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

Abstract

The present disclosure includes an ultrasonic diagnostic device comprising: a first acquisition unit that acquires measurement data including tissue-derived information and blood flow-derived information on the basis of ultrasonic signals reflected in a living body; a second acquisition unit that acquires blood flow data obtained by extracting or emphasizing the blood flow-derived information of the measurement data, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; and a generation unit that generates a plurality of pieces of feature separation data having mutually different image features from a plurality of frames of the blood flow data, respectively, and combines, for the plurality of frames, feature points extracted from each of the plurality of pieces of feature separation data to generate display image data.

Description

Ultrasound diagnostic device, medical information processing device, information processing method, and program

[0001] The present invention relates to an ultrasonic diagnostic apparatus, a medical information processing apparatus, an information processing method, and a program.

[0002] Ultrasound diagnostic devices are widely used for observing and diagnosing blood flow in living bodies. Ultrasound diagnostic devices generate and display blood flow information from reflected ultrasound waves using the Doppler method, which is 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 CFM, ultrasound waves are transmitted and received multiple times along multiple scan lines. Then, 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. The CFM method estimates blood flow information, such as blood flow velocity, blood flow dispersion, and blood flow power, from the blood flow signals, and displays the distribution of the estimated results as a Doppler image.

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

[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 integrating high pixel values ​​from multiple blood flow images obtained in time series.

[0006] 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)

[0007] In the technique disclosed in Non-Patent Document 1, for example, when adjacent blood vessels with different flow velocities, i.e., different diameters, are included within the observation area, only the thick blood vessels with fast flow velocities are extracted, and the thin blood vessels with slow flow velocities may not appear in the blood flow image with improved resolution.

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

[0009] The present disclosure includes an ultrasound diagnostic device having: a first acquisition unit that acquires measurement data including tissue-derived information and blood flow-derived information based on ultrasound signals reflected within a living body; a second acquisition unit that acquires blood flow data in which the blood flow-derived information from the measurement data has been extracted or emphasized, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; and 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 combining feature points extracted from each of the plurality of feature separation data for a plurality of frames.

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

[0011] FIG. 1 is a block diagram showing an example of the configuration of an ultrasound diagnostic apparatus. FIG. 2 is a block diagram showing an example of the functions of a received signal processing block. FIG. 3 is a diagram showing the flow of super-resolution image generation processing in the first embodiment. FIGS. 4A to 4D are diagrams explaining the generation of super-resolution images in the first embodiment. FIG. 5 is a diagram showing the flow of super-resolution image generation processing in the conventional technology. FIGS. 6A to 6C are diagrams explaining the generation of super-resolution images in the conventional technology. FIG. 7 is a diagram explaining a Rayleigh distribution. FIG. 8A is an example of a blood flow image, FIG. 8B is an example of a super-resolution image in the conventional technology, and FIG. 8C is an example of a super-resolution image in the first embodiment. FIGS. 9A and 9B are diagrams explaining the generation of feature-separated images in the second embodiment. FIGS. 10A and 10B are diagrams explaining the generation of feature-separated images in the third embodiment. FIGS. 11A and 11B are diagrams explaining the generation of feature-separated images in the fourth embodiment. FIGS. 12A and 12B are diagrams explaining the generation of feature-separated images in the fifth embodiment.

[0012] As mentioned above, conventional super-resolution technology generates super-resolution images by extracting and accumulating high-pixel-value portions from the original blood flow data. Therefore, when various blood vessels are mixed within the observation area, only the pixels of blood vessels with relatively high pixel values ​​(e.g., blood vessels with high flow speeds, 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 low flow speeds, thin blood vessels, etc.).

[0013] Therefore, the inventors have adopted a new approach in which multiple blood flow data sets (called "feature-separated data sets") 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-separated data set. Specifically, the super-resolution image can be generated using 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 ultrasound signals reflected within the living body. The "measurement data" may be, for example, received signals obtained by transmitting and receiving ultrasound signals, or data obtained by performing phased summation and quadrature detection processing on the received signals. Note that the information derived from blood flow may include not only information derived from blood, but also information derived from contrast agents in the blood. Multiple 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. "Blood flow data" refers to data having extracted or emphasized information derived from blood flow as pixel values, and data representing blood flow data in a two-dimensional image format is also called a "blood flow image." "Extracting information derived from blood flow" refers, for example, to an operation of selectively extracting image features of blood flow components from the measurement data. "Emphasizing information derived from blood flow" refers, for example, to an operation of making image features of blood flow components more prominent relative to image features of tissue components. Note that 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 by the second acquisition unit may include a process of calculating information such as blood flow velocity, variance, and power value (also referred to as "blood flow information") using the Doppler method.

[0016] Next, the generating unit generates multiple feature separation data sets, each with different image features, from each of the multiple frames of blood flow data. The "feature separation data" can also be considered blood flow data containing only some of the image features (i.e., some blood flow information) of the original blood flow data. Data representing 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 multiple frames of feature points extracted from each of the multiple feature separation data sets. The "feature points" are preferably portions where blood flow characteristics are particularly strongly expressed (i.e., portions that are likely to indicate the location of blood flow). For example, if feature points are extracted from an image containing blood flow-related information (e.g., blood flow velocity, variance, power value) as pixel values, the pixel with the maximum pixel value in the image or a pixel with a pixel value above a predetermined threshold may be extracted as the feature point. The range of pixel values ​​(extraction range) from which feature points are extracted can be set arbitrarily. "Synthesis" is an operation of combining information on multiple feature points extracted from multiple frames to generate a single blood flow image, and can be, for example, a process of accumulating (plotting) multiple feature points on a single image. "Display image data" is data that visualizes high-resolution blood flow information and is displayed as a processing result, and is also called a super-resolution blood flow image.

