by the receiving circuit 105, and stores the received signals after these processing in the signal storage unit 202. The phasing addition processing refers to the processing of changing the delay time and weight according to each vibrator 101 and adding the received signals of multiple vibrators 101 to form a received ultrasonic beam, which is also called Delay and Sum (DAS) beam forming. The quadrature detection processing is the processing of converting the received signal into an in-phase signal (I signal) and a quadrature signal (Q signal) of the baseband frequency band. The phasing addition processing and the quadrature detection processing are performed based on the component configuration input from the system control unit 109 and various conditions (aperture control, signal filtering) in image generation. The received signal after the phasing addition processing and the quadrature detection processing is stored in the signal storage unit 202. Although a representative example of DAS beamforming is shown here, the phasing and adding processing unit 201 may use any processing as long as it is a processing for forming a reception ultrasonic beam, such as adaptive beamforming, model-based processing, processing using machine learning, or the like.

[0052] The B-mode processing unit 203 performs B-mode processing such as envelope detection processing and logarithmic compression processing on the received signal for generating the B-mode image stored in the signal storage unit 202, and generates image data in which the signal intensity at each point in the observation area is represented by the brightness intensity. Alternatively, the B-mode processing unit 203 may perform B-mode processing on the received signal whose displacement has been corrected by the displacement correction unit 204 described later.

[0053] The displacement amount correction unit 204 calculates the amount of tissue displacement caused by body movement etc. between frames based on the received signals of the acquired multiple frames. The amount of tissue displacement is calculated, for example, by a block matching operation (Block Matching) called speckle tracking method. The displacement amount correction unit 204 sets a region of interest in a frame, tracks the region of interest between frames using correlation, and thereby calculates the displacement of the region of interest. In addition, the displacement amount correction unit 204 sets multiple regions of interest in a frame, and thereby calculates the displacement of the entire frame. Here, an example of a method using correlation between frames is shown, but any method can be used as long as the displacement of the region of interest can be obtained. The scattered image of ultrasound reflected from the scatter body in the body tissue, i.e., speckle, is tracked by using an ultrasonic signal, so it is called speckle tracking. Alternatively, the correlation operation of the region of interest between frames may be performed on the received signal after any of the phase addition processing, orthogonal detection processing, and envelope detection processing. In addition, the displacement correction unit 204 can perform correlation calculation on the time waveform of the received signal, or can perform correlation calculation on the frequency space data obtained by discrete Fourier transforming the received signal. The displacement correction unit 204 uses the calculated displacement to move the received signal relative to the reference frame to correct the displacement. The corrected displacement can be a value that is consistent with the entire frame, such as the average value of the displacement of each region of interest, or can be changed within the frame according to each region of interest. Alternatively, the displacement of the entire frame calculated according to each region of interest can be interpolated in the time direction of the data sequence within the frame and multiple frames using linear interpolation, spline interpolation, etc. Alternatively, when correcting the displacement, the displacement correction unit 204 can interpolate the received signal in a manner that can correct it more finely, and then move the received signal relative to the reference frame.

[0054] The Doppler processing unit 205 extracts blood flow information (Doppler information) from the received signal for generating a blood flow image stored in the signal storage unit 202, and generates a blood flow image (blood flow image data) in which the blood flow information is visualized. Alternatively, the Doppler processing unit 205 may perform Doppler processing on the received signal whose displacement has been corrected by the displacement correction unit 204.

[0055] The processing content of the Doppler processing unit 205 is described in detail. The Doppler processing unit 205 performs frequency analysis on the received signal for generating the Doppler image stored in the signal storage unit 202, thereby extracting blood flow information based on the Doppler effect of the object within the scanning range. In this embodiment, the example of the object being blood is mainly used for explanation, but the object may also be an object such as a tissue in the body or a contrast agent. In addition, examples of blood flow information include at least one of velocity, discrete value, and energy value. In addition, the Doppler processing unit 205 can obtain the blood flow information of a point (a position) in the subject, and can also obtain the blood flow information of multiple positions in the depth direction. Alternatively, the Doppler processing unit 205 can obtain the blood flow information at multiple time points in the time series in a manner that can display the time changes of the blood flow information.

[0056] When a blood flow image is generated by the Doppler method, a received signal data sequence of multiple frames in the time direction is acquired at one or more positions. The Doppler processing unit 205 applies an MTI (Moving Target Indicator) filter to the received signal data sequence, thereby reducing the components (clutter components) from tissues that are stationary or have little movement between frames, and extracting components from the blood flow. In addition, the Doppler processing unit 205 calculates blood flow information such as blood flow velocity, blood flow dispersion, and blood flow energy based on the blood flow components.

[0057] As an MTI filter, you can use a Butterworth-type IIR (infinite impulse response) filter, a polynomial regression filter, or other filters with fixed filter coefficients, or you can use an adaptive filter that uses eigenvalue decomposition or singular value decomposition to change the coefficients according to the input signal. You can also use eigenvalue decomposition or singular value decomposition to decompose the received signal data into one or more bases, extract only a specific base, and remove the noise component.

[0058] Alternatively, the Doppler processing unit 205 may use a method such as a vector Doppler method, a speckle tracking method, or a vector flow mapping method to obtain a velocity vector at each coordinate in the image and obtain a blood flow vector indicating the magnitude and direction of the blood flow.

[0059] The super-resolution processing unit 206 generates super-resolution blood flow image data, which is a blood flow image with improved resolution, based on the blood flow image data generated by the Doppler processing unit 205. A method of acquiring a super-resolution blood flow image according to the present embodiment will be described in the description of the processing flow.

[0060] Image data output from the B-mode processing unit 203, the Doppler processing unit 205, and the super-resolution processing unit 206 are processed by the image processing unit 107 and finally displayed on the display device 108. The respective image data may be displayed superimposed or in parallel, or only a part of the image data may be displayed.

[0061] Alternatively, the received signal processing unit 106 may be composed of one or more processors and memories. In this case, Figure 2 The functions of the components 201, 203 to 206 shown are implemented by computer programs. Figure 2 The received signal storage unit 200 and the signal storage unit 202 shown are implemented by a memory. For example, a CPU (Central Processing Unit) reads and executes a program stored in the memory, thereby providing the functions of each unit 201, 203 to 206. In addition to the CPU, the received signal processing unit 106 may also include a processor (GPU, FPGA, etc.) responsible for the calculation of the B-mode processing unit 203, the displacement correction unit 204, the Doppler processing unit 205, and the super-resolution processing unit 206. The memory may include a memory for non-temporary storage of programs, a memory for temporarily storing data such as received signals, a working memory used by the CPU, etc.

[0062] The overall configuration of the ultrasonic diagnostic apparatus according to the first embodiment has been described above. Next, a processing flow for acquiring a super-resolution blood flow image according to the present embodiment will be described.

[0063] (Processing Flow for Acquiring Super-resolution Blood Flow Images in the First Embodiment)

[0064] Reference Figure 3 , describing the processing flow for acquiring a super-resolution blood flow image in this embodiment. Figure 3 This is a diagram showing an example of a process flow for acquiring a super-resolution blood flow image executed by the ultrasonic diagnostic apparatus according to the first embodiment.

