Ultrasonic diagnostic apparatus, medical information processing apparatus, information processing method, and program

Abstract

To provide a technology to acquire high resolution blood flow information.SOLUTION: An ultrasonic diagnostic apparatus includes: a first acquisition unit for acquiring measurement data including tissue-derived information and blood flow-derived information on the basis of an ultrasonic signal reflected in a living body; a second acquisition unit for acquiring blood flow data having the blood flow-derived information that is extracted or emphasized as a pixel value, the blood flow data extracting or emphasizing the blood flow-derived information of the measurement data; and a generation unit for generating display image data by generating a plurality of pieces of feature separation data whose image features are mutually different from each piece of the blood flow data of a plurality of frames, and synthesizing feature points extracted from each of the plurality of pieces of feature separation data for a plurality of frames.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an ultrasonic diagnostic apparatus, a medical information processing apparatus, an information processing method, and a program. [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 captured using the Color Flow Mapping (CFM) method. In the CFM method, ultrasonic waves are transmitted and received multiple times on multiple scanning lines. Then, by applying an MTI (Moving Target Indicator) filter to the data sequence at the same position, signals (clutter signals) originating from stationary or slow-moving tissues are suppressed and signals originating from blood flow are extracted. In the CFM method, blood flow information such as blood flow speed, blood flow dispersion, and blood flow power are estimated from this blood flow signal, and the distribution of the estimated results 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 extracting and accumulating high pixel values ​​from multiple blood flow images obtained in time series. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Jorgen Arendt Jensen et al., "Fast super resolution ultrasound imaging using the erythrocytes," Proc. SPIE 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography, 120380E (4 April 2022) Summary of the Invention [Problem to be solved by the invention]

[0007] In the technology disclosed in Non-Patent Document 1, for example, when adjacent blood vessels with a difference in flow velocity, i.e., a difference in diameter, are included within the observation region, only the thick blood vessels with a fast flow velocity are extracted, and the thin blood vessels with a slow flow velocity may not appear in the blood flow image with improved resolution.

[0008] The present invention has been made in consideration of the above-mentioned problems, and has an object to provide a technique for obtaining high-resolution blood flow information. [Means for solving the problem]

[0009] The present disclosure relates to a first acquisition unit that acquires measurement data including information derived from tissue and information derived from blood flow based on an ultrasonic signal reflected within a living body, a second acquisition unit that acquires blood flow data in which the information derived from the blood flow of the measurement data is extracted or emphasized, the blood flow data having the extracted or emphasized information derived from the blood flow as pixel values, and a first acquisition unit that generates a plurality of feature separation data having mutually different image features from each of a plurality of frames of blood flow data, and and a generating unit that generates display image data by synthesizing feature points extracted from the respective frames for a plurality of frames. Effect of the Invention

[0010] According to the present invention, high-resolution blood flow information can be obtained. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an ultrasound diagnostic apparatus. [Diagram 2] FIG. 4 is a block diagram showing an example of functions of a reception signal processing block. [Diagram 3] FIG. 4 is a flowchart showing a super-resolution image generation process according to the first embodiment. [Figure 4] 5A to 5C are views for explaining generation of a super-resolution image in the first embodiment. [Diagram 5] FIG. 1 is a diagram showing a flow of a super-resolution image generation process according to the prior art. [Figure 6] 1A and 1B are diagrams for explaining generation of a super-resolution image in the prior art. [Figure 7] FIG. 1 is a diagram illustrating a Rayleigh distribution. [Figure 8] 4 shows examples of super-resolution images according to the conventional technology and the first embodiment. [Figure 9] 10A to 10C are views for explaining generation of a feature separated image in the second embodiment. [Figure 10] 13A to 13C are views for explaining generation of a feature separated image according to the third embodiment. [Figure 11] 13A to 13C are views for explaining generation of a feature separated image in the fourth embodiment. [Figure 12] 13A to 13C are views for explaining generation of a feature separated image in the fifth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] As mentioned above, conventional super-resolution technology generates super-resolution images by extracting and accumulating high pixel values ​​from the original blood flow data. Therefore, when various blood vessels are mixed in the observation area, only the pixels of blood vessels with relatively high pixel values ​​(e.g., blood vessels with a fast flow rate, thick blood vessels, etc.) are used to generate the super-resolution image. As a result, the super-resolution image may lack blood flow information of blood vessels with relatively low pixel values ​​(e.g., blood vessels with a slow flow rate, thin blood vessels, etc.).

[0013] Therefore, the present inventors adopt a new approach in which multiple blood flow data (called "feature separation data") with different image features are generated from the original blood flow data, and pixels to be used for generating a super-resolution image are extracted from each feature separation data. Specifically, it is preferable to generate a super-resolution image by the following procedure.

[0014] First, the first acquisition unit acquires measurement data including information derived from tissue (tissue components) and information derived from blood flow (blood flow components) based on an ultrasonic signal reflected within a living body. The "measurement data" may be, for example, a received signal obtained by transmitting and receiving an ultrasonic signal, or data obtained by performing phasing and summation and quadrature detection processing on the received signal. Note that the information derived from blood flow may include not only information derived from blood, but also information derived from a contrast agent in blood. A plurality of frames of time-series measurement data are acquired at a predetermined frame rate.

[0015] Next, the second acquisition unit acquires blood flow data in which information derived from blood flow is extracted or emphasized from the measurement data. The "blood flow data" is data having the extracted or emphasized information derived from blood flow as pixel values, and data representing the blood flow data in a two-dimensional image format is also called a "blood flow image." "Extracting information derived from blood flow" is, for example, an operation of selectively extracting image features of blood flow components from the measurement data. "Emphasis on information derived from blood flow" is, for example, an operation of making image features of blood flow components stand out relatively more than image features of tissue components. The second acquisition unit may acquire blood flow data by performing a process of extracting or emphasizing information derived from blood flow, or conversely, may acquire blood flow data by performing a process of removing or reducing information derived from tissue. For example, the process of the second acquisition unit may include a process of calculating information such as blood flow velocity, variance value, and power value (also called "blood flow information") by the Doppler method.

[0016] Next, the generating unit generates a plurality of feature separation data having different image features from each of the plurality of frames of blood flow data. The "feature separation data" can be said to be blood flow data including only a part of the image features (i.e., a part of blood flow information) of the original blood flow data. Data expressing the feature separation data in a two-dimensional image format is also called a "feature separation image". The generating unit then generates display image data by synthesizing feature points extracted from each of the plurality of feature separation data for a plurality of frames. The "feature points" are preferably parts where the blood flow features are particularly strongly expressed (i.e., parts that are highly likely to indicate the location of blood flow). For example, if feature points are extracted from an image having information derived from blood flow (such as blood flow speed, variance value, and power value) as pixel values, pixels having the maximum pixel value in the image or pixels having pixel values ​​equal to or greater than a predetermined threshold value may be extracted as feature points. The range of pixel values ​​(extraction range) from which feature points are extracted can be set arbitrarily. "Synthesis" is an operation to generate one blood flow image by combining information of multiple feature points extracted from each of multiple frames, and can be, for example, a process of accumulating (plotting) multiple feature points on one image. "Display image data" is data that visualizes high-resolution blood flow information displayed as a processing result, and is also called a super-resolution blood flow image.

