Ultrasonic diagnostic apparatus, image processing apparatus, and image processing method

Abstract

To acquire information highly convenient for a user.SOLUTION: An ultrasonic diagnostic apparatus includes a Doppler processing part and a super-resolution processing part. The Doppler processing part reduces a tissue-derived clutter component and estimates first blood flow information from a data string of reflection wave data collected in a plurality of frames in a time direction at one or more positions by the ultrasonic transmission and reception. The super-resolution processing part generates second blood flow information, which is an image of a scalar value of a blood flow signal, from the first blood flow information; detects a topical maximum value of the second blood flow information, and integrates the topical maximum values or images emphasizing the topical maximum values so as to generates a scalar value integration image; calculates an autocorrelation function in a frame direction of the first blood flow information at the position for taking the topical maximum value, and integrates the autocorrelation functions so as to generate an autocorrelation function integration image; generates third blood flow information in which the blood flow is color coded, from the scalar value integration image and the autocorrelation function integration image; and causes a display part to display the third blood flow information.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The embodiments disclosed in this specification and the drawings relate to an ultrasound diagnostic apparatus, an image processing apparatus, and an image processing method. [Background technology]

[0002] Ultrasound diagnostic devices are widely used to observe and diagnose blood flow in living bodies. Ultrasound diagnostic devices generate and display blood flow information from reflected ultrasonic waves using the Doppler method based on the Doppler effect. Examples of blood flow information generated and displayed by ultrasound diagnostic devices include color Doppler images and Doppler waveforms (Doppler spectra).

[0003] Color Doppler images are obtained by imaging using the Color Flow Mapping (CFM) method. In the CFM method, ultrasonic waves are transmitted and received multiple times on multiple scanning lines. In the CFM method, a moving target indicator (MTI) filter is applied to a data sequence at the same position to suppress signals (clutter signals) originating from stationary or slow-moving tissues and extract signals originating from blood flow (blood flow signals). In the CFM method, blood flow information such as the blood flow speed, blood flow dispersion, and blood flow power are estimated from the blood flow signal, and the distribution of the estimated results (blood flow information) is displayed as a Doppler image.

[0004] It is known that the resolution of B-mode images and Doppler images decreases due to the point spread function (PSF), which is determined by the wavelength of the transmitted ultrasound, the transmit / receive aperture width, etc. Although there are solutions such as increasing the frequency of the transmitted ultrasound, there is a limit to the resolution of the images that can be obtained because there is also a limit to the frequency band of the probe.

[0005] Non-Patent Document 1 describes a super-resolution technology for blood flow images that achieves a resolution of about 1 / 5 of the wavelength of the transmitted ultrasound. A blood flow image with improved resolution is generated by acquiring many normal Doppler images and extracting and accumulating the parts with high image values. Non-Patent Document 1 only mentions super-resolution display of the power of blood flow in grayscale. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] 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) Summary of the Invention [Problem to be solved by the invention]

[0007] One of the problems that the embodiments disclosed in this specification and the drawings aim to solve is to obtain information that is highly convenient for users. However, the problems that the embodiments disclosed in this specification and the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0008] The ultrasonic diagnostic apparatus according to the embodiment includes an acquisition unit, a Doppler processing unit, and a super-resolution processing unit. The acquisition unit acquires a data sequence of reflected wave data collected in multiple frames in the time direction at one or multiple positions by ultrasonic transmission and reception. The Doppler processing unit estimates first blood flow information by reducing clutter components derived from tissue from the data sequence of the multiple frames. The super-resolution processing unit generates second blood flow information, which is an image of scalar values ​​of blood flow signals, from the first blood flow information, detects a local maximum value of the second blood flow information, and generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, calculates an autocorrelation function in the frame direction of the first blood flow information at the position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function, generates third blood flow information in which blood flows are color-coded from the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on a display unit. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing an example of a hardware configuration of an ultrasonic diagnostic apparatus according to the first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of functions of the received signal processing unit according to the first embodiment. [Diagram 3] FIG. 3 is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image, which is executed by the ultrasound diagnostic apparatus according to the first embodiment. [Figure 4A] FIG. 4A shows images of renal blood flow in various species. [Figure 4B] FIG. 4B shows images of renal blood flow in various species. [Figure 4C] FIG. 4C shows images of renal blood flow in various species. [Figure 4D] FIG. 4D shows images of renal blood flow in various species. [Diagram 5] FIG. 5 is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image, which is executed by the ultrasound diagnostic apparatus according to the second embodiment. [Figure 6A]FIG. 6A is a diagram showing an example of a blood flow image of multiple frames generated in step S430 in the second embodiment. [Figure 6B] FIG. 6B is a diagram showing an example of image division into groups based on three features in the second embodiment. [Figure 6C] FIG. 6C is a diagram showing an example of an image obtained by the process of step S460 in the second embodiment. [Figure 6D] FIG. 6D is a diagram showing an example of a super-resolution blood flow image according to the second embodiment. [Figure 7] FIG. 7 is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image according to the conventional technology. [Figure 8A] FIG. 8A is a diagram for explaining an example of a processing method according to the conventional technology. [Figure 8B] FIG. 8B is a diagram for explaining an example of a processing method according to the conventional technology. [Figure 8C] FIG. 8C is a diagram for explaining an example of a processing method according to the conventional technology. [Figure 9] FIG. 9 is a diagram illustrating an example of a Rayleigh distribution. [Figure 10A] FIG. 10A is a diagram for explaining an example of a problem with the conventional technology. [Figure 10B] FIG. 10B is a diagram for explaining an example of a problem with the conventional technology. [Figure 10C] FIG. 10C is a diagram for explaining an example of a problem with the conventional technology. [Figure 11A] FIG. 11A shows images of renal blood flow obtained by various methods. [Figure 11B] FIG. 11B shows images of renal blood flow obtained by various methods. [Figure 11C] FIG. 11C shows images of renal blood flow obtained by various methods. [Figure 11D] FIG. 11D shows images of renal blood flow obtained by various methods. [Figure 11E]FIG. 11E shows images of renal blood flow obtained by various methods. [Figure 11F] FIG. 11F shows images of renal blood flow obtained by various methods. [Figure 11G] FIG. 11G shows images of renal blood flow obtained by various methods. [Figure 11H] FIG. 11H shows images of renal blood flow obtained by various methods. [Figure 12] FIG. 12 is a diagram for explaining the process after step S450 in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, with reference to the drawings, embodiments and modifications of an ultrasound diagnostic device, an image processing device, and an image processing method will be described in detail. Note that the ultrasound diagnostic device, the image processing device, and the image processing method according to the present application are not limited to the embodiments and modifications shown below. Furthermore, the embodiments can be combined with other embodiments, modifications, or conventional techniques as long as no inconsistency occurs in the processing content. Similarly, the modifications can be combined with the embodiments, other modifications, or conventional techniques as long as no inconsistency occurs in the processing content.

[0011] (First embodiment) An ultrasonic diagnostic apparatus according to a first embodiment described below includes a processing circuit. The processing circuit acquires a data sequence of reflected wave data collected in a plurality of frames in the time direction at one or a plurality of positions by ultrasonic transmission and reception. The processing circuit estimates first blood flow information from the data sequence of the plurality of frames by reducing clutter components derived from tissue. The processing circuit generates second blood flow information, which is an image of scalar values ​​of blood flow signals, from the first blood flow information, detects a local maximum value of the second blood flow information, and generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, calculates an autocorrelation function in the frame direction of the first blood flow information at the position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function, generates third blood flow information in which blood flows are color-coded from the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on a display.

[0012] 1 is a block diagram showing an example of a hardware configuration of an ultrasonic diagnostic apparatus according to the first embodiment. The ultrasonic diagnostic apparatus includes an ultrasonic probe (ultrasonic probe) 102, a probe connection unit 103, a transmission electric circuit 104, a reception electric 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 a subject 100, receiving reflected ultrasonic waves reflected inside the subject 100, and generating image information (ultrasonic image) of the inside of the subject 100 using the received reflected ultrasonic waves. Ultrasonic images obtained by the ultrasonic diagnostic apparatus are used in various clinical examinations.

[0013] The ultrasonic probe 102 is an electronic scanning type probe, and has a plurality of transducers 101 arranged one-dimensionally or two-dimensionally at the tip of the ultrasonic probe 102. The transducer 101 is an electromechanical conversion element that converts between an electric signal (voltage pulse signal) and an ultrasonic wave (acoustic wave). The ultrasonic probe 102 transmits ultrasonic waves from the plurality of transducers 101 to the subject 100, and receives reflected ultrasonic waves from the subject 100 by the plurality of transducers 101. The reflected ultrasonic waves reflect the difference in acoustic impedance within the subject 100. When the transmitted ultrasonic pulse is reflected by the surface of a moving blood flow or a heart wall, the reflected wave signal undergoes a frequency shift due to the Doppler effect depending on the velocity component of the moving body in the ultrasonic transmission direction.

