Ultrasonic image processing apparatus

Abstract

To prevent or reduce the blurring of the edge of a blood flow in the display of flow velocity data.SOLUTION: The processor removes noise from the power data 110 extracted from the color flow data 100 (block 14), and obtains a gradient at each pixel as the blood flow form information 114 from the power data 112 after noise removal (block 16). Further, the processor performs noise removal on the flow velocity data 120 extracted from the color flow data 100 using the blood flow form information 114 (block 20). In this denoising, the processor uses, for example, a denoising filter that considers, for each pixel, only a pixel group along a direction perpendicular to the gradient at the pixel.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an ultrasound imaging device, and more particularly to removing or reducing noise in color Doppler images. [Background technology]

[0002] In the color flow mode of an ultrasound diagnostic device, echo signals from inside the subject detected by an ultrasound probe are subjected to Doppler signal processing, which generates color flow data, which is then displayed in various modes, such as flow velocity display and power display.

[0003] In flow velocity displays, for example, flow approaching the probe is colored red and flow away from the probe is colored blue, and the magnitude of the flow velocity is expressed by changes in hue, saturation, or brightness. The degree of flow velocity turbulence (i.e., dispersion) is sometimes displayed using the green component. Flow velocity displays are also called color Doppler displays or color flow mapping. In power displays, the strength (i.e., amplitude) of the Doppler signal is expressed by changes in hue or brightness.

[0004] Color flow data contains noise such as electrical noise, residual body motion signals, black spots in blood flow, jagged edges of blood flow, etc. These noises must be removed or reduced to generate high-quality display images.

[0005] The power data used for power display is color flow data with no positive or negative polarity. Therefore, noise removal algorithms similar to those used for black-and-white B-mode tomographic images, which also have no polarity, can be used to remove noise from power data. While advanced noise removal algorithms using nonlinear filters, multi-resolution filters, etc. are used for B-mode tomographic images, similar advanced noise removal algorithms can also be used for power display.

[0006] In contrast, because the flow velocity data used for flow velocity display has both positive and negative polarity, advanced noise reduction algorithms using nonlinear filters, multi-resolution filters, etc. cannot be used as is. While it is possible to modify these advanced noise reduction algorithms to take polarity into account, this would impose a very large computational load. For this reason, simple smoothing processes using averaging filters, Gaussian filters, etc. have traditionally been used to remove noise from flow velocity displays. However, such simple smoothing processes result in blurred edges of blood flow in the flow velocity display.

[0007] Patent Document 1 discloses a conventional technique for noise removal in an ultrasound diagnostic device. This conventional device addresses the problem that when intracardiac noise removal is performed on an ultrasound image of the heart, signals from the myocardium are also removed, blurring the boundary between the cardiac chamber and the myocardium, while enhancing signals from the myocardium also enhances noise within the cardiac chamber. This device includes a signal enhancement processing unit that performs signal enhancement processing on medical image data, a noise removal unit that performs noise removal processing on the medical image data, a first signal compression processing unit that compresses the medical image data that has been subjected to signal enhancement processing and noise removal processing, a second signal compression processing unit that compresses the medical image data, and a synthesis processing unit that synthesizes the medical image data compressed by the first signal compression processing unit and the medical image data compressed by the second signal compression processing unit.

[0008] In addition, the ultrasound diagnostic device disclosed in Patent Document 2 includes a transmitting means for transmitting an ultrasound beam into a living body, a receiving means for receiving a reflected echo signal from the living body, a blood flow signal processing means for performing Doppler signal processing on the reflected echo signal to generate color flow data consisting of an average flow velocity, a power value, and a velocity variance, an averaging means for performing averaging on at least the power value of the color flow data, a comparing means for comparing the color flow data averaged by the averaging means with a set threshold value, and a display means for displaying the color flow data based on the comparison result of the comparing means. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Re-tabled publication 2014 / 069374 [Patent Document 2] Japanese Patent Application Publication No. 7-308318 Summary of the Invention [Problem to be solved by the invention]

[0010] The present invention aims to prevent or reduce blurring of the edges of blood flow in the display of flow velocity data. [Means for solving the problem]

[0011] In one aspect, an ultrasound image processing device according to the present disclosure includes a processor, which acquires a flow velocity value and a power value of each pixel in color flow data, calculates morphological information indicating the shape of the blood flow based on the power value of each pixel, and performs noise removal on the flow velocity value of each pixel based on the morphological information.

