Medical information processing device, ultrasonic diagnostic device, and program

Abstract

To obtain ultrasonic data having high responsibility and high resolution.SOLUTION: A medical information processing device according to an embodiment includes an acquisition part, a conversion part, an extraction part, and an output part. The acquisition part acquires a plurality of first ultrasonic data obtained on the basis of a result of ultrasonic scanning on a subject. The conversion part applies conversion to the plurality of first ultrasonic data and generates a plurality of second ultrasonic data characterized by a parameter different from a time. The extraction part continuously extracts a signal component representing a target object from the plurality of second ultrasonic data. The output part outputs third ultrasonic data on the basis of the signal component representing the continuously extracted target object.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a medical information processing device, an ultrasound diagnostic device, 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 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 the CFM method, ultrasound waves are transmitted and received multiple times on multiple scan lines. Then, by applying an MTI (Moving Target Indicator) filter to the data sequence at the same position, signals originating from stationary or slow-moving tissue (clutter signals) are suppressed and signals originating from blood flow are extracted. The CFM method estimates blood flow information such as blood flow velocity, variance, and power values ​​from this blood flow signal, and displays the distribution of the estimated results as a Doppler image.

[0004] It is known that resolution of B-mode and Doppler data is reduced due to the point spread function (PSF), which is determined by the wavelength of the transmitted ultrasound and 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 images that can be obtained.

[0005] Non-Patent Document 1 describes a method for generating blood flow data with improved resolution by extracting and integrating high signal (amplitude) portions from multiple blood flow data sets that are continuous in the time direction. More specifically, Non-Patent Document 1 describes a method for utilizing the fact that the amplitude distribution characteristics of the speckle pattern of blood flow data can be approximately approximated by a probability distribution called a Rayleigh distribution. As a result, high-intensity, spatially sparse signals that have a low occurrence probability but a large signal (amplitude) value are extracted from each blood flow data set and integrated.

[0006] However, in the method described in Non-Patent Document 1, if the number of frames used to extract the object is small, the object is extracted discretely (discontinuously), and high-resolution blood flow data (ultrasound data) cannot be obtained. On the other hand, if the number of frames used is large, the calculation load increases and responsiveness deteriorates. [Prior art documents] [Non-patent literature]

[0007] [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, April 4, 2022 Summary of the Invention [Problem to be solved by the invention]

[0008] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to obtain high-resolution ultrasound data with high responsiveness. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0009] A medical image processing apparatus according to an embodiment includes an acquisition unit, a conversion unit, an extraction unit, and an output unit. The acquisition unit acquires a plurality of first ultrasound data obtained based on the results of an ultrasound scan of a subject. The conversion unit applies a transformation to the plurality of first ultrasound data to generate a plurality of second ultrasound data characterized by a parameter different from time. The extraction unit continuously extracts signal components representing an object from the plurality of second ultrasound data. The output unit outputs third ultrasound data based on the continuously extracted signal components representing the object. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing the configuration of an ultrasonic diagnostic apparatus according to an embodiment. [Figure 2] FIG. 2 is a diagram for explaining the Rayleigh distribution. [Figure 3] FIG. 3 is a flowchart showing the flow of processing performed by the ultrasound diagnostic apparatus according to the embodiment. [Figure 4] FIG. 4 is a schematic diagram for explaining the processing performed by the ultrasound diagnostic apparatus according to the first embodiment. [Figure 5] FIG. 5 is a diagram illustrating the transformation for generating the second ultrasound data in the first embodiment. [Figure 6] FIG. 6 is a diagram showing an example of a kernel used in the process of step S500 in FIG. [Figure 7A] FIG. 7A is a diagram showing an example of second ultrasound data from which signal components are extracted. [Figure 7B] FIG. 7B is a diagram showing the result of extraction of an object when a kernel according to a comparative example is used. [Figure 7C] FIG. 7C is a diagram showing the extraction result of the object when the kernel according to the embodiment is used. [Figure 8]FIG. 8 is a diagram illustrating the transformation for generating the second ultrasound data in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] (First embodiment) Hereinafter, embodiments of a medical information processing apparatus, an ultrasound diagnostic apparatus, and a program will be described in detail with reference to the drawings.