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

[0018] Although the present invention has been described above as being applied to an ultrasound diagnostic device, it may also be applied to modalities (medical information processing devices) other than ultrasound diagnostic devices. For example, in a medical information processing device that acquires measurement data from a living body, including tissue-derived information and blood flow-derived information, similar to ultrasound images, high-resolution blood flow information can be obtained.

[0019] Next, more specific embodiments of the present invention will be described by way of example.

[0020] First Embodiment A first embodiment of the present invention will be described. In this embodiment, a plurality of feature-separated 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-separated images, and a super-resolution blood flow image is generated by integrating the many extracted feature points.

[0021] Consider a feature space formed by all image features contained 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 contained in that partial space, a feature-separated image can be generated in which only some of the image features of the original blood flow image are separated. 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 separate, or the partial spaces may partially overlap.

[0022] 1 is a block diagram showing an example of the hardware configuration of an ultrasound diagnostic apparatus according to this embodiment. The ultrasound diagnostic apparatus 1 includes an ultrasound 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 ultrasound diagnostic apparatus 1 is a system for transmitting pulsed ultrasound signals from the ultrasound probe 102 to a subject 100, receiving ultrasound signals reflected within the living body, and generating image information of the interior of the subject 100. Ultrasound images such as B-mode images and blood flow images obtained by the ultrasound diagnostic apparatus 1 are used in various clinical examinations.

[0023] The ultrasound probe 102 is an electronic scanning probe having a plurality of transducers 101 arranged one-dimensionally or two-dimensionally at its tip. The transducers 101 are electromechanical transducers that convert between electrical signals (voltage pulse signals) and ultrasound waves (acoustic waves). The ultrasound probe 102 transmits ultrasound waves from the plurality of transducers 101 to the subject 100 and receives reflected ultrasound waves from the subject 100 via the plurality of transducers 101. The reflected acoustic waves reflect differences in acoustic impedance within the subject 100. When a transmitted ultrasound pulse is reflected by a moving blood flow, the surface of a heart wall, or the like, the reflected ultrasound signal undergoes a frequency shift due to the Doppler effect, depending on the velocity component of the moving object relative to the ultrasound transmission direction.

[0024] The transmission electrical circuit 104 is a transmitting unit that outputs pulse signals (drive signals) to the multiple transducers 101. By applying pulse signals to the multiple transducers 101 with a time difference, the multiple transducers 101 transmit ultrasound waves with different delay times, thereby forming a transmitted ultrasound beam. The direction and focus of the transmitted ultrasound 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 ultrasound beam, an observation area within the subject 100 is scanned. Furthermore, by changing the delay time of the pulse signal, a transmitted ultrasound beam that is a plane wave (focused at a distance) or a diverging wave (focused in the opposite direction to the ultrasound transmission direction for the multiple transducers 101) may be formed. Alternatively, a transmitted ultrasound beam may be formed using one transducer or a portion of the multiple transducers 101. The transmission electrical circuit 104 transmits a pulse signal with a predetermined drive waveform to the transducer 101, causing the transducer 101 to generate a transmitted ultrasound wave with a predetermined transmission waveform. The receiving electrical circuit 105 is a receiving section that receives, as a received signal, an electrical signal output from the transducer 101 that has received the reflected ultrasonic waves. The received signal is input to a received signal processing section 106.

[0025] The operations of the transmitting electric circuit 104 and the receiving electric circuit 105, i.e., the transmission and reception of ultrasound waves, are controlled by a system control unit 109. The system control unit 109 changes the voltage signal and the position at which the transmitted ultrasound waves are 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, received signals of reflected ultrasound obtained by scanning the observation area are acquired and used for image generation. When generating a blood flow image, ultrasound is transmitted and received multiple times along one or more scanning lines within the observation area, and multiple frames of received signals of reflected ultrasound are acquired and used for image generation, i.e., for extracting blood flow information. Scanning for generating a blood flow image may be performed by performing multiple transmissions and receptions along one scanning line and then transmitting and receiving on the next scanning line, or by repeating multiple transmissions and receptions along each scanning line. Furthermore, B-mode images and blood flow images may be generated by reducing the number of scanning lines, i.e., by transmitting plane waves or diverging waves to transmit ultrasound over a wide range of the observation area. Furthermore, multiple transmissions and receptions over a wide range of the observation area may be performed by changing the transmission angle of the plane waves or diverging waves or the range of the observation area to be transmitted, and the received signals may be added together and used.