[0014] The transmission electric circuit 104 is a transmitting section that outputs a pulse signal (driving signal) to the multiple transducers 101. The transmission electric circuit 104 applies a pulse signal to the multiple transducers 101 with a time difference, so that ultrasonic waves with different delay times are transmitted from the multiple transducers 101. This forms a transmitted ultrasonic beam. The transmission electric circuit 104 can control the direction and focus of the transmitted ultrasonic beam by selectively changing the transducer 101 to which the pulse signal is applied (i.e., the transducer 101 to be driven) or by changing the delay time (application timing) of the pulse signal. By sequentially changing the direction and focus of this transmitted ultrasonic beam, the observation area inside the subject 100 is scanned. In addition, the transmission electric circuit 104 may form a transmitted ultrasonic beam that is a plane wave (focus is far away) or a diverging wave (focus point is opposite to the ultrasonic transmission direction for the multiple transducers 101) by changing the delay time of the pulse signal, or may form a transmitted ultrasonic beam using one transducer 101 or a part of the multiple transducers 101. The transmitting electric circuit 104 transmits a pulse signal of a predetermined drive waveform to the transducer 101, thereby causing the transducer 101 to generate a transmitted ultrasonic wave having a predetermined transmission waveform. The receiving electric circuit 105 is a receiving section that inputs, as a received signal, an electric signal output from the transducer 101 that has received a reflected ultrasonic wave. The received signal is input to a received signal processing section 106. The receiving electric circuit 105 converts an analog received signal (analog signal) input from the transducer 101 into a digital received signal (digital signal), and outputs the digital received signal to the received signal processing section 106.

[0015] In this specification, the analog signal output from the transducer 101 and the digital data obtained by sampling (digital conversion) the analog signal are both referred to as the received signal without any particular distinction. However, depending on the context, the received signal may be referred to as the received data in order to clearly indicate that it is digital data.

[0016] The operations of the transmitting electric circuit 104 and the receiving electric circuit 105, i.e., the transmission and reception of ultrasonic waves, are controlled by a system control unit 109. The system control unit 109 changes the position at which the voltage signal or the transmitted ultrasonic wave is formed in accordance with the generation of a B-mode image and a blood flow image, which will be described later, for example.

[0017] When generating a B-mode image, the ultrasound diagnostic device acquires a reception signal of reflected ultrasound obtained by scanning the observation area, and uses the acquired reception signal for image generation. When generating a blood flow image, the ultrasound diagnostic device transmits and receives ultrasound multiple times on one or multiple scanning lines in the observation area, acquires reception signals of reflected ultrasound for multiple frames, and uses the acquired reception signals for image generation, i.e., extraction of blood flow information. Scanning for generating a blood flow image may be a method of transmitting and receiving ultrasound multiple times on one scanning line and then performing the same transmission and reception on the next scanning line, or a method of repeating transmission and reception multiple times by performing one transmission and reception on each scanning line. In addition, the generation of B-mode images and blood flow images may be such that, for example, plane waves or diverging waves are transmitted to transmit ultrasound over a wide range of the observation area so as to reduce the number of scanning lines. In addition, the ultrasound diagnostic device may change the transmission angle of the plane waves or diverging waves or the range of the observation area to be transmitted, perform transmission and reception multiple times over a wide range of the observation area, and add and use the received signals.

[0018] The received signal processing unit 106 is an image generating unit that generates various images (various image data) based on the received signal obtained from the ultrasonic probe 102. The image processing unit 107 performs image processing such as brightness adjustment, interpolation, and filter processing on the image data generated by the received 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 transmitting electric circuit 104, the receiving electric circuit 105, the received signal processing unit 106, the image processing unit 107, the display device 108, etc.

[0019] (Configuration of the receiving signal processing block) 2 is a block diagram showing an example of functions of the received signal processing unit 106 according to the first embodiment. The received signal processing unit 106 includes a received signal storage unit 200, a delay-and-sum processing unit 201, a signal storage unit 202, a B-mode processing unit 203, a displacement amount correction unit 204, a Doppler processing unit 205, and a super-resolution processing unit 206.

[0020] The received signal storage unit 200 stores the received signal output from the receiving electrical circuit 105. Note that, depending on the device configuration and the type of received signal, the received signal storage unit 200 may not store the received signal, and the received signal after processing by a phasing addition processing unit 201 described later may be stored in the signal storage unit 202. The received signal storage unit 200 may be formed of a block common to the signal storage unit 202 described later, and may store (store) the received signal from the receiving electrical circuit 105 and the received signal after processing by the phasing addition processing unit 201.

[0021] The delay and sum processing unit 201 performs delay and sum processing and quadrature detection processing on the reception signal obtained by the reception electric circuit 105, and stores the reception signal after these processes in the signal storage unit 202. The delay and sum processing is a process of forming a reception ultrasonic beam by changing the delay time and weight for each transducer 101 and adding up the reception signals of the multiple transducers 101, and is also called delay and sum (DAS) beamforming. The quadrature detection processing is a process of converting the reception signal into an in-phase signal (I signal) and a quadrature signal (Q signal) in the baseband. The delay and sum processing and quadrature detection processing are performed based on the element arrangement and various conditions (aperture control, signal filter) in image generation input from the system control unit 109. The reception signal after the delay and sum processing and quadrature detection processing is stored in the signal storage unit 202. Here, a typical example of DAS beamforming is shown, but the delay and sum processing unit 201 may use any processing that forms a reception ultrasonic beam, such as adaptive beamforming, model-based processing, and processing using machine learning.

[0022] The B-mode processing unit 203 performs B-mode processing such as envelope detection processing and logarithmic compression processing on the received signals for generating B-mode images stored in the signal storage unit 202, and generates image data in which the signal intensity at each point in the observation region is represented by luminance intensity. The B-mode processing unit 203 may also perform B-mode processing on received signals whose displacement has been corrected by a displacement correction unit 204 described later.

[0023] The displacement amount correction unit 204 calculates the amount of tissue displacement caused by body movement between frames from the received signals of multiple frames acquired. The amount of tissue displacement is calculated, for example, by a block matching calculation called a speckle tracking method. The displacement amount correction unit 204 calculates the amount of displacement of the region of interest by setting a region of interest within a frame and tracking the region of interest between frames by correlation. In addition, the displacement amount correction unit 204 calculates the amount of displacement of the entire frame by setting multiple regions of interest within the frame. Here, an example of a method using correlation between frames is shown, but any method may be used as long as it can obtain the amount of displacement of the region of interest. In the case of an ultrasonic signal, it is called speckle tracking because it tracks speckles, which are scattered images of ultrasonic waves reflected from scatterers in the body tissue. The correlation calculation of the region of interest between frames may be performed on the received signals after any of delay and sum processing, quadrature detection processing, and envelope detection processing. In addition, the displacement amount correction unit 204 may perform correlation calculation on the time waveform of the received signal, or may perform correlation calculation on frequency space data obtained by discrete Fourier transform of the received signal. The displacement amount correction unit 204 corrects the displacement amount by moving the received signal relative to the reference frame using the calculated displacement amount. The displacement amount to be corrected may be a uniform value for the entire frame, such as an average value of the displacement amounts for each region of interest, or may be changed within the frame, such as for each region of interest. In addition, the displacement amount for the entire frame calculated for each region of interest may be interpolated in the time direction within the frame and in the data strings of multiple frames using linear interpolation, spline interpolation, or the like. In addition, when correcting the displacement amount, the displacement amount correction unit 204 may similarly interpolate the received signal to allow for more fine correction, and then move the received signal relative to the reference frame.

[0024] The Doppler processing unit 205 extracts blood flow information (Doppler information) from the reception signal for generating a blood flow image stored in the signal storage unit 202, and generates a blood flow image (blood flow image data) by visualizing the blood flow information. The Doppler processing unit 205 may also perform Doppler processing on the reception signal whose displacement amount has been corrected by the above-mentioned displacement amount correction unit 204.

[0025] The processing contents of the Doppler processing unit 205 will be described in detail. The Doppler processing unit 205 extracts blood flow information based on the Doppler effect of an object within the scanning range by frequency analyzing the received signal for generating a Doppler image stored in the signal storage unit 202. In this embodiment, an example in which the object is blood will be mainly described, but the object may be an object such as an internal tissue or a contrast agent. Examples of blood flow information include at least one of velocity, dispersion value, and power value. The Doppler processing unit 205 may obtain blood flow information at one point (one position) in the object, or may obtain blood flow information at multiple positions in the depth direction. The Doppler processing unit 205 may obtain blood flow information at multiple points in time in a time series so that the time change of blood flow information can be displayed.

[0026] In generating a blood flow image by the Doppler method, a series of received signal data of a plurality of frames is acquired in the time direction at one or a plurality of positions. The Doppler processing unit 205 applies an MTI (Moving Target Indicator) filter to the series of received signal data to reduce components (clutter components) originating from tissues that are stationary between frames or tissues with little movement, and extract components originating from blood flow. The Doppler processing unit 205 then calculates blood flow information such as blood flow velocity, blood flow dispersion, and blood flow power from the blood flow components.