[0017] According to the above procedure, the pixels used to generate the display image data are not directly extracted from the original blood flow data, but are extracted from each of a plurality of feature separation data with different image features, thereby making it possible to generate high-resolution display image data including a variety of image features compared to the conventional method. Therefore, even if the observation area includes blood vessels with various flow velocities, diameters, or shapes, it is possible to obtain a blood flow image that depicts the blood flows of those blood vessels with high resolution.

[0018] Although the present invention has been described so far as applied to an ultrasonic diagnostic device, the present invention may be applied to modalities (medical information processing devices) other than ultrasonic diagnostic devices. For example, in a medical information processing device that acquires measurement data including tissue-derived information and blood flow-derived information from a living body in the same manner as an ultrasonic image, high-resolution blood flow information can be obtained.

[0019] Next, some specific embodiments of the present invention will be described as examples.

[0020] <First embodiment> A first embodiment of the present invention will be described. In this embodiment, a plurality of feature separation images with different image features are generated from a plurality of frames of blood flow images, feature points are extracted from each of the feature separation images, and a super-resolution blood flow image is generated by accumulating the many extracted feature points.

[0021] Consider a feature space formed by all image features included in a blood flow image. For example, by separating a partial space from the feature space and reconstructing an image using only the image features included in that partial space, a feature-separated image in which only a portion of the image features of the original blood flow image are separated can be generated. In this case, by varying the image feature of interest (i.e., the partial space separated from the feature space), multiple feature-separated images with different image features can be generated from a single blood flow image. Note that the partial spaces of each feature-separated image may be completely separated, or the partial spaces may overlap.

[0022] 1 is a block diagram showing an example of the hardware configuration of an ultrasonic diagnostic apparatus according to this embodiment. The ultrasonic diagnostic apparatus 1 includes an 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 1 transmits a pulsed ultrasonic signal from the ultrasonic probe 102 to a subject 100, and detects ultrasonic waves reflected within the living body. This is a system for receiving an acoustic signal and generating image information of the inside of a subject 100. Ultrasonic images such as B-mode images and blood flow images obtained by the ultrasonic diagnostic device 1 are used in various clinical examinations.

[0023] The ultrasonic probe 102 is an electronic scanning type probe, and has a plurality of transducers 101 arranged one-dimensionally or two-dimensionally at its tip. 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 acoustic 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 ultrasonic signal undergoes a frequency shift due to the Doppler effect depending on the velocity component of the moving body in the ultrasonic transmission direction.

[0024] The transmission electric circuit 104 is a transmission unit that outputs a pulse signal (drive signal) to the multiple transducers 101. By applying a pulse signal to the multiple transducers 101 with a time difference, ultrasonic waves with different delay times are transmitted from the multiple transducers 101, and a transmitted ultrasonic beam is formed. The direction and focus of the transmitted ultrasonic beam can be controlled 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 the transmitted ultrasonic beam, the observation area inside the subject 100 is scanned. In addition, by changing the delay time of the pulse signal, a transmitted ultrasonic beam that is a plane wave (focus is far away) or a diverging wave (focus point is the opposite ultrasonic transmission direction for the multiple transducers 101) may be formed. Alternatively, a transmitted ultrasonic beam may be formed using one transducer or a part of the multiple transducers 101. The transmission electric circuit 104 transmits a pulse signal of a predetermined drive waveform to the transducer 101, causing the transducer 101 to generate a transmitted ultrasonic wave having a predetermined transmission waveform. The receiving electrical circuit 105 is a receiving section that inputs, as a received signal, an electrical signal output from the transducer 101 that has received a reflected ultrasonic wave. The received signal is input to a received signal processing section .

[0025] 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.

[0026] When generating a B-mode image, the reflected ultrasound reception signal obtained by scanning the observation area is acquired and used for image generation. When generating a blood flow image, ultrasound transmission and reception is performed multiple times on one or multiple scanning lines in the observation area to obtain multiple frames of reflected ultrasound reception signals, which are used for image generation, i.e., extraction of blood flow information. Scanning for generating a blood flow image may be a method of performing multiple transmissions and receptions on one scanning line and then transmission and reception on the next scanning line, or a method of performing transmission and reception once on each scanning line multiple times. In addition, B-mode images and blood flow images may be generated so as to reduce the number of scanning lines, that is, by transmitting plane waves or diverging waves to transmit ultrasound over a wide range of the observation area. In addition, the transmission angle of the plane waves or diverging waves, the range of the observation area to be transmitted, etc. may be changed, transmission and reception may be performed multiple times over a wide range of the observation area, and the received signals may be added and used.

[0027] In this specification, both the analog signal output from the transducer 101 and the digital data sampled (digitally converted) from the analog signal are 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 or the measured data in order to clearly indicate that it is digital data.

[0028] The received signal processing unit 106 is an image generating unit that generates 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 configured, for example, with a liquid crystal display or an organic EL display. The system control unit 109 is a control unit that generally 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.

[0029] (Configuration of the receiving signal processing block) 2 is a block diagram showing an example of functions of the received signal processing unit 106. The received signal processing unit 106 has 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.

[0030] The received signal storage unit 200 stores the received signal received by the receiving electrical circuit 105. Note that, depending on the device configuration and the type of received signal, the received signal may not be stored in the received signal storage unit 200, but may be stored in a signal storage unit 202 after a phasing and summing processing unit 201, which will be described later. The received signal storage unit 200 may be formed of a block common to the signal storage unit 202, which will be described later, and may store the received signal from the receiving electrical circuit 105 and the received signal after the phasing and summing processing unit 201.

[0031] 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 processed reception signal in the signal storage unit 202. The delay and sum processing is a processing to form 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 processing to convert the reception signal into an in-phase signal (I signal) and a quadrature signal (Q signal) of the baseband. The delay and sum processing and quadrature detection processing are performed based on the element arrangement and various conditions for image generation (aperture control, signal filter) 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 any processing to form a reception ultrasonic beam, such as adaptive beamforming, model-based processing, and processing using machine learning, may be used.

[0032] The reception signal (digital data) obtained by the receiving electrical circuit 105 is also called RAW data. The reception signal (digital data) obtained by the phasing addition processing unit 201 is also called RF data. In this embodiment, the RAW data and the RF data are examples of measurement data acquired based on an ultrasonic signal reflected within a living body, and the receiving electrical circuit 105 and the phasing addition processing unit 201 are examples of a first acquisition unit that acquires the measurement data.

[0033] The B-mode processing unit 203 performs envelope detection processing, logarithmic compression processing, etc. 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 the received signals whose displacement has been corrected by the displacement correction unit 204 described later.