[0027] 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 received signals without any particular distinction. However, depending on the context, the received signal may also be referred to as received data or measured data 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 received signals obtained from the ultrasound probe 102. The image processing unit 107 performs image processing such as brightness adjustment, interpolation, and filtering on the image data generated by the received signal processing unit 106. The display device 108 is a display unit that displays 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 comprehensively 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] 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 (described later). The received signal storage unit 200 may also be formed from a block common to the signal storage unit 202 (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 and quadrature detection processing on the received signals obtained by the receiving electrical circuit 105, and stores the processed received signals in the signal storage unit 202. The delay-and-sum processing is a process of forming a received ultrasonic beam by adding together received signals from multiple transducers 101 while changing the delay time and weight for each transducer 101, and is also called delay-and-sum (DAS) beamforming. The quadrature detection processing is a process of converting the received 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 image generation conditions (aperture control, signal filter) input from the system control unit 109. The received signals after the delay-and-sum processing and quadrature detection processing are stored in the signal storage unit 202. While a typical example of DAS beamforming is shown here, any process of forming a received ultrasonic beam, such as adaptive beamforming, model-based processing, or processing using machine learning, may be used.

[0032] The received signal (digital data) obtained by the receiving electrical circuit 105 is also called RAW data. The received signal (digital data) obtained by the phasing addition processing unit 201 is also called RF data. In this embodiment, the RAW data and RF data are examples of measurement data acquired based on ultrasound signals 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 expressed as 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 amount correction unit 204, which will be described later.

[0034] The displacement amount correction unit 204 calculates the amount of tissue displacement between frames due to body movement, etc., from received signals acquired over multiple frames. This calculation is typically performed using a block matching calculation known as a speckle tracking method. A region of interest is set within a frame, and the displacement of the region of interest between frames is calculated by tracking the region of interest through correlation. By setting multiple regions of interest within a frame, the displacement of the entire frame is calculated. While an example of a method using correlation between frames is shown here, any method that can calculate the displacement of the region of interest may be used. For ultrasound signals, speckles, which are scattered images of ultrasound reflected from scatterers in body tissue, are tracked, making this method known as speckle tracking. The correlation calculation for the region of interest between frames may be performed on received signals after any of delay-and-sum processing, quadrature detection processing, and envelope detection processing. Furthermore, the correlation calculation may be performed on the time waveform of the received signal, or on frequency space data obtained by discrete Fourier transforming the received signal. The displacement amount correction unit 204 corrects the displacement by moving the received signal relative to a reference frame using the calculated displacement amount. The amount of displacement to be corrected may be a uniform value for the entire frame, such as the average value of the amount of displacement for each region of interest, or may be changed within the frame, such as for each region of interest. Furthermore, the amount of displacement for the entire frame calculated for each region of interest may be interpolated in the time direction within the frame or in the data sequence of multiple frames using linear interpolation, spline interpolation, or the like. Furthermore, when correcting the amount of displacement, the received signal may be similarly interpolated and then moved to allow for more precise correction.

[0035] The Doppler processing unit 205 extracts blood flow information (Doppler information) from the received signals for generating blood flow images stored in the signal storage unit 202, and generates blood flow image data that visualizes the blood flow information. The Doppler processing unit 205 may also perform Doppler processing on the received signals whose displacements have been corrected by the displacement amount correction unit 204.

[0036] The processing content 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 signals 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 also be an object such as 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 a single point (one position) within the object, or may obtain blood flow information at multiple positions in the depth direction. The Doppler processing unit 205 may also obtain blood flow information at multiple time points in a time series so that changes in blood flow information over time can be displayed.

[0037] When generating a blood flow image using the Doppler method, multiple frames of received signal data sequences are 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 sequence. This reduces information (clutter components) derived from tissues that are stationary or have little movement between frames, and extracts information derived from blood flow (blood flow components). The Doppler processing unit 205 then calculates blood flow information such as blood flow velocity, blood flow dispersion, and blood flow power from these 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 may be a filter with fixed filter coefficients, such as a Butterworth-type infinite impulse response (IIR) filter or a polynomial regression filter. The MTI filter may be an adaptive filter that changes its coefficients according to the 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 vector Doppler, speckle tracking, or vector flow mapping to calculate a velocity vector for each coordinate in the image and thereby calculate a blood flow vector representing the magnitude and direction of 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 processing unit 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 processing unit 205. A method for obtaining the super-resolution blood flow image will be explained when explaining the processing flow. In this embodiment, the super-resolution processing unit 206 is an example of a generating unit 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 is processed 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 a memory. In this case, the functions of each unit 201 to 206 shown in FIG. 2 are realized by a computer program. For example, the functions of each unit 201 to 206 can be provided by a CPU reading and executing a program stored in memory. In addition to the CPU, the received signal processing unit 106 may also include a processor (GPU, FPGA, etc.) responsible for the calculations of the B-mode processing unit 203, displacement amount correction unit 205, Doppler processing unit 205, and super-resolution processing unit 206. The memory may include a memory for non-temporarily storing programs, a memory for temporarily storing data such as received signals, a working memory used by the CPU, etc.

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

[0043] (Processing Flow for Acquiring Super-Resolution Blood Flow Image) 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 to include the region of interest for blood flow information observation, and ultrasonic transmission and reception are repeated while fixed at the same measurement position to acquire a received signal containing a data sequence of multiple frames in the time direction (S310). The number of frames of the received 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 received signal when generating a normal Doppler image is approximately 5 to 20, whereas the number of frames of the received signal when generating a super-resolution blood flow image is at least 100 frames or more, preferably several hundred to several tens of thousands. The frame rate of the ultrasound diagnostic device 1 is approximately several hundred Hz to several thousand Hz, depending on the model and measurement method. Therefore, the measurement time of the received signal to generate a super-resolution blood flow image is approximately several hundred msec to several tens of seconds. The received signal containing a data sequence of multiple frames undergoes phased addition and quadrature detection processing by the phased addition processing unit 201 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 obtained by transmitting and receiving ultrasound multiple times to include the observation area. For example, one frame of data may be obtained by transmitting a plane wave or a diverging wave to include the observation area, and adding together multiple received signals at different transmission angles.