[0027] The MTI filter may be a filter with fixed filter coefficients, such as a Butterworth-type infinite impulse response (IIR) filter or a polynomial regression filter, or an adaptive filter that changes coefficients according to the input signal using eigenvalue decomposition or singular value decomposition, or the received signal data may be decomposed into one or more bases using eigenvalue decomposition or singular value decomposition, and clutter components may be removed by extracting only a specific base.

[0028] In addition, 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 for each coordinate in the image, and obtain a blood flow vector representing the magnitude and direction of the blood flow.

[0029] The super-resolution processing unit 206 generates super-resolution blood flow image data, which is a blood flow image with improved resolution, from the blood flow image data generated by the Doppler processing unit 205. A method for acquiring a super-resolution blood flow image according to this embodiment will be described when explaining the processing flow.

[0030] The 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 then finally displayed on the display device 108. The respective image data may be displayed in a superimposed manner, may be displayed in parallel, or only a portion of the image data may be displayed.

[0031] The received signal processing unit 106 may be configured with one or more processors and memories. In this case, the functions of the units 201, 203 to 206 shown in FIG. 2 are realized by a computer program. The received signal storage unit 200 and the signal storage unit 202 shown in FIG. 2 are realized by a memory. For example, the functions of the units 201, 203 to 206 can be provided by the CPU reading and executing a program stored in the memory. The received signal processing unit 106 may include a processor (GPU, FPGA, etc.) that handles the calculations of the B-mode processing unit 203, the displacement amount correction unit 204, the Doppler processing unit 205, and the super-resolution processing unit 206 in addition to the CPU. The memory may include a memory for non-temporarily storing a program, a memory for temporarily storing data such as a received signal, a working memory used by the CPU, etc.

[0032] Above, we have described the overall configuration of the ultrasound diagnostic apparatus according to the first embodiment. Next, we will explain the processing flow for acquiring a super-resolution blood flow image according to this embodiment.

[0033] (Processing flow for acquiring a super-resolution blood flow image in the first embodiment) A processing flow for acquiring a super-resolution blood flow image in this embodiment will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image, which is executed by the ultrasound diagnostic apparatus according to the first embodiment.

[0034] The ultrasonic probe 102 repeatedly transmits and receives ultrasonic waves at the same position so as to include the target region for acquiring blood flow information, and the receiving electric circuit 105 and the delay-and-sum processing unit 201 create a receiving signal including a data sequence of a plurality of frames in the time direction (step S310). That is, the receiving electric circuit 105 and the delay-and-sum processing unit 201 acquire a data sequence of reflected wave data collected in a plurality of frames in the time direction at one or a plurality of positions by ultrasonic transmission and reception. The receiving electric circuit 105 and the delay-and-sum processing unit 201 are, for example, an example of an acquisition unit. The number of frames of the data sequence when generating a super-resolution blood flow image according to this embodiment may be larger than that when generating a normal Doppler image. For example, the number of frames of the data sequence when generating a normal Doppler image is about 5 to 20, whereas the number of frames of the data sequence when generating a super-resolution blood flow image may be about several hundred to several tens of thousands. The delay-and-sum processor 201 performs delay-and-sum processing and quadrature detection processing on the received signal including a data sequence of multiple frames, and the received signal that has been subjected to delay-and-sum processing and quadrature detection processing is stored in the signal storage unit 202. Furthermore, one frame in the data sequence may be a received signal obtained by performing one ultrasonic transmission and reception so as to include the observation region, or may be a sum of received signals that have been transmitted and received multiple times so as to include the observation region. For example, the ultrasonic probe 102 transmits a plane wave or a diverging wave so as to include the observation region, and the delay-and-sum processor 201 adds multiple received signals with different transmission angles to obtain one frame of data.

[0035] The displacement amount correction unit 204 calculates the amount of tissue displacement between frames due to body movement or the like from a received signal including a data sequence of multiple frames, and corrects the displacement amount by moving the received signal using the calculated displacement amount so that the tissue positions are aligned within the frames in which the integration process in step S370 described later is performed (step S320). At this time, the reference frame for calculating the displacement amount may be one frame for the entire data sequence, or multiple reference frames may be provided by changing the position in the time direction, or the displacement amount may be calculated between adjacent frames.

[0036] The Doppler processing unit 205 applies an MTI filter to the received signal including the data sequence of the multiple frames whose displacement amount has been corrected by the displacement amount correction unit 204, thereby reducing clutter components and extracting components derived from blood flow, thereby generating blood flow images of multiple frames (step S330). At this time, a part of the data sequence may be extracted and used for designing the MTI filter, or the entire data sequence may be used. Thus, in step S330, the Doppler processing unit 205 estimates the first blood flow information by reducing clutter components derived from tissues from the data sequence of the multiple frames.

[0037] The Doppler processing unit 205 increases the number of reception scanning lines by interpolation to improve the resolution of the local maximum value described below (step S340). Since the blood flow signal is a complex signal, interpolation in the scanning line direction is performed to perform phase-inclusive interpolation, improving the resolution. Although it is better to increase the number of reception scanning lines by beamforming, the processing load can be reduced by performing interpolation at this location. In other words, the processing load during beamforming can be reduced by performing interpolation after beamforming, rather than at the timing of beamforming. If a sufficient number of reception scanning lines is secured by beamforming, the processing of step S340 is not necessary.

[0038] Next, the super-resolution processor 206 extracts (detects) the local maximum value of the power value of each frame and calculates the autocorrelation function of those positions (step S350). The local maximum value refers to, for example, the maximum value of the power value in each of multiple local regions of each frame. The super-resolution processor 206 then integrates the local maximum value of the power value and the value of the autocorrelation function for each frame (step S360). The power value c0(x,y) after integration at position (x,y) and the autocorrelation function c1(x,y) after integration can be expressed by the following formulas (1) and (2).

number

number

[0039] Here, z(x,y,n) is the IQ signal (baseband complex signal; I: Inphase Q: Quadrature phase) input in step S350 at position (x,y) of the nth frame. * represents complex conjugate. p(x,y,n) is a function that returns 1 if the point at position (x,y) of the nth frame is a local maximum, and 0 if it is not a local maximum. N is the total number of frames to be processed.

[0040] Thus, in steps S350 and S360, the super-resolution processor 206 generates second blood flow information, which is an image of a power value that is a scalar value of a blood flow signal, from the first blood flow information. The super-resolution processor 206 may generate an image of the amplitude of the blood flow signal, which is a scalar value of the blood flow signal, as the second blood flow information from the first blood flow information. The super-resolution processor 206 may also generate an image of another scalar value of the blood flow signal as the second blood flow information from the first blood flow information. In step S360, the super-resolution processor 206 may accumulate an image in which the local maximum value is emphasized by attenuating pixel values ​​other than the local maximum point, instead of accumulating an image in which the local maximum value is extracted. The image obtained as a result is used for processing in step S370 described later. Although the original image will be included to some extent, the amount of attenuation may be set to infinity to extract only the pixel value of the local maximum point. In step S360, the super-resolution processor 206 detects a location of a predetermined range of image values ​​or a local maximum point from the second blood flow information, and creates an image in which the pixel value of the local maximum point is emphasized. The predetermined range of image points may be the portion with the maximum image value in each image, or the position of the image value in a range where a threshold value is set for the maximum image value. Alternatively, a pixel value of a point of interest that is greater than the pixel values ​​of neighboring pixels may be determined as a local maximum point. A maximum point for each local region may be extracted to extract multiple local maximum points within an image.

[0041] In addition, in steps S350 and S360, the super-resolution processor 206 detects a local maximum value of the second blood flow information and generates a scalar value integrated image by integrating the local maximum value. In addition, in steps S350 and S360, the super-resolution processor 206 calculates an autocorrelation function in the frame direction (time axis direction) of the first blood flow information at the position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function. In addition, the super-resolution processor 206 detects a local maximum value of the power of the second blood flow information and generates a power value integrated image as a scalar value integrated image by integrating the local maximum value.

[0042] Next, the super-resolution processor 206 colors the power value using the integrated power value and the integrated autocorrelation function value (step S370). For example, the super-resolution processor 206 calculates the blood flow velocity from the autocorrelation function value, converts the blood flow velocity (blood flow velocity value) and power value into RGB using a two-dimensional color map, and displays the image. That is, the super-resolution processor 206 displays the image obtained by converting the image into RGB using the two-dimensional color map on the display device 108. The power value for display is calculated by logarithmically compressing c0(x,y) in equation (1). The velocity value for display is calculated from the argument of the complex number c1(x,y) in equation (2).

[0043] Alternatively, in step S370, the super-resolution processor 206 calculates only the direction of blood flow from the value of the autocorrelation function, and displays the power value on the display device 108 in a different color according to the direction. Since the direction of blood flow is determined by the sign of the imaginary part of the autocorrelation function, when only the direction of blood flow is required, the super-resolution processor 206 calculates the imaginary part c1 of the autocorrelation function c1(x, y) according to the following formula (3): im Find (x,y) and calculate the imaginary part c1 im The direction of blood flow can be calculated from the signs of (x,y).

number

[0044] Note that the subscript "re" represents the real part, and the subscript "im" represents the imaginary part.