[0034] The displacement amount correction unit 204 calculates the amount of tissue displacement caused by body movement between frames from received signals acquired over multiple frames. Generally, this is calculated using a block matching operation known as a speckle tracking method. A region of interest is set within a frame, and the region of interest between frames is tracked using correlation to calculate the amount of displacement in the region of interest. By setting multiple regions of interest within a frame, the amount of displacement for the entire frame is calculated. Here, an example of a method using correlation between frames has been shown, but if the amount of displacement in a region of interest can be calculated, what other methods can be used? A method may be used. In the case of an ultrasound signal, it is called speckle tracking because it tracks speckles, which are scattered images of ultrasound reflected from scatterers in body tissue. The correlation calculation of the region of interest between frames may be performed on the received signal after any of delay and sum processing, quadrature detection processing, and envelope detection processing. In addition, the correlation calculation may be performed on the time waveform of the received signal, or on frequency space data obtained by performing a discrete Fourier transform on the received signal. The displacement amount correction unit 204 corrects the displacement amount by moving the received signal with respect 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 the 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 string of multiple frames using linear interpolation, spline interpolation, or the like. In addition, when correcting the displacement amount, the received signal may be similarly interpolated and then moved so that more fine correction can be performed.

[0035] The Doppler processing unit 205 extracts blood flow information (Doppler information) from the reception signals for generating a blood flow image stored in the signal storage unit 202, and generates blood flow image data by imaging the blood flow information. The Doppler processing unit 205 may also perform Doppler processing on the reception signals whose displacement amount has been corrected by the above-mentioned displacement amount correction unit 204.

[0036] 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 a scanning range by frequency analyzing the received signal for generating a blood flow 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 the blood flow information can be displayed.

[0037] In generating a blood flow image using the Doppler method, a series of received signal data of multiple frames is acquired in the time direction for the same measurement position. The Doppler processing unit 205 applies an MTI (Moving Target Indicator) filter to the received signal data series. This reduces information (clutter components) derived from tissues that are stationary between frames or tissues with little movement, and extracts information derived from blood flow (blood flow components). The Doppler processing unit 205 then calculates blood flow information such as blood flow speed, blood flow dispersion, and blood flow power from the blood flow components. Data representing the calculated blood flow information in the form of a two-dimensional image is called a blood flow image.

[0038] The MTI filter is a Butterworth type IIR (infinite impulse A filter with fixed filter coefficients, such as a multi-level time response filter or a polynomial regression filter, may be used. The MTI filter may be an adaptive filter that changes coefficients according to an input signal using eigenvalue decomposition or singular value decomposition. Alternatively, the Doppler processing unit 205 may decompose the received signal into one or more bases using eigenvalue decomposition or singular value decomposition, extract only a specific base, and remove clutter components. The Doppler processing unit 205 may also 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. In addition to the methods exemplified here, any method may be used as long as it can extract or emphasize information derived from blood flow contained in the received signal (measurement data) or remove or reduce information derived from tissue.

[0039] The super-resolution processor 206 generates super-resolution blood flow image data, which is a blood flow image with improved resolution, from multiple frames of blood flow image data generated by the Doppler processor 205. A method for acquiring the super-resolution blood flow image will be described when explaining the processing flow. In this embodiment, the super-resolution processor 206 is an example of a generator that generates display image data.

[0040] The image data output from the B-mode processing unit 203, the Doppler processing unit 205, and the super-resolution processing unit 206 are subjected to processing by the image processing unit 107 shown in Fig. 1, 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.

[0041] 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 to 206 shown in FIG. 2 are realized by a computer program. For example, the functions of the units 201 to 206 can be provided by the CPU reading and executing a program stored in the memory. In addition to the CPU, the received signal processing unit 106 may also include a processor (GPU, FPGA, etc.) that handles the calculations of the B-mode processing unit 203, the displacement amount correction unit 205, the Doppler processing unit 205, and the super-resolution processing unit 206. 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.

[0042] The above has described the overall configuration of the ultrasound diagnostic apparatus 1 according to the first embodiment. Next, a processing flow for acquiring a super-resolution blood flow image will be described.

[0043] (Processing flow for obtaining super-resolution blood flow images) A processing flow for acquiring a super-resolution blood flow image in the first embodiment will be described with reference to FIG.

[0044] A measurement position is set so as to include a region in which blood flow information is to be observed, and ultrasonic transmission and reception are repeated while fixed at the same measurement position to obtain a reception signal including a data sequence of multiple frames in the time direction (S310). The number of frames of the reception signal when generating a super-resolution blood flow image according to this embodiment is generally greater than that when generating a normal Doppler image. For example, the number of frames (number of packets) of the reception signal when generating a normal Doppler image is about 5 to 20, whereas the number of frames of the reception signal when generating a super-resolution blood flow image is at least 100 frames or more, preferably about several hundred to tens of thousands. The frame rate of the ultrasound diagnostic device 1 is about several hundred Hz to several thousand Hz, depending on the model and measurement method. Therefore, the measurement time of the reception signal to generate a super-resolution blood flow image is generally about several hundred msec to several tens of sec. The reception signal including the data sequence of multiple frames is subjected to a delay-and-sum processing unit 201 to a delay-and-sum processing and a quadrature detection processing, and is stored in the signal storage unit 202. Furthermore, one frame in the data sequence may be a received signal obtained by transmitting and receiving ultrasound once to include the observation area, or may be a sum of received signals transmitted and received multiple times to include the observation area. For example, a plane wave or diverging wave is transmitted to include the observation area, and multiple received signals with different transmission angles are added together to obtain one frame of data.

[0045] The displacement amount correction unit 204 calculates the amount of tissue displacement between frames due to body movement, etc., from a received signal including a data sequence of multiple frames. Then, the displacement amount correction unit 204 corrects the displacement amount by using the calculated displacement amount to move the received signal so that its position is aligned within the frame where the accumulation process in S360 and S550 described below is performed (S320). The reference frame for calculating the displacement amount may be only one frame in the entire data sequence, or the reference frame may be changed depending on the position in the time direction (i.e., multiple reference frames may be provided), or the previous frame in an adjacent frame may be used as the reference frame. The reference frame may be selected arbitrarily from the data sequence, for example, the first frame, an intermediate frame, the last frame, Either of these may be used as the reference frame.

[0046] The Doppler processing unit 205 applies an MTI filter to the received signal including the data sequence of the multiple frames with the displacement corrected, thereby reducing clutter components and extracting components derived from blood flow, thereby generating blood flow images of multiple frames (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. In addition, the parameters of the MTI filter (cutoff frequency, number of packets / overlap of the extracted frame, etc.) may be automatically or user-adjustable according to the nature of the received signal, the subject to be measured, etc. An example of a blood flow image of multiple frames generated in this way is shown in FIG. 4A. The shade of the pixel value at each position within the position 401 including the blood flow information is due to the difference in flow velocity. The flow velocity is fast at the location with a large pixel value (dark color in the figure), and the flow velocity is slow at the location with a small pixel value (light color in the figure).

