Blood flow visualization method, apparatus, digital scan converter, and ultrasound imaging system

Abstract

This application discloses a blood flow imaging method, apparatus, digital scan converter, and ultrasound imaging system. The method includes: acquiring real-time acquired ultrasound echo data; the ultrasound echo data includes blood flow signal data and noise signal data, wherein the average value of the blood flow signal data is higher than the average value of the noise signal data; performing gain amplification processing on the ultrasound echo data; performing numerical inversion processing on the ultrasound echo data so that the average value of the blood flow signal data is lower than the average value of the noise signal data; performing data compensation processing to shift the ultrasound echo data of each pixel to the positive domain after data compensation of the same amplitude; and mapping the values ​​of the compensated ultrasound echo data onto a chromatogram to generate a blood flow imaging map. This application expands the numerical difference between the noise signal and the blood flow signal, improves the anti-noise interference capability of the blood flow signal, and significantly improves the image quality of the blood flow imaging map.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging technology, and in particular to a blood flow imaging method, apparatus, digital scan converter, and ultrasound imaging system. Background Technology

[0002] Noise is an inherent signal of ultrasound equipment. Noise signals, which can exist across the entire frequency band, interfere with the imaging and display of true blood flow signals. To address this, blood flow imaging solutions typically suppress noise by using various algorithms to calculate the data characteristics of noise and blood flow signals, thereby minimizing noise and preserving the blood flow (energy) signal. However, filtering and suppression methods cannot completely eliminate noise, especially under low signal-to-noise ratio conditions. Furthermore, to better observe blood flow, particularly when the blood flow signal is weak, signal gain adjustment is usually necessary. However, in weak signal conditions, noise is far more sensitive to gain than the signal itself; as gain increases, noise becomes increasingly amplified, even exceeding the blood flow signal, rendering noise filtering and suppression methods meaningless. Therefore, providing a solution to the aforementioned technical problems is of great interest to those skilled in the art. Summary of the Invention

[0003] The purpose of this application is to provide a blood flow imaging method, apparatus, digital scan converter, and ultrasound imaging system to effectively reduce the interference of noise signals on blood flow signals and enhance the blood flow imaging effect.

[0004] To address the aforementioned technical problems, this application discloses a blood flow imaging method applied to an ultrasound imaging system, comprising:

[0005] Acquire real-time ultrasound echo data; the ultrasound echo data includes blood flow signal data and noise signal data, wherein the average value of the blood flow signal data is higher than the average value of the noise signal data;

[0006] The ultrasonic echo data is then amplified by gain.

[0007] The ultrasound echo data is inverted to make the average value of the blood flow signal data lower than the average value of the noise signal data.

[0008] By performing data compensation processing, the ultrasonic echo data of each pixel is shifted to the positive number domain after being compensated by the same amplitude.

[0009] The values ​​of the compensated ultrasound echo data are mapped onto the chromatogram to generate a blood flow imaging map.

[0010] Optionally, before performing gain amplification processing on the ultrasonic echo data, the method further includes:

[0011] The ultrasound echo data is subjected to wall filtering to remove tissue signal data from the ultrasound echo data.

[0012] Optionally, before performing numerical inversion on the ultrasonic echo data, the method further includes:

[0013] The ultrasonic echo data is subjected to frame correlation processing between consecutive frames.

[0014] Optionally, before the step of shifting the ultrasonic echo data of each pixel to the positive domain after data compensation of equal amplitude through data compensation processing, the method further includes:

[0015] Numerical transformation is performed on the ultrasound echo data of each pixel to amplify the numerical difference between blood flow signal data and noise signal data.

[0016] Optionally, the numerical transformation of the ultrasonic echo data of each pixel includes:

[0017] For any frame of ultrasound echo data:

[0018] The type of ultrasound echo data for each pixel in the current frame is identified using the short-time integration method. The types include blood flow signal data and noise signal data.

[0019] Calculate the first mean value of each blood flow signal data and the second mean value of each noise signal data;

[0020] An initial linear transformation relationship that meets the target value range requirement is determined based on the first and second numerical averages.

[0021] The initial linear transformation relationship is adjusted so that the absolute value of the transformation value of the first numerical mean increases after adjustment, and the absolute value of the transformation value of the second numerical mean decreases after adjustment.