[0045] As described above, in step S370, the super-resolution processor 206 generates third blood flow information from the scalar value accumulated image and the autocorrelation function accumulated image, the third blood flow information having a resolution higher than that of the first blood flow information and in which the blood flow is color-coded, and displays the third blood flow information on the display device 108. Also, in step S370, the super-resolution processor 206 calculates information on the direction of blood flow (information on the direction of blood flow) from the autocorrelation function accumulated image, and generates third blood flow information color-coded for each direction of blood flow from the scalar value accumulated image and the information on the direction of blood flow. That is, the super-resolution processor 206 generates third blood flow information color-coded for each direction of blood flow from the scalar value accumulated image and information on the direction of blood flow at the position where the local maximum value is obtained.

[0046] The effects obtained by the ultrasound diagnostic apparatus according to the first embodiment will be described with reference to Figs. 4A to 4D. Figs. 4A to 4D are images of various renal blood flows. The image shown in Fig. 4A is an example of a grayscale image (grayscale image) obtained by a conventional technique that does not perform super-resolution. The image shown in Fig. 4B is an example of a directional color image (color image) by a conventional technique that does not perform super-resolution. The image shown in Fig. 4C is an example of a grayscale image of super-resolution without direction. The image shown in Fig. 4D is an example of a super-resolution image color-coded according to direction in this embodiment.

[0047] In the image shown in FIG. 4D, the area hatched with diagonal lines going up to the right is, for example, displayed in red, and is the area of ​​the renal artery flowing in the direction toward the ultrasonic probe 102. In addition, in the image shown in FIG. 4D, the area hatched with diagonal lines going down to the right is, for example, displayed in blue, and is the area of ​​the renal vein flowing in the direction away from the ultrasonic probe 102. The image shown in FIG. 4D has an overwhelmingly higher resolution than the image shown in FIG. 4A and the image shown in FIG. 4B, and compared to the image shown in FIG. 4C, 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, and the amount of information is increased. In this way, by displaying the power value of the blood flow, etc. in different colors according to the speed and direction of the blood flow, when the blood vessels running parallel to each other are arteries and veins, the blood vessels can be displayed in different colors in an easy-to-understand manner.

[0048] In the above description, the ultrasonic diagnostic apparatus uses only the direction of blood flow from the blood flow information. However, the Doppler processing unit 205 may calculate the blood flow velocity v(x, y) from the following equation (4), and the ultrasonic diagnostic apparatus may display a color map of only velocity or a two-dimensional color map of velocity and power.

number

[0049] Here, angle(c1) is a function that calculates the argument of complex number c1.

[0050] Furthermore, the super-resolution processor 206 may calculate the variance σ(x, y) using the following formula (5), and the ultrasonic diagnostic apparatus may display a two-dimensional color map of velocity and variance.

number

[0051] In this way, the super-resolution processor 206 calculates the velocity and variance of the blood flow from the power value integrated image and the autocorrelation function integrated image. The super-resolution processor 206 may calculate information on the direction of the blood flow from the autocorrelation function integrated image, and display two or three pieces of information on the velocity, variance, and power value in color on the display device 108 as third blood flow information.

[0052] The first embodiment has been described above. According to the first embodiment, the power of blood flow and the like can be displayed in super-resolution by coloring the blood flow according to the direction and speed of the blood flow. That is, according to the first embodiment, the blood flow can be displayed in super-resolution by coloring it. Therefore, according to the first embodiment, it is possible to obtain information that is highly convenient for the user.

[0053] Second embodiment Next, the second embodiment will be described. It is generally known that in a blood flow image by the Doppler method, a thick blood vessel with a fast flow velocity has a higher image value (pixel value) than a thin blood vessel with a slow flow velocity. Therefore, in the technology disclosed in the above-mentioned Non-Patent Document 1, when adjacent blood vessels with a flow velocity difference, i.e., a diameter difference, are included in an image, only the thick blood vessel with a fast flow velocity is extracted, and the thin blood vessel with a slow flow velocity does not appear in the blood flow image with improved resolution. In addition, Non-Patent Document 1 does not describe a method of utilizing the difference in the direction of blood flow or the blood flow velocity for super-resolution.

[0054] Therefore, as an ultrasonic diagnostic device, an image processing device, and an image processing method according to the second embodiment, an ultrasonic diagnostic device, an image processing device, and an image processing method capable of displaying blood flow information (blood flow image) with improved resolution in blood vessels with a wide range of flow velocities or various diameters or shapes will be described. In the following description of the second embodiment, the description of the same configuration as the first embodiment will be omitted, and the configuration different from the first embodiment will be mainly described.

[0055] 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 image as a base, and processing is performed to emphasize image values ​​corresponding to a predetermined numerical range or a local maximum value for each image in the image group based on the features, and the emphasized image values ​​of each base are added. In this embodiment, image values ​​are extracted for each image in the image group based on the features divided by flow velocity range, so that blood vessels with both fast and slow flow velocities can be extracted, improving the resolution.

[0056] An ultrasonic diagnostic apparatus according to 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 multiple positions by transmitting and receiving ultrasonic waves. The processing circuit estimates first blood flow information from a data sequence of multiple frames by reducing clutter components derived from tissue. The processing circuit calculates second blood flow information by performing an orthogonal transformation in the frame direction from the first blood flow information, creates a scalar value image for each frequency of the second blood flow information, detects a local maximum point of the scalar value image at each frequency, and performs processing to emphasize the local maximum point, generates third blood flow information from the frequency information and an image in which the local maximum point of the scalar value of each frequency is emphasized, and displays the third blood flow information on a display.

[0057] The super-resolution processing unit 206 according to the second embodiment generates super-resolution blood flow image data, which is a blood flow image with improved resolution, from the blood flow image data generated by the Doppler processing unit 205. The methods for acquiring the conventional and present super-resolution blood flow images will be described when explaining the processing flow. Next, the processing flow for acquiring the super-resolution blood flow image according to the present embodiment will be described.

[0058] (Processing flow for acquiring a super-resolution blood flow image in the second embodiment) A process flow for acquiring a super-resolution blood flow image in this embodiment will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of a process flow for acquiring a super-resolution blood flow image, which is executed by the ultrasound diagnostic apparatus according to the second embodiment. Note that the process of step S420 and the process of step S440 indicated by the dotted line blocks do not have to be executed.

[0059] 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.

[0060] The displacement amount correction unit 204 calculates the amount of tissue displacement between frames due to body movement or the like from the received signal including a data sequence of multiple frames, and corrects the amount of displacement by moving the received signal using the calculated amount of displacement (step S420). At this time, the reference frame for calculating the amount of displacement may be one frame for the entire data sequence, or multiple reference frames may be provided by changing the position in the time direction, or the displacement may be calculated between adjacent frames.

[0061] The Doppler processing unit 205 applies an MTI filter to the received signal including the data sequence of the multiple frames in which the displacement amount has been corrected by the displacement amount correction unit 204, thereby reducing clutter components and extracting components derived from blood flow to generate blood flow images of multiple frames (step S430). At this time, a part of the data sequence may be extracted and used for designing the MTI filter, or the entire data sequence may be used. Thus, in step S430, the Doppler processing unit 205 estimates the first blood flow information by reducing clutter components derived from tissues from the data sequence of the multiple frames. An example of the blood flow images of the multiple frames generated in step S430 is shown in FIG. 6A. FIG. 6A is a diagram showing an example of the blood flow images of the multiple frames generated in step S430 in the second embodiment. The shading of the image value at each position in the position 401 including the blood flow information shown in FIG. 6A is due to the difference in flow speed, and the flow speed is fast at a location with a large image value (dark color) and slow at a location with a small image value (light color).

[0062] Here, for comparison, a processing flow for acquiring a super-resolution blood flow image by the conventional technology will be described, and after describing the problems thereof, the processing flow for acquiring a super-resolution blood flow image by this embodiment will be described in more detail. FIG. 7 is a diagram showing an example of a processing flow for acquiring a super-resolution blood flow image by the conventional technology, and FIGS. 8A to 8C are diagrams for explaining an example of a processing method by the conventional technology. The processing of steps S510 to S530 in FIG. 7 is similar to the processing of steps S410 to S430 in FIG. 5. FIG. 8A shows a blood flow image of multiple frames obtained as a result of the processing of steps S510 to S530 in FIG. 7, but the blood flow image of multiple frames shown in FIG. 8A is similar to the blood flow image of multiple frames shown in FIG. 6A.

[0063] In each of the blood flow images of multiple frames, a portion having an image value within a predetermined range is extracted (S540). The predetermined range of image values ​​may be the maximum image value of the entire image, or a threshold value may be set for the maximum image value. In other words, by extracting a portion having an image value equal to or greater than a certain value within each blood vessel image, an image such as that shown in FIG. 8B is obtained. The dashed line portion in FIG. 8B represents the position 401 including blood flow information in FIG. 8A.