[0047] For comparison, the processing flow for acquiring a super-resolution blood flow image by the conventional technology will be described first, and the problems associated with the processing flow will be described. The processing flow for acquiring a super-resolution blood flow image by the conventional technology is shown in FIG. 5, and the processing method is explained in FIG. 6A to FIG. 6C. Steps S510 to S530 in FIG. 5 are the same as steps S310 to S330 in FIG. 3. The resulting multiple frames of blood flow images (FIG. 6A) are also the same as those shown in FIG. 4A.

[0048] Pixels having pixel values ​​within a predetermined range are extracted from each of the blood flow images of multiple frames (S540). The pixel value within the predetermined range may be the maximum pixel value in the image, or a range of pixel values ​​greater than a certain threshold. By extracting only pixels having pixel values ​​within the predetermined range from each blood vessel image, an image such as that shown in FIG. 6B is obtained. The dashed line in FIG. 6B indicates the position 401 containing blood flow information in FIG. 6A.

[0049] Since the pixel values ​​in a certain range move between frames, a super-resolution blood flow image with improved resolution as shown in FIG. 6C is generated by accumulating multiple blood flow images from which pixel values ​​have been extracted, compared to each blood flow image, i.e., a normal Doppler image (S550). Since only the parts with high pixel values ​​are extracted, if the point spread function is assumed to be a Gaussian distribution, only the parts with high sharpness at the center of the distribution are extracted. In an image of one frame, only points or small ranges are extracted, but since the parts with high pixel values ​​move between frames, a super-resolution blood flow image can be obtained by accumulating multiple blood flow images from which pixel values ​​have been extracted.

[0050] Here, the problem with the conventional technology is that, as shown in Figures 6B and 6C, when only pixels with large pixel values ​​are extracted from a blood flow image, only pixels within blood vessels with large pixel values, i.e., blood vessels with fast flow speeds, are extracted.

[0051] The problems of the conventional technology will be described in detail. It is generally known that the amplitude distribution characteristics of ultrasonic reception signals from a uniform scattering medium can be approximately approximated by a probability distribution called Rayleigh distribution shown in FIG. 7. Extracting the high pixel value part in S540 means extracting "points with a low probability in the Rayleigh distribution but a high amplitude value" from the reflection signals from many red blood cells, extracting only the reflection signals from a small number of red blood cells and making them spatially sparse. However, when blood vessels with different flow velocities exist adjacent to each other, simply extracting the high pixel value part extracts only the reflection signals from "points with a low probability in the Rayleigh distribution of a thick blood vessel with a fast flow velocity but a high amplitude value", and therefore does not extract the reflection signals from thin blood vessels with a slow flow velocity.

[0052] Figure 8A shows an example of a blood flow image in which 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. However, because the blood vessel gap is below the resolution, it cannot be separated in the blood flow image. FIG. 8B shows a super-resolution blood flow image by the conventional technology, which is the result of performing the processes of S540 and S550 on each blood flow image of multiple frames. As can be seen by comparing with FIG. 8C, which is a super-resolution blood flow image by the present embodiment described later, in the super-resolution blood flow image by the conventional technology in FIG. 8B, the resolution of only the thick blood vessels with a fast flow speed is improved, and the thin blood vessels with a slow flow speed are not imaged.

[0053] It is possible to widen the pixel value extraction range so that blood vessels with small pixel values, i.e., blood vessels with slow flow speeds, can also be extracted. However, when considering extraction from the above-mentioned Gaussian distribution, the range of the distribution to be extracted is widened, so the effect of improving the resolution is reduced.

[0054] Returning to the description of the processing flow for acquiring a super-resolution blood flow image according to this embodiment, in consideration of the problems of the conventional method described above, in this embodiment, the super-resolution processor 206 generates a plurality of feature separated images having different image features from each blood flow image (S340).

[0055] Here, the effect of dividing a blood flow image into multiple feature separation images will be described. In general, in a blood flow image, the pixel value of the thick blood vessel with a fast flow speed is larger than that of the thin blood vessel with a slow flow speed. Therefore, when a pixel group having a pixel value in a predetermined range is extracted from a blood flow image in which a thick blood vessel with a fast flow speed and a thin blood vessel with a slow flow speed are adjacent to each other, the pixels of the thin blood vessel with a slow flow speed may not be extracted. In order to solve this problem, it is preferable to generate a feature separation image using image features related to the blood flow speed. That is, from the original blood flow image, multiple feature separation images with different flow speed ranges are generated, such as a first feature separation image with image features corresponding to a first flow speed range, a second feature separation image with image features corresponding to a second flow speed range, etc. Then, an appropriate extraction range is set for each feature separation image, and feature points are extracted from each feature separation image. With this method, pixels of thick blood vessels with a fast flow speed and pixels of thin blood vessels with a slow flow speed can be extracted as feature points.

[0056] Specifically, a discrete Fourier transform is performed on the change in pixel value of each pixel in the time direction using two or more frames of the blood flow image of multiple frames. The discrete Fourier transform is a sine wave basis and represents frequency components. Therefore, the frequency components of the change between frames of each pixel of the blood flow image, i.e., flow velocity components, can be separated by the discrete Fourier transform, and a feature separation image having blood flow information in the flow velocity range corresponding to each basis can be generated. The feature separation image having blood flow information in the flow velocity range corresponding to each basis may be a frequency domain image after the discrete Fourier transform, or a time domain image obtained by performing an inverse discrete Fourier transform on the frequency domain image. By changing some of the frames used for the discrete Fourier transform among the blood flow images of multiple frames in the time direction, a feature separation image with a different time series corresponding to each basis can be obtained. In this case, the blood flow image is separated into multiple feature separation images by focusing on image features related to flow velocity (frequency components of the change in pixel value over time), i.e., by using the flow velocity as a basis. Figure 4B shows an example of separation into three feature separated images, with blood flows with different flow velocities visualized in each basis. By increasing the number of separations, i.e., the number of frames used for the discrete Fourier transform, the flow velocity range corresponding to each basis becomes smaller, so that blood flow images can be separated into finer flow velocity ranges. Note that any basis other than the discrete Fourier transform based on a sine wave can be used as long as it can separate the blood flow images into images for each flow velocity range, and polynomial regression such as the Legendre polynomial can be used to separate the blood flow images into images for each flow velocity range.

[0057] The super-resolution processor 206 extracts feature points from the information derived from blood flow from the multiple feature separation images generated in S340 (S350). The feature separation image of this embodiment is an image that represents information derived from blood flow by pixel values ​​(brightness; density), and pixels with more (stronger) information derived from blood flow (i.e., pixels corresponding to locations where blood flow exists) have larger pixel values. Therefore, the super-resolution processor 206 extracts points (pixels) with large pixel values ​​as feature points from the feature separation images. By performing the operation of S350, an image is obtained in which only points where information derived from blood flow is particularly strongly expressed are extracted from each feature separation image, as shown in Figure 4C.