[0012] The ultrasound diagnostic apparatus according to this embodiment causes an ultrasound probe to perform an ultrasound scan and collects multiple frames of data (multiple frames of data within a predetermined time period) that are consecutive in the time direction and obtained by the execution of the ultrasound scan. The frame data is collected at a predetermined frame rate by the execution of the ultrasound scan. The frame data refers to any of received data, measurement data, blood flow data, and tissue data. The received data is, for example, a received signal of ultrasound (e.g., CH data) received by the ultrasound probe. The measurement data is data (e.g., IQ data) obtained by performing phasing addition and quadrature detection processing on the received signal of ultrasound. The blood flow data is data (e.g., power signal data) in which information derived from blood flow in the measurement data has been extracted or emphasized. The tissue data is data (e.g., B-mode data) in which information derived from tissue has been extracted or emphasized.

[0013] The measurement data includes, for example, information derived from tissue (tissue signal components (clutter)) and information derived from blood flow (blood flow signal components). The information derived from blood flow may include not only information derived from blood but also information derived from contrast agents in blood. Furthermore, the blood flow data is data in which information derived from blood flow is extracted or emphasized, and includes blood flow velocity values, variance values, and power values.

[0014] Extracting information derived from blood flow is, for example, an operation of extracting blood flow signal components from measurement data. Emphasizing information derived from blood flow is, for example, an operation of making blood flow signal components more prominent relative to tissue signal components. Note that blood flow data may be obtained by a process of extracting or emphasizing information derived from blood flow, or by a process of removing or reducing information derived from tissue.

[0015] The ultrasound diagnostic device also acquires a plurality of first ultrasound data obtained based on the results of an ultrasound scan of the subject. Subsequently, the ultrasound diagnostic device applies a transform to the plurality of first ultrasound data to generate second ultrasound data characterized by a parameter different from time. The parameter different from time is, for example, frequency. The ultrasound diagnostic device also continuously extracts signal components representing the object from the plurality of second ultrasound data. The object includes, for example, any of blood, body tissue, and contrast agent.

[0016] The ultrasound diagnostic device outputs third ultrasound data based on the signal components representing the continuously extracted object. The third ultrasound data may be synthesized data obtained by synthesizing the signal components representing the continuously extracted object with the second ultrasound data, corrected data obtained by correcting data obtained from the first ultrasound data or the second ultrasound data based on the signal components representing the continuously extracted object, or data including the signal components representing the continuously extracted object.

[0017] According to the ultrasound diagnostic device of this embodiment, the second ultrasound data characterized by a parameter different from time is used to extract the object, so that the object can be extracted continuously even if the number of frames used for object extraction is small. This allows for the acquisition of high-resolution ultrasound data (high-resolution data) with high responsiveness. Note that "high resolution" here includes both cases where the resolution is improved by increasing the pixel density of the image, and cases where the spatial resolution is improved by extracting peak signals without increasing the pixel density of the image.

[0018] Although the above description has been given of an example in which the present invention is applied to an ultrasound diagnostic device, the present invention may also be applied to modalities (medical information processing devices) other than ultrasound diagnostic devices. For example, the present invention may be applied to medical information processing devices such as workstations and servers that acquire ultrasound data obtained based on the results of ultrasound scans of a subject.

[0019] FIG. 1 is a block diagram showing the configuration of an ultrasound diagnostic apparatus according to an embodiment. The ultrasound diagnostic apparatus 110 is an apparatus that generates ultrasound data based on received signals (reflected wave signals) received from an ultrasound probe 105. The ultrasound diagnostic apparatus 110 shown in FIG. 1 is an apparatus that can generate two-dimensional ultrasound data based on two-dimensional received signals and three-dimensional ultrasound data based on three-dimensional received signals. However, the embodiment is also applicable to cases where the ultrasound diagnostic apparatus 110 is an apparatus dedicated to two-dimensional data. The ultrasound diagnostic apparatus 110 includes a transmission circuit 109, a reception circuit 111, and a medical information processing apparatus 100.

[0020] The ultrasonic probe 105 is, for example, an electronic scanning probe, and has a plurality of transducers 101 arranged one-dimensionally or two-dimensionally at its tip. The transducers 101 are piezoelectric elements (electromechanical transducers) that convert between electrical signals (voltage pulse signals) and ultrasound waves (acoustic waves). The ultrasonic probe 105 transmits ultrasound waves from the plurality of transducers 101 to a subject and receives reflected ultrasound waves from the subject via the plurality of transducers 101. The reflected acoustic waves reflect differences in acoustic impedance within the subject. When a transmitted ultrasound pulse is reflected by the surface of a moving blood flow, heart wall, or the like, the reflected ultrasound undergoes a frequency shift due to the Doppler effect, depending on the velocity signal component of the moving object relative to the ultrasound transmission direction.