[0064] Since the location of the image value in a predetermined range moves between frames, a plurality of blood flow images from which image values ​​have been extracted are integrated to generate and display a super-resolution blood flow image with improved resolution as shown in FIG. 8C compared to each blood flow image, i.e., a normal Doppler image (step S550). Since only the portion with high image values ​​is extracted, if the point spread function is assumed to be a Gaussian distribution, only the portion with high sharpness at the center of the distribution is extracted. In an image of one frame, only points or a small range are extracted, but since the portion with high image values ​​moves between frames, a super-resolution blood flow image is obtained by integrating the plurality of blood flow images from which image values ​​have been extracted.

[0065] Here, the problem with the conventional technology is that, as shown in Figures 8B and 8C, when extracting points having the maximum image value or an image value above a certain level within each blood vessel image, only blood vessels with large image values, i.e., blood vessels with fast flow velocities, are extracted.

[0066] The problems of the conventional technology will be described in detail. FIG. 9 is a diagram showing an example of a Rayleigh distribution. It is generally known that the amplitude distribution characteristic of an ultrasonic reception signal from a uniform scattering medium can be approximately approximated by a probability distribution called a Rayleigh distribution shown in FIG. 9. In step S540, extracting a portion with a large amplitude value as an image value in a predetermined range means extracting a portion with a small probability in the Rayleigh distribution but a large amplitude value from a large number of reflected signals originating from red blood cells, thereby extracting only a small number of reflected signals originating from red blood cells and making them spatially sparse. However, when blood vessels with different flow velocities are adjacent to each other, extracting a portion with a large amplitude value as an image value in a predetermined range extracts only reflected signals from portions with a large amplitude value but a small probability in the Rayleigh distribution of a thick blood vessel with a fast flow velocity, and therefore does not extract reflected signals from thin blood vessels with a slow flow velocity.

[0067] FIG. 10A shows an example of a blood flow image in which two blood vessels, a thick blood vessel with a fast flow velocity and a thin blood vessel with a slow flow velocity, run parallel to each other in the depth direction. A thin blood vessel with a slow flow velocity is located at a shallow depth, and a thick blood vessel with a fast flow velocity is located at a deep depth, but the gap between the two blood vessels is less than the resolution, so they cannot be separated in the blood flow image. FIG. 10B shows an example of a super-resolution blood flow image generated by a conventional technology, which is a result of performing the processes of steps S540 and S550 on each blood flow image of multiple frames. As can be seen by comparing with the super-resolution blood flow image generated by the present embodiment shown in FIG. 10C, the resolution of only the thick blood vessels with a fast flow velocity is improved in the super-resolution blood flow image generated by the conventional technology shown in FIG. 10B, and the thin blood vessels with a slow flow velocity are not imaged.

[0068] It is conceivable to widen the predetermined range of image values ​​so that blood vessels with small image values, i.e., blood vessels with slow flow velocities, can also be extracted. However, when considering extraction from the above-mentioned Gaussian distribution, the range of distribution to be extracted is widened, and the effect of improving resolution is reduced.

[0069] The process flow for acquiring a super-resolution blood flow image according to this embodiment in consideration of the above-mentioned problems will be continued. The Doppler processing unit 205 executes the process in step S440. The process in step S440 is the same as the process in step S340 described above. Note that, as in the first embodiment, if a sufficient number of reception scanning lines is secured in beamforming, the process in step S440 is not necessary.

[0070] The super-resolution processor 206 divides each blood flow image of a plurality of frames into images with different flow velocity ranges (step S450). Specifically, the super-resolution processor 206 performs a discrete Fourier transform on the change in the image value of each pixel in the time direction using some of the blood flow images of a plurality of frames. The discrete Fourier transform is a sine wave basis and represents frequency components. Therefore, the super-resolution processor 206 can divide the image into each 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 processor 206 can divide into images having blood flow information in a flow velocity range corresponding to each basis. The images having blood flow information in a flow velocity range corresponding to each basis may be frequency domain images after discrete Fourier transform, or may be time domain images obtained by inverse discrete Fourier transform of the frequency domain images. Here, a method of processing with frequency domain images without inverse discrete Fourier transform will be described. By changing some of the frames used for the discrete Fourier transform among the blood flow images of multiple frames in the time direction, images of different time series corresponding to each base can be obtained. These are called image groups based on features, and in the second embodiment, the feature is the flow velocity, that is, the base is the flow velocity. FIG. 6B shows an example of division into image groups based on three features. In FIG. 6B, blood flows with different flow velocities are imaged in each base. By increasing the number of divisions, that is, the number of frames used for the discrete Fourier transform, the super-resolution processing unit 206 can divide the blood vessels into finer flow velocity ranges because the flow velocity range corresponding to each base becomes smaller. Note that any basis that can divide the blood flow images into flow velocity ranges may be used other than the discrete Fourier transform based on a sine wave, and the blood flow images may be divided into image groups based on the features for each flow velocity range by polynomial regression such as the Legendre polynomial. As described above, in step S450, the super-resolution processor 206 calculates the second blood flow information by performing a Fourier transform in the frame direction from the first blood flow information. Note that the super-resolution processor 206 may perform another orthogonal transform instead of the Fourier transform. In addition, the super-resolution processor 206 creates an image of a scalar value for each frequency of the second blood flow information.While the images for each frequency are often complex numbers, the images sent to the next step S460 are converted to images whose pixels are scalar values ​​that allow for size comparison, for example, power values ​​(the sum of the squares of the real and imaginary parts). Power values ​​are easy to handle because, according to Parseval's theorem, the integral on the time axis and the integral on the frequency axis have the same property, so from here on, the explanation will be given using power values.

[0071] The super-resolution processor 206 detects a location that is a predetermined range of image values ​​or a local maximum point for each image of the power image group based on the features of each base, and creates an image (peak-emphasized image) in which the pixel value of the local maximum point is emphasized (step S460). The image point in the predetermined range may be a portion with the maximum image value in each image, or a position of an image value in a range where a threshold value is set for the maximum image value. Alternatively, when the pixel value of the target point is larger than the pixel value of the neighboring pixels, it may be set as a local maximum point. The maximum point for each local region may be extracted to extract multiple local maximum points within the image. Next, an image in which the local maximum point is emphasized (peak-emphasized image) is created, and the peak-emphasized image can be created by, for example, attenuating pixel values ​​other than the local maximum point by a certain amount. As a result, the original image is included to some extent, but it is also possible to set the attenuation amount to infinity and extract only the pixel value of the local maximum point.

[0072] 5, three types of processing are shown as the processing subsequent to the processing of step S460. In steps S470 and S480, the super-resolution processor 206 adds images in which the local maximum points of all frequencies have been enhanced, and causes the image obtained by the addition to be displayed on the display device 108. The processing of steps S470 and S480 will be described in detail later.

[0073] In steps S490 and S491, the super-resolution processor 206 divides the frequency into two types, positive and negative, adds the local maximum values, and displays the positive and negative values ​​in different colors on the display device 108. That is, in steps S490 and S491, the super-resolution processor 206 adds a peak-emphasized image for each type of frequency, and colors the positive and negative frequencies differently for the image obtained by the addition, and displays the colored positive frequency image and the colored negative frequency image on the display device 108. In steps S492 and S493, the super-resolution processor 206 divides the frequency into a plurality of groups, adds the local maximum values, and displays the different colors for each group on the display device 108. That is, in steps S492 and S493, the super-resolution processor 206 adds a peak-emphasized image for each frequency group, and colors the image obtained by the addition in different colors for each group, and displays the colored images for each group on the display device 108. The process of steps S450 to S480 is shown in Fig. 12 in a schematic diagram. In step S450, N frames of data are Fourier transformed in the frame direction. In step S460, the local maximum value of the power value is extracted for each frequency. In step S470, the local maximum values ​​of each frequency are added. The region to be Fourier transformed is partially overlapped with the previous region.

[0074] The super-resolution processor 206 may also extract a location with a predetermined range of image values ​​or a local maximum value after linearly or spline-interpolating the number of pixels of each image of the image group based on the features of each basis or the blood flow images of the original frames. This is because the pixel size differs between deep and shallow areas in a convex probe or a sector probe, and when the pixels are coarse, the effect of improving the resolution is small even if the image values ​​are extracted and the addition process of step S470 is performed to perform super-resolution processing. In the case of linear processing, the same result is obtained whether the processing is performed on the time axis or on the frequency axis, but the results are different when nonlinear processing called local maximum is performed. In the case of Fourier transform basis, a local maximum value is obtained for each blood flow velocity, making it easier to separate and extract blood vessels with different blood flow velocities.