[0012] In one aspect, the morphological information is the gradient of the power value at each pixel, and the noise removal for the flow velocity value includes a calculation for each pixel to determine the flow velocity value of the pixel after noise removal from the flow velocity values ​​of the pixel and its surrounding pixels, and in the calculation, when the direction perpendicular to the gradient at the pixel is called the first direction, a greater weight is assigned to the flow velocity values ​​of the surrounding pixels that are on the first direction side of the pixel than to the flow velocity values ​​of pixels that are not.

[0013] In another aspect, the morphological information is the contour of the blood flow, and the noise removal for the flow velocity value is a process of calculating, for each pixel, the flow velocity value of the pixel after noise removal from the flow velocity values ​​of the pixel and pixels surrounding the pixel, and in the calculation, when the direction in which the contour extends in the vicinity of the pixel is called the second direction, a greater weight is assigned to the flow velocity value of the surrounding pixels that are on the second direction side of the pixel in question than to the flow velocity value of pixels that are not.

[0014] In yet another aspect, the processor may perform noise reduction on the power values ​​of the acquired pixels, and the morphological information may be calculated on the power values ​​after noise reduction.

[0015] In another aspect, a program according to the present disclosure may cause a computer to perform the following process: acquire a flow velocity value and a power value of each pixel in color flow data; determine morphological information indicating the shape of the blood flow based on the power value of each pixel; and perform noise removal on the flow velocity value of each pixel based on the morphological information. [Effects of the Invention]

[0016] According to the present invention, blurring of the edges of blood flow can be prevented or reduced when displaying flow velocity data. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 2 illustrates an example of a hardware configuration of a computer that executes the processing of the embodiment. [Figure 2] FIG. 10 is a diagram showing an example of a processing flow for displaying flow velocity (i.e., color Doppler) in an embodiment. [Figure 3] FIG. 10 is a diagram showing an example of a procedure for removing noise from flow velocity data. [Figure 4] FIG. 10 is a diagram illustrating an example of a kernel of a noise removal filter stored in the ultrasound image processing device. [Figure 5]FIG. 10 is a diagram showing another example of a procedure for removing noise from flow velocity data. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. An ultrasound image processing device in this embodiment generates an ultrasound diagnostic image to be displayed by an ultrasound diagnostic device. One example of the ultrasound diagnostic image generated by the ultrasound image processing device is a color Doppler image for displaying flow velocity.

[0019] The processing for image generation performed by the ultrasound image processing device of this embodiment is executed by, for example, a computer. In one example, this computer is built into an ultrasound diagnostic device. In this example, the ultrasound image processing device of this embodiment is the ultrasound diagnostic device itself or an image processing system built into the ultrasound diagnostic device.

[0020] In another example, the image generation process in this embodiment may be performed by a computer external to the ultrasound diagnostic device. In this example, the external computer is connected to the ultrasound diagnostic device via a communication path such as a data communication network. In this example, the external computer may be a single computer, or may be composed of multiple computers that cooperate to perform the process by communicating via the data communication network.

[0021] In another example, the processing may be performed by a computer built into the ultrasound diagnostic device in cooperation with an external computer connected to the ultrasound diagnostic device.

[0022] 1 shows an example of the hardware configuration of a computer built into or connected to an ultrasound diagnostic apparatus. The illustrated computer has a circuit configuration in which a processor 1002, a memory (main storage device) 1004 such as a random access memory (RAM), a controller for controlling an auxiliary storage device 1006 which is a nonvolatile storage device such as a flash memory, an SSD (solid state drive), or an HDD (hard disk drive), interfaces with various input / output devices 1008, a network interface 1010 for controlling connection to a network such as a local area network, and the like are connected via a data transmission path such as a bus 1012. For example, a program describing the processing of this embodiment is installed in the computer and stored in the auxiliary storage device 1006. The program stored in the auxiliary storage device 1006 is executed by the processor 1002 using the memory 1004, thereby realizing the ultrasound image processing device of this embodiment.