[0058] The super-resolution processor 206 may have a function as a setting unit (not shown) that automatically sets the pixel value range (hereinafter referred to as "extraction range") from which feature points are extracted, or allows the user to set it. The setting unit may read a setting value of the extraction range stored in advance in a memory and set it in the super-resolution processor 206. The setting unit may dynamically set the extraction range according to the pixel value distribution (histogram) or maximum pixel value of the feature separation image. For example, the setting unit may set the maximum pixel value of the feature separation image as the upper limit and a value obtained by multiplying the maximum pixel value by a predetermined coefficient (e.g., 0.8) as the lower limit. In this case, if the coefficient is set to 1.0, only the pixel having the maximum pixel value in the feature separation image is extracted, and the coefficient can be adjusted so that more pixels are extracted as the coefficient is made smaller. Alternatively, the extraction range may be set to include the top N pixels of the histogram (N is an integer that is set arbitrarily).

[0059] The super-resolution processor 206 may extract a pixel that is a local maximum value as a feature point. For example, the blood flow image is divided into a plurality of local regions, and feature points are extracted for each local region. In this case, the super-resolution processor 206 may fix the extraction range of each local region, or may set the extraction range for each local region according to the local maximum pixel value. In addition, the local maximum value may be extracted by extracting a pixel that has a larger pixel value than adjacent pixels (for example, when the pixel value of the central pixel is the largest in a local region of 3 pixels x 3 pixels), that is, a pixel that is a maximum. Here, an example of a representative method for extracting a local maximum value is shown, but any process for extracting a local maximum value may be used. By setting an extraction range for each local region, more pixels can be extracted as feature points than when a uniform extraction range is set for the entire image, and the number of pixels used for the accumulation process in S360 described later can be increased.

[0060] The setting unit may allow the user to input or select an extraction range (upper limit, lower limit, local region, etc.) on a setting screen displayed on the display device 108, or may allow the user to specify the extraction range using a GUI such as a slider bar. At this time, the pixel value distribution (histogram) of the feature separated image may be displayed on the setting screen, or the pixel group extracted in S350 may be displayed on the setting screen. Such displays can assist the user in setting the extraction range.

[0061] Alternatively, before performing the integration process of S360, the super-resolution processor 206 may perform a process of actively removing pixels (noise) in tissue from the pixel group extracted in S350. Specifically, a threshold may be set for the pixel value distribution (histogram) of the pixel group extracted in S350, and pixels with pixel values ​​smaller than the threshold may be regarded as noise and removed. The threshold may be set by the user. For example, the threshold may be input or selected on a setting screen displayed on the display device 108, or may be specified by a GUI such as a slider bar. At this time, the pixel value distribution (histogram) of the pixel group extracted in S350 may be displayed on the setting screen, and auxiliary information such as the position of Nσ and the position of a valley in the pixel value distribution may be displayed. Such a display can assist the user in selecting the threshold. Alternatively, the super-resolution processor 206 may automatically set the threshold. In this case, a fixed threshold may be used, or the threshold may be dynamically determined based on the variance, bimodality, outliers, etc. of the pixel value distribution. Alternatively, from the extracted pixel group, the lowest M pixels (M is an integer that is set arbitrarily) starting from the smallest pixel value may be mechanically removed as noise.

[0062] Furthermore, in a blood flow image, pixel values ​​vary depending on the depth from the body surface due to attenuation by the living body and the diffusion attenuation of ultrasonic waves. Therefore, in the process of S350, the super-resolution processor 206 may set the above-mentioned extraction range according to the "depth from the body surface" of each local region in the feature separation image. In other words, if a first local region and a second local region having different depths from the body surface are present in the feature separation image, When a first local region and a second local region are included, the extraction range of the first local region is made different from the extraction range of the second local region, taking into consideration the depth from the body surface. For example, when the second local region is deeper from the body surface than the first local region and the pixel value of the second local region is smaller due to the influence of ultrasonic wave attenuation, the extraction range of the second local region may be made wider than that of the first local region (for example, the lower limit may be lowered). Alternatively, instead of changing the extraction range, a process for correcting biological attenuation or diffusion attenuation may be applied to the feature separation image before the process for extracting feature points.

[0063] Furthermore, the super-resolution processor 206 may average feature separation images of two or more frames to generate a feature separation image with improved SNR, and then perform feature point extraction in S350 on the averaged feature separation image. Furthermore, the super-resolution processor 206 may not use feature separation images of all frames in the feature point extraction process in S350 or the accumulation process in S360. For example, frames with poor SNR may be excluded from the processing targets of S350 and S360.

[0064] In addition, the super-resolution processing unit 206 may apply the feature point extraction process of S350 after performing a process of increasing the resolution (up-conversion) of the feature separation image. At this time, the feature separation image itself may be up-converted, or the original blood flow image may be up-converted and the feature separation image may be generated from the up-converted blood flow image. Alternatively, the received signal may be up-converted, a blood flow image may be generated using a plurality of frames of the up-converted received signal, and a feature separation image may be further generated. The up-conversion method is arbitrary, and for example, an interpolated pixel may be generated by nearest neighbor, linear interpolation, spline interpolation, or the like. Alternatively, the up-conversion may be performed by applying a filter generated by machine learning. Since the resolution (number of pixels) of the super-resolution blood flow image obtained by the accumulation process of S360 is the same as the resolution (number of pixels) of the original image used to extract the feature points, when the resolution of the original image is low, the effect of improving the resolution (resolution) by the super-resolution process is small. Therefore, by increasing the resolution (number of pixels) of the original image in advance, the resolution of the final super-resolution blood flow image can be improved. In addition, in images obtained using a convex probe or sector probe, the pixel size (resolution) differs between deep and shallow regions in the image. By increasing the resolution of the shallow regions in advance through up-conversion, it is expected that the resolution of the final super-resolution blood flow image will also be improved.

[0065] The extraction process of S350 produces an image as shown in FIG. 4C. Since the process of extracting feature points is performed for each feature separation image for each flow velocity, it can be seen that not only pixels of blood vessels with large pixel values ​​(thick blood vessels with fast flow velocity) but also pixels of blood vessels with small pixel values ​​(thin blood vessels with slow flow velocity) can be extracted. In other words, blood vessels with a wider range of flow velocity can be extracted than with the conventional technology. 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 noise energy, the noise energy is dispersed to each base by generating a feature separation image separated by flow velocity by the process of S340. This reduces the noise energy compared to the image information of the blood flow component, so that the SNR (signal-to-noise ratio) or CNR (contrast-to-noise ratio) of each feature separation image can be improved. Therefore, there is also the advantage that blood vessels that could not be distinguished from noise in the blood flow image can be depicted in the feature separation image. Furthermore, by separating into multiple images by the process of S340, the pixel values ​​of each image become sparse, and for example, in the process of S360, more feature points can be extracted than with the conventional technology with the same number of frames. This not only improves the quality of the final super-resolution blood flow image, but also shortens the measurement time required to generate a super-resolution blood flow image, since a super-resolution blood flow image of equivalent quality can be generated from fewer frames than conventional technology.