[0022] The adjusted initial linear transformation relationship is used as the target linear transformation relationship to perform numerical transformation on the ultrasonic echo data of each pixel in the current frame.

[0023] Optionally, adjusting the initial linear transformation relationship so that the absolute value of the transformation value of the first numerical mean increases after adjustment and the absolute value of the transformation value of the second numerical mean decreases after adjustment includes:

[0024] Determine the initial transformation curve in the two-dimensional plane that corresponds to the initial linear transformation relationship;

[0025] The point on the initial transformation curve corresponding to the first numerical mean is determined as the blood flow mean point, and the point on the initial transformation curve corresponding to the second numerical mean is determined as the noise mean point;

[0026] Use a point on the initial transformation curve located between the blood flow mean point and the noise mean point as the rotation pivot point;

[0027] The initial transformation curve is rotated around the rotation pivot in a target rotation direction to obtain the target transformation curve; the target rotation direction is the direction that increases the absolute value of the transformation value of the blood flow mean point after rotation.

[0028] The corresponding linear transformation relationship of the target is determined based on the target transformation curve.

[0029] Optionally, the step of identifying the type of ultrasonic echo data for each pixel in the current frame using the short-time integration method includes:

[0030] For any single pixel:

[0031] Integral calculation is performed on the ultrasonic echo data of the pixel in multiple consecutive frames before and after the current frame;

[0032] Determine whether the slope of the integral curve changes linearly;

[0033] If so, the ultrasonic echo data of the pixel in the current frame will be identified as noise signal data;

[0034] If not, the ultrasound echo data of the pixel in the current frame will be identified as blood flow signal data.

[0035] In another aspect, this application also discloses a blood flow imaging device for use in an ultrasound imaging system, comprising:

[0036] The acquisition module is used to acquire real-time ultrasound echo data; the ultrasound echo data includes blood flow signal data and noise signal data, and the average value of the blood flow signal data is higher than the average value of the noise signal data.

[0037] The processing module is used to perform gain amplification processing on the ultrasound echo data; and to perform numerical inversion processing on the ultrasound echo data so that the average value of the blood flow signal data is lower than the average value of the noise signal data.

[0038] The compensation module is used to shift the ultrasonic echo data of each pixel to the positive domain after data compensation of the same amplitude through data compensation processing.

[0039] The mapping module is used to map the values ​​of the compensated ultrasound echo data onto the chromatogram to generate a blood flow imaging map.

[0040] Optionally, before performing gain amplification processing on the ultrasonic echo data, the processing module is further configured to:

[0041] The ultrasound echo data is subjected to wall filtering to remove tissue signal data from the ultrasound echo data.

[0042] Optionally, before performing numerical inversion processing on the ultrasonic echo data, the processing module is further configured to:

[0043] The ultrasonic echo data is subjected to frame correlation processing between consecutive frames.

[0044] Optionally, before the compensation module shifts the ultrasonic echo data of each pixel to the positive domain after data compensation of equal amplitude by the compensation module, the processing module is further configured to:

[0045] Numerical transformation is performed on the ultrasound echo data of each pixel to amplify the numerical difference between blood flow signal data and noise signal data.

[0046] Optionally, when performing numerical transformation on the ultrasonic echo data of each pixel, the processing module is specifically used for:

[0047] For any frame of ultrasound echo data:

[0048] The type of ultrasound echo data for each pixel in the current frame is identified using the short-time integration method. The types include blood flow signal data and noise signal data.

[0049] Calculate the first mean value of each blood flow signal data and the second mean value of each noise signal data;

[0050] An initial linear transformation relationship that meets the target value range requirement is determined based on the first and second numerical averages.

[0051] The initial linear transformation relationship is adjusted so that the absolute value of the transformation value of the first numerical mean increases after adjustment, and the absolute value of the transformation value of the second numerical mean decreases after adjustment.

[0052] The adjusted initial linear transformation relationship is used as the target linear transformation relationship to perform numerical transformation on the ultrasonic echo data of each pixel in the current frame.