[0023] 2 shows an example of the flow of processing executed by the processor 1002 of the ultrasound image processing apparatus according to this embodiment. This flow will be described below.

[0024] The Doppler processor 10 generates color flow data 100 by performing known Doppler signal processing on echo signals detected by an ultrasound probe in an ultrasound diagnostic device. The color flow data 100 includes information such as flow velocity, power value (i.e., Doppler signal amplitude), and flow velocity variance at each position within the subject. The "position" here corresponds to a pixel in an ultrasound image representing the subject. When the color flow data 100 is composed of information for each pixel in an ultrasound image, the color flow data 100 includes, for example, information such as the flow velocity, power value, and variance of the pixel for each pixel. The Doppler processor 10 may also use an MTI (Moving Target Indicator) filter or the like to remove low-velocity signals from surrounding tissues and the like to obtain data on blood flow components.

[0025] The processor 1002 of the ultrasound imaging device extracts (block 12) power data 110 indicating the power value at each position (i.e., each pixel) from the color flow data 100. The extracted power data 110 includes information about the power value at each position.

[0026] The processor 1002 then performs noise removal on the power data 110 (block 14). Because the power values ​​at each position in the power data 110 are non-negative, any of a variety of techniques used for removing noise from B-mode tomographic images may be used for this noise removal. This noise removal results in noise-removed power data 112.

[0027] The processor 1002 generates blood flow morphology information 114 indicating the morphology of the blood flow from the noise-removed power data 112 (block 16). The blood flow morphology here refers to the shape of the blood flow flowing inside the blood vessel. For example, the shape of the edge of the blood flow (i.e., the boundary between the blood flow and the surrounding tissue) is a representative example. Another example of blood flow morphology information is the gradient at each position of the power data 112.

[0028] In parallel with the extraction of the power data 110 (block 12), the processor 1002 also extracts flow velocity data 120 indicating the flow velocity value at each position (i.e., each pixel) from the color flow data 100 (block 18). The extracted flow velocity data 120 includes information on the flow velocity at each position. Note that if the signals of the surrounding tissue outside the blood flow have been removed by the MTI filter, the flow velocity at each position in the surrounding tissue will be approximately zero.

[0029] Next, the processor 1002 performs a noise removal calculation on the flow velocity data 120 (block 20). This noise removal calculation is performed with reference to the blood flow morphology information 114. The process performed in noise removal (block 20) ​​will be described in detail later with an example. This noise removal results in noise-removed flow velocity data 122.

[0030] The processor 1002 generates an image for displaying the flow velocity (i.e., a color Doppler image) using the flow velocity value at each position (i.e., each pixel) in the noise-removed flow velocity data 122, and displays the image on a display device not shown (block 22).

[0031] According to the process shown in FIG. 2, noise is removed from the flow velocity data 120 by referring to the blood flow morphology information 114, thereby preventing the flow velocity values ​​at positions near the edge of the blood flow from being blurred due to the influence of the velocity values ​​of the surrounding tissue.

[0032] Next, an example of the processing contents of noise removal (block 20) ​​for flow velocity data in the processing flow shown in FIG. 2 will be described with reference to FIG.

[0033] In this example, in block 16 of the processing flow in Fig. 2, the gradient value of each pixel is calculated as blood flow morphology information for the power data 112, which is a scalar field. In the processing procedure in Fig. 3, the gradient of each pixel is referenced.

[0034] The processor 1002 performs the procedure of FIG. 3 for each pixel included in the flow velocity data 120, treating that pixel as a target pixel.

[0035] In this procedure, the processor 1002 first obtains the gradient value of the target pixel (block 202) from the blood flow morphology information obtained in block 16. Next, the processor 1002 calculates the direction perpendicular to the gradient obtained in block 202 (block 204).

[0036] Next, the processor 1002 selects a noise reduction filter corresponding to the direction calculated in block 204 (block 206). That is, the ultrasound image processing device has a plurality of noise reduction filters corresponding to different directions, and the processor 1002 selects from among the plurality of filters the filter with the direction closest to the direction calculated in block 204.