[0066] Finally, the super-resolution processor 206 generates a super-resolution blood flow image by accumulating pixel values ​​of feature points extracted from the feature separation images of multiple frames (S360). 4D and 8C show examples of super-resolution blood flow images. Because pixels with large pixel values ​​are concentrated in the blood flow range by the integration process, the blood flow is imaged (visualized) with higher resolution and more clearly in the super-resolution blood flow image than in the original blood flow image. Also, compared to a conventional super-resolution blood flow image (FIG. 8B), it can be seen that adjacent blood vessels of different thicknesses are both imaged clearly in the super-resolution blood flow image of this embodiment (FIG. 8C). The super-resolution processing unit 206 displays the generated super-resolution blood flow image on the display device 108.

[0067] All the feature separation images generated from the blood flow images of all the frames stored in the signal storage unit 202 may be used for feature point extraction and integration processing to generate one super-resolution blood flow image, or only frames in a part of the time range may be used to generate the super-resolution blood flow image. In the latter case, the user may select the time range used to generate the super-resolution blood flow image. Also, multiple super-resolution blood flow images in time series may be generated while shifting the time range used to generate the super-resolution blood flow image in sequence. Furthermore, multiple super-resolution blood flow images may be displayed in sequence on the display device 108 to display a moving image of the super-resolution blood flow image. Alternatively, multiple super-resolution blood flow images may be displayed side by side on the display device 108 to allow comparison of changes over time.

[0068] All feature points extracted from a plurality of feature separation images corresponding to different image features may be simply integrated, or only some feature points may be selected and integrated, or weighted according to the image features may be added. For example, by simply integrating all feature points extracted from a plurality of feature separation images with different flow velocity ranges, all blood vessels (blood flows) from blood vessels with slow flow velocities to blood vessels with fast flow velocities can be imaged. By selecting only feature points of a feature separation image corresponding to a certain flow velocity range, or by increasing the weight of the feature points and then integrating, an image in which blood vessels in the certain flow velocity range are emphasized can be obtained. A GUI may be provided to the user for arbitrarily selecting or setting the range of image features (feature separation images) used for integration or the weighting according to the image features. By using such a GUI, a suitable super-resolution blood flow image can be generated according to the object to be observed (blood vessel thickness and flow velocity).

[0069] According to the configuration of this embodiment described above, blood vessels with a wide range of flow velocities can be extracted from blood flow images of multiple frames to generate a super-resolution blood flow image with improved resolution.

[0070] <Second embodiment> In the first embodiment, a feature separation image is generated by focusing on image features related to flow velocity, but the image features are not limited to this. In the second embodiment, a feature separation image is generated by focusing on image features related to the shape or thickness of blood vessels. In other words, the blood vessel diameters extracted differ for each base.

[0071] For example, the super-resolution processor 206 performs a two-dimensional Fourier transform on the spatial change of pixel values ​​in the blood flow image to calculate the spatial frequency of each blood flow image. A high spatial frequency, i.e., a spatially fine structure, is an image characteristic of a thin blood vessel, and a low spatial frequency, i.e., a spatially coarse structure, is an image characteristic of a thick blood vessel. Therefore, for example, the super-resolution processor 206 can generate a feature separation image having image characteristics corresponding to a desired blood vessel diameter by extracting only the spatial frequency components corresponding to a desired blood vessel diameter from the two-dimensional Fourier transform of the blood flow image and performing an inverse Fourier transform. Then, by appropriately changing the spatial frequency components to be extracted, a plurality of feature separation images corresponding to different blood vessel diameters can be obtained, such as a feature separation image corresponding to a blood vessel diameter of 1 mm to 2 mm, a feature separation image corresponding to a blood vessel diameter of 2 mm to 3 mm, etc. FIG. 9A is an example of an original blood flow image, and FIG. 9B is an example of feature separation images of three types of blood vessel diameters generated from the original blood flow image.

[0072] In addition, by applying line enhancement processing based on eigenvalue analysis of the Hessian matrix to blood flow images, A feature separation image in which image features corresponding to a desired blood vessel diameter are emphasized may be generated by selectively emphasizing the brightness or contrast of blood vessel-like (linear, columnar) structures of a desired thickness. In this case, too, by appropriately changing the thickness to be emphasized by the line emphasis process, multiple feature separation images corresponding to different blood vessel diameters can be generated.

[0073] Also, a similarity search process such as template matching may be used. For example, a similarity map (a two-dimensional map showing the similarity (correlation value) with the template image for each local region) is generated by searching the entire blood flow image using a template image of a blood vessel pattern of a desired thickness. This similarity map can be regarded as a two-dimensional image filter in which a larger coefficient is set for pixels corresponding to the blood vessel diameter of the template image. Therefore, by multiplying the blood flow image by the similarity map, a feature separation image in which the image feature corresponding to the blood vessel diameter of the template image is emphasized can be obtained. In this case, by preparing multiple template images with different blood vessel diameters, multiple feature separation images corresponding to different blood vessel diameters can be generated.

[0074] A feature separated image may be generated using any method other than those described here, as long as it is a method for selectively emphasizing or extracting image features corresponding to the thickness or shape of blood vessels. Note that the process flow and the device configuration other than the process of generating the feature separated image in S340 may be the same as those in the first embodiment.

[0075] <Third embodiment> In the third embodiment, a feature separation image is generated based on two image features, namely, "temporal change and spatial structure", that is, "flow velocity and shape".

[0076] For example, the super-resolution processor 206 performs a discrete Fourier transform (three-dimensional discrete Fourier transform) on some frames of the blood flow images of multiple frames in both the time direction and the spatial direction within the image of the pixel value of each pixel. Then, by extracting only the spatial frequency components corresponding to the desired flow velocity range and the desired blood vessel diameter and performing an inverse Fourier transform, a feature separation image having image features corresponding to the desired flow velocity range and the desired blood vessel diameter can be generated. By appropriately changing the spatial frequency components to be extracted, a plurality of feature separation images having different combinations of flow velocity range and blood vessel diameter can be obtained. Alternatively, it can be realized by converting each frame of some frames of the blood flow images of multiple frames into a one-dimensional vector, and using singular value decomposition on matrix data in which each frame is arranged in the column direction. This allows blood vessels of different sizes spatially to be further divided into images with different flow velocity ranges. Specifically, it is sufficient to generate a group of images using only each singular value component. This method of dividing into bases by singular value decomposition is also used in the clutter removal process performed in S330 of FIG. 3, so if this process is performed at the same time, the amount of calculation for the entire process can be reduced.