[0045] 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 containing a data sequence of multiple frames. The displacement amount correction unit 204 then corrects the displacement amount by using the calculated displacement amount to move the received signal so that its position is aligned within the frames where the integration process in S360 and S550 (described later) 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 the 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, or the last frame may be used as the reference frame.

[0046] The Doppler processing unit 205 applies an MTI filter to the received signal containing the data sequence of multiple frames corrected for displacement, thereby reducing clutter components and extracting components derived from blood flow, thereby generating multiple frames of blood flow images (S330). In this case, the MTI filter may be designed using a portion of the data sequence or the entire data sequence. Furthermore, the MTI filter parameters (cutoff frequency, number of packets and overlaps of frames to be extracted, etc.) may be automatically or user-adjustable depending on the characteristics of the received signal, the subject being measured, etc. An example of multiple frames of blood flow images generated in this manner is shown in Figure 4A. The shading of pixel values ​​at each position within the position 401 containing blood flow information is due to differences in flow velocity. Locations with large pixel values ​​(dark colors in the figure) indicate fast flow velocity, while locations with small pixel values ​​(light colors in the figure) indicate slow flow velocity.

[0047] For comparison, we will first describe the processing flow for acquiring a super-resolution blood flow image using conventional technology, and then explain the problems associated with that technology. The processing flow for acquiring a super-resolution blood flow image using this embodiment will then be explained. The processing flow for acquiring a super-resolution blood flow image using conventional technology is shown in FIG. 5, and an explanation of the processing method is shown in FIGS. 6A to 6C. Steps S510 to S530 in FIG. 5 are the same as steps S310 to S330 in FIG. 3. The resulting multiple-frame 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 values ​​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 within each blood vessel image that have pixel values ​​within the predetermined range, an image like that shown in Figure 6B is obtained. The dashed line in Figure 6B represents the position 401 in Figure 6A that contains blood flow information.

[0049] Because the pixel values ​​in a predetermined range move between frames, by integrating multiple blood flow images from which pixel values ​​have been extracted, a super-resolution blood flow image with improved resolution, as shown in Figure 6C, is generated compared to each blood flow image, i.e., a normal Doppler image (S550). Because only high pixel value areas are extracted, if the point spread function is assumed to be a Gaussian distribution, only the sharpest areas at the center of the distribution are extracted. Although only points or small ranges are extracted in one frame of image, because the high pixel value areas move between frames, by integrating multiple blood flow images from which pixel values ​​have been extracted, a super-resolution blood flow image can be obtained.

[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 velocities, are extracted.

[0051] The problems with the conventional technology will be described in detail. It is generally known that the amplitude distribution characteristics of ultrasound signals received from a uniform scattering medium can be roughly approximated by a probability distribution called the Rayleigh distribution shown in FIG. 7. Extracting high pixel values ​​in S540 means extracting "points with low probability but large amplitude values ​​in the Rayleigh distribution" from among the many reflected signals from red blood cells, thereby extracting only the reflected signals from a small number of red blood cells and making them spatially sparse. However, when blood vessels with different flow velocities are present adjacently, simply extracting high pixel values ​​will extract only the reflected signals from "points with low probability but large amplitude values ​​in the Rayleigh distribution of thick blood vessels with fast flow velocities," and therefore will not extract reflected signals from thin blood vessels with slow flow velocities.

[0052] FIG. 8A 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 in the depth direction. The "thin blood vessel with a slow flow velocity" is located at a shallow position, while the "thick blood vessel with a fast flow velocity" is located at a deep position. However, because the gap between the blood vessels is below the resolution, they cannot be separated in the blood flow image. FIG. 8B shows a super-resolution blood flow image obtained by conventional technology, which is the result of performing the processes of S540 and S550 on each of multiple frames of blood flow images. As can be seen by comparing with FIG. 8C, which shows a super-resolution blood flow image obtained by this embodiment (described later), the super-resolution blood flow image obtained by conventional technology in FIG. 8B shows improved resolution of only the thick blood vessels with a fast flow velocity, while the thin blood vessels with a slow flow velocity 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 velocities, can also be extracted. However, when considering extraction from the Gaussian distribution described above, the range of the extracted distribution is widened, which reduces the effect of improving resolution.

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

[0055] The effect of dividing a blood flow image into multiple feature-separated images will now be described. Generally, in a blood flow image, the pixel values ​​of the larger blood vessels with a faster flow velocity are larger than those of the smaller blood vessels with a slower flow velocity. Therefore, when extracting a group of pixels with pixel values ​​within a predetermined range from a blood flow image in which a larger blood vessel with a faster flow velocity and a smaller blood vessel with a slower flow velocity are adjacent to each other, there is a possibility that the pixels of the smaller blood vessels with a slower flow velocity will not be extracted. To solve this problem, it is effective to generate feature-separated images using image features related to the blood flow velocity. Specifically, multiple feature-separated images with different flow velocity ranges are generated from the original blood flow image, such as a first feature-separated image with image features corresponding to a first flow velocity range, a second feature-separated image with image features corresponding to a second flow velocity range, and so on. An appropriate extraction range is then set for each feature-separated image, and feature points are extracted from each feature-separated image. This method allows both pixels of larger blood vessels with a faster flow velocity and pixels of smaller blood vessels with a slower flow velocity to be extracted as feature points.