[0021] The probe connection unit 103 connects to the ultrasonic probe 105 and transmits and receives ultrasonic waves to and from the ultrasonic probe 105. The connection means of the ultrasonic probe 105 by the probe connection unit 103 may be either wired or wireless. In the wired case, the probe connection unit 103 has a connector unit (receptacle) for connecting the connector (plug) of the ultrasonic probe 105. In the wireless case, it has a communication unit for wireless communication with the ultrasonic probe 105.

[0022] The transmission circuit 109 is a transmission 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, ultrasonic waves with different delay times are transmitted from the multiple transducers 101, thereby forming a transmitted ultrasonic beam. The direction and focus of the transmitted ultrasonic beam can be controlled by selectively changing the transducers 101 to which the pulse signals are applied or by changing the delay time (application timing) of the pulse signals. By sequentially changing the direction and focus of the transmitted ultrasonic beam, an observation area inside the subject is scanned. Furthermore, by changing the delay time of the pulse signal, a transmitted ultrasonic beam that is a plane wave (focused at a distance) or a diverging wave (focus point is in the opposite direction of the ultrasonic transmission direction relative to the multiple transducers 101) may be formed. Alternatively, a transmitted ultrasonic beam may be formed using one transducer or some of the multiple transducers 101. The transmission circuit 109 transmits pulse signals with a predetermined drive waveform to the transducers 101, causing the transducers 101 to generate transmitted ultrasonic waves with a predetermined transmission waveform.

[0023] The receiving circuit 111 is a receiving unit that inputs, as a received signal, an electrical signal output from the transducer 101 that has received reflected ultrasound. The received signal is input to the processing circuit 150. In this embodiment, the analog signal output from the transducer 101 and the digital data obtained by sampling (digital conversion) the analog signal are both referred to as the received signal without any particular distinction. However, depending on the context, the received signal may also be referred to as received data or measured data to clearly indicate that it is digital data.

[0024] The medical information processing device 100 is connected to a transmission circuit 109 and a reception circuit 111, and processes signals received from the reception circuit 111 and controls the transmission circuit 109. The medical information processing device 100 includes a processing circuit 150, a memory 132, an input device 134, and a display 135.

[0025] The memory 132 is composed of a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory 132 is a memory for storing data such as image data for display generated by the processing circuit 150. The memory 132 can also store received signals (reflected wave signals) output by the receiving circuit 111. In addition, the memory 132 stores, as necessary, control programs for transmitting and receiving ultrasound, image processing, and display processing, as well as various data such as diagnostic information (e.g., patient ID, doctor's findings, etc.), diagnostic protocols, and various body marks.

[0026] The input device 134 receives various instructions and information input from an operator and is configured from input interface devices such as a mouse, a keyboard, buttons, and a trackball.

[0027] The display 135 displays a GUI (Graphical User Interface) for receiving input of imaging conditions and various images under the control of the processing circuitry 150. The display 135 is configured by a display interface device such as a liquid crystal display, for example.

[0028] The processing circuitry 150 controls each component of the ultrasound diagnostic device 110, thereby controlling the entire ultrasound diagnostic device 110. Although the processing circuitry 150 is described as being implemented as a single circuit in Fig. 1, it may also be implemented as multiple circuits by combining multiple independent processors. Furthermore, it may be configured as an independent circuit dedicated to a specific function, such as an ASIC (Application Specific Integrated Circuit).

[0029] Furthermore, the term "processor" used in the above description refers to circuits such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor realizes its functions by reading and executing programs stored in memory 132.

[0030] The processing circuitry 150 causes the ultrasonic probe 105 to perform an ultrasonic scan and collects multiple frames of data (multiple frame data within a predetermined time) that are consecutive in the time direction and obtained by the execution of the ultrasonic scan. The processing circuitry 150 performs delay-and-sum processing and quadrature detection processing on the received signals (CH data) collected via the receiving circuitry 111. The delay-and-sum processing is processing that adds together received signals from multiple transducers 101 by changing the delay time and weight for each transducer 101, and is also called delay-and-sum (DAS) beamforming. The quadrature detection processing is processing that converts received signals into in-phase signals and quadrature signals (IQ data (measurement data)) in the baseband. Note that other processing such as adaptive beamforming, model-based processing, and machine learning may also be performed on the received signals.