[0075] By the process of step S460, an image as shown in FIG. 6C is obtained. Unlike the conventional technology, the super-resolution processor 206 performs a process of extracting image values ​​for each image divided by flow speed, and therefore can extract blood vessels with large image values, i.e., thick blood vessels with a fast flow speed, and blood vessels with small image values, i.e., thin blood vessels with a slow flow speed. That is, the super-resolution processor 206 can extract blood vessels with a wide range of flow speeds. In addition, when the energy level of the pixel values ​​of a certain blood vessel (for example, the standard deviation of the pixel values) is close to the energy of noise, the noise energy is distributed to each base by generating an image divided by flow speed by the process of step S450, and the SNR or CNR (Contrast to Noise Ratio) of the image divided by flow speed can be improved.

[0076] Therefore, the super-resolution processor 206 can extract blood vessels that could not be distinguished from noise in the conventional technology. Furthermore, by dividing into multiple images by the process of step S450, the pixel values ​​of each image become sparse, and for example, in the process of step S460 where local peaks are selected, more pixels can be selected with the same number of frames than in the conventional technology. This makes it possible to shorten the measurement time required to obtain the final image.

[0077] As described above, in step S460, the super-resolution processor 206 detects local maximum points of the scalar value image at each frequency and performs processing to enhance the scalar value of the local maximum point.

[0078] A predetermined range of image values ​​or a location of a local maximum value moves between each image of the image group based on the feature. Therefore, the super-resolution processor 206 generates a super-resolution blood flow image with improved resolution as shown in FIG. 6D by adding each image of the image group based on the feature that emphasizes a predetermined range of image values ​​or a local maximum point between the image groups based on the feature and between each base, and displays the generated super-resolution blood flow image on the display device 108 (step S480). The super-resolution processor 206 may display the result of adding the images and bases of all the image groups based on the feature as an image on the display device 108, or may display a moving image on the display device 108 by sequentially updating intermediate images added in chronological order and displaying them on the display device 108, or may display intermediate images on the display device 108 in parallel without updating them. The super-resolution processor 206 may not use the images and bases of all the image groups based on the feature for the addition, but may use images and bases in the time series range to be used designated by the user.

[0079] The super-resolution processor 206 can extract blood vessels with a wide range of flow velocities by making the weights for the image values ​​of each base uniform when adding. On the other hand, the super-resolution processor 206 can also emphasize blood vessels with a certain flow velocity by changing the weights for the image values ​​of each base. For example, the super-resolution processor 206 can emphasize thin blood vessels with a slow flow velocity by increasing the weights for the bases corresponding to blood vessels with a slow flow velocity.

[0080] In this way, blood vessels with a wide range of flow velocities can be extracted from multiple frames of blood flow images, and a super-resolution blood flow image with improved resolution can be generated.

[0081] A specific display method in step S480 will be described. A power value obtained by adding a peak-enhanced image for each frequency has been calculated by step S470. That is, between the processing of step S460 and the processing of step S480, the super-resolution processor 206 calculates a power value obtained by adding a peak-enhanced image for all frequencies by adding a peak-enhanced image for each frequency as processing of step S470. Alternatively, the super-resolution processor 206 adds a peak-enhanced image for each positive frequency and negative frequency as in the processing of step S490. Alternatively, the super-resolution processor 206 divides frequencies into groups and adds a peak-enhanced image for each group as in the processing of step S492.

[0082] FIG. 10C shows an image in which the power values ​​obtained in the process of step S470 are added together at all frequencies. As shown in FIG. 6B, since the frequency and the blood flow velocity can be contrasted, only a specific blood flow velocity can be displayed by displaying a specific frequency. It is also possible to display different colors depending on the frequency. For example, since a positive frequency is an oncoming blood flow, the super-resolution processor 206 can display only the blood flow in the direction toward the ultrasonic probe 102 at a super-resolution level by displaying an image to which the power of a positive frequency has been added on the display device 108. In addition, the super-resolution processor 206 can display only the blood flow in the direction away from the ultrasonic probe 102 at a super-resolution level by displaying an image to which the power of a negative frequency has been added on the display device 108. In addition, the super-resolution processor 206 can simultaneously display the oncoming blood flow and the receding blood flow by expressing them in different hues and adding them together in RGB. Furthermore, the super-resolution processor 206 sets a threshold value for the power of the oncoming blood flow, and displays signals above the threshold value on the front side, thereby allowing the arteries to be clearly displayed. These aspects are shown in FIGS. 11A to 11H.

[0083] 11A to 11H are diagrams showing images of kidney blood flow obtained by various methods, displayed on the display device 108. The image shown in FIG. 11A is an image in which blood flow power values ​​are displayed in grayscale without super-resolution display. The image shown in FIG. 11B is an image in which blood flow power values ​​are color-coded according to direction without super-resolution display. In the image shown in FIG. 11B, approaching blood flow is displayed in red, and receding blood flow is displayed in blue. Only one of the approaching or receding blood flow can be displayed at one position. The image shown in FIG. 11C is an image in which super-resolution processing is performed on the time axis of the conventional example and blood flow power values ​​are displayed in grayscale. The image shown in FIG. 11D is an image in which super-resolution processing is performed on the frequency axis according to this embodiment and the power of all frequencies is added and displayed in grayscale. Comparing the image inside the circle set in the image shown in FIG. 11C with the image inside the circle set in the image shown in FIG. 11D, it can be seen that the blood vessels are better separated and displayed in the image inside the circle set in the image shown in FIG. 11D than in the image inside the circle set in the image shown in FIG. 11C. The image shown in FIG. 11E is an image in which the power of only positive frequencies (oncoming blood flow) is displayed. In the image shown in FIG. 11E, the oncoming blood flow is displayed in red. The image shown in FIG. 11F is an image in which the power of only negative frequencies (receding blood flow) is displayed. In the image shown in FIG. 11F, the receding blood flow is displayed in blue. The image shown in FIG. 11G is an image in which the image shown in FIG. 11E and the image shown in FIG. 11F are added together in RGB. The image shown in FIG. 11H is an image in which positive frequencies above a certain threshold are displayed on the front side. In the case of the kidney, the blood flow toward us is the renal artery, and the blood flow away from us is the renal vein, so the arteries and veins can be displayed separately, and even if the blood vessels are overlapping, the arteries and veins are displayed separately as in the image shown in Figure 11G and the image shown in Figure 11H. It is also possible to display only the arteries as in the image shown in Figure 11E, and to display only the veins as in the image shown in Figure 11F.

[0084] As described above, in steps S480, S491, and S493, the super-resolution processor 206 generates third blood flow information having a higher resolution than that of the first blood flow information from the frequency information and a scalar value sum image of each frequency (an image in which a local maximum point of the scalar value of each frequency is emphasized), and displays the third blood flow information on the display device 108. The super-resolution processor 206 also adds a plurality of scalar value sum images of positive frequencies to calculate positive frequency blood flow information, adds scalar value sum images of negative frequencies to calculate negative frequency blood flow information, and changes the color of the positive frequency blood flow information and the negative frequency blood flow information to display the same on the display device 108.

[0085] In step S491, the super-resolution processor 206 causes the display device 108 to display a third RGB image obtained by adding the first RGB image showing blood flow information of positive frequencies and the second RGB image showing blood flow information of negative frequencies, or causes the display device 108 to display either the first RGB image or the second RGB image preferentially on the front side. The super-resolution processor 206 also adds a plurality of scalar value added images of positive frequencies to calculate blood flow information of positive frequencies, or adds a plurality of scalar value added images of negative frequencies to calculate blood flow information of negative frequencies, and causes the display device 108 to display the blood flow information of positive frequencies, or causes the display device 108 to display the blood flow information of negative frequencies.

[0086] In the above description, the super-resolution processor 206 classifies frequencies into two types, positive and negative frequencies, but may color all frequencies differently. Each Doppler frequency corresponds to the velocity of blood flow. Therefore, the super-resolution processor 206 colors differently according to the blood flow velocity. That is, the super-resolution processor 206 displays the scalar value added image of each frequency on the display device 108 with a different hue for each frequency. The super-resolution processor 206 may display the blood flow velocity as a hue and the power of the blood flow at that velocity as brightness. Furthermore, the super-resolution processor 206 calculates the center of gravity frequency (first moment) of the power on the frequency axis, which becomes the average flow velocity of the blood flow. The super-resolution processor 206 may display only this average flow velocity or a two-dimensional color map of the average flow velocity and the power. Furthermore, the super-resolution processor 206 calculates the second moment of the power on the frequency axis to obtain the variance of the blood flow velocity. The super-resolution processor 206 may display this variance in combination with the velocity and power. The super-resolution processor 206 may also create an image of the power value for each frequency of the second blood flow information, detect a local maximum value of the image of the power value at each frequency, calculate an image in which the local maximum point of the power value is emphasized by integrating the local maximum value for each frequency, calculate the average velocity and variance of the blood flow from the image in which the local maximum point of the power value at each frequency is emphasized, and display one or more of the average velocity, variance, and power value on the display device 108 as a multidimensional color map.

[0087] The second embodiment has been described above. According to the second embodiment, blood flow information (blood flow image) with improved resolution can be obtained for blood vessels with a wide range of flow velocities or various diameters or shapes. For example, according to the second embodiment, even overlapping blood vessels can be displayed independently 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 that is highly convenient for the user can be obtained.