[0056] Specifically, using two or more frames of blood flow images, a discrete Fourier transform is performed on the temporal change in pixel value of each pixel. The discrete Fourier transform is based on a sinusoidal wave and represents frequency components. Therefore, the discrete Fourier transform can separate the frequency components, i.e., flow velocity components, of the inter-frame changes in each pixel of the blood flow image, and generate feature-separated images with blood flow information for the flow velocity range corresponding to each base. The feature-separated images with blood flow information for the flow velocity range corresponding to each base may be frequency-domain images after discrete Fourier transform, or may be time-domain images obtained by performing an inverse discrete Fourier transform on the frequency-domain images. By shifting some of the frames used for the discrete Fourier transform among the multiple frames of blood flow images in the temporal direction, different time-series feature-separated images corresponding to each base can be obtained. In this case, the blood flow image is separated into multiple feature-separated images by focusing on image features related to flow velocity (frequency components of temporal changes in pixel values), i.e., using flow velocity as a base. 4B shows an example of separation into three feature-separated images, with blood flow images of different flow velocities being visualized for each base. Increasing the number of separations, i.e., the number of frames used for the discrete Fourier transform, reduces the flow velocity range corresponding to each base, allowing blood flow images to be separated into finer flow velocity ranges. Note that any basis other than the discrete Fourier transform based on a sine wave may be used as long as it can separate the blood flow images into flow velocity range-specific images. The blood flow images may also be separated into images for each flow velocity range using polynomial regression such as Legendre polynomials.

[0057] The super-resolution processor 206 extracts feature points from the blood flow-derived information from the multiple feature separation images generated in S340 (S350). The feature separation images of this embodiment are images that represent blood flow-derived information using pixel values ​​(brightness; density), with pixels with more (stronger) blood flow-derived information (i.e., pixels corresponding to locations where blood flow is present) having 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 blood flow-derived information is particularly strongly expressed are extracted from each feature separation image, as shown in FIG. 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 the "extraction range") from which feature points are extracted, or allows the user to set the range. The setting unit may read a setting value for the extraction range stored in advance in memory and set it in the super-resolution processor 206. The setting unit may dynamically set the extraction range based on the pixel value distribution (histogram) or maximum pixel value of the feature-separated image. For example, the setting unit may set the maximum pixel value of the feature-separated image as the upper limit and the maximum pixel value multiplied 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 with the maximum pixel value from the feature-separated image is extracted. The coefficient can be adjusted so that more pixels are extracted as the coefficient is reduced. Alternatively, the extraction range may be set to include the top N pixels of the histogram (N is an integer that can be set arbitrarily).

[0059] The super-resolution processor 206 may extract a pixel with a local maximum value as a feature point. For example, the blood flow image is divided into multiple local regions, and feature points are extracted for each local region. In this case, the super-resolution processor 206 may fix the extraction range for each local region, or may set the extraction range for each local region according to the local maximum pixel value. Alternatively, the super-resolution processor 206 may extract a pixel with a larger pixel value than its neighboring pixels (e.g., when the pixel value of the central pixel in a 3 x 3 pixel local region is the largest), i.e., a local maximum pixel, to extract the local maximum value. While a typical example of a method for extracting a local maximum value is shown here, 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 by setting a uniform extraction range for the entire image, and this increases the number of pixels used in the integration process in S360, described below.

[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 in S360, the super-resolution processor 206 may actively remove pixels in tissue (noise) 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 considered 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 using a GUI such as a slider bar. In this case, 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 valleys in the pixel value distribution may be displayed. Such displays can assist the user in selecting a 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 bottom M pixels (M is an integer that is set arbitrarily) in terms of the smallest pixel value may be mechanically removed as noise.

[0062] Furthermore, in blood flow images, pixel values ​​vary depending on the depth from the body surface due to attenuation by the living body and the diffusion attenuation of ultrasound waves. Therefore, in the process of S350, the super-resolution processor 206 may set the extraction range described above according to the "depth from the body surface" of each local region in the feature separation image. That is, when the feature separation image includes a first local region and a second local region at different depths from the body surface, the extraction range of the first local region and the extraction range of the second local region are differentiated taking into account the depth from the body surface. For example, if 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 ultrasound attenuation, it is advisable to widen the extraction range of the second local region (e.g., lower the lower limit) compared to the extraction range of the first local region. Alternatively, instead of changing the extraction range, a process for correcting for living body attenuation and diffusion attenuation may be applied to the feature separation image before the feature point extraction process.