[0065] The ultrasonic probe 102 repeats ultrasonic transmission and reception at the same position in a manner that includes the area of ​​the object for which blood flow information is to be obtained, and the receiving circuit 105 and the phased addition processing unit 201 create a received signal including a data column of multiple frames in the time direction (step S310). That is, the receiving circuit 105 and the phased addition processing unit 201 obtain a data column of reflected wave data collected in multiple frames in the time direction at one or more positions through ultrasonic transmission and reception. The receiving circuit 105 and the phased addition processing unit 201 are, for example, an example of an acquisition unit. Compared with the generation of a normal Doppler image, the number of frames of the data column when generating the super-resolution blood flow image involved in this embodiment is sometimes more. For example, the number of frames of the data column when generating a normal Doppler image is about 5 to 20, compared with which, the number of frames of the data column when generating a super-resolution blood flow image sometimes requires hundreds to tens of thousands. The phased addition processing unit 201 performs phased addition processing and orthogonal detection processing on the received signal including the data sequence of multiple frames, and the received signal subjected to the phased addition processing and orthogonal detection processing is stored in the signal storage unit 202. In addition, one frame in the data sequence may be a received signal in which ultrasound is transmitted and received once in a manner including the observation area, or may be a received signal in which multiple transmissions and receptions are added together in a manner including the observation area to form one frame. For example, the ultrasonic probe 102 transmits a plane wave or a diffuse wave in a manner including the observation area, and the phased addition processing unit 201 adds together multiple received signals with the transmission angle changed to form one frame of data.

[0066] The displacement amount correction unit 204 calculates the amount of tissue displacement caused by body movement etc. between frames based on the received signal including the data sequence of multiple frames, and uses the calculated displacement amount to correct the displacement amount by moving the received signal in such a manner that the position of the tissue is aligned within the frame to be accumulated in step S370 described later (step S320). At this time, the reference frame for calculating the displacement amount may be the entire data sequence as one frame, or may be set to multiple reference frames by changing the position in the time direction, or may be calculated between adjacent frames.

[0067] The Doppler processing unit 205 applies an MTI filter to the received signal including the data sequence of multiple frames whose displacements are corrected by the displacement correction unit 204, thereby reducing the clutter components and extracting the components from the blood flow to generate blood flow images of multiple frames (step S330). At this time, when designing the MTI filter, a part of the data sequence can be taken out for use, or the entire data sequence can be used. In this way, in step S330, the Doppler processing unit 205 reduces the clutter components from the tissue based on the data sequence of multiple frames to estimate the first blood flow information.

[0068] The Doppler processing unit 205 increases the number of receiving scan lines by interpolation, thereby improving the resolution of the local maximum value described later (step S340). Since the blood flow signal is a complex signal, interpolation is performed in the scan line direction, thereby performing phase interpolation to improve the resolution. Originally, it is preferred to increase the number of receiving scan lines by using beamforming, but interpolation is performed at this point, thereby reducing the processing load. In other words, interpolation is performed at a timing after beamforming instead of at the timing of beamforming, thereby reducing the processing load during beamforming. When the number of receiving scan lines is sufficiently ensured by beamforming, the processing of step S340 is not required.

[0069] Then, the super-resolution processing unit 206 extracts (detects) the local maximum of the energy value of each frame and calculates the autocorrelation function of those positions (step S350). The local maximum refers to, for example, the maximum value of the energy value of each of the multiple local regions of each frame. In addition, the super-resolution processing unit 206 accumulates the local maximum of the energy value and the value of the autocorrelation function for each frame (step S360). The accumulated energy value c0(x, y) and the accumulated autocorrelation function c1(x, y) of the position (x, y) can be expressed by the following equations (1) and (2).

[0070] [Formula 1]

[0071]

[0072] [Formula 2]

[0073]

[0074] Here, z(x, y, n) is the IQ signal (complex signal of baseband. I: Inphse, real part, Q: Quadrature phase, imaginary part) of step S350 at the position (x, y) of the nth frame. * represents complex conjugate. p(x, y, n) is a function that returns 1 when the point at the position (x, y) of the nth frame is a local maximum and returns 0 when it is not a local maximum. N is the total number of frames to be processed.

[0075] Thus, in step S350 and step S360, the super-resolution processing unit 206 generates the second blood flow information based on the first blood flow information, and the second blood flow information is an image of the scalar value of the blood flow signal, that is, the energy value. It is also possible that the super-resolution processing unit 206 generates an image of the scalar value of the blood flow signal, that is, the amplitude of the blood flow signal, as the second blood flow information based on the first blood flow information. It is also possible that the super-resolution processing unit 206 generates an image of other scalar values ​​of the blood flow signal as the second blood flow information based on the first blood flow information. It is also possible that in step S360, the super-resolution processing unit 206 does not accumulate the image from which the local maximum value is extracted, but accumulates the following image, which is an image that emphasizes the local maximum value by attenuating the pixel values ​​outside the local maximum point. As a result, the obtained image is used for the processing in step S370 described later. Moreover, although the original image is also included to some extent, it is also possible to make the attenuation amount infinite and extract only the pixel value of the local maximum point. In step S360, the super-resolution processing unit 206 detects the image value of a predetermined range or the position of the local maximum point with respect to the second blood flow information, and creates an image that emphasizes the pixel value of the local maximum point. The image point of the predetermined range may refer to the portion of the maximum image value in each image, or may be the position of the image value of the range with a threshold set relative to the maximum image value. Alternatively, when the pixel value of the focus point is greater than the adjacent pixel value, it may be set as the local maximum point. Alternatively, the maximum point of each local area may be extracted, and multiple local maximum points may be extracted in the image.

[0076] In addition, in step S350 and step S360, the super-resolution processing unit 206 detects the local maximum value of the second blood flow information, and accumulates the local maximum value to generate a scalar value accumulation image. In addition, in step S350 and step S360, the super-resolution processing unit 206 calculates the autocorrelation function of the first blood flow information in the frame direction (time axis direction) at the position of the local maximum value, and accumulates the autocorrelation function to generate an autocorrelation function accumulation image. In addition, the super-resolution processing unit 206 detects the local maximum value of the energy of the second blood flow information, and accumulates the local maximum value to generate an energy value accumulation image as a scalar value accumulation image.

[0077] Then, the super-resolution processing unit 206 uses the accumulated energy value and the accumulated autocorrelation function value to color the energy value (step S370). For example, the super-resolution processing unit 206 calculates the blood flow velocity based on the value of the autocorrelation function, converts the blood flow velocity (blood flow velocity value) and the energy value into RGB based on a two-dimensional color map (colormap) and displays the image. That is, the super-resolution processing unit 206 displays the image obtained by converting the two-dimensional color map into RGB on the display device 108. The energy value for display is calculated by log compression of c0(x, y) of formula (1). The velocity value for display is calculated based on the complex deflection angle of c1(x, y) of formula (2).

[0078] Alternatively, in step S370, the super-resolution processing unit 206 calculates only the direction of blood flow based on the value of the autocorrelation function, and distinguishes colors according to the direction, so that the energy value is displayed on the display device 108. The direction of blood flow is determined by the sign of the imaginary part of the autocorrelation function. Therefore, when only the direction of blood flow is needed, the super-resolution processing unit 206 calculates the imaginary part c1im(x, y) of the autocorrelation function c1(x, y) according to the following formula (3), and the direction of blood flow can be calculated based on the sign of the imaginary part c1im(x, y).

[0079] [Formula 3]

[0080]

[0081] Furthermore, the subscript re represents a real part, and the subscript im represents an imaginary part.

[0082] As described above, in step S370, the super-resolution processing unit 206 generates the third blood flow information having a higher resolution than the first blood flow information and in which the blood flow is differentiated by color based on the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on the display device 108. In addition, in step S370, the super-resolution processing unit 206 calculates the information on the direction of the blood flow (information related to the direction of the blood flow) based on the autocorrelation function integrated image, and generates the third blood flow information differentiated by color for each blood flow direction based on the scalar value integrated image and the information on the direction of the blood flow. That is, the super-resolution processing unit 206 generates the third blood flow information differentiated by color for each blood flow direction based on the scalar value integrated image and the information related to the direction of the blood flow at the position where the local maximum value is obtained.