[0077] As shown in FIG. 10A, when blood vessels with different diameters but the same flow velocity exist, they cannot be separated by the basis of the first embodiment. In contrast, by using the basis of the present embodiment, blood vessels with spatially different sizes can be further divided into images with different flow velocity ranges as shown in FIG. 10B. This makes the pixel values ​​of each image sparser, and in the extraction of feature points in the subsequent processing, more feature points can be extracted than with the conventional technology with the same number of frames. This improves the quality of the final super-resolution blood flow image, and since a super-resolution blood flow image of the same quality can be generated from fewer frames than with the conventional technology, the measurement time required to generate the super-resolution blood flow image can be shortened.

[0078] Any method other than the methods mentioned here may be used to generate a feature-separated image as long as it is a method for selectively emphasizing or extracting image features corresponding to the flow velocity and shape of blood vessels. Note that the process flow and the device configuration other than the process of generating the feature-separated image in S340 are the same as those in the first embodiment. The same as the embodiment may be used.

[0079] <Fourth embodiment> In the fourth embodiment, a feature separation image is generated by focusing on the position in the blood flow image as an image feature. For example, the super-resolution processor 206 divides the blood flow image into a plurality of regions, and the divided images (small images) for each region are regarded as feature separation images. FIG. 11B shows an example in which each blood flow image in FIG. 11A is divided into four regions. By making the difference in pixel values ​​of the plurality of blood vessels contained in each feature separation image small, it is possible to extract the pixels of each blood vessel.

[0080] In Fig. 11B, the blood flow image is divided into four, but the number of divisions can be set arbitrarily. The size of each region may be the same or different. The process flow and the device configuration other than the generation process of the feature separation image in S340 may be the same as those in the first embodiment.

[0081] <Fifth embodiment> In the fifth embodiment, a feature separation image is generated by focusing on image features related to the direction of blood flow.

[0082] The Doppler processing unit 205 obtains a velocity vector for each coordinate in the image by using a method such as a vector Doppler method, a speckle tracking method, or a vector flow mapping method in addition to a blood flow image by the Doppler method. The arrow in FIG. 12A indicates a velocity vector. The direction of this velocity vector indicates the direction of blood flow. To generate a feature separation image corresponding to a desired blood flow direction, for example, a coincidence map is generated that indicates the degree of coincidence between the desired blood flow direction and the velocity vector for each coordinate. This coincidence map can be regarded as a two-dimensional image filter in which a larger coefficient is set for a coordinate having a velocity vector closer to the desired blood flow direction. Therefore, by multiplying the blood flow image by the coincidence map, a feature separation image in which the image feature corresponding to the desired blood flow direction is emphasized can be obtained. By generating a coincidence map for each blood flow direction and multiplying it by the blood flow image, multiple feature separation images corresponding to different blood flow directions can be generated. It is to be noted that the coincidence map and the feature separation image may be generated by considering not only the direction of the vector but also the magnitude of the vector.

[0083] An example of a feature separation image separated by blood flow direction (direction of velocity vector) is shown in Figure 12B. It is expected that more blood vessel pixels can be extracted by separating the image by blood flow direction (Figure 12B) and then extracting feature points, rather than extracting feature points from the original blood flow image (Figure 12A) in which various blood flow directions are mixed.

[0084] A feature separated image may be generated using any method other than those described here as long as it selectively emphasizes or extracts image features corresponding to the direction of blood flow. Note that the process flow and the device configuration other than the process of generating the feature separated image in S340 may be the same as those in the first embodiment.

[0085] <Other embodiments> The above-described embodiments merely show specific examples of the present invention. The scope of the present invention is not limited to the configurations of the above-described embodiments, and various embodiments can be adopted without changing the gist of the present 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 gist of the invention.

[0086] The disclosed technology can be embodied as, for example, a system, an apparatus, a method, a program, or a recording medium (storage medium). Specifically, the technology may be applied to a system composed of multiple devices (for example, a host computer, an interface device, an imaging device, a web application, etc.), or may be applied to an apparatus composed of a single device. is also good.

[0087] Needless to say, the object of the present invention can be achieved by the following: That is, a recording medium (or storage medium) on which is recorded a program code (computer program) of software that realizes the functions of the above-mentioned embodiments is supplied to a system or device. Needless to say, such a storage medium is 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.

[0088] The disclosure of this specification includes the following configurations, methods, and programs.

[0089] (Configuration 1) a first acquisition unit that acquires measurement data including information derived from tissue and information derived from blood flow based on an ultrasonic signal reflected within a living body; a second acquisition unit that acquires blood flow data obtained by extracting or emphasizing information derived from the blood flow of the measurement data, the blood flow data having the extracted or emphasized information derived from the blood flow as pixel values; a generating unit that generates a plurality of feature separation data having different image features from each of a plurality of frames of blood flow data, and generates display image data by synthesizing feature points extracted from each of the plurality of feature separation data for a plurality of frames; An ultrasound diagnostic device having the above configuration.

[0090] (Configuration 2) The image features include image features related to a blood flow velocity. 2. The ultrasonic diagnostic apparatus according to claim 1.

[0091] (Configuration 3) The generating unit performs a Fourier transform on a change in pixel value in a time direction in the blood flow data of two or more frames, thereby generating a feature separation image for each frequency component of the change in pixel value over time. 3. The ultrasonic diagnostic apparatus according to claim 2.

[0092] (Configuration 4) The image features include image features related to a shape of a blood vessel. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 3.

[0093] (Configuration 5) the generating unit generates a feature separation image for each spatial frequency component of the blood flow data by performing a Fourier transform on a spatial change in pixel values ​​in the blood flow data. 5. The ultrasound diagnostic apparatus according to claim 4.

[0094] (Configuration 6) The image features include image features related to a blood flow velocity and a blood vessel shape. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 5.

[0095] (Configuration 7) The generating unit divides the blood flow data into a plurality of regions to generate a feature separation image for each region. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 6.

[0096] (Configuration 8) the generating unit extracts pixels having pixel values ​​within a predetermined extraction range from the feature separation data as the feature points; The ultrasonic diagnostic apparatus according to any one of configurations 1 to 7.

[0097] (Configuration 9) A setting unit that automatically sets the extraction range or allows a user to set the extraction range. 9. The ultrasound diagnostic apparatus according to claim 8.

[0098] (Configuration 10) 10. The ultrasound diagnostic apparatus according to configuration 9, wherein the setting unit sets the extraction range in accordance with a maximum pixel value of the feature separation data.

[0099] (Configuration 11) the setting unit divides the feature separation data into a plurality of local regions, and sets the extraction range for each of the local regions according to a maximum pixel value for each of the local regions. 11. The ultrasound diagnostic apparatus according to configuration 9 or 10.

[0100] (Configuration 12) the setting unit divides the feature separation data into a plurality of local regions, and sets the extraction range for each of the local regions according to a depth from a body surface of each of the local regions. The ultrasonic diagnostic apparatus according to any one of configurations 9 to 11.