[0063] The super-resolution processor 206 may also average feature-separated images of two or more frames to generate a feature-separated image with improved SNR, and then perform feature point extraction on the averaged feature-separated image in S350. The super-resolution processor 206 may not use the feature-separated 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] The super-resolution processor 206 may also apply the feature point extraction process of S350 after performing a process to increase the resolution (upconvert) of the feature-separated image. In this case, the feature-separated image itself may be upconverted, or the original blood flow image may be upconverted and the feature-separated image may be generated from the upconverted blood flow image. Alternatively, the received signal may be upconverted, and a blood flow image may be generated using multiple frames of the upconverted received signal, and then a feature-separated image may be generated. Any upconversion method may be used. For example, interpolated pixels may be generated using nearest neighbor, linear interpolation, spline interpolation, or the like. Alternatively, upconversion may be performed by applying a filter generated by machine learning. The resolution (number of pixels) of the super-resolution blood flow image obtained by the integration process of S360 is the same as the resolution (number of pixels) of the original image used to extract feature points. Therefore, if 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. Furthermore, in images obtained using convex and sector probes, pixel size (resolution) differs between deep and shallow regions in the image. Increasing the resolution of shallow regions in advance through upconversion can be expected to improve the resolution of the final super-resolution blood flow image.

[0065] The extraction process in S350 yields an image such as that shown in FIG. 4C . Because feature points are extracted from each feature-separated image for each flow velocity, it is possible to extract not only pixels of blood vessels with large pixel values ​​(thick blood vessels with fast flow velocities) but also pixels of blood vessels with small pixel values ​​(thin blood vessels with slow flow velocities). This means that blood vessels with a wider range of flow velocities can be extracted than with conventional techniques. Furthermore, if the energy level of the pixel values ​​of a blood vessel (e.g., standard deviation of pixel values) is close to the noise energy, the process in S340 generates a feature-separated image separated by flow velocity, dispersing the noise energy among the bases. This reduces the noise energy compared to the image information of the blood flow component, thereby improving the signal-to-noise ratio (SNR) or contrast-to-noise ratio (CNR) of each feature-separated image. Therefore, another advantage is that blood vessels that could not be distinguished from noise in a blood flow image can be depicted in a feature-separated image. Furthermore, by separating the image 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 conventional techniques using 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 the super-resolution blood flow image, since it is now possible to generate a super-resolution blood flow image of equivalent quality using fewer frames than with conventional techniques.

[0066] Finally, the super-resolution processing unit 206 generates a super-resolution blood flow image by integrating pixel values ​​of feature points extracted from multiple feature separation images of multiple frames (S360). Examples of super-resolution blood flow images are shown in Figures 4D and 8C. Because the integration process concentrates pixels with large pixel values ​​in the blood flow area, the super-resolution blood flow image visualizes blood flow with higher resolution and clarity than the original blood flow image. Furthermore, compared to a conventional super-resolution blood flow image (Figure 8B), the super-resolution blood flow image of this embodiment (Figure 8C) clearly depicts adjacent blood vessels of different thicknesses. The super-resolution processing unit 206 displays the generated super-resolution blood flow image on the display device 108.

[0067] All feature-separated images generated from all frame blood flow images stored in the signal storage unit 202 may be used for feature point extraction and integration processing to generate a single super-resolution blood flow image, or only frames within a certain time range may be used to generate a super-resolution blood flow image. In the latter case, the user may be allowed to select the time range used to generate the super-resolution blood flow image. Alternatively, multiple super-resolution blood flow images may be generated in time series by sequentially shifting the time range used to generate the super-resolution blood flow image. Furthermore, multiple super-resolution blood flow images may be displayed sequentially on the display device 108 to display a video of the super-resolution blood flow images. 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 multiple feature-separated 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. For example, simply integrating all feature points extracted from multiple feature-separated images with different flow velocity ranges can image all blood vessels (blood flows), from those with slow flow velocities to those with fast flow velocities. Selecting only feature points from feature-separated images corresponding to a specific flow velocity range, or integrating them with a high weighting, can produce an image in which blood vessels in that specific flow velocity range are emphasized. A GUI may be provided to the user to allow the user to arbitrarily select or set the range of image features (feature-separated images) used for integration or the weighting according to the image features. Using such a GUI, a super-resolution blood flow image appropriate for the target (blood vessel size and flow velocity) to be observed can be generated.

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

[0070] Second Embodiment In the first embodiment, a feature-separated 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-separated image is generated by focusing on image features related to the shape or thickness of blood vessels. In other words, the blood vessel diameter extracted varies for each base.

[0071] For example, the super-resolution processor 206 performs a two-dimensional Fourier transform on the spatial changes in 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 thin blood vessels, while a low spatial frequency, i.e., a spatially coarse structure, is an image characteristic of thick blood vessels. Therefore, for example, the super-resolution processor 206 can generate a feature-separated image having image characteristics corresponding to the desired blood vessel diameter by extracting only the spatial frequency components corresponding to the 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 extracted spatial frequency components, multiple feature-separated images corresponding to different blood vessel diameters can be obtained, such as a feature-separated image corresponding to a blood vessel diameter of 1 mm to 2 mm, a feature-separated image corresponding to a blood vessel diameter of 2 mm to 3 mm, etc. FIG. 9A shows an example of an original blood flow image, and FIG. 9B shows examples of feature-separated images of three different blood vessel diameters generated from the original blood flow image.

[0072] Furthermore, by applying line enhancement processing based on eigenvalue analysis of the Hessian matrix to the blood flow image, the brightness and contrast of blood vessel-like (linear, columnar) structures of a desired thickness can be selectively enhanced, thereby generating a feature-separated image in which image features corresponding to a desired blood vessel diameter are enhanced. In this case, too, by appropriately changing the thickness to be enhanced by the line enhancement processing, multiple feature-separated images corresponding to different blood vessel diameters can be generated.