[0037] FIG. 4 shows an example of a kernel of a noise removal filter stored in an ultrasound image processing device. In this example, the ultrasound image processing device stores kernels for six directions with angles varying in 30-degree increments, such as -75 degrees, -45 degrees, -15 degrees, +15 degrees, +45 degrees, and +75 degrees. Each cell constituting the illustrated kernel corresponds to a pixel in the ultrasound image, and the central cell corresponds to the target pixel that is the target of the noise removal calculation. In addition, in the illustrated kernel, the value of each cell is a binary value of 0 or 1. In this example, a pixel corresponding to a cell with a value of 1 is a valid pixel, i.e., a pixel that is referenced in the noise removal calculation. Furthermore, a pixel with a value of 0 is an invalid pixel that is not referenced in the calculation. For example, when a -45-degree kernel is used, in the filter processing, the filter output is calculated by referring to a group of pixels on a diagonal line sloping upward at 45 degrees to the right, centered on the target pixel.

[0038] For example, when a median filter is used as a noise removal filter, the median value of the pixel values ​​of multiple valid pixels indicated by the kernel (i.e., flow velocity values ​​in this example) is obtained as the filter output. When an averaging filter is used, the average value of the pixel values ​​of the valid pixels is obtained as the filter output. In this example, the values ​​of invalid pixels are not reflected in the filter output.

[0039] In the example of Figure 4, all the values ​​of the cells corresponding to valid pixels in the kernel are 1, but this is just one example. As another example of a kernel, a different value may be used for each cell corresponding to a valid pixel. By making the value different for each cell corresponding to a valid pixel, a filter that performs noise removal by weighted averaging can be configured. For example, a Gaussian filter kernel can be configured by maximizing the value of the cell corresponding to the target pixel and decreasing the value of cells farther away from that cell according to a Gaussian distribution.

[0040] Furthermore, in the above examples, a median filter, a simple averaging filter, and a weighted averaging filter are exemplified, but other types of filters may also be used as noise removal filters.

[0041] In the above example, the value of the cells corresponding to invalid pixels is set to 0, but this is merely an example. Alternatively, the value of the cells corresponding to invalid pixels may be set to a positive value that is significantly smaller than the value of the cells corresponding to valid pixels. Even in this case, the value of the filter output is determined almost entirely by the value of the valid pixels, and the influence of the invalid pixels is negligible or minimal.

[0042] In block 206, a filter having a direction closest to the direction calculated in block 204 is selected from among the plurality of prepared filters (i.e., kernels).

[0043] The processor 1002 then applies the selected filter to the target pixel to determine a filter output value corresponding to the target pixel (block 208). That is, the processor 1002, for example, aligns the selected filter with the target pixel in the image indicated by the flow velocity data. The processor 1002 then performs a filter operation on the image using the values ​​of each cell indicated by the filter to calculate the filter output value. The content of the filter operation depends on the type of filter used as the noise removal filter (for example, a median filter or an averaging filter).

[0044] In this manner, in this embodiment, noise removal for the flow velocity value of the target pixel is performed along a direction perpendicular to the gradient of the power value at the target pixel.

[0045] This noise removal involves a calculation (e.g., the filter calculation described above) to determine the noise-removed flow velocity value of the target pixel from the flow velocity values ​​of the target pixel and its surrounding pixels. Here, the surrounding pixels of the target pixel refer to pixels within a predetermined range (e.g., distance in pixel units) from the target pixel. For example, when the kernel described above is aligned with the target pixel, the pixels located at positions corresponding to each cell in the kernel are examples of the "surrounding pixels" described above. Furthermore, in the calculation to determine the noise-removed flow velocity value, a greater weight is assigned to the flow velocity values ​​of the "surrounding pixels" located on the first direction side of the target pixel than to the flow velocity values ​​of other pixels. Here, the first direction refers to the direction perpendicular to the gradient at the target pixel. The pixels located on the first direction side of the target pixel are not limited to pixels located strictly in the first direction relative to the target pixel, but may also be pixels located within a predetermined range (e.g., a predetermined distance or a predetermined angle) from a position along the first direction. The weight assigned to pixels not on the first direction side may be 0 as in the kernel illustrated in FIG. 4, or may be a small positive value close to 0.