[0053] Optionally, when the processing module adjusts the initial linear transformation relationship so that the absolute value of the transformation value of the first numerical mean increases after adjustment and the absolute value of the transformation value of the second numerical mean decreases after adjustment, it is specifically used for:

[0054] Determine the initial transformation curve in the two-dimensional plane that corresponds to the initial linear transformation relationship;

[0055] The point on the initial transformation curve corresponding to the first numerical mean is determined as the blood flow mean point, and the point on the initial transformation curve corresponding to the second numerical mean is determined as the noise mean point;

[0056] Use a point on the initial transformation curve located between the blood flow mean point and the noise mean point as the rotation pivot point;

[0057] The initial transformation curve is rotated around the rotation pivot in a target rotation direction to obtain the target transformation curve; the target rotation direction is the direction that increases the absolute value of the transformation value of the blood flow mean point after rotation.

[0058] The corresponding linear transformation relationship of the target is determined based on the target transformation curve.

[0059] Optionally, when the processing module identifies the type of ultrasonic echo data for each pixel in the current frame using the short-time integration method, it is specifically used for:

[0060] For any single pixel:

[0061] Integral calculation is performed on the ultrasonic echo data of the pixel in multiple consecutive frames before and after the current frame;

[0062] Determine whether the slope of the integral curve changes linearly;

[0063] If so, the ultrasonic echo data of the pixel in the current frame will be identified as noise signal data;

[0064] If not, the ultrasound echo data of the pixel in the current frame will be identified as blood flow signal data.

[0065] Furthermore, this application also discloses a digital scan converter, comprising:

[0066] Memory, used to store computer programs;

[0067] A processor for executing the computer program to implement the steps of any of the blood flow imaging methods described above.

[0068] In another aspect, this application also discloses an ultrasonic imaging system, including an ultrasonic transducer, a transmitting and receiving unit, a display recorder, and a digital scanning converter as described above;

[0069] The digital scanning converter is communicatively connected to the display recorder and the transmitting and receiving unit, respectively, and is used to receive the ultrasound echo data sent by the transmitting and receiving unit, and send the generated blood flow imaging image data to the display recorder for display.

[0070] The beneficial effects of the blood flow imaging method, apparatus, digital scan converter, and ultrasound imaging system provided in this application are as follows: Based on the numerical inversion and compensation processing of ultrasound echo data, this application can increase the value of noise signals and decrease the value of blood flow signals, thereby widening the numerical difference between noise and blood flow signals. Especially under low signal-to-noise ratio conditions, this application can effectively solve the problem of weak blood flow signals being easily confused by noise; at the same time, the enhancement of noise signals in this application also effectively solves the problem of noise flicker. Therefore, this application can effectively improve the anti-noise interference capability of blood flow signals, enhance the contrast effect between blood flow areas and non-blood flow areas in blood flow imaging images, and significantly improve image accuracy. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the prior art and the embodiments of this application, the accompanying drawings used in the description of the prior art and the embodiments of this application will be briefly introduced below. Of course, the accompanying drawings described below with respect to the embodiments of this application are only a part of the embodiments in this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort, and such other drawings also fall within the protection scope of this application.

[0072] Figure 1 This is a flowchart of a blood flow imaging method disclosed in an embodiment of this application;

[0073] Figure 2 This is a signal energy map of filtered ultrasonic echo data disclosed in an embodiment of this application;

[0074] Figure 3 This is a signal energy diagram of ultrasonic echo data after inversion, as disclosed in an embodiment of this application.

[0075] Figure 4 This application discloses a signal energy map of ultrasonic echo data after compensation operation in an embodiment of the present application.

[0076] Figure 5 This is a schematic diagram of an integral curve of ultrasonic echo data disclosed in an embodiment of this application;

[0077] Figure 6 The embodiments disclosed in this application Figure 5 A schematic diagram showing the slope change of the integral curve;

[0078] Figure 7This is a schematic diagram illustrating an adjustment of the linear transformation relationship of ultrasonic echo data disclosed in an embodiment of this application;

[0079] Figure 8 This is a structural block diagram of a blood flow imaging device disclosed in an embodiment of this application;

[0080] Figure 9 This is a structural block diagram of a digital scanning converter disclosed in an embodiment of this application;

[0081] Figure 10 This is a structural block diagram of an ultrasound imaging system disclosed in an embodiment of this application. Detailed Implementation

[0082] The core of this application is to provide a blood flow imaging method, apparatus, digital scan converter, and ultrasound imaging system, so as to effectively reduce the interference of noise signals on blood flow signals and enhance the blood flow imaging effect.