[0083] Reference FIG. 4A to FIG. 4D The effects obtained by the ultrasonic diagnostic apparatus according to the first embodiment will be described. FIG. 4A to FIG. 4D are images of various renal blood flows. Figure 4AThe image shown is an example of an image displayed in grayscale (grayscale image) obtained by conventional technology without performing super-resolution. Figure 4B The image shown is an example of a conventional directional color-displayed image (color image) without super-resolution. Figure 4C The image shown is an example of an image displayed in grayscale with super-resolution without direction. Figure 4D The image shown is an example of an image displayed by super-resolution in which colors are differentiated according to directions in this embodiment.

[0084] exist Figure 4D In the image shown, the area shaded by the diagonal lines slanting upward to the right is displayed in red, for example, and is the area of ​​the renal artery flowing in the direction toward the ultrasonic probe 102. Figure 4D In the illustrated image, a region shaded by a downwardly sloping line, for example, is displayed in blue, and is a region of the renal vein flowing in a direction away from the ultrasonic probe 102 . Figure 4D The images shown are Figure 4A The image shown, Figure 4B The images shown have an overwhelmingly high resolution, comparable to Figure 4C Compared with the image shown in which the colors are differentiated only by direction, the renal artery flowing in the direction toward the ultrasonic probe 102 and the renal vein flowing in the direction away from the ultrasonic probe 102 can be distinguished and displayed, thereby increasing the amount of information. In this way, compared with the case where the energy value of the blood flow is simply displayed by super-resolution using grayscale, the energy value of the blood flow is differentiated by color according to the speed and direction of the blood flow, and thus, when the parallel blood vessels are arteries and veins, the blood vessels can be separated by different colors, making the display easier to understand.

[0085] Moreover, the above description describes a case where the ultrasonic diagnostic apparatus only uses the direction of blood flow in the blood flow information, but it is also possible that the Doppler processing unit 205 calculates the blood flow velocity v(x, y) according to the following formula (4), and the ultrasonic diagnostic apparatus displays it using a color mapping of the velocity only or a two-dimensional color mapping of the velocity and energy.

[0086] [Formula 4]

[0087] v(x,y)=agnle(c1(x,y))·(4)

[0088] Here, angle(c1) is a function that calculates the deflection angle of the complex number c1.

[0089] Alternatively, the super-resolution processing unit 206 may calculate the discrete value σ(x, y) using the following equation (5), and the ultrasonic diagnostic apparatus may display the discrete value using a two-dimensional color map of the velocity and the discrete value.

[0090] [Formula 5]

[0091]

[0092] In this way, the super-resolution processing unit 206 calculates the velocity and dispersion of the blood flow based on the energy value cumulative image and the autocorrelation function cumulative image. Alternatively, the super-resolution processing unit 206 calculates the information on the direction of the blood flow based on the autocorrelation function cumulative image, and displays two or three of the velocity, dispersion, and energy value as the third blood flow information in color on the display device 108.

[0093] The first embodiment is described above. According to the first embodiment, the energy of the blood flow can be colored according to the direction and speed of the blood flow and displayed by super-resolution. That is, according to the first embodiment, the blood flow can be distinguished by color and displayed by super-resolution. Therefore, according to the first embodiment, information that is highly convenient for the user can be obtained.

[0094] (Second Embodiment)

[0095] Next, the second embodiment is described. Here, it is known that there is a situation as follows: Generally, in a blood flow image obtained by the Doppler method, the image value (pixel value) of a blood vessel with a fast flow rate and a large size is higher than that of a blood vessel with a slow flow rate and a small size. Therefore, in the technique disclosed in the above-mentioned non-patent document 1, when an image includes adjacent blood vessels with a flow rate difference, that is, a diameter difference, only the blood vessels with a fast flow rate and a large size are extracted, and the blood vessels with a slow flow rate and a small size are not represented in the blood flow image with improved resolution. In addition, non-patent document 1 does not record a method for super-resolution using the difference in blood flow direction and blood flow velocity.

[0096] Therefore, as an ultrasonic diagnostic apparatus, an image processing apparatus, and an image processing method according to a second embodiment, an ultrasonic diagnostic apparatus, an image processing apparatus, and an image processing method are described that can display blood flow information (blood flow images) with improved resolution in a wide range of flow velocities or blood vessels of various diameters or shapes. In the following, in the description of the second embodiment, the description of the same structure as the first embodiment is omitted, and the description of the structure different from the first embodiment is mainly performed.

[0097] In the second embodiment, a plurality of blood flow images are divided into image groups based on features with the flow velocity ranges of each of the images as bases, and processing is performed on each image of the image group formed based on the features to emphasize the image values ​​corresponding to a predetermined numerical range or a local maximum value, and the emphasized image values ​​of each base are added. In this embodiment, image values ​​are extracted from each image of the image group formed based on the features divided according to each flow velocity range, so that both blood vessels with a fast flow velocity and blood vessels with a slow flow velocity can be extracted, thereby improving resolution.

[0098] The ultrasonic diagnostic device involved in the second embodiment described below includes a processing circuit. The processing circuit acquires reflected wave data collected in multiple frames in the time direction at one or more positions by ultrasonic transmission and reception. The processing circuit estimates the first blood flow information by reducing the clutter components from the tissue based on the data columns of the multiple frames. The processing circuit performs an orthogonal transformation in the frame direction based on the first blood flow information to thereby calculate the second blood flow information, creates an image of a scalar value according to each frequency of the second blood flow information, detects the local maximum point of the image of the scalar value at each frequency and performs processing to emphasize the local maximum point, generates the third blood flow information based on the frequency information and the image of the local maximum point of the scalar value at each frequency, and displays the third blood flow information on the display.

[0099] The super-resolution processing unit 206 according to the second embodiment generates a blood flow image with improved resolution, i.e., super-resolution blood flow image data, based on the blood flow image data generated by the Doppler processing unit 205. The conventional method of acquiring a super-resolution blood flow image according to the present embodiment is described when the processing flow is described. Next, the processing flow for acquiring a super-resolution blood flow image according to the present embodiment is described.

[0100] (Processing Flow for Acquiring Super-resolution Blood Flow Images in Second Embodiment)

[0101] Reference Figure 5 , describing the processing flow for acquiring a super-resolution blood flow image in this embodiment. Figure 5 1 is a diagram showing an example of a process flow for acquiring a super-resolution blood flow image executed by the ultrasonic diagnostic apparatus according to Embodiment 2. In addition, the process of step S420 and the process of step S440 indicated by the blocks with dotted lines may not be executed.

[0102] The ultrasonic diagnostic apparatus according to the second embodiment executes the process in step S410. The process in step S410 is the same as the process in step S310 described above.

[0103] The displacement amount correction unit 204 calculates the amount of tissue displacement between frames due to body movement, etc., based on the received signal including the data sequence of multiple frames, and uses the calculated displacement to move the received signal to correct the displacement (step S420). At this time, the reference frame for calculating the displacement may be the entire data sequence as one frame, or may be a plurality of reference frames set according to the change in the time direction position, or may be calculated between adjacent frames.

[0104] The Doppler processing unit 205 applies an MTI filter to the received signal including the data sequence of multiple frames whose displacements are corrected by the displacement correction unit 204, thereby reducing the clutter components and extracting the components from the blood flow to generate blood flow images of multiple frames (step S430). At this time, when designing the MTI filter, a part of the data sequence can be taken out for use, or the entire data sequence can be used. In this way, in step S430, the Doppler processing unit 205 reduces the clutter components from the tissue based on the data sequence of multiple frames to estimate the first blood flow information. Fig. 6A An example of a plurality of frames of blood flow images generated in step S430 is shown. Fig. 6A FIG. 4 is a diagram showing an example of a plurality of frames of blood flow images generated in step S430 in the second embodiment. Fig. 6A The lightness or darkness of the image value at each position within the position 401 including the blood flow information shown is caused by the difference in flow velocity. Therefore, the flow velocity is fast at a location with a large image value (dark color) and the flow velocity is slow at a location with a small image value (light color).