[0046] The above processing procedure performs noise removal by referencing the pixel group along the shape of the blood flow, i.e., the direction perpendicular to the gradient of the power value in this example, thereby suppressing or reducing blurring of the edges of the blood flow, which occurs in conventional simple filtering processes.

[0047] Furthermore, in the above processing procedure, blood flow morphology information is obtained based on power data after noise removal, so that the influence of noise contained in the power data can be reduced, and smooth blood flow morphology information can be obtained.

[0048] Next, another example of the process of calculating blood flow morphology information (block 16) and removing noise (block 20) ​​will be described with reference to FIG.

[0049] In this example, the blood flow contour is obtained as blood flow morphology information. It is well known that the blood flow contour is clearly visible in a power Doppler display that displays power data. This contour information is used as blood flow morphology information.

[0050] 5, the processor 1002 obtains a blood flow contour as blood flow morphology information from the power data 112 after noise removal (block 162). In the calculation of block 162, for example, the contour may be detected by applying an edge detection filter to the image represented by the power data 112. Alternatively, the power data 112 may be binarized to separate it into a blood flow portion and other portions, and then the contour may be detected by applying an edge detection filter.

[0051] Next, the processor 1002 performs noise removal on the flow velocity data 120 using the information on the blood flow contour obtained in block 162 (block 20). In block 20, the processor 1002 performs the steps of blocks 212 to 216 in FIG. 5 for each pixel included in the flow velocity data 120, with the pixel being a target pixel.

[0052] First, in block 212, the processor 1002 estimates the direction of blood flow at a target pixel from the contour portion of the blood flow contour obtained in block 162 that is close to the target pixel. In this estimation, the processor 1002, for example, finds the point on the blood flow contour that is closest to the target pixel and finds the direction of the tangent to the contour at that point. The processor 1002 then estimates the direction of the tangent as the direction of blood flow at the target pixel. As another example, the processor 1002 may estimate the direction of the contour within a predetermined range (e.g., several pixels) near the point of the blood flow contour that is closest to the target pixel as the direction of blood flow.

[0053] Next, the processor 1002 selects a noise reduction filter (i.e., a kernel) from among the noise reduction filters (i.e., kernels) stored in the ultrasound image processing device that corresponds to the direction estimated in block 212, for example, a kernel that corresponds to the direction closest to the direction estimated in block 212 (block 214). The processor 1002 then applies the selected filter to the target pixel to obtain a filter output value corresponding to the target pixel (block 216). The processing of blocks 214 and 216 may be similar to the processing of blocks 206 and 208 in the procedure of FIG. 3.

[0054] In this way, in the example of FIG. 5, noise removal for the flow velocity value of the target pixel is performed along the direction in which the portion of the blood flow contour near the target pixel extends.

[0055] This noise removal involves calculating the noise-removed flow velocity value of the target pixel from the flow velocity values ​​of the target pixel and its surrounding pixels. Here, the surrounding pixels of the target pixel refer to pixels within a predetermined range from the target pixel. For example, when the kernel illustrated above is aligned with the target pixel, the pixels located at positions corresponding to each cell in the kernel are examples of the "surrounding pixels" described above. Furthermore, in the calculation to calculate the noise-removed flow velocity value, a greater weight is assigned to the flow velocity values ​​of the "surrounding pixels" located on the second direction side of the target pixel than to the flow velocity values ​​of other pixels. Here, the second direction refers to the direction in which the portion of the blood flow contour near the target pixel extends. The pixels located on the second direction side of the target pixel do not necessarily have to be located strictly in the second direction relative to the target pixel, but may also be pixels within a predetermined range (e.g., a predetermined distance or a predetermined angle) from a position along the second direction. The weight assigned to pixels not on the second direction side may be 0, as in the kernel illustrated in FIG. 4, or a small positive value close to 0.

[0056] Although the embodiments of the present disclosure have been described above, they are merely illustrative examples for explaining the present disclosure. For example, in the above embodiments, the gradient of power data and the contour of blood flow are used as blood flow morphology information, but this is merely an example. As another example, a contour of power data may be calculated using a known contour calculation algorithm, and information on this contour may be used as blood flow morphology information. In this case, the processor 1002 performs noise removal by applying a kernel corresponding to the extension direction of the contour passing through the target pixel (e.g., the tangent direction of the contour at the target pixel) to the target pixel.