[0031] Furthermore, the processing circuitry 150 may estimate the amount of tissue displacement due to the subject's body movement or the like between multiple frame data, and correct each frame data based on the estimated result. Specifically, the processing circuitry 150 calculates the amount of tissue displacement due to the body movement or the like between frames from multiple frame data. The processing circuitry 150 corrects the frame data based on the calculated amount of displacement.

[0032] The processing circuitry 150 also performs envelope detection processing, logarithmic compression processing, etc. to generate B-mode data (data in which tissue-derived information is extracted or emphasized) that represents the signal intensity at each point in the observation region as brightness. The processing circuitry 150 also generates blood flow data (power signal data) in which blood flow-derived information from the measurement data is extracted or emphasized.

[0033] For example, the processing circuitry 150 applies an MTI (Moving Target Indicator) filter to multiple frame data. This reduces information (tissue signal components (clutter)) derived from tissues that are stationary or move little between frames, and extracts information derived from blood flow (blood flow signal components). The MTI filter may be a filter with fixed filter coefficients, such as a Butterworth-type IIR (Infinite Impulse Response) filter or a Polynomial Regression Filter. The MTI filter may also be an adaptive filter that changes its coefficients according to the input signal using eigenvalue decomposition or singular value decomposition.

[0034] The processing circuitry 150 may also decompose the frame data into multiple bases using eigenvalue decomposition or singular value decomposition, remove tissue-derived information by extracting a specific base, and extract information derived from blood flow. The processing circuitry 150 may also use a method such as vector Doppler, speckle tracking, or vector flow mapping to obtain a velocity vector for each coordinate in the received signal data and obtain 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 blood flow-derived information contained in the frame data (received data, measured data) or remove or reduce tissue-derived information.

[0035] Furthermore, the processing circuit 150 reads and executes a program stored in the memory 132, thereby causing the acquisition function 150a, conversion function 150b, extraction function 150c, and output function 150d to function, and performs processing to output high-resolution ultrasound data.

[0036] Before describing the process of outputting high-resolution ultrasound data using the ultrasound diagnostic device 110 configured as above, a conventional method for generating high-resolution data (for example, the method described in Non-Patent Document 1) will be described.

[0037] As shown in Figure 2, it is known that the amplitude distribution characteristic 10 of the speckle pattern of blood flow data can be roughly approximated by a probability distribution called a Rayleigh distribution. A conventional high-resolution data generation method extracts, from a plurality of blood flow data corresponding to each of a plurality of frame data, high-intensity, spatially sparse signals (points 11 shown in Figure 2) that have a low occurrence probability but a large signal value (amplitude value), and then integrates these to generate ultrasound data.

[0038] In the high-resolution data generation method according to the related art, high-intensity, spatially sparse signals are extracted for each frame labeled by time, whereas in the high-resolution data generation method according to the embodiment, as will be described later, high-intensity, spatially sparse signals are extracted for each frame labeled by a parameter other than time, such as frequency.

[0039] The processing performed by the medical information processing device 100 will be described below with reference to Figures 3 and 4. The processing circuitry 150, using the acquisition function 150a, collects and acquires frame data (received signals, measurement data, etc.) that are continuous in the time direction and obtained based on the results of an ultrasound scan of the subject, as shown in Figure 4 (step S100). The number of frames of frame data when generating high-resolution data is approximately the same as when generating general Doppler data. For example, the number of frames (number of packets) when generating Doppler data is approximately several tens. On the other hand, the number of frame data collected by the acquisition function 150a is approximately several tens to several hundreds, which is significantly smaller than the number of frame data collected by conventional techniques (approximately several thousands to tens of thousands).

[0040] Furthermore, the processing circuitry 150 estimates the amount of tissue displacement due to the subject's body movement or the like between multiple frame data, and corrects each frame data based on the estimation result (step S200). Specifically, the processing circuitry 150 calculates the amount of tissue displacement due to body movement or the like between frames from a received signal including a data sequence of multiple frames. The processing circuitry 150 corrects the amount of displacement by using the calculated amount of displacement to move the received signal so that its position is aligned within the frame. Here, the reference frame for calculating the amount of displacement 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, or the last frame may be used as the reference frame.