[0105] Here, for comparison, a processing flow for acquiring a super-resolution blood flow image in the related art will be described, and after describing the problem, the continuation of the processing flow for acquiring a super-resolution blood flow image according to the present embodiment will be described. Figure 7 is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image in the prior art. FIG. 8A to FIG. 8C This is a diagram for explaining an example of a processing method in the related art. Figure 7 The processing of steps S510 to S530 is Figure 5 The processing of steps S410 to S430 is the same. In addition, Fig. 8A Show Figure 7 As a result of the processing of steps S510 to S530, a plurality of frames of blood flow images are obtained, but Fig. 8A The blood flow images of multiple frames shown are Fig. 6A The blood flow images of the multiple frames shown are similar.

[0106] In each blood flow image of multiple frames, a portion of an image value within a predetermined range is extracted (S540). The image value within the predetermined range may refer to the portion of the maximum image value of the entire image, or a threshold may be set for the maximum image value. That is, a portion having an image value above a predetermined value in each blood vessel image is extracted, thereby obtaining Figure 8B As shown in the image. Figure 8B The dotted line indicates Fig. 8A The position 401 containing the blood flow information.

[0107] Since the part with the image value in a predetermined range moves between frames, a plurality of blood flow images from which the image values ​​are extracted are integrated, and thus a Doppler image is generated and displayed in comparison with each blood flow image, that is, a normal Doppler image. Figure 8CThe super-resolution blood flow image with improved resolution is obtained (step S550). Only the portion with high image value is extracted, so when the point spread function is assumed to be a Gaussian distribution, only the portion with high sharpness in the center of the distribution is extracted. Only points or a small range are extracted in an image of one frame, but the portion with high image value is moved between frames, so that when a plurality of blood flow images with extracted image values ​​are accumulated, a super-resolution blood flow image is obtained.

[0108] Here, the problem of the prior art is that Figure 8B as well as Figure 8C As shown, when a portion having a maximum image value or an image value greater than a certain value in each blood vessel image is extracted, only blood vessels having a large image value, that is, blood vessels having a fast flow velocity, are extracted.

[0109] Describe the problems of the prior art in detail. Fig. 9 is a diagram showing an example of Rayleigh distribution. It is known that, generally speaking, the amplitude distribution characteristics of an ultrasonic reception signal from a uniform scattering medium can be approximated to Fig. 9 The probability distribution shown is called Rayleigh distribution. In step S540, extracting a portion with a large amplitude value as an image value of a predetermined range means extracting a portion with a large amplitude value although the probability in Rayleigh distribution is small from the reflection signals from a plurality of red blood cells, thereby extracting only the reflection signals from a small number of red blood cells and making them sparse in space. However, in the case where blood vessels with a flow velocity difference exist adjacent to each other, when extracting a portion with a large amplitude value as an image value of a predetermined range, although the probability in Rayleigh distribution of a blood vessel with a fast flow velocity is small, only the reflection signal of the portion with a large amplitude value is extracted, and therefore the reflection signal of a blood vessel with a slow flow velocity and a small flow velocity is not extracted.

[0110] Fig. 10A An example of a blood flow image is shown in which a blood vessel with a fast flow rate and a small blood vessel with a slow flow rate are parallel to each other in the depth direction. A small blood vessel with a slow flow rate is arranged at a shallow depth, and a large blood vessel with a fast flow rate is arranged at a deep depth, but the gap between the two blood vessels is below the resolution, so they cannot be separated in the blood flow image. Fig. 10B FIG. 1 is a diagram showing an example of a super-resolution blood flow image generated by the conventional technology as a result of performing the processing of steps S540 and S550 on each of the blood flow images of a plurality of frames. Fig. 10C By comparing the super-resolution blood flow image generated by the present embodiment described later, it can be seen that Fig. 10B In the super-resolution blood flow image generated by the conventional technique shown, only the resolution of large blood vessels with a fast flow rate is improved, and small blood vessels with a slow flow rate are not imaged.

[0111] In order to extract blood vessels with small image values, that is, blood vessels with slow flow velocity, it is conceivable to expand the predetermined range of image values. However, considering the extraction of Gaussian distribution, the range of the extracted distribution will be expanded, so the effect of improving resolution will be reduced.

[0112] The following is a description of the subsequent processing flow for acquiring a super-resolution blood flow image according to the present embodiment in view of the above-mentioned problem. The Doppler processing unit 205 performs the processing in step S440. The processing in step S440 is the same as the processing in step S340 described above. Moreover, as in the first embodiment, when the number of reception scan lines is sufficiently ensured by beamforming, the processing in step S440 is not necessary.

[0113] The super-resolution processing unit 206 divides each blood flow image of a plurality of frames into images with different flow velocity ranges (step S450). Specifically, the super-resolution processing unit 206 uses a portion of the frames of the blood flow images of the plurality of frames to perform discrete Fourier transform on the changes in the image value of each pixel in the time direction. Discrete Fourier transform is a sine wave basis, which represents the frequency component. Therefore, the super-resolution processing unit 206 can divide the image according to the frequency component of the change between frames of each pixel of the blood flow image, that is, each flow velocity component. That is, in step S450, the super-resolution processing unit 206 can make it possible to divide the image into images with blood flow information of flow velocity ranges corresponding to each basis. The image with blood flow information of flow velocity ranges corresponding to each basis can also be an image of the frequency region after discrete Fourier transform, or an image of the time region obtained by inverse discrete Fourier transforming the image of the frequency region, but here, a method of processing in the image of the frequency region without inverse discrete Fourier transform is described. In this regard, a portion of the frames used for discrete Fourier transform in the blood flow images of multiple frames are changed in the time direction, thereby obtaining images with different time series corresponding to each basis. These are called feature-based image groups, and in the second embodiment, flow velocity is used as a feature, that is, flow velocity is used as a basis. Figure 6B , an example of dividing images into groups based on three features is shown. Figure 6BIn the above, the blood flow with different flow velocities in each basis is imaged. The super-resolution processing unit 206 increases the number of divisions, that is, the frames used in the discrete Fourier transform, thereby reducing the flow velocity range corresponding to each basis, so that the blood vessels can be divided according to each finer flow velocity range. Moreover, if it is a basis that can be divided according to the flow velocity range, in addition to the discrete Fourier transform based on the sine wave, it can also be a polynomial regression such as Legendre polynomial to divide the blood flow image into image groups based on the characteristics of each flow velocity range. As described above, in step S450, the super-resolution processing unit 206 performs Fourier transform in the frame direction based on the first blood flow information to calculate the second blood flow information. Moreover, instead of Fourier transform, the super-resolution processing unit 206 can also perform other orthogonal transforms. In addition, the super-resolution processing unit 206 creates a scalar value image according to each frequency of the second blood flow information. The image of each frequency is often a complex number, but the image sent to the next step S460 is converted into an image with a scalar value such as an energy value (the sum of the squares of the real part and the imaginary part) as a pixel that can be compared in size. The energy value has the property that the integral on the time axis and the integral on the frequency axis are the same according to Parseval's theorem, so it is easy to handle, so the energy value will be used for explanation below.

[0114] The super-resolution processing unit 206 detects the image value of a predetermined range or the position of the local maximum point for each image of the energy image group formed based on the characteristics of each base, and creates an image (peak-emphasized image) that emphasizes the pixel value of the local maximum point (step S460). The image point of the predetermined range can refer to the part of the maximum image value in each image, or the position of the image value in the range of setting a threshold for the maximum image value. Alternatively, when the pixel value of the focus point is greater than the adjacent pixel value, it is set as the local maximum point. Alternatively, the maximum point of each local area is extracted, and multiple local maximum points are extracted in the image. Then, an image (peak-emphasized image) that emphasizes the local maximum point is created, but for example, a peak-emphasized image can be created by attenuating the pixel values ​​other than the local maximum point by a fixed amount. This also contains the original image to some extent, but the attenuation amount can also be made infinite and only the pixel value of the local maximum point is extracted.