[0073] Alternatively, a similarity search process such as template matching may be used. For example, a similarity map (a two-dimensional map representing the similarity (correlation value) between each local region and the template image) may be generated by searching the entire blood flow image using a template image of a blood vessel pattern of a desired diameter. 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-separated image in which image features corresponding to the blood vessel diameter of the template image are emphasized can be obtained. In this case, by preparing multiple template images with different blood vessel diameters, multiple feature-separated images corresponding to different blood vessel diameters can be generated.

[0074] In addition to the methods described here, any method may be used to generate a feature-separated image as long as it selectively emphasizes or extracts image features corresponding to the thickness or shape of blood vessels. Note that the processing flow and device configuration other than the process of generating a feature-separated image in S340 may be the same as those in the first embodiment.

[0075] Third Embodiment In a 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 (3D discrete Fourier transform) on some of the multiple blood flow images in both the time direction and the spatial direction within the image. Then, by extracting only the spatial frequency components corresponding to the desired flow velocity range and the desired vascular diameter and performing an inverse Fourier transform, a feature-separated image with image features corresponding to the desired flow velocity range and the desired vascular diameter can be generated. By appropriately changing the extracted spatial frequency components, multiple feature-separated images with different combinations of flow velocity range and vascular diameter can be obtained. Alternatively, this can be achieved by converting each of the multiple blood flow images into a one-dimensional vector and then applying singular value decomposition to the matrix data in which each frame is arranged in the column direction. This allows blood vessels of different spatial sizes to be further divided into images with different flow velocity ranges. Specifically, a group of images can be generated using only the singular value components. This method of dividing into bases by singular value decomposition is also used in clutter removal performed in the process of S330 in 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, the basis set of the first embodiment cannot separate them. In contrast, by using the basis set of this embodiment, blood vessels of different spatial 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 feature point extraction process in the subsequent stage, more feature points can be extracted than with conventional techniques using 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 the super-resolution blood flow image, since super-resolution blood flow images of equivalent quality can be generated using fewer frames than with conventional techniques.

[0078] In addition to the methods described here, any method may be used to generate a feature-separated image as long as it selectively emphasizes or extracts image features corresponding to the flow velocity and shape of blood vessels. Note that the processing flow and device configuration other than the process of generating a feature-separated image in S340 may be the same as those in the first embodiment.

[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 multiple regions, and the divided images (small images) for each region are used as feature separation images. Fig. 11B shows an example in which each blood flow image in Fig. 11A is divided into four regions. By minimizing the difference in pixel values ​​between multiple blood vessels included in each feature separation image, it is possible to extract the pixels of each blood vessel.

[0080] 11B, the blood flow image is divided into four, but the number of divisions can be set arbitrarily. The sizes of the regions may be the same or different. The processing flow and device configuration other than the process of generating a feature-separated image in S340 may be the same as those in the first embodiment.

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

[0082] The Doppler processing unit 205 obtains velocity vectors for each coordinate in the image by using techniques such as vector Doppler, speckle tracking, and vector flow mapping in addition to blood flow images obtained by the Doppler method. The arrows in FIG. 12A indicate velocity vectors. The direction of these velocity vectors represents the direction of blood flow. To generate a feature-separated image corresponding to a desired blood flow direction, for example, a correspondence map is generated that indicates the degree of correspondence between the desired blood flow direction and the velocity vector for each coordinate. This correspondence map can be considered as a two-dimensional image filter in which larger coefficients are set for coordinates with velocity vectors closer to the desired blood flow direction. Therefore, by multiplying the blood flow image by the correspondence map, a feature-separated image in which image features corresponding to the desired blood flow direction are emphasized can be obtained. By generating a correspondence map for each blood flow direction and multiplying it by the blood flow image, multiple feature-separated images corresponding to different blood flow directions can be generated. The correspondence map and feature-separated image may be generated by taking into account not only the vector direction but also the vector magnitude.

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

[0084] In addition to the methods described here, any method that selectively emphasizes or extracts image features corresponding to the direction of blood flow may be used to generate a feature-separated image. Note that the processing flow and device configuration other than the process of generating a feature-separated image in S340 may be the same as those in the first embodiment.

[0085] <Other Embodiments> The above-described embodiments merely illustrate 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 as long as the gist of the present invention is not changed. These embodiments and their modifications are intended to be included in the scope of the invention and its equivalents as defined in the accompanying claims, as well as in the scope and gist of the inventions.

[0086] Furthermore, the disclosed technology can be embodied as, for example, a system, a device, a method, a program, or a recording medium (storage medium), etc. Specifically, the technology may be applied to a system consisting 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 consisting of a single device.

[0087] Needless to say, the object of the present invention can be achieved by the following: A recording medium (or storage medium) on which software program code (computer program) that realizes the functions of the above-described embodiments is recorded is supplied to a system or device. This storage medium is, of course, a computer-readable storage medium. The computer (or CPU or MPU) of the system or device then reads and executes the program code stored on the recording medium. In this case, the program code itself read from the recording medium realizes the functions of the above-described 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) An ultrasound diagnostic device comprising: a first acquisition unit that acquires measurement data including tissue-derived information and blood flow-derived information based on ultrasound signals reflected within a living body; a second acquisition unit that acquires blood flow data in which the blood flow-derived information from the measurement data has been extracted or emphasized, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; and a generation unit that generates multiple feature separation data having different image features from each of multiple frames of blood flow data, and generates display image data by combining feature points extracted from each of the multiple feature separation data for multiple frames.