[0041] Furthermore, the processing circuitry 150 applies an MTI filter to the plurality of frame data whose displacement has been corrected. This reduces information (tissue signal components (clutter)) derived from tissue that remains stationary or moves little between frames, and extracts information derived from blood flow (blood flow signal components) (step S300). As a result, as shown in FIG. 4, the processing circuitry 150 removes the clutter components and generates a plurality of first ultrasound data 30. The plurality of first ultrasound data 30 is ultrasound data of a plurality of frames arranged in time series. In this way, the processing circuitry 150 uses the acquisition function 150a to acquire a plurality of first ultrasound data 30 obtained based on the results of an ultrasound scan of the subject.

[0042] Next, in step S400, the processing circuitry 150 uses the transform function 150b to apply a transform to the plurality of first ultrasound data 30 to generate a plurality of second ultrasound data 31 characterized by a parameter different from the time t. In the first embodiment, a case will be described in which the transform applied to the first ultrasound data 30 in step S400 is a (discrete) Fourier transform, and the parameter different from the time t is frequency.

[0043] Here, the meaning of applying a transformation to a plurality of first ultrasound data 30 to generate a plurality of second ultrasound data 31 characterized by a parameter different from the time t will be explained. By generating a plurality of ultrasound data characterized by a parameter different from the time t, the transformation function 150b can separate, for example, a blood flow image into a plurality of feature-separated images. Here, a feature-separated image is an image in which, for example, an image in which a flow velocity component in a slow flow velocity range is extracted and an image in which a flow velocity component in a fast flow velocity range is extracted are separated. By selecting a parameter different from the time t as the frequency, the processing circuitry 150 can separate the images, for example, by flow velocity range.

[0044] As a specific processing method, the processing circuitry 150 applies a discrete Fourier transform to the plurality of first ultrasound data 30 using the transform function 150b to generate a plurality of second ultrasound data 31.

[0045] Specifically, when the plurality of first ultrasound data 30 is expressed as F(t) as a function of time t, and F(t) is expanded using a basis obtained by analytically connecting trigonometric functions to a complex plane, the plurality of first ultrasound data 30 can be expanded as shown in equation (1), where N is the number of frames, and the expansion coefficient f(x) is a function of a parameter x different from time t. This expansion coefficient f(x) is defined as the plurality of second ultrasound data 31.

[0046]

number

[0047] The plurality of second ultrasound data 31, which are the expansion coefficients f(x) defined in this way, can be calculated from the plurality of first ultrasound data 30, F(t), by using the following equation (2).

[0048]

number

[0049] 5, the transform function 150b applies a (discrete) Fourier transform 20 to the plurality of first ultrasound data 31a, 31b, 31c, ..., 31y, 31z to calculate the plurality of second ultrasound data 32a, 32b, 32c, ..., 32y, 32z using equation (2). Here, the first ultrasound data 31a, 31b, 31c, 31y, 31z are data corresponding to times t=0, t=1, t=2, t=N-1, t=N, respectively, and the second ultrasound data 32a, 32b, 32c, 32y, 32z are data corresponding to a parameter x different from the time t=0, x=1, x=2, x=N-1, x=N, respectively.

[0050] Returning to FIG. 3, the extraction function 150c performs a process of continuously extracting signal components representing an object from the plurality of second ultrasound data 31 generated in step S400 for each parameter x different from time t (step S500). The object here refers to a high-resolution image, including at least one of blood, body tissue, and contrast agent. Continuously extracting signal components representing the object refers to extracting the positions of signals (pixels) representing the object without any gaps in signal space or image space. For example, if the object is blood, the extraction function 150c extracts the positions of signals (pixels) representing the blood without any gaps in signal space or image space. As a result, the extraction function 150c obtains signal-extracted ultrasound data 32, which is extracted data representing the object, as shown in FIG. 4. The signal extraction in step S500 is performed for each parameter x different from time t, i.e., for each frequency in the first embodiment.

[0051] An example of a method for continuously extracting signal components representing an object will be described with reference to FIGS. 6 and 7A to 7C. For each parameter x different from time t, the extraction function 150c extracts the signal component of each of the positions of interest included in the second ultrasound data 31 as a signal component representing the object based on a comparison result between the signal value of the position of interest and a signal value located in a predetermined direction and range of interest relative to the position of interest. As an example, if the signal value of the position of interest is greater than or equal to the signal value located in the predetermined direction and range, the extraction function 150c extracts the signal component of the position of interest as a signal component representing the object. For example, as shown in FIG. 6, the extraction function 150c extracts the position of a signal (pixel) representing blood using a kernel 40 based on a ratio of 3 vertical x 1 horizontal. For convenience of explanation, an example has been shown here in which the pixel value (brightness value) of the position of interest is compared with pixel values ​​located within a predetermined range for each direction of interest relative to the position of interest on a two-dimensional image, but a one-dimensional comparison may be made between the signal value of the position of interest and signal values ​​located within a predetermined range for the second ultrasound data 31. In other words, the signal component representing the object may be extracted without expanding the second ultrasound data 31 into an image.