[0115] Figure 5 In FIG. 1 , three types of processing are illustrated as processing after the processing of step S460. In steps S470 and S480, the super-resolution processing unit 206 adds all the images with the local maximum points of the frequencies emphasized, and displays the image obtained by the addition on the display device 108. The details of the processing of steps S470 and S480 will be described later.

[0116] In steps S490 and S491, the super-resolution processing unit 206 divides the local maximum into two types, positive frequency and negative frequency, and adds them, and displays them on the display device 108 with different colors for positive and negative. That is, in steps S490 and S491, the super-resolution processing unit 206 adds the peak-emphasized images according to the type of each frequency, and colors the images obtained by the addition differently for positive frequency and negative frequency, so that the colored positive frequency image and the colored negative frequency image are displayed on the display device 108. In steps S492 and S493, the super-resolution processing unit 206 divides the frequency into a plurality of groups, adds the local maximum, and displays them on the display device 108 with different colors for each group. That is, in steps S492 and S493, the super-resolution processing unit 206 adds the peak-emphasized images according to each frequency group, and colors the images obtained by the addition differently for each group, so that the colored images of each group are displayed on the display device 108. Fig.12 Schematic diagram showing the processing of steps S450 to S480. In step S450, the data of N frames are Fourier transformed in the frame direction. In step S460, the local maximum of the energy value is extracted for each frequency. In step S470, the local maximum of each frequency is added. The Fourier transform area partially overlaps with a part of the previous area.

[0117] Alternatively, the super-resolution processing unit 206 may interpolate the number of pixels of each image of the image group or the original blood flow images of multiple frames based on the characteristics of each basis by linear interpolation, spline interpolation, etc., and then extract the image value within a predetermined range or the location of the local maximum value. This is because the pixel size is different in the deep and shallow parts of the convex array probe and the sector probe. When the pixel is coarse, even if the image value is extracted and the addition process of step S470 is performed to perform super-resolution processing, the resolution improvement effect is small. In addition, in the case of linear processing, the same result is obtained whether the processing is performed on the time axis or the frequency axis, but in the case of non-linear processing of the local maximum value, the results of the two are different. In the case of the Fourier transform basis, the local maximum value is taken for each blood flow velocity, so it is easy to separate and extract blood vessels with different blood flow velocities.

[0118] Obtained through the processing of step S460 Figure 6CThe image shown in FIG. 4 is different from the prior art because the super-resolution processing unit 206 extracts image values ​​from each image divided by each flow rate, and thus can extract both blood vessels with large image values, i.e., blood vessels with fast flow rate and large size, and blood vessels with small image values, i.e., blood vessels with slow flow rate and small size. That is, the super-resolution processing unit 206 can extract blood vessels with a wide range of flow rates. In addition, when the energy level of the pixel value of a certain blood vessel (e.g., the standard deviation of the pixel value) is close to the energy of the noise, the images divided by each flow rate are generated by the processing of step S450, whereby the noise energy is discrete on each basis, and the SNR (Signal-to-Noise Ratio) or CNR (Contrast to Noise Ratio) of the images divided by each flow rate can be improved.

[0119] Therefore, even for blood vessels that cannot be distinguished from noise in the conventional technology, the super-resolution processing unit 206 can extract blood vessels. In addition, by dividing into multiple images through the processing of step S450, the pixel values ​​of each image become sparse, and for example, in the processing of step S460 for selecting a local peak, multiple pixels can be selected with the same number of frames compared with the conventional technology. As a result, the measurement time required to obtain the final image can be shortened.

[0120] As described above, in step S460 , the super-resolution processing unit 206 performs processing for detecting the local maximum point of the image of the scalar value at each frequency and emphasizing the scalar value of the local maximum point.

[0121] The image values ​​of a predetermined range or the location of the local maximum point are shifted between the images in the image group based on the feature. Therefore, the super-resolution processing unit 206 adds the images in the image group based on the predetermined range of image values ​​or the features that emphasize the local maximum point between the image groups based on the feature and between the bases to generate Fig.6D The super-resolution blood flow image with improved resolution as shown is displayed on the display device 108 (step S480). The super-resolution processing unit 206 may display the result of adding the image and the basis of the image group based on all the features as an image on the display device 108, or may display the moving image on the display device 108 by successively updating and displaying the intermediate images added in time series order on the display device 108, or may display the intermediate images in parallel on the display device 108 without updating them. In addition, the super-resolution processing unit 206 may use the image and the basis of the time series range to be used specified by the user instead of using the image and the basis of the image group based on the features for addition.

[0122] The super-resolution processing unit 206 can extract blood vessels with a wide range of flow velocities by making the weights of the image values ​​of each basis uniform during addition. On the other hand, the super-resolution processing unit 206 can also emphasize blood vessels with a certain flow velocity by changing the weights of the image values ​​of each basis. For example, the super-resolution processing unit 206 can emphasize blood vessels with a slow flow velocity by making the weights of the basis corresponding to the blood vessels with a slow flow velocity large.

[0123] In this way, blood vessels with a wide range of flow velocities can be extracted from blood flow images of a plurality of frames to generate a super-resolution blood flow image with improved resolution.

[0124] The specific display method in step S480 is described. The energy value obtained by adding the peak-emphasized images for each frequency up to step S470 is calculated. That is, the super-resolution processing unit 206 adds the peak-emphasized images for each frequency as the processing of step S470 between the processing of step S460 and the processing of step S480, thereby calculating the energy value obtained by adding the peak-emphasized images for the entire frequency. Alternatively, as in the processing of step S490, the super-resolution processing unit 206 adds the peak-emphasized images for each positive frequency and negative frequency. Alternatively, as in the processing of step S492, the super-resolution processing unit 206 groups the frequencies and adds the peak-emphasized images for each group.

[0125] Previous Fig. 10C FIG. 4 shows an image in which the energy values ​​obtained by the process of step S470 are added and displayed in the entire frequency. Figure 6B As shown, the frequency and the blood flow velocity can be compared, so a specific frequency can be displayed, thereby only a specific blood flow velocity can be displayed. It is also possible to change the color according to the frequency for display. For example, the positive frequency is the incoming blood flow, so the super-resolution processing unit 206 displays an image obtained by adding the energy of the positive frequency on the display device 108, thereby only the blood flow in the direction toward the ultrasonic probe 102 can be super-resolution displayed. In addition, the super-resolution processing unit 206 displays an image obtained by adding the energy of the negative frequency on the display device 108, thereby only the blood flow in the direction away from the ultrasonic probe 102 can be super-resolution displayed. In addition, the super-resolution processing unit 206 expresses the two with different hues and adds them with RGB, thereby simultaneously expressing the incoming blood flow and the distant blood flow. In addition, the super-resolution processing unit 206 sets a threshold for the energy of the incoming blood flow and displays the signal above the threshold in the front, thereby clearly expressing the artery. FIG. 11A to FIG. 11H Show what they look like.