[0057] In this embodiment, each process is executed by an arbitrary computer. Furthermore, the arbitrary computer may execute these processes by a processor as hardware, a program as software, or a combination thereof. In this case, the processor may function as each unit or each means that executes the various processes in this embodiment. Furthermore, the order in which the processes are executed by the processor is not limited to the order described above and may be changed as appropriate. The arbitrary computer may be a general-purpose computer, a computer for specific applications, a workstation, or any other system capable of executing each process.

[0058] A processor may be configured with one or more pieces of hardware, and the type of hardware is not limited. For example, a processor may be configured with hardware such as a programmable logic device such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or an FPGA (Field Programmable Gate Array), a dedicated circuit for executing specific processes such as an ASIC (Application Specific Integrated Circuit), a GPU (Graphic Processing Unit), or an NPU (Neural Processing Unit). The type of hardware may also be a combination of different types of hardware. When multiple pieces of hardware are configured to execute one or more processes of a certain processor, the multiple pieces of hardware may exist in devices physically separated from each other or in the same device. In any embodiment, the order of each process performed by the processor is not limited to the order described above and may be changed as appropriate. The hardware may be configured with an electric circuit or the like that combines circuit elements such as semiconductor elements.

[0059] Furthermore, the program may be software, such as firmware or microcode. The program may also be, for example, a group of program modules, each function of which may be implemented by a processor configured to perform the respective function. The program may be program code or multiple code segments stored in one or more non-transitory computer-readable media (e.g., storage media or other storages). The program may be stored across multiple non-transitory computer-readable media that reside in physically separate devices. The program code or code segment may represent a procedure, a function, a subprogram, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. The program code or code segment may be connected to another code segment or a hardware circuit by sending or receiving information, data, arguments, parameters, or memory contents.

[0060] Furthermore, the hardware that constitutes the processor may include a digital signal processing circuit and an analog signal processing circuit. [Explanation of symbols]

[0061] 10 Doppler processing section, 12 power data extraction, 14 noise removal (for power data), 16 blood flow morphology information generation, 18 flow velocity data extraction, 20 noise removal, 22 flow velocity display.

Claims

1. a processor; The processor: Obtain the flow velocity value and power value of each pixel in the color flow data, determining morphological information indicating the morphology of the blood flow based on the power value of each pixel; performing noise removal on the flow velocity value of each pixel based on the morphological information; 1. An ultrasonic image processing device comprising:

2. the morphological information is a gradient of the power value at each pixel; The noise removal for the flow velocity value includes a calculation for determining, for each pixel, the flow velocity value of the pixel after noise removal from the flow velocity values ​​of the pixel and pixels surrounding the pixel, and in the calculation, when a direction perpendicular to the gradient at the pixel is referred to as a first direction, a larger weight is assigned to the flow velocity value of a pixel of the surrounding pixels that is on the first direction side of the pixel than to the flow velocity value of a pixel that is not on the first direction side.

2. The ultrasonic image processing apparatus according to claim 1.

3. the morphological information is a contour of the blood flow; The noise removal for the flow velocity value is a process of calculating, for each pixel, a flow velocity value after noise removal for the pixel from the flow velocity values ​​of the pixel and pixels surrounding the pixel, and in the calculation, when the direction in which the contour extends in the vicinity of the pixel is called a second direction, a larger weight is assigned to the flow velocity value of a pixel of the surrounding pixels that is on the second direction side of the pixel than to the flow velocity value of a pixel that is not on the second direction side.

2. The ultrasonic image processing apparatus according to claim 1.

4. the processor performs noise reduction on the power value of each of the obtained pixels; The morphological information is calculated on the power values ​​after noise removal.

4. The ultrasonic image processing apparatus according to claim 1, wherein the ultrasonic image processing apparatus is a multi-function apparatus.

5. Obtain the flow velocity value and power value of each pixel in the color flow data, determining morphological information indicating the morphology of the blood flow based on the power value of each pixel; performing noise removal on the flow velocity value of each pixel based on the morphological information; A program that causes a computer to execute a process.

Images