[0088] (Other embodiments) The disclosed technology can be embodied as, for example, a system, a device, a method, a program, or a recording medium (storage medium). Specifically, the technology may be applied to a system composed of a plurality of devices (for example, a host computer, an interface device, an imaging device, a web application, etc.), or may be applied to a device composed of one device. For example, the technology described above may be applied to an image processing device. For example, a memory included in the image processing device stores a received signal including a data sequence of a plurality of frames in the time direction acquired by an ultrasound diagnostic device. That is, the memory stores in advance a data sequence of reflected wave data collected in a plurality of frames in the time direction at one or a plurality of positions by ultrasound transmission and reception performed by the ultrasound diagnostic device. Then, a processor included in the image processing device acquires the above-mentioned received signal (the data sequence of reflected wave data collected in a plurality of frames) from the memory. Then, the processor executes the same process as the process executed by the above-mentioned ultrasound diagnostic device on the acquired received signal.

[0089] Needless to say, the object of the present invention can be achieved by the following: A recording medium (or storage medium) on which is recorded software program code (computer program) for realizing the functions of the above-mentioned embodiments is supplied to a system or device. Such a storage medium is, of course, a computer-readable storage medium. Then, a computer (or a CPU or MPU) of the 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 above-mentioned embodiments, and the recording medium on which the program code is recorded constitutes the present invention.

[0090] The term "processor" used in the description of the above-mentioned embodiments means a circuit such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). Here, instead of storing a program in the memory circuit 152, the program may be directly embedded in the circuit of the processor. In this case, the processor realizes its function by reading and executing the program embedded in the circuit. In addition, each processor in each embodiment is not limited to being configured as a single circuit for each processor, and may be configured as one processor by combining multiple independent circuits to realize its function.

[0091] Here, the program executed by the processor is provided in advance in a ROM (Read Only Memory) or a storage circuit. The program may be provided by being recorded in a computer-readable non-transitory storage medium such as a CD (Compact Disk)-ROM, a FD (Flexible Disk), a CD-R (Recordable), or a DVD (Digital Versatile Disk) in a format that can be installed in these devices or in a format that can be executed. The program may also be provided or distributed by being stored on a computer connected to a network such as the Internet and downloaded via the network. For example, the program is composed of modules including each of the above-mentioned processing functions. As actual hardware, a CPU reads out the program from a storage medium such as a ROM and executes it, so that each module is loaded onto a main storage device and generated on the main storage device.

[0092] Regarding the above embodiment, the following supplementary notes are disclosed as one aspect and optional features of the invention.

[0093] (Appendix 1) An ultrasonic diagnostic apparatus provided in one aspect of the present invention includes an acquisition unit that acquires a data sequence 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 that estimates first blood flow information from the data sequence of the multiple frames by reducing clutter components derived from tissue, and a super-resolution processing unit that generates second blood flow information which is an image of scalar values ​​of blood flow signals from the first blood flow information, detects a local maximum value of the second blood flow information, and generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, calculates an autocorrelation function in the frame direction of the first blood flow information at the position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function, generates third blood flow information in which blood flow is color-coded from the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on a display unit.

[0094] (Appendix 2) The super-resolution processing unit may calculate information on the direction of blood flow from the autocorrelation function integrated image, and generate the third blood flow information color-coded for each direction of blood flow from the scalar value integrated image and the information on the direction of blood flow.

[0095] (Appendix 3) The scalar value may be a power value of the blood flow signal, and the super-resolution processing unit may detect the local maximum value of the power value of the second blood flow information, generate a power value accumulated image as a scalar value accumulated image by accumulating the local maximum value or an image in which the local maximum value is emphasized, calculate the velocity and variance of the blood flow from the power value accumulated image and the autocorrelation function accumulated image, and display two or three pieces of information of the velocity, the variance, and the power value in color on the display unit as the third blood flow information.

[0096] (Appendix 4) The Doppler processing unit may increase the number of scan lines by interpolating the first blood flow information in which clutter components derived from the tissue have been reduced, in a direction of ultrasonic scan lines.

[0097] (Appendix 5) An image processing device provided in one aspect of the present invention includes an acquisition unit that acquires a data sequence of reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasound, a Doppler processing unit that estimates first blood flow information from the data sequence of the multiple frames by reducing clutter components derived from tissue, and a super-resolution processing unit that generates second blood flow information which is an image of scalar values ​​of blood flow signals from the first blood flow information, detects a local maximum value of the second blood flow information, and generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, calculates an autocorrelation function in the frame direction of the first blood flow information at the position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function, generates third blood flow information in which blood flow is color-coded from the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on a display unit.

[0098] (Appendix 6) An image processing method provided in one aspect of the present invention executes the steps of: acquiring a data sequence of reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasound; estimating first blood flow information from the data sequence of the multiple frames by reducing clutter components derived from tissue; generating second blood flow information which is an image of scalar values ​​of blood flow signals from the first blood flow information; detecting a local maximum value of the second blood flow information and generating a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized; calculating the autocorrelation function in the frame direction of the first blood flow information at the position where the local maximum value is obtained and generating an autocorrelation function integrated image by integrating the autocorrelation function; generating third blood flow information in which blood flow is color-coded from the scalar value integrated image and the autocorrelation function integrated image; and displaying the third blood flow information on a display unit.

[0099] (Appendix 7) An ultrasonic diagnostic apparatus provided in another aspect of the present invention includes an acquisition unit that acquires a data sequence 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 that estimates first blood flow information from the data sequence of the multiple frames by reducing clutter components derived from tissue, and a super-resolution processing unit that generates second blood flow information which is an image of scalar values ​​of blood flow signals from the first blood flow information, detects a local maximum value of the second blood flow information, generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, generates third blood flow information in which blood flow is color-coded from the scalar value integrated image and information regarding the direction of blood flow at the position where the local maximum value is obtained, and displays the third blood flow information on a display unit.

[0100] (Appendix 8) An ultrasound diagnostic apparatus provided in another aspect of the present invention includes an acquisition unit that acquires reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasound, a Doppler processing unit that estimates first blood flow information by reducing clutter components derived from tissue from the data sequence of the multiple frames, and a super-resolution processing unit that calculates second blood flow information by performing an orthogonal transformation in the frame direction from the first blood flow information, creates a scalar value image for each frequency of the second blood flow information, detects local maxima of the scalar value image at each frequency and performs processing to emphasize the scalar value of the local maxima, generates third blood flow information from the frequency information and the image in which the local maxima of the scalar value of each frequency are emphasized, and displays the third blood flow information on a display unit.

[0101] (Appendix 9) The super-resolution processing unit may calculate positive frequency blood flow information by adding images in which local maximum points of multiple positive frequency scalar values ​​are emphasized, calculate negative frequency blood flow information by adding images in which local maximum points of negative frequency scalar values ​​are emphasized, and display the positive frequency blood flow information and the negative frequency blood flow information on the display unit with different colors.

[0102] (Appendix 10) The super-resolution processing unit may cause the display unit to display a third RGB image obtained by adding a first RGB image indicating the blood flow information of the positive frequency and a second RGB image indicating the blood flow information of the negative frequency, or may cause the display unit to display either the first RGB image or the second RGB image preferentially on the front side.

[0103] (Appendix 11) The super-resolution processing unit may calculate positive frequency blood flow information by adding together images in which local maximum points of multiple positive frequency scalars are emphasized, or calculate negative frequency blood flow information by adding together images in which local maximum points of multiple negative frequency scalar values ​​are emphasized, and display the positive frequency blood flow information on the display unit, or display the negative frequency blood flow information on the display unit.

[0104] (Appendix 12) The super-resolution processor may display, on the display unit, an image in which a local maximum point of a scalar value of each frequency is emphasized, in a different hue for each frequency.

[0105] (Appendix 13) The scalar value may be a power value of blood flow, and the super-resolution processing unit may create an image of the power value for each frequency of the second blood flow information, detect a local maximum value of the image of the power value at each frequency, calculate an image in which the local maximum point of the power value is emphasized by integrating the local maximum value for each frequency, calculate an average velocity and variance of the blood flow from the image in which the local maximum point of the power value at each frequency is emphasized, and display one or more of the average velocity, the variance, and the power value on the display unit as a multidimensional color map.

[0106] (Appendix 14) The Doppler processing unit may increase the number of scan lines by interpolating the first blood flow information in which clutter components derived from the tissue have been reduced, in a direction of ultrasonic scan lines.

[0107] (Appendix 15) An image processing device provided in another aspect of the present invention includes an acquisition unit that acquires reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasound, a Doppler processing unit that reduces clutter components derived from tissue from the data sequence of the multiple frames and estimates first blood flow information, and a super-resolution processing unit that calculates second blood flow information by performing an orthogonal transformation in the frame direction from the first blood flow information, creates a scalar value image for each frequency of the second blood flow information, detects local maxima of the scalar value image at each frequency and performs processing to emphasize the scalar values ​​of the local maxima, generates third blood flow information from the frequency information and the image in which the local maxima of the scalar values ​​of each frequency are emphasized, and displays the third blood flow information on a display unit.