[0083] To provide a clearer and more complete description of the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0084] An ultrasound imaging system is a medical device that uses the echolocation of ultrasound waves to create images. It uses ultrasound waves to irradiate the human body, and by receiving and processing the echoes carrying information about the properties and characteristics of human tissues or structures, it obtains visible images of the tissues and structures. It is commonly used to determine the location, size, and shape of organs, identify the extent and physical properties of lesions, provide anatomical diagrams of glandular tissues, and differentiate between normal and abnormal fetuses. Therefore, ultrasound imaging is widely used in ophthalmology, obstetrics and gynecology, as well as in the cardiovascular, digestive, and urinary systems.

[0085] In ultrasound imaging, blood flow imaging helps physicians visually examine blood flow in target areas of the patient, aiding in the identification of tissue structure and size. Therefore, the accuracy and quality of blood flow imaging images are crucial. However, noise is an inherent signal of ultrasound equipment and can exist across the entire frequency band, interfering with the imaging of blood flow. When the noise is sufficiently high, the chromatographic brightness difference between blood flow and non-blood flow areas becomes smaller and harder to distinguish in the resulting images.

[0086] To address this, blood flow imaging techniques typically employ noise suppression methods. This involves using various algorithms to calculate the data characteristics of noise and blood flow signals, thereby minimizing noise and preserving the blood flow signal as much as possible. However, filtering and suppression methods cannot completely eliminate noise, especially under low signal-to-noise ratio conditions. Generally, to better observe blood flow, particularly when the blood flow signal is weak, gain adjustment is usually necessary. However, in weak signal conditions, noise is far more sensitive to gain than the signal itself. As the gain increases, the noise becomes increasingly larger, even exceeding the blood flow signal, rendering noise filtering and suppression methods meaningless. Therefore, this application provides a blood flow imaging scheme that effectively solves the above problems.

[0087] See Figure 1 As shown in the embodiments of this application, a blood flow imaging method is disclosed, applied to an ultrasound imaging system, mainly including:

[0088] S101: Acquire real-time ultrasound echo data; the ultrasound echo data includes blood flow signal data and noise signal data, with the average value of the blood flow signal data being higher than the average value of the noise signal data.

[0089] S102: Perform gain amplification processing on ultrasonic echo data.

[0090] Specifically, since the signal strength of blood flow signals is generally greater than that of noise signals, noise suppression is usually performed in conventional techniques. However, because the noise signal in ultrasound echo signals is distributed across the entire frequency band, it cannot be completely filtered out using frequency band filtering methods.

[0091] In reality, completely filtering out noise signals is unlikely to be achieved. Firstly, this is because the energy of blood flow signals has an upper limit, while noise signals do not. When the gain increases to a certain level, the energy of the amplified noise will be very close to that of the blood flow signal. Once the energy intensity of the noise signal and the blood flow signal is not significantly different, it becomes difficult to selectively filter out the noise signal using traditional filtering and suppression methods while preserving the blood flow signal.

[0092] On the other hand, noise fluctuates in energy as human tissues move. When noise is at its energy peak, it is difficult to distinguish blood flow from noise numerically. When noise is at its energy trough, its value is very small and almost disappears, with a strong flickering sensation, which increases the difficulty of targeted suppression.

[0093] Furthermore, some tissues in the human body have strong echo energy, and these tissue signals are also mixed in with the blood flow signals, becoming another kind of "noise signal." This "noise signal," whose energy intensity is not much different from that of the blood flow signal, is also difficult to filter out specifically.

[0094] Therefore, given that noise signals cannot be completely filtered out, the blood flow imaging method provided in this application adopts an alternative processing approach to reduce the interference of noise signals on blood flow signals. Specifically, this application no longer suppresses noise signals in ultrasound echo signals, but instead enhances and amplifies noise signals through a series of signal processing operations, making the fluctuations in noise signal values ​​less significant, while simultaneously reducing the value of blood flow signals. This widens the difference between noise signals and blood flow signals, thereby enhancing the blood flow imaging effect.

[0095] S103: Perform numerical inversion processing on the ultrasound echo data to make the average value of the blood flow signal data lower than the average value of the noise signal data.