[0052] Each square in Fig. 7A represents a pixel value (brightness value) of the image represented by the second ultrasound data 31. When extracting a position having a relatively high pixel value from the second ultrasound data 31, a 3 x 3 kernel of a total of 9 squares is used, including the position of interest and 8 squares adjacent to the position of interest, as shown in Fig. 7B, for example. In this case, when the pixel value of the position of interest is higher than any of the 8 adjacent squares, the processing circuitry 150 extracts the position of interest as the position of a pixel (signal) representing blood.

[0053] However, when the kernel shown in FIG. 7B is used, the object (blood vessel, etc.) is extracted discretely, and its structure is not fully represented. On the other hand, when the kernel 40 shown in FIG. 6 is used, the object is extracted continuously, as shown in FIG. 7C. Specifically, the kernel 40 has three squares in one column, and each square corresponds to one pixel. The pixel corresponding to the center square is set as the position of interest, and the pixels corresponding to the two squares adjacent to the center square are set as positions to be compared. More specifically, when the kernel 40 is set at the position shown in FIG. 6, the pixel value of the position of interest is higher than the pixel values ​​of the adjacent positions, and therefore the position of interest is extracted as the object.

[0054] In the above example, the kernel 40 extracts signal components representing an object in four directions: vertical, horizontal, diagonally upper right, and diagonally upper left, but the shape and size of the kernel are not limited to these. Furthermore, the extraction function 150c may extract a position of interest as an object even if the pixel value of the position of interest is equal to or greater than the pixel values ​​of pixels located in a predetermined direction and range.

[0055] Although the above example shows an example of extracting an object pixel by pixel, the object may be extracted continuously in groups each having multiple pixels. When extracting an object in groups each having multiple pixels, the average or integrated value of each pixel constituting the group may be used as the pixel value (signal value) of the group. In this case, the extraction function 150c extracts a signal component representing the object for each group each having multiple pixels based on the comparison result between the pixel values ​​of other groups located in a predetermined direction and range and the pixel values ​​of the group at the target position.

[0056] As another example, for each of a plurality of positions of interest included in the first ultrasound data 29, the extraction function 150c may compare the signal value (pixel value) of the position of interest with the signal value (pixel value) of a position within a predetermined range for each direction of interest relative to the position of interest, and weight the signal component of the position of interest based on the comparison result and a weighting coefficient predetermined for each direction of interest. For example, if the four directions of interest are vertical, horizontal, diagonally upper right, and diagonally lower left, a weighting coefficient is set in advance for each direction. Then, if the pixel value of the position of interest in the three directions of vertical, horizontal, and diagonally upper right is greater than or equal to the adjacent pixel value, the extraction function 150c weights the pixel value of the position of interest by the weighting coefficient predetermined for each of the three directions (multiplying the pixel value by the weighting coefficient). As a result, the pixel value of the position of interest is changed and emphasized based on the comparison result and the weighting coefficient predetermined for each direction.

[0057] Next, the output function 150d performs processing to output third ultrasound data 33 based on the ultrasound data 32 (extracted data) representing the object continuously extracted in step S500 (step S600). As an example, the output function 150d performs processing to output the third ultrasound data 33 by performing an addition process on the ultrasound data 32 extracted for each parameter x. As an example, the output function 150d outputs the third ultrasound data 33 by simply adding the ultrasound data 32 extracted for each parameter x. As another example, the processing circuit 150d generates the third ultrasound data 33 from the ultrasound data 32 while performing weighted addition depending on the value of the parameter x.

[0058] Note that the embodiment is not limited to this, and as another example, the output function 150d may generate composite data or corrected data as the third ultrasound data 33. As one example, the output function 150d may generate the third ultrasound data 33 by combining data obtained by adding together multiple pieces of second ultrasound data 31 for the parameter x or data obtained by adding together multiple pieces of first ultrasound data 30 for the time t with data obtained by adding together ultrasound data 32 for the parameter x. As another example, the output function 150d may correct the data obtained by adding together multiple pieces of second ultrasound data 31 for the parameter x or data obtained by adding together multiple pieces of first ultrasound data 30 for the time t based on the ultrasound data 32 to generate the third ultrasound data 33. Also, the embodiment is not limited to this, and as another example, the output function 150d may perform an inverse transformation of the transformation in step S400 on each piece of ultrasound data 32 extracted for each parameter x, and then perform an addition process on the inversely transformed data.