[0126] Fig.11A to Fig.11H The figures show images of renal blood flow obtained by various methods and displayed on the display device 108 . Fig.11AThe image shown is an image that displays the blood flow energy value in grayscale without super-resolution display. Fig. 11B The image shown is an image in which the blood flow energy values ​​are differentiated by color according to the direction without super-resolution display. Fig. 11B In the image shown, the incoming blood flow is expressed in red, and the outgoing blood flow is expressed in blue. It is not possible to express only one of the incoming blood flow and the outgoing blood flow at one location. Fig. 11C The image shown is an image in which the blood flow energy value is displayed in grayscale after super-resolution processing is performed on the time axis of the conventional example. Fig.11D The image shown is an image that is super-resolved in the frequency axis according to this embodiment and the energy of the entire frequency is added to display the grayscale. Fig. 11C The image shown is set to the same as the image inside the circle. Fig.11D When compared with the image inside the circle of the image setting shown in Fig. 11C The image shown is set inside the circle compared to the image in Fig.11D The blood vessels in the image within the circle of the image setting shown are well separated and displayed. Fig.11E The image shown is an image showing energy of only positive frequencies (incoming blood flow). Fig.11E In the image shown, the oncoming blood flow is represented in red. Fig.11F The image shown is an image showing energy of only negative frequencies (away from blood flow). Fig.11F In the image shown, the distal blood flow is shown in blue. Fig.11G The image shown is a RGB Fig.11E The image shown is similar to Fig.11F The images shown are added to the image displayed. Fig.11H The image shown is an image that displays positive frequencies above a certain threshold in the front. In the case of the kidney, the incoming blood flow is the renal artery, and the outgoing blood flow is the renal vein. Therefore, the artery and vein can be displayed separately, even if the blood vessels are overlapped. Fig.11G The image shown, Fig.11H The arteries and veins can be displayed separately as shown in the image shown in FIG. Fig.11E The image shown shows only the arteries, or Fig.11F Only the veins are shown as in the image shown.

[0127] As described above, in steps S480, S491, and S493, the super-resolution processing unit 206 generates third blood flow information having a higher resolution than the first blood flow information based on the frequency information and the scalar value addition image of each frequency (an image that emphasizes the local maximum point of the scalar value of each frequency), and displays the third blood flow information on the display device 108. In addition, the super-resolution processing unit 206 adds a plurality of scalar value addition images of positive frequencies to calculate blood flow information of positive frequencies, adds a scalar value addition image of negative frequencies to calculate blood flow information of negative frequencies, and changes the coloring of the blood flow information of positive frequencies and the blood flow information of negative frequencies and displays them on the display device 108.

[0128] In addition, in step S491, the super-resolution processing unit 206 displays a third RGB image obtained by adding a first RGB image representing blood flow information of a positive frequency and a second RGB image representing blood flow information of a negative frequency on the display device 108, or displays either the first RGB image or the second RGB image in the front with priority on the display device 108. In addition, the super-resolution processing unit 206 calculates blood flow information of a positive frequency by adding a plurality of scalar value addition images of positive frequencies, or calculates blood flow information of a negative frequency by adding a plurality of scalar value addition images of negative frequencies, and displays the blood flow information of a positive frequency on the display device 108, or displays the blood flow information of a negative frequency on the display device 108.

[0129] In the above description, the super-resolution processing unit 206 classifies the frequencies into two types, positive and negative, but different colors may be applied to all frequencies. Each Doppler frequency corresponds to the velocity of blood flow. Therefore, the super-resolution processing unit 206 applies different colors to the blood flow velocity. That is, the super-resolution processing unit 206 displays the scalar value addition image of each frequency on the display device 108 with different hues for each frequency. Alternatively, the super-resolution processing unit 206 displays the blood flow velocity as the hue and the energy of the blood flow of the velocity as the brightness. Furthermore, when the super-resolution processing unit 206 calculates the centroid frequency (first-order moment) of the energy on the frequency axis, it becomes the average flow velocity of the blood flow. Alternatively, the super-resolution processing unit 206 displays only the average flow velocity or the two-dimensional color mapping of the average flow velocity and the energy. Furthermore, when the super-resolution processing unit 206 calculates the second-order moment of the energy on the frequency axis, it obtains the discreteness of the blood flow velocity. Alternatively, the super-resolution processing unit 206 displays the discreteness in combination with the velocity and the energy. Alternatively, the super-resolution processing unit 206 may create an image of energy values ​​at each frequency of the second blood flow information, detect a local maximum value of the image of the energy value at each frequency, accumulate the local maximum values ​​at each frequency, calculate an image that emphasizes the local maximum points of the energy values, calculate the average velocity and dispersion of the blood flow based on the image that emphasizes the local maximum points of the energy values ​​at each frequency, and display one or more of the average velocity, dispersion and energy value on the display device 108 using multi-dimensional color mapping.

[0130] The second embodiment is described above. According to the second embodiment, blood flow information (blood flow image) with improved resolution can be obtained in a wide range of flow velocities or blood vessels of various diameters or shapes. For example, according to the second embodiment, even overlapping blood vessels can be independently displayed with high resolution, and not only morphological information but also information such as the direction and speed of blood flow can be displayed simultaneously. Therefore, according to the second embodiment, information with high convenience for the user can be obtained.

[0131] (Other Embodiments)

[0132] The disclosed technology can be implemented, for example, in the form of a system, an apparatus, a method, a program, or as a recording medium (storage medium). Specifically, it can be applied to a system composed of multiple devices (for example, a host computer, an interface device, a camera device, a network application, etc.), and can also be applied to a device formed by one device. For example, the above technology can also be applied to an image processing device. For example, a memory included in the image processing device pre-stores a received signal including a data column of multiple frames in the time direction acquired by an ultrasonic diagnostic device. That is, the memory pre-stores a data column of reflected wave data collected in multiple frames in the time direction at one or more positions through ultrasonic transmission and reception performed by the ultrasonic diagnostic device. Moreover, the processor included in the image processing device acquires the above-mentioned received signal (the data column of reflected wave data collected in multiple frames) from the memory. Moreover, the processor performs the same processing as the processing performed by the above-mentioned ultrasonic diagnostic device on the acquired received signal.

[0133] In addition, the purpose of the present invention is of course achieved by the following. That is, a recording medium (or storage medium) recording the program code (computer program) of the software that realizes the functions of the described embodiment is supplied to the system or device. The storage medium is of course a computer-readable storage medium. Moreover, the computer (or CPU, MPU) of its system or device reads and executes the program code stored in the recording medium. In this case, the program code itself read from the recording medium realizes the functions of the described embodiment, and the recording medium recording its program code constitutes the present invention.

[0134] The term "processor" used in the description of the above-mentioned embodiments refers to, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an Application Specific Integrated Circuit (ASIC), a programmable logic device (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)) and other circuits. Here, instead of storing the program in the storage circuit 152, it is also possible to configure the program to be directly incorporated into the circuit of the processor. In this case, the processor reads out and executes the program incorporated into the circuit to thereby realize the function. In addition, each processor of each embodiment is not limited to being configured as a single circuit for each processor, and multiple independent circuits can also be combined to form a processor to realize its function.

[0135] Here, the program executed by the processor is provided by pre-assembling ROM (Read Only Memory), storage circuit, etc. In addition, the program may be provided by recording on a non-disposable storage medium readable by a computer such as CD (Compact Disk)-ROM (Read Only Optical Disk), FD (Flexible Disk), CD-R (Recordable Optical Disk), DVD (Digital Versatile Disk) in a folder in a form that can be installed by these devices or in an executable form. Alternatively, the program may be stored in a computer connected to a network such as the Internet and provided or transmitted via network download. For example, the program is composed of components including the above-mentioned processing functions. As actual hardware, the CPU reads out and executes the program from a storage medium such as ROM, whereby each component is loaded into the main storage device and generated on the main storage device.

[0136] Regarding the above-mentioned embodiment, the following supplementary notes are disclosed as one aspect and optional features of the invention.