[0096] Specifically, this application employs a method of numerical inversion combined with numerical compensation to enhance the noise signal and weaken the blood flow signal. Specifically, as... Figure 2 As shown, in the initially acquired ultrasound echo signal, the average energy level of the mixed noise signal is generally lower than that of the blood flow signal. This application uniformly performs gain amplification and numerical inversion processing on all ultrasound echo signal data. The signal energy diagram after numerical inversion is shown below. Figure 3 As shown, all data has been flipped to the negative domain. Thus, data that originally had higher values ​​(corresponding to blood flow signals) becomes data with lower values ​​after the inversion operation, while data that originally had lower values ​​(corresponding to noise signals) becomes data with higher values ​​after the inversion operation.

[0097] S104: Through data compensation processing, the ultrasonic echo data of each pixel is shifted to the positive domain after being compensated by the same amplitude.

[0098] Specifically, after the numerical inversion operation, this application will add a positive offset to all data for compensation, such as... Figure 4 As shown, this shifts all data upwards to the positive domain to facilitate proper blood flow mapping and imaging. The data obtained after the upward shift are all positive, with higher values ​​for noise signals and lower values ​​for blood flow signals.

[0099] Those skilled in the art can reasonably set the size of the positive offset according to the actual situation so that the values ​​of each data after compensation are all positive.

[0100] It's easy to understand that before the value inversion operation, the numerical value reflects the actual signal energy; that is, the higher the energy of the actual blood flow signal, the larger the corresponding signal value, which can be called a "positive reflection." However, the value obtained after the inversion and compensation operations "reflects" the energy of the actual blood flow signal in reverse; that is, the lower the value, the stronger the actual blood flow energy in the corresponding area.

[0101] S105: Map the values ​​of the compensated ultrasound echo data onto the chromatogram to generate a blood flow imaging map.

[0102] Specifically, in the blood flow imaging image generated by blood flow mapping, different regions will be displayed with different levels of brightness and darkness according to their respective numerical values. Generally, the higher the numerical value, the brighter the corresponding color, and the lower the numerical value, the darker the corresponding color. Thus, after blood flow mapping imaging of the values ​​processed in the above steps of this application, the blood flow display mode of the generated blood flow imaging image is that noise signals (higher numerical values) are displayed in bright colors, and blood flow signals (lower numerical values) are displayed in dark colors. In other words, in the blood flow imaging image obtained by the method protected in this application, the darker the color, the stronger the blood flow signal.

[0103] As can be seen, the blood flow imaging method provided in this application, based on the numerical inversion and compensation processing of ultrasound echo data, can increase the value of the noise signal and decrease the value of the blood flow signal, thus widening the numerical difference between the noise signal and the blood flow signal. Especially under low signal-to-noise ratio conditions, this application can effectively solve the problem of weak blood flow signals being easily confused by noise; at the same time, the enhancement of the noise signal in this application also effectively solves the problem of noise flicker. Therefore, this application can effectively improve the anti-noise interference capability of blood flow signals, enhance the contrast effect between blood flow areas and non-blood flow areas in blood flow imaging images, and significantly improve the accuracy of blood flow imaging images.

[0104] As a specific embodiment, the blood flow imaging method provided in this application, based on the above content, further includes the following step before performing gain amplification processing on the ultrasound echo data:

[0105] Wall filtering is performed on the ultrasound echo data to remove tissue signal data from the ultrasound echo data.

[0106] Specifically, the ultrasound echo signals acquired by ultrasound equipment typically include tissue signals, blood flow signals, and noise signals. If we denote the ultrasound echo signal as Signal_echo, then:

[0107] Signal_echo=Tissue+Blood+Noise.

[0108] Among these, tissue signals have low frequency but high energy; blood flow signals have a frequency related to blood flow velocity, but their energy is weaker than that of tissue signals; noise signals cover all frequency bands and have the lowest energy. During blood flow imaging, a series of blood flow algorithms are needed to remove tissue and noise signals while preserving the blood flow signal.

[0109] For tissue signals, wall filtering, or high-pass filtering, can be used. The signal obtained after wall filtering (denoted as Signal) is as follows:

[0110] Signal = Blood + Noise.

[0111] As a specific embodiment, the blood flow imaging method provided in this application, based on the above content, further includes the following step before performing numerical inversion processing on the ultrasound echo data:

[0112] Frame correlation processing is performed on multiple consecutive frames of ultrasonic echo data.