[0108] (Appendix 16) An image processing method provided in another aspect of the present invention performs the following processes: acquiring reflected wave data collected in multiple frames in the time direction at one or more positions by transmitting and receiving ultrasound; estimating first blood flow information from the data sequence of the multiple frames by reducing clutter components derived from tissue; calculating second blood flow information by performing an orthogonal transformation in the frame direction from the first blood flow information; creating a scalar value image for each frequency of the second blood flow information, detecting local maxima of the scalar value image at each frequency and emphasizing the scalar value of the local maxima; and generating third blood flow information from the frequency information and the image in which the local maxima of the scalar value of each frequency are emphasized, and displaying the third blood flow information on a display unit.

[0109] According to at least one of the embodiments described above, it is possible to obtain information that is highly convenient for the user.

[0110] Although some embodiments have been described, these embodiments are presented 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 modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0111] 102 Ultrasound probe 105 Receiving electrical circuit 106 Receiving signal processing section 107 Image Processing Unit 108 Display device 109 System Control Unit 205 Doppler processing section 206 Super-resolution processing section

Claims

1. 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 a plurality of positions by transmitting and receiving ultrasonic waves; a Doppler processor that reduces clutter components derived from tissues and estimates first blood flow information from the data sequence of the plurality of frames; a super-resolution processing unit that generates second blood flow information which is an image of a scalar value of a blood flow signal from the first blood flow information, detects a local maximum value of the second blood flow information, generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, calculates an autocorrelation function in a frame direction of the first blood flow information at a position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function, generates third blood flow information in which blood flows are color-coded from the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on a display unit; An ultrasound diagnostic device comprising:

2. 2. The ultrasound diagnostic apparatus according to claim 1, wherein the super-resolution processor calculates information on a direction of blood flow from the autocorrelation function integrated image, and generates the third blood flow information color-coded for each direction of blood flow from the scalar value integrated image and the information on the direction of blood flow.

3. the scalar value is a power value of the blood flow signal, 2. The ultrasonic diagnostic apparatus according to claim 1, wherein the super-resolution processing unit detects the local maximum value of a power value of the second blood flow information, generates a power value accumulated image as a scalar value accumulated image by accumulating the local maximum value or an image in which the local maximum value is emphasized, calculates a blood flow velocity and variance from the power value accumulated image and the autocorrelation function accumulated image, and displays two or three pieces of information of the velocity, the variance, and the power 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 increases the number of scan lines by interpolating the first blood flow information in which clutter components derived from the tissue have been reduced, in a direction of ultrasonic scan lines.

5. 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 a plurality of positions by transmitting and receiving ultrasonic waves; a Doppler processor that reduces clutter components derived from tissues and estimates first blood flow information from the data sequence of the plurality of frames; a super-resolution processing unit that generates second blood flow information which is an image of a scalar value of a blood flow signal from the first blood flow information, detects a local maximum value of the second blood flow information, generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, calculates an autocorrelation function in a frame direction of the first blood flow information at a position where the local maximum value is obtained, and generates an autocorrelation function integrated image by integrating the autocorrelation function, generates third blood flow information in which blood flows are color-coded from the scalar value integrated image and the autocorrelation function integrated image, and displays the third blood flow information on a display unit; An image processing device comprising:

6. A process of acquiring a data sequence of reflected wave data collected in multiple frames in the time direction at one or multiple positions by transmitting and receiving ultrasound; A process of estimating first blood flow information by reducing clutter components derived from tissue from the data sequence of the multiple frames; A process of generating second blood flow information, which is an image of a scalar value of a blood flow signal, from the first blood flow information; a process of detecting a local maximum value of the second blood flow information, and generating a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized; A process of calculating an autocorrelation function in a frame direction of the first blood flow information at the position where the local maximum value is obtained, and generating an autocorrelation function integrated image by integrating the autocorrelation function; generating third blood flow information in which blood flows are color-coded from the scalar value accumulated image and the autocorrelation function accumulated image; A process of displaying the third blood flow information on a display unit; An image processing method that performs

7. 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 a plurality of positions by transmitting and receiving ultrasonic waves; a Doppler processor that reduces clutter components derived from tissues and estimates first blood flow information from the data sequence of the plurality of frames; a super-resolution processing unit that generates second blood flow information, which is an image of a scalar value of a blood flow signal, from the first blood flow information, detects a local maximum value of the second blood flow information, generates a scalar value integrated image by integrating the local maximum value or an image in which the local maximum value is emphasized, generates third blood flow information in which blood flow is color-coded from the scalar value integrated image and information regarding the direction of blood flow at the position where the local maximum value is obtained, and displays the third blood flow information on a display unit; An ultrasound diagnostic device comprising:

8. an acquisition unit that acquires reflected wave data collected in multiple frames in a time direction at one or multiple positions by transmitting and receiving ultrasonic waves; a Doppler processor that reduces clutter components derived from tissues and estimates first blood flow information from the data sequence of the plurality of frames; a super-resolution processing unit that calculates second blood flow information by performing an orthogonal transformation in a frame direction from the first blood flow information, creates a scalar value image for each frequency of the second blood flow information, detects a local maximum point of the scalar value image at each frequency and performs processing to emphasize the scalar value of the local maximum point, generates third blood flow information from the frequency information and the image in which the local maximum point of the scalar value of each frequency is emphasized, and displays the third blood flow information on a display unit; An ultrasound diagnostic device comprising:

9. 9. The ultrasound diagnostic apparatus according to claim 8, wherein the super-resolution processing unit calculates positive frequency blood flow information by adding images in which local maximum points of a plurality of positive frequency scalar values ​​are emphasized, calculates negative frequency blood flow information by adding images in which local maximum points of negative frequency scalar values ​​are emphasized, and displays the positive frequency blood flow information and the negative frequency blood flow information on the display unit with different colors.

10. 10. The ultrasound diagnostic apparatus according to claim 9, wherein the super-resolution processor causes the display unit to display a third RGB image obtained by adding a first RGB image indicating blood flow information of positive frequencies and a second RGB image indicating blood flow information of negative frequencies, or causes the display unit to display either the first RGB image or the second RGB image preferentially on a front side.

11. 9. The ultrasound diagnostic apparatus according to claim 8, wherein the super-resolution processing unit calculates positive frequency blood flow information by adding together images in which local maximum points of multiple positive frequency scalars are emphasized, or calculates negative frequency blood flow information by adding together images in which local maximum points of multiple negative frequency scalar values ​​are emphasized, and displays the positive frequency blood flow information on the display unit, or displays the negative frequency blood flow information on the display unit.

12. The ultrasonic diagnostic apparatus according to claim 8 , wherein the super-resolution processor displays, on the display unit, an image in which a local maximum point of a scalar value of each frequency is emphasized, in a different hue for each frequency.

13. the scalar value is a power value of blood flow, 9. The ultrasonic diagnostic apparatus according to claim 8, wherein the super-resolution processing unit creates an image of the power values ​​for each frequency of the second blood flow information, detects a local maximum value of the image of the power values ​​at each frequency, calculates an image in which the local maximum points of the power values ​​are emphasized by integrating the local maximum values ​​for each frequency, calculates an average velocity and variance of the blood flow from the image in which the local maximum points of the power values ​​at each frequency are emphasized, and displays one or more of the average velocity, the variance, and the power values ​​in a multidimensional color map on the display unit.

14. The ultrasonic diagnostic apparatus according to claim 8 , wherein the Doppler processing unit increases the number of scanning lines by interpolating the first blood flow information in which clutter components derived from the tissue have been reduced, in a direction of ultrasonic scanning lines.

15. an acquisition unit that acquires reflected wave data collected in multiple frames in a time direction at one or multiple positions by transmitting and receiving ultrasonic waves; a Doppler processor that reduces clutter components derived from tissues and estimates first blood flow information from the data sequence of the plurality of frames; a super-resolution processing unit that calculates second blood flow information by performing an orthogonal transformation in a frame direction from the first blood flow information, creates a scalar value image for each frequency of the second blood flow information, detects a local maximum point of the scalar value image at each frequency and performs processing to emphasize the scalar value of the local maximum point, generates third blood flow information from the frequency information and the image in which the local maximum point of the scalar value of each frequency is emphasized, and displays the third blood flow information on a display unit; An image processing device comprising:

16. A process of acquiring reflected wave data collected in multiple frames in the time direction at one or multiple positions by transmitting and receiving ultrasonic waves; A process of estimating first blood flow information by reducing clutter components derived from tissue from the data sequence of the multiple frames; A process of calculating second blood flow information by performing an orthogonal transformation in a frame direction from the first blood flow information; a process of creating a scalar value image for each frequency of the second blood flow information, detecting a local maximum point of the scalar value image at each frequency, and emphasizing the scalar value of the local maximum point; generating third blood flow information from the frequency information and an image in which the local maximum points of the scalar values ​​of the respective frequencies are emphasized, and displaying the third blood flow information on a display unit; An image processing method that performs

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