[0059] As described above, the medical image processing apparatus 100 according to the embodiment applies transformation to a plurality of first ultrasound data 30 to generate a plurality of second ultrasound data 31 characterized by a parameter x different from the time t, and performs signal extraction using this. As a result, for example, by separating a blood flow image into a plurality of feature-separated images, efficient signal extraction can be performed.

[0060] (Second embodiment) In the first embodiment, the case where the transform function 150b performs a Fourier transform in step S400 to generate a plurality of second ultrasound data based on a plurality of first ultrasound data 30 has been described. However, the embodiment is not limited to this. The transform performed in step S400 is not limited to a Fourier transform, and more generally, may be an expansion process using an orthogonal basis.

[0061] In the processing of steps S100 to S300, the second embodiment also performs the same processing as the first embodiment. In step S400, in the second embodiment, as in the first embodiment, the processing circuitry 150 applies a transformation to the plurality of first ultrasound data 30 using the transformation function 150b to generate a plurality of second ultrasound data 31 characterized by a parameter different from the time t. Here, in the second embodiment, in step S400, the processing circuitry 150 uses the transformation function 150b to expand the plurality of first ultrasound data 30 in an orthogonal basis to find expansion coefficients, thereby generating a plurality of second ultrasound data 31.

[0062] This allows, for example, a blood flow image to be separated into a plurality of feature separation images classified by the orthogonal basis set. As an example, when the orthogonal basis set is an orthogonal polynomial, the second ultrasound data when the parameter x different from time t is the lowest becomes a feature separation image in which the vibration component of the fundamental wave mode is extracted, and the second ultrasound data when the parameter x different from time t is high-order becomes a feature separation image in which high-order vibration components are extracted.

[0063] Here, if φ is an orthonormal basis set standardized by equation (3), the plurality of first ultrasound data 30 is defined as F(t) as a function of time t, the second ultrasound data 31 is defined as f(x) as a function of a parameter x different from time t, and N is the number of frames. F(t) is expanded by the following equation (4) using f(x).

[0064]

number

[0065]

number

[0066] In this case, f(x) can be calculated using F(t) as shown in the following equation (5).

[0067]

number

[0068] 8, the processing circuit 150 applies the process 21 of expanding the first ultrasound data 31a, 31b, 31c, ..., 31y, 31z using an orthogonal basis set using the transformation function 150b to calculate the second ultrasound data 32a, 32b, 32c, ..., 32y, 32z using Equation (5). Here, the first ultrasound data 31a, 31b, 31c, 31y, 31z are data corresponding to times t=0, t=1, t=2, t=N-1, t=N, respectively, and the second ultrasound data 32a, 32b, 32c, 32y, 32z are data corresponding to parameters x different from the time t=0, x=1, x=2, x=N-1, x=N, respectively.

[0069] Examples of orthonormal basis sets used herein include Legendre polynomials, Chebyshev polynomials, Laguerre polynomials, and Bessel functions. The Fourier transform described in the first embodiment is used when trigonometric functions are used as the orthonormal basis set. The domains of the integrands in these orthonormal basis sets vary depending on the function system. To properly expand the function in the orthonormal basis set, the transformation function 150b may appropriately transform the domain of the integrand by appropriately transforming variables before expanding the function in the orthonormal basis set. If the orthogonal basis set is not normalized, similar processing can be performed by appropriately normalizing it.

[0070] In step S500, the extraction function 150c of the processing circuit 150 performs processing to continuously extract signal components representing the object from the plurality of second ultrasound data 31 generated in step S400, for each parameter x different from the time t, using a kernel similar to that used in the first embodiment.

[0071] Next, in step S600, the output function 150d performs processing to output third ultrasound data 33 based on the ultrasound data 32 (extracted data) representing the object continuously extracted in step S500. As an example, the output function 150d performs processing to output the third ultrasound data 33 by performing an addition process on the ultrasound data 32 extracted for each parameter x. As an example, the output function 150d outputs the third ultrasound data 33 by simply adding the ultrasound data 32 extracted for each parameter x. As another example, the output function 150d generates the third ultrasound data 33 from the ultrasound data 32 while performing weighted addition depending on the value of the parameter x. Furthermore, the embodiment is not limited to this. As another example, the output function 150d may perform an inverse transformation of the transformation in step S400 on each of the ultrasound data 32 extracted for each parameter x, and then perform an addition process on the inversely transformed data.