[0113] Specifically, frame correlation processing refers to the smoothing of corresponding pixel grayscale values ​​between image frames, which can further reduce noise. Since the noise superimposed on the image is uncorrelated and has a mean of zero, if the original image is represented by the average value of several frames under the same conditions, the image noise intensity can be reduced.

[0114] In ultrasound imaging systems, correlation processing is typically performed between the current frame and the previous frame. It's easy to understand that the more frames of image data involved in the correlation processing, the better the smoothing effect. Therefore, to further improve accuracy, this embodiment specifically performs correlation processing on multiple frames of data.

[0115] Specifically, the nth frame of ultrasound echo data is denoted as:

[0116] Signal(n)=Blood(n)+Noise(n);

[0117] Let gain be the factor by which the signal is amplified. Then, the amplified signal can be obtained as follows:

[0118] Signal(n)=gain×[Blood(n)+Noise(n)];

[0119] Assuming correlation processing is performed on consecutive (2f+1) frames of data, we can obtain:

[0120]

[0121] After inverting the value, we get:

[0122]

[0123] As a specific embodiment, the blood flow imaging method provided in this application, based on the above content, further includes, before shifting the ultrasound echo data of each pixel to the positive domain after data compensation of equal amplitude through data compensation processing:

[0124] Numerical transformation is performed on the ultrasound echo data of each pixel to amplify the numerical difference between blood flow signal data and noise signal data.

[0125] Specifically, in order to further amplify the numerical difference between the noise signal and the blood flow signal, this embodiment further performs numerical transformation. It is easy to understand that the numerical transformation occurs before the data compensation operation; therefore, the result of the transformation is to specifically reduce the absolute value of the noise signal and increase the absolute value of the blood flow signal.

[0126] As a specific embodiment, the blood flow imaging method provided in this application, based on the above content, performs numerical transformation on the ultrasound echo data of each pixel, including:

[0127] For any frame of ultrasound echo data:

[0128] The short-time integration method is used to identify the type of ultrasound echo data of each pixel in the current frame, including blood flow signal data and noise signal data.

[0129] Calculate the first mean value of each blood flow signal data and the second mean value of each noise signal data;

[0130] The initial linear transformation relationship that meets the requirements of the target numerical range is determined based on the first and second numerical mean values.

[0131] The initial linear transformation relationship is adjusted so that the absolute value of the transformation value of the first numerical mean increases after adjustment, and the absolute value of the transformation value of the second numerical mean decreases after adjustment;

[0132] The adjusted initial linear transformation relationship is used as the target linear transformation relationship to perform numerical transformation on the ultrasonic echo data of each pixel in the current frame.

[0133] Specifically, as will be readily understood by those skilled in the art, before mapping the echo data to different color levels in a chromatogram to generate a blood flow imaging map, all data needs to be adjusted to a reasonable numerical range through linear transformation. To this end, this embodiment calculates the first mean value of the blood flow signal data and the second mean value of the noise signal data in each pixel of the current frame, and determines an initial linear transformation relationship based on the first and second mean values. Through this initial linear transformation relationship, all data can be transformed to the target numerical value range. The target numerical value range can be specifically determined based on factors such as the processor's data processing bit width.

[0134] It should be noted that this embodiment further adjusts the initial linear transformation relationship. The adjusted linear transformation relationship, i.e., the target linear transformation relationship, can further amplify the numerical difference between blood flow signal data and noise signal data. It is easy to understand that, since the absolute value of the blood flow signal data is higher than the absolute value of the noise signal data before the data compensation operation, amplifying the numerical difference between the blood flow signal and noise signal specifically means that, compared to the initial linear transformation relationship, the target linear transformation relationship can increase the absolute value of the transformed blood flow signal data while decreasing the absolute value of the transformed noise signal data.

[0135] As a specific embodiment, the blood flow imaging method provided in this application, based on the above content, adjusts the initial linear transformation relationship so that the absolute value of the transformation value of the first numerical mean increases after adjustment and the absolute value of the transformation value of the second numerical mean decreases after adjustment, including:

[0136] Determine the initial transformation curve in the two-dimensional plane that corresponds to the initial linear transformation relationship;

[0137] The point on the initial transformation curve corresponding to the first numerical mean is determined as the blood flow mean point, and the point on the initial transformation curve corresponding to the second numerical mean is determined as the noise mean point.