[0137] (Note 1)

[0138] An ultrasonic diagnostic apparatus provided in one aspect of the present invention includes: an acquisition unit that acquires a data column of reflected wave data collected in multiple frames in a time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit that reduces clutter components from tissues based on the data column of the multiple frames to estimate first blood flow information; and a super-resolution processing unit that generates a scalar value image of a blood flow signal, i.e., second blood flow information, based on the first blood flow information, detects a local maximum value of the second blood flow information, accumulates the local maximum value or an image with the local maximum value emphasized to generate a scalar value accumulation image, calculates an autocorrelation function in the frame direction of the first blood flow information at the position of the local maximum value, accumulates the autocorrelation function to generate an autocorrelation function accumulation image, generates third blood flow information in which blood flow is distinguished by color based on the scalar value accumulation image and the autocorrelation function accumulation image, and displays the third blood flow information on a display unit.

[0139] (Note 2)

[0140] The super-resolution processing unit may calculate information on the direction of blood flow based on the autocorrelation function cumulative image, and generate the third blood flow information having colors differentiated according to the direction of blood flow based on the scalar value cumulative image and the information on the direction of blood flow.

[0141] (Note 3)

[0142] The scalar value may also be the energy value of the blood flow signal. The super-resolution processing unit may also detect the local maximum value of the energy value of the second blood flow information, accumulate the local maximum value or an image that emphasizes the local maximum value to generate an energy value accumulation image as a scalar value accumulation image, calculate the blood flow velocity and discreteness based on the energy value accumulation image and the autocorrelation function accumulation image, and display two or three of the velocity, discreteness and energy value as the third blood flow information in color on the display unit.

[0143] (Note 4)

[0144] The Doppler processing unit may interpolate the first blood flow information in which clutter components from the tissue are reduced in an ultrasonic scanning line direction to increase the number of scanning lines.

[0145] (Note 5)

[0146] An image processing device provided in one aspect of the present invention includes: an acquisition unit, which acquires a data column of reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit, which reduces clutter components from a group based on the data column of the multiple frames to estimate first blood flow information; and a super-resolution processing unit, which generates an image of a scalar value of a blood flow signal, i.e., second blood flow information, based on the first blood flow information, detects a local maximum value of the second blood flow information, accumulates the local maximum value or an image with the local maximum value emphasized to generate a scalar value accumulation image, calculates an autocorrelation function in the frame direction of the first blood flow information at the position of the local maximum value, accumulates the autocorrelation function to generate an autocorrelation function accumulation image, generates third blood flow information in which blood flow is distinguished by color based on the scalar value accumulation image and the autocorrelation function accumulation image, and displays the third blood flow information on a display unit.

[0147] (Note 6)

[0148] The image processing method provided in one aspect of the present invention performs the following processing: a process of acquiring a data column of reflected wave data collected in multiple frames in the time direction at one or more positions by ultrasonic transmission and reception; a process of reducing the clutter components from the tissue to estimate the first blood flow information based on the data column of the multiple frames; a process of generating an image of the scalar value of the blood flow signal, i.e., the second blood flow information, based on the first blood flow information; a process of detecting the local maximum value of the second blood flow information, and accumulating the local maximum value or the image with the local maximum value emphasized to generate a scalar value accumulated image; a process of calculating the autocorrelation function in the frame direction of the first blood flow information at the position of the local maximum value, and accumulating the autocorrelation function to generate an autocorrelation function accumulated image; a process of generating third blood flow information in which the blood flow is distinguished by color based on the scalar value accumulated image and the autocorrelation function accumulated image; and a process of displaying the third blood flow information on a display unit.

[0149] (Note 7)

[0150] The ultrasonic diagnostic apparatus provided in another aspect of the present invention includes: an acquisition unit, which acquires a data column of reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit, which reduces clutter components from tissues based on the data column of the multiple frames to estimate first blood flow information; and a super-resolution processing unit, which generates an image of a scalar value of a blood flow signal, i.e., second blood flow information, based on the first blood flow information, detects a local maximum value of the second blood flow information, accumulates the local maximum value or an image with the local maximum value emphasized to generate a scalar value accumulation image, generates third blood flow information in which the blood flow is distinguished by color based on the scalar value accumulation image and information related to the direction of the blood flow at the position where the local maximum value is taken, and displays the third blood flow information on the display unit.

[0151] (Note 8)

[0152] The ultrasonic diagnostic apparatus provided in another aspect of the present invention includes: an acquisition unit, which acquires reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit, which reduces the clutter components from the tissue based on the data columns of the multiple frames to estimate the first blood flow information; and a super-resolution processing unit, which performs an orthogonal transformation in the frame direction based on the first blood flow information to thereby calculate the second blood flow information, creates an image of a scalar value according to each frequency of the second blood flow information, detects the local maximum point of the image of the scalar value at each frequency, performs a process of emphasizing the scalar value of the local maximum point, generates third blood flow information based on the frequency information and the image with the local maximum point of the scalar value emphasized at each frequency, and displays the third blood flow information on the display unit.

[0153] (Note 9)

[0154] Alternatively, the super-resolution processing unit may add images of local maximum points of multiple scalar values ​​that emphasize positive frequencies to calculate blood flow information of positive frequencies, and may add images of local maximum points of scalar values ​​that emphasize negative frequencies to calculate blood flow information of negative frequencies, so that the coloring of the blood flow information of the positive frequencies and the blood flow information of the negative frequencies are changed and displayed on the display unit.

[0155] (Note 10)

[0156] Alternatively, the super-resolution processing unit displays on the display unit a third RGB image obtained by adding a first RGB image representing the blood flow information of the positive frequency and a second RGB image representing the blood flow information of the negative frequency, or displays either the first RGB image or the second RGB image in the front in priority on the display unit.

[0157] (Note 11)

[0158] Alternatively, the super-resolution processing unit may add images of local maximum points of scalar values ​​of multiple positive frequencies to calculate blood flow information of positive frequencies, or add images of local maximum points of scalar values ​​of multiple negative frequencies to calculate blood flow information of negative frequencies, so that the blood flow information of positive frequencies is displayed on the display unit, or the blood flow information of negative frequencies is displayed on the display unit.

[0159] (Note 12)

[0160] The super-resolution processing unit may display an image in which a local maximum point of a scalar value of each frequency is emphasized on the display unit with a different hue for each frequency.

[0161] (Note 13)

[0162] The scalar value may be an energy value of the blood flow, and the super-resolution processing unit may create an image of the energy value at each frequency of the second blood flow information, detect a local maximum value of the image of the energy value at each frequency, accumulate the local maximum values ​​at each frequency to calculate an image that emphasizes the local maximum point of the energy value, calculate the average velocity and discreteness of the blood flow based on the image that emphasizes the local maximum point of the energy value at each frequency, and display one or more of the average velocity, the discreteness and the energy value on the display unit using multi-dimensional color mapping.

[0163] (Note 14)

[0164] The Doppler processing unit may interpolate the first blood flow information in which clutter components from the tissue are reduced in an ultrasonic scanning line direction to increase the number of scanning lines.

[0165] (Note 15)

[0166] The image processing device provided in other aspects of the present invention includes: an acquisition unit, which acquires reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit, which reduces the clutter components from the tissue based on the data columns of the multiple frames to estimate the first blood flow information; and a super-resolution processing unit, which performs an orthogonal transformation in the frame direction based on the first blood flow information to thereby calculate the second blood flow information, creates an image of a scalar value according to each frequency of the second blood flow information, detects the local maximum point of the image of the scalar value at each frequency to perform processing to emphasize the scalar value of the local maximum point, generates third blood flow information based on the frequency information and the image with the local maximum point of the scalar value emphasized at each frequency, and displays the third blood flow information on the display unit.

[0167] (Note 16)

[0168] The image processing method provided in other aspects of the present invention performs the following processing: a process of acquiring reflected wave data collected in multiple frames in the time direction at one or more positions by ultrasonic transmission and reception; a process of reducing the clutter components from the tissue to estimate the first blood flow information based on the data columns of the multiple frames; a process of performing an orthogonal transformation in the frame direction based on the first blood flow information to thereby calculate the second blood flow information; a process of creating an image of a scalar value at each frequency of the second blood flow information, detecting the local maximum point of the image of the scalar value at each frequency to emphasize the scalar value of the local maximum point; a process of generating third blood flow information based on the frequency information and the image with the local maximum point of the scalar value emphasized at each frequency, and displaying the third blood flow information on a display unit.