[0072] As described above, the second embodiment has generally described a case where a plurality of second ultrasound data are generated based on a plurality of first ultrasound data 30. In the embodiment, various transformations other than the Fourier transform can be applied.

[0073] (Other embodiments) The embodiments are not limited to the above examples, and various modifications are possible without departing from the spirit of the invention.

[0074] In the above-described embodiment, the case where each of the plurality of first ultrasound data 30 is, for example, two-dimensional or three-dimensional data related to pixel values ​​has been described, but the embodiment is not limited thereto. As an example, the transformation function 150b may apply a Fourier transform not only in the time t direction but also in the spatial direction, and perform the processing of the first or second embodiment using the plurality of first ultrasound data whose frequencies are displayed in the spatial direction. More generally, the transformation function 150b may perform expansion in an orthonormal basis, such as Legendre polynomials, Bessel functions, spherical harmonics, or spherical Bessel functions, not only in the time t direction but also in the spatial direction, and perform the processing of the first or second embodiment using the plurality of first ultrasound data whose frequencies are displayed using expansion coefficients.

[0075] In the above embodiment, the extraction function 150c continuously extracts signal components representing an object from the first ultrasound data using, for example, the kernel 40 shown in FIG. 7 . However, the extraction function 150c may also extract signal components representing an object without using a kernel. For example, the extraction function 150c may input the second ultrasound data 31 to a trained model that continuously outputs signal components representing an object contained in the ultrasound data by inputting a single piece of ultrasound data having a higher S / N ratio than multiple time-sequential frame data obtained by executing an ultrasound scan, and acquire ultrasound data 32 representing the object continuously extracted from the trained model. This trained model is trained using a dataset that uses ultrasound data with an improved S / N ratio as input data and the signal components continuously representing the object in the ultrasound data as training data. The extraction function 150c inputs the second ultrasound data to the trained model and acquires the signal components continuously representing the object output from the trained model. This allows continuous extraction of signal components representing the object from the second ultrasound data 31 without using a kernel.

[0076] According to at least one of the embodiments described above, it is possible to obtain high-resolution ultrasound data with high response.

[0077] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0078] 150 Processing Circuit 150a Acquisition Function 150b conversion function 150c extraction function 150d output function

Claims

1. an acquisition unit that acquires a plurality of first ultrasound data obtained based on the results of an ultrasound scan of a subject; a transform unit that applies a transform to the plurality of first ultrasound data to generate a plurality of second ultrasound data characterized by a parameter different from time; an extracting unit that continuously extracts signal components representing an object from the plurality of second ultrasound data; an output unit that outputs third ultrasound data based on the continuously extracted signal components representing the object; Medical information processing equipment.

2. The medical image processing apparatus according to claim 1 , wherein the parameter is a frequency.

3. The medical image processing apparatus according to claim 1 , wherein the transformation is a Fourier transform.

4. The medical image processing apparatus according to claim 1 , wherein the transforming unit generates the plurality of second ultrasound data by expanding the plurality of first ultrasound data in an orthogonal basis to obtain expansion coefficients.

5. The medical image processing device according to claim 1 , wherein the object includes at least one of blood, body tissue, and a contrast agent.

6. an execution unit that causes the ultrasound probe to perform an ultrasound scan on the subject; an acquisition unit that acquires a plurality of first ultrasound data obtained based on the results of the ultrasound scan; a transform unit that applies a transform to the plurality of first ultrasound data to generate a plurality of second ultrasound data characterized by a parameter different from time; an extracting unit that continuously extracts signal components representing an object from the plurality of second ultrasound data; an output unit that outputs second ultrasound data based on the continuously extracted signal components that represent the object.

7. On the computer, acquiring a plurality of first ultrasound data obtained based on the results of an ultrasound scan of the subject; applying a transform to the plurality of first ultrasound data to generate a plurality of second ultrasound data characterized by a parameter different from time; continuously extracting signal components representing an object from the plurality of second ultrasound data; a program for executing a process of outputting second ultrasound data based on the continuously extracted signal components representing the object;

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