[0138] Use a point on the initial transformation curve located between the blood flow mean point and the noise mean point as the rotation pivot point;

[0139] The initial transformation curve is rotated around the pivot point in the target rotation direction to obtain the target transformation curve; the target rotation direction is the direction that makes the absolute value of the transformation value of the blood flow mean point increase after rotation.

[0140] The corresponding linear transformation relationship of the target is determined based on the target transformation curve.

[0141] See Figure 7 , Figure 7 This is a schematic diagram illustrating an adjustment of the linear transformation relationship of ultrasonic echo data disclosed in an embodiment of this application. Figure 7 As shown, the horizontal axis represents the values ​​before transformation, and the vertical axis represents the values ​​after transformation. Curve 1 is the initial transformation curve, point Blood is the mean blood flow point, and point Noise is the mean noise point. Choosing a point between these two points as the pivot, curve 1 is rotated counterclockwise to obtain curve 2, which serves as the target transformation curve.

[0142] It should be noted that since points corresponding to blood flow signal data are likely to be distributed around the point "Blood," and points corresponding to noise signal data are likely to be distributed around the point "Noise," the midpoint between these two points is likely to be close to the boundary between the noise signal data and the blood flow signal data. Therefore, as a specific embodiment, such as... Figure 7 As shown, the midpoint between the mean blood flow point and the mean noise point can be used as the pivot point when rotating the initial transformation curve.

[0143] As a specific embodiment, the blood flow imaging method provided in this application, based on the above content, uses a short-time integration method to identify the type of ultrasound echo data of each pixel in the current frame, including:

[0144] For any single pixel:

[0145] Integral calculation is performed on the ultrasonic echo data of a pixel in multiple consecutive frames before and after the current frame;

[0146] Determine whether the slope of the integral curve changes linearly;

[0147] If so, the ultrasonic echo data of the pixel in the current frame will be identified as noise signal data;

[0148] If not, the ultrasound echo data of the pixel in the current frame will be identified as blood flow signal data.

[0149] The specific formula for calculating the integral curve is as follows:

[0150]

[0151] The formula for calculating the slope of the integral curve is as follows:

[0152] slope(k)=curve(k)-curve(k-1); k=1,2,…,n-1.

[0153] Specifically, because the noise signal has a small amplitude and stable energy, the slope of the integral curve of the multi-frame noise signal changes continuously and linearly. See [link to relevant documentation] for details. Figure 5 The integral curve shown and Figure 6The curve shown illustrates the slope change of the integral curve. The slope of the blood flow signal integral curve, however, exhibits a non-linear change due to the instability of blood flow velocity. Therefore, this embodiment can specifically identify whether the data at each pixel location in the current frame is noise data or blood flow data based on the slope change of the integral curve.

[0154] See Figure 8 As shown in the figure, this application discloses a blood flow imaging device 200, which mainly includes:

[0155] The acquisition module 201 is used to acquire real-time ultrasound echo data; the ultrasound echo data includes blood flow signal data and noise signal data, and the average value of the blood flow signal data is higher than the average value of the noise signal data.

[0156] Processing module 202 is used to perform gain amplification processing on ultrasound echo data; and to perform numerical inversion processing on ultrasound echo data so that the average value of blood flow signal data is lower than the average value of noise signal data.

[0157] The compensation module 203 is used to shift the ultrasonic echo data of each pixel to the positive domain after data compensation of the same amplitude through data compensation processing.

[0158] The mapping module 204 is used to map the values ​​of the compensated ultrasound echo data onto the chromatogram to generate a blood flow imaging map.

[0159] As can be seen, the blood flow imaging device disclosed in this application, based on the numerical inversion and compensation processing of ultrasound echo data, can increase the value of the noise signal and decrease the value of the blood flow signal, thus widening the numerical difference between the noise signal and the blood flow signal. Especially under low signal-to-noise ratio conditions, this application can effectively solve the problem of weak blood flow signals being easily confused by noise; at the same time, the enhancement of the noise signal in this application also effectively solves the problem of noise flicker. Therefore, this application can effectively improve the anti-noise interference capability of blood flow signals, enhance the contrast effect between blood flow areas and non-blood flow areas in blood flow imaging images, and significantly improve the accuracy of the images.