[0101] (Configuration 13) the generating unit removes noise from pixel groups extracted as the feature points from the plurality of feature separation data, and generates the display image data by synthesizing the pixel groups after the noise removal. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 12.

[0102] (Configuration 14) The generating unit extracts the feature points from data obtained by averaging two or more pieces of feature separation data. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 13.

[0103] (Configuration 15) The generating unit up-converts the plurality of feature separation data and extracts the feature points from the up-converted feature separation data. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 14.

[0104] (Configuration 16) the generating unit, when generating the display image data, combines the feature points extracted from each of the plurality of feature separated images by weighting them according to the image features. The ultrasonic diagnostic apparatus according to any one of configurations 1 to 15.

[0105] (Configuration 17) obtaining blood flow data in which information derived from blood flow in a living body is extracted or emphasized from measurement data including information derived from tissue and information derived from blood flow, the blood flow data having the extracted or emphasized information derived from blood flow as pixel values; a generation unit that generates a plurality of feature separation data having different image features from each of a plurality of frames of blood flow data, and generates display image data by synthesizing feature points extracted from each of the plurality of feature separation data for a plurality of frames; A medical information processing device having the above configuration.

[0106] (Method 18) A method for processing information by a computer, comprising the steps of: acquiring measurement data including information derived from tissue and information derived from blood flow based on an ultrasonic signal reflected within a living body; acquiring blood flow data obtained by extracting or emphasizing information derived from the blood flow from the measurement data, the blood flow data having the extracted or emphasized information derived from the blood flow as pixel values; generating a plurality of feature separation data having different image features from each of the plurality of frames of blood flow data, and generating display image data by synthesizing feature points extracted from each of the plurality of feature separation data for a plurality of frames; An information processing method comprising:

[0107] (Program 19) acquiring measurement data including information derived from tissue and information derived from blood flow based on an ultrasonic signal reflected within a living body; acquiring blood flow data obtained by extracting or emphasizing information derived from the blood flow from the measurement data, the blood flow data having the extracted or emphasized information derived from the blood flow as pixel values; generating a plurality of feature separation data having different image features from each of the plurality of frames of blood flow data, and generating display image data by synthesizing feature points extracted from each of the plurality of feature separation data for a plurality of frames; A program for causing a computer to execute an information processing method including the steps of: [Explanation of symbols]

[0108] 1: Ultrasound diagnostic equipment

Claims

1. A first acquisition unit acquires measurement data including tissue-derived information and blood flow-derived information based on ultrasound signals reflected within the living body, A second acquisition unit acquires blood flow data in which the blood flow-derived information of the measurement data has been extracted or enhanced, and the extracted or enhanced blood flow-derived information is used as pixel values. A generation unit generates multiple feature-separated data sets, each with different image features, from multiple frames of blood flow data, and generates display image data by synthesizing the feature points extracted from each of the multiple feature-separated data sets for multiple frames. An ultrasound diagnostic device.

2. The aforementioned image features include image features related to blood flow velocity. The ultrasound diagnostic apparatus according to claim 1.

3. The generation unit generates a feature-separated image of each frequency component of the time-dependent change in pixel values ​​by performing a Fourier transform on the temporal change in pixel values ​​in blood flow data of two or more frames. The ultrasound diagnostic apparatus according to claim 2.

4. The aforementioned image features include image features related to the shape of blood vessels. The ultrasound diagnostic apparatus according to claim 1.

5. The generation unit generates a feature-separated image of each spatial frequency component of the blood flow data by performing a Fourier transform on the spatial changes in pixel values ​​in the blood flow data. The ultrasound diagnostic apparatus according to claim 4.

6. The aforementioned image features include image features related to blood flow velocity and vascular shape. The ultrasound diagnostic apparatus according to claim 1.

7. The generation unit generates feature-separated images for each region by dividing the blood flow data into multiple regions. The ultrasound diagnostic apparatus according to claim 1.

8. The generation unit extracts pixels having pixel values ​​within a predetermined extraction range from the feature separation data as feature points. The ultrasound diagnostic apparatus according to claim 1.

9. The system has a setting unit that automatically sets the extraction range or allows the user to set it. The ultrasound diagnostic apparatus according to claim 8.

10. The ultrasonic diagnostic apparatus according to claim 9, wherein the setting unit sets the extraction range according to the maximum pixel value of the feature separation data.

11. The setting unit divides the feature separation data into a plurality of local regions and sets the extraction range for each local region according to the maximum pixel value for each local region. The ultrasound diagnostic apparatus according to claim 9.

12. The setting unit divides the feature separation data into a plurality of local regions and sets the extraction range for each local region according to the depth from the body surface for each local region. The ultrasound diagnostic apparatus according to claim 9.

13. The generation unit generates the display image data by removing noise from the pixel group extracted as feature points from the plurality of feature separation data and synthesizing the noise-removed pixel group. The ultrasound diagnostic apparatus according to claim 1.

14. The generation unit extracts the feature points from data obtained by adding and averaging two or more feature-separated data. The ultrasound diagnostic apparatus according to claim 1.

15. The generation unit upconverts the plurality of feature-separated data and extracts the feature points from the upconverted feature-separated data. The ultrasound diagnostic apparatus according to claim 1.

16. When generating the display image data, the generation unit synthesizes the feature points extracted from each of the plurality of feature-separated data by assigning weights corresponding to the image features. The ultrasound diagnostic apparatus according to claim 1.

17. Blood flow data obtained by extracting or enhancing the blood flow-derived information from measurement data including tissue-derived information and blood flow-derived information in a living organism, wherein the extracted or enhanced blood flow-derived information is obtained as pixel values, A generation unit generates multiple feature-separated data sets, each with different image features, from multiple frames of blood flow data, and generates display image data by synthesizing the feature points extracted from each of the multiple feature-separated data sets for multiple frames. A medical information processing device having [a specific feature / function].

18. A computer-based information processing method, Based on ultrasound signals reflected within the body, measurement data including information derived from tissue and information derived from blood flow is acquired. Obtaining blood flow data in which the blood flow-derived information of the measurement data is extracted or enhanced, and in which the extracted or enhanced blood flow-derived information is represented as pixel values, The process involves generating multiple feature-separated data sets, each with distinct image characteristics, from multiple frames of blood flow data, and then synthesizing the feature points extracted from each of these feature-separated data sets across multiple frames to generate display image data. Information processing methods including

19. Based on ultrasound signals reflected within the body, measurement data including information derived from tissue and information derived from blood flow is acquired. Obtaining blood flow data in which the blood flow-derived information of the measurement data is extracted or enhanced, and in which the extracted or enhanced blood flow-derived information is represented as pixel values, The process involves generating multiple feature-separated data sets, each with distinct image characteristics, from multiple frames of blood flow data, and then synthesizing the feature points extracted from each of these feature-separated data sets across multiple frames to generate display image data. A program that causes a computer to execute an information processing method that includes such methods.

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