[0090] (Configuration 2) The ultrasound diagnostic device according to Configuration 1, wherein the image features include image features related to blood flow velocity.

[0091] (Configuration 3) The ultrasound diagnostic device according to Configuration 2, wherein the generating unit generates a feature separated image for each frequency component of the temporal change in pixel values ​​by performing a Fourier transform on the temporal change in pixel values ​​in the blood flow data of two or more frames.

[0092] (Configuration 4) The ultrasound diagnostic device according to any one of configurations 1 to 3, wherein the image features include image features related to the shape of blood vessels.

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

[0094] (Configuration 6) The ultrasonic diagnostic device according to any one of Configurations 1 to 5, wherein the image features include image features related to blood flow velocity and blood vessel shape.

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

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

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

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

[0099] (Configuration 11) The ultrasound diagnostic device according to Configuration 9 or 10, wherein 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.

[0100] (Configuration 12) The ultrasound diagnostic device according to any one of Configurations 9 to 11, wherein 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.

[0101] (Configuration 13) The ultrasound diagnostic device according to any one of Configurations 1 to 12, wherein the generation unit generates the display image data by removing noise from pixel groups extracted as the feature points from the plurality of feature separation data and synthesizing the pixel groups after the noise removal.

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

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

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

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

[0106] (Method 18) An information processing method by a computer, comprising: acquiring measurement data including tissue-derived information and blood flow-derived information based on an ultrasound signal reflected within a living body; acquiring blood flow data in which the blood flow-derived information from the measurement data has been extracted or emphasized, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; generating multiple feature separation data having different image features from each of multiple frames of blood flow data, and generating display image data by synthesizing feature points extracted from each of the multiple feature separation data for multiple frames.

[0107] (Program 19) A program for causing a computer to execute an information processing method, including: acquiring measurement data including tissue-derived information and blood flow-derived information based on an ultrasound signal reflected within a living body; acquiring blood flow data in which the blood flow-derived information from the measurement data has been extracted or emphasized, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; and generating multiple feature separation data having different image features from each of multiple frames of blood flow data, and generating display image data by synthesizing feature points extracted from each of the multiple feature separation data for multiple frames.

[0108] The present invention is not limited to the above-described embodiments, and various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the following claims are appended to apprise the public of the scope of the present invention.

[0109] This application claims priority based on Japanese Patent Application No. 2023-100640, filed on June 20, 2023, the entire contents of which are incorporated herein by reference.

[0110] 1: Ultrasound diagnostic equipment

Claims

1. An ultrasound diagnostic device having: a first acquisition unit that acquires measurement data including information derived from tissue and information derived from blood flow based on ultrasound signals 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 has been extracted or emphasized, the blood flow data having the extracted or emphasized information derived from blood flow as pixel values; and 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.

2. The ultrasound diagnostic device according to claim 1, wherein the image features include image features related to blood flow velocity.

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

4. The ultrasound diagnostic device according to claim 1, wherein the image features include image features related to the shape of blood vessels.

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

6. The ultrasound diagnostic device according to claim 1, wherein the image features include image features related to blood flow velocity and blood vessel shape.

7. The ultrasound diagnostic device according to claim 1, wherein the generating unit generates a feature separated image for each region by dividing the blood flow data into a plurality of regions.

8. The ultrasound diagnostic device according to claim 1, wherein the generating section extracts pixels having pixel values ​​within a predetermined extraction range from the feature separation data as the feature points.

9. The ultrasonic diagnostic apparatus according to claim 8, further comprising a setting unit which sets the extraction range automatically or allows a user to set the extraction range.

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

11. The ultrasound diagnostic device according to claim 9, wherein 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.

12. The ultrasound diagnostic device according to claim 9, wherein 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 a depth from the body surface of each local region.

13. The ultrasound diagnostic device according to claim 1, wherein the generation 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 noise removal.

14. The ultrasound diagnostic device according to claim 1, wherein the generating unit extracts the feature points from data obtained by averaging two or more pieces of feature separation data.

15. The ultrasound diagnostic device according to claim 1, wherein the generating unit up-converts the plurality of feature separation data and extracts the feature points from the up-converted feature separation data.

16. The ultrasound diagnostic device according to claim 1, wherein, when generating the display image data, the generation unit combines the feature points extracted from each of the plurality of feature separated images by weighting them according to the image features.

17. A medical information processing device having a generation unit that acquires blood flow data in which information derived from blood flow in a living body is extracted or emphasized from measurement data containing information derived from tissues and information derived from blood flow, the blood flow data having the extracted or emphasized information derived from blood flow as pixel values, 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.

18. An information processing method by a computer, comprising: acquiring measurement data including tissue-derived information and blood flow-derived information based on an ultrasonic signal reflected within a living body; acquiring blood flow data in which the blood flow-derived information from the measurement data has been extracted or emphasized, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; generating a plurality of feature separation data having different image features from each of a 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.

19. A program for causing a computer to execute an information processing method including: acquiring measurement data including tissue-derived information and blood flow-derived information based on ultrasonic signals reflected within a living body; acquiring blood flow data in which the blood flow-derived information from the measurement data has been extracted or emphasized, the blood flow data having the extracted or emphasized blood flow-derived information as pixel values; generating a plurality of feature separation data having different image features from each of a 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.

Images