[0169] According to at least one of the embodiments described above, it is possible to obtain information that is highly convenient for the user.

[0170] Although several embodiments have been described, these embodiments are given as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the scope of the main purpose of the invention. These embodiments and their variations are included in the scope and main purpose of the invention, and are also included in the scope equivalent to the invention recorded in the claims.

[0171] While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments.The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. An ultrasonic diagnostic device, comprising: an acquisition unit that acquires a data sequence of reflected wave data collected in a plurality of frames in a time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit for reducing clutter components from tissues to estimate first blood flow information based on the data sequences of the plurality of frames; as well as A super-resolution processing unit generates an image of a scalar value of a blood flow signal, i.e., second blood flow information, based on the first blood flow information, detects a local maximum value of the second blood flow information, accumulates the local maximum value or an image with the local maximum value emphasized to generate a scalar value accumulated image, calculates an autocorrelation function in a frame direction of the first blood flow information at the position of the local maximum value, accumulates the autocorrelation function to generate an autocorrelation function accumulated image, generates third blood flow information in which blood flow is distinguished by color based on the scalar value accumulated image and the autocorrelation function accumulated image, and displays the third blood flow information on a display unit.

2. The ultrasonic diagnostic apparatus according to claim 1, wherein: The super-resolution processing unit calculates information on the direction of blood flow based on the autocorrelation function integrated image, and generates the third blood flow information having colors differentiated according to the direction of blood flow based on the scalar value integrated image and the information on the direction of blood flow.

3. The ultrasonic diagnostic apparatus according to claim 1, wherein: The scalar value is the energy value of the blood flow signal. The super-resolution processing unit detects the local maximum value of the energy value of the second blood flow information, accumulates the local maximum value or an image that emphasizes the local maximum value to generate an energy value accumulation image as a scalar value accumulation image, calculates the blood flow velocity and discreteness based on the energy value accumulation image and the autocorrelation function accumulation image, and displays two or three of the velocity, discreteness and energy value in color on the display unit as the third blood flow information.

4. The ultrasonic diagnostic apparatus according to claim 1, wherein: The Doppler processing unit interpolates the first blood flow information in which clutter components from the tissue are reduced in an ultrasonic scanning line direction to increase the number of scanning lines.

5. An image processing method, performing the following processing, Processing of acquiring a data sequence of reflected wave data collected in a plurality of frames in the time direction at one or more positions by transmitting and receiving ultrasonic waves; Processing of estimating first blood flow information by reducing clutter components from tissues according to the data columns of the plurality of frames; generating an image of a scalar value of a blood flow signal, namely, a second blood flow information according to the first blood flow information; detecting a local maximum value of the second blood flow information, and accumulating the local maximum value or an image emphasizing the local maximum value to generate a scalar value accumulated image; Calculating the autocorrelation function of the first blood flow information in the frame direction at the position of the local maximum value, and accumulating the autocorrelation function to generate an autocorrelation function accumulation image; generating third blood flow information in which blood flow is differentiated by color according to the scalar value cumulative image and the autocorrelation function cumulative image; as well as A process of causing a display unit to display the third blood flow information.

6. An ultrasonic diagnostic device, comprising: an acquisition unit that acquires a data sequence of reflected wave data collected in a plurality of frames in a time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit for reducing clutter components from tissues to estimate first blood flow information based on the data sequences of the plurality of frames; as well as A super-resolution processing unit generates an image of a scalar value of a blood flow signal, i.e., second blood flow information, based on the first blood flow information, detects a local maximum value of the second blood flow information, accumulates the local maximum value or an image with the local maximum value emphasized to generate a scalar value accumulation image, generates third blood flow information in which the blood flow is distinguished by color based on the scalar value accumulation image and information related to the direction of the blood flow at the position where the local maximum value is taken, and displays the third blood flow information on a display unit.

7. An ultrasonic diagnostic device, comprising: an acquisition unit that acquires reflected wave data collected in a plurality of frames in a time direction at one or more positions by transmitting and receiving ultrasonic waves; a Doppler processing unit for reducing clutter components from tissues to estimate first blood flow information based on the data sequences of the plurality of frames; as well as A super-resolution processing unit performs an orthogonal transformation in a frame direction based on the first blood flow information to thereby calculate the second blood flow information, creates an image of a scalar value according to each frequency of the second blood flow information, detects a local maximum point of the image of the scalar value at each frequency, performs processing to emphasize the scalar value of the local maximum point, generates third blood flow information based on the frequency information and the image with the local maximum point of the scalar value emphasized at each frequency, and displays the third blood flow information on a display unit.

8. The ultrasonic diagnostic apparatus according to claim 7, wherein: The super-resolution processing unit adds images of local maximum points of multiple scalar values ​​of positive frequencies to calculate blood flow information of positive frequencies, and adds images of local maximum points of scalar values ​​of negative frequencies to calculate blood flow information of negative frequencies, so that the coloring of the blood flow information of the positive frequency and the blood flow information of the negative frequency are changed and displayed on the display unit.

9. The ultrasonic diagnostic apparatus according to claim 8, characterized in that: The super-resolution processing unit displays a third RGB image obtained by adding the first RGB image representing the blood flow information of the positive frequency and the second RGB image representing the blood flow information of the negative frequency on the display unit, or displays either the first RGB image or the second RGB image in the front on the display unit with priority.

10. The ultrasonic diagnostic apparatus according to claim 7, wherein: The super-resolution processing unit adds images that emphasize the local maximum points of the scalar values ​​of multiple positive frequencies to calculate the blood flow information of the positive frequencies, or adds images that emphasize the local maximum points of the scalar values ​​of multiple negative frequencies to calculate the blood flow information of the negative frequencies, so that the blood flow information of the positive frequencies is displayed on the display unit, or the blood flow information of the negative frequencies is displayed on the display unit.

11. The ultrasonic diagnostic apparatus according to claim 7, wherein: The super-resolution processing unit causes the display unit to display an image in which a local maximum point of a scalar value of each frequency is emphasized, using a different hue for each frequency.

12. The ultrasonic diagnostic apparatus according to claim 7, wherein: The scalar value is the energy value of blood flow, The super-resolution processing unit creates an image of the energy value according to each frequency of the second blood flow information, detects the local maximum of the image of the energy value at each frequency, accumulates the local maximum at each frequency to calculate an image that emphasizes the local maximum point of the energy value, calculates the average velocity and dispersion of the blood flow based on the image that emphasizes the local maximum point of the energy value at each frequency, and displays one or more of the average velocity, the dispersion and the energy value on the display unit using multi-dimensional color mapping.

13. The ultrasonic diagnostic apparatus according to claim 7, wherein: The Doppler processing unit interpolates the first blood flow information in which clutter components from the tissue are reduced in an ultrasonic scanning line direction to increase the number of scanning lines.

14. An image processing method, performing the following processing, A process of acquiring reflected wave data collected in a plurality of frames in the time direction at one or more locations by transmitting and receiving ultrasonic waves; Processing of estimating first blood flow information by reducing clutter components from tissues according to the data columns of the plurality of frames; Processing of performing an orthogonal transformation in a frame direction according to the first blood flow information to thereby calculate the second blood flow information; Creating a scalar value image at each frequency of the second blood flow information, detecting a local maximum point of the scalar value image at each frequency to emphasize the scalar value of the local maximum point; A process of generating third blood flow information based on the frequency information and the image in which the local maximum point of the scalar value of each frequency is emphasized, and displaying the third blood flow information on a display unit.

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