[0160] For details regarding the aforementioned blood flow imaging device, please refer to the aforementioned detailed introduction to blood flow imaging methods; further details will not be repeated here.

[0161] As a specific embodiment, the blood flow imaging device disclosed in this application, based on the above content, further includes, before the processing module 202 performs gain amplification processing on the ultrasound echo data, the following:

[0162] Wall filtering is performed on the ultrasound echo data to remove tissue signal data from the ultrasound echo data.

[0163] As a specific embodiment, the blood flow imaging device disclosed in this application, based on the above content, further includes, before performing numerical inversion processing on the ultrasound echo data, the processing module 202 being used to:

[0164] Frame correlation processing is performed on multiple consecutive frames of ultrasonic echo data.

[0165] As a specific embodiment, the blood flow imaging device disclosed in this application, based on the above content, further includes the following in the processing module 202: before the compensation module shifts the ultrasound echo data of each pixel to the positive domain after data compensation of equal amplitude through data compensation processing:

[0166] Numerical transformation is performed on the ultrasound echo data of each pixel to amplify the numerical difference between blood flow signal data and noise signal data.

[0167] As a specific embodiment, the blood flow imaging device disclosed in this application, based on the above content, has its processing module 202 specifically used for: When performing numerical transformation on the ultrasound echo data of each pixel, it is further configured to:

[0168] For any frame of ultrasound echo data:

[0169] The short-time integration method is used to identify the type of ultrasound echo data of each pixel in the current frame, including blood flow signal data and noise signal data.

[0170] Calculate the first mean value of each blood flow signal data and the second mean value of each noise signal data;

[0171] The initial linear transformation relationship that meets the requirements of the target numerical range is determined based on the first and second numerical mean values.

[0172] The initial linear transformation relationship is adjusted so that the absolute value of the transformation value of the first numerical mean increases after adjustment, and the absolute value of the transformation value of the second numerical mean decreases after adjustment;

[0173] The adjusted initial linear transformation relationship is used as the target linear transformation relationship to perform numerical transformation on the ultrasonic echo data of each pixel in the current frame.

[0174] As a specific embodiment, the blood flow imaging device disclosed in this application, based on the above content, has a processing module 202 that, when adjusting the initial linear transformation relationship so that the absolute value of the transformation value of the first numerical mean increases after adjustment and the absolute value of the transformation value of the second numerical mean decreases after adjustment, specifically used for:

[0175] Determine the initial transformation curve in the two-dimensional plane that corresponds to the initial linear transformation relationship;

[0176] The point on the initial transformation curve corresponding to the first numerical mean is determined as the blood flow mean point, and the point on the initial transformation curve corresponding to the second numerical mean is determined as the noise mean point.

[0177] Use a point on the initial transformation curve located between the blood flow mean point and the noise mean point as the rotation pivot point;

[0178] The initial transformation curve is rotated around the pivot point in the target rotation direction to obtain the target transformation curve; the target rotation direction is the direction that makes the absolute value of the transformation value of the blood flow mean point increase after rotation.

[0179] The corresponding linear transformation relationship of the target is determined based on the target transformation curve.

[0180] As a specific embodiment, the blood flow imaging device disclosed in this application, based on the above content, has the following specific uses for the processing module 202 when identifying the type of ultrasound echo data of each pixel in the current frame using the short-time integration method:

[0181] For any single pixel:

[0182] Integral calculation is performed on the ultrasonic echo data of a pixel in multiple consecutive frames before and after the current frame;

[0183] Determine whether the slope of the integral curve changes linearly;

[0184] If so, the ultrasonic echo data of the pixel in the current frame will be identified as noise signal data;

[0185] If not, the ultrasound echo data of the pixel in the current frame will be identified as blood flow signal data.

[0186] See Figure 9 As shown in the figure, this application discloses a digital scan converter 300, including:

[0187] Memory 301 is used to store computer programs;

[0188] Processor 302 is configured to execute the computer program to implement the steps of any of the blood flow imaging methods described above.

[0189] See Figure 10 As shown in the figure, this application discloses an ultrasonic imaging system 400, characterized in that it includes an ultrasonic transducer 401, a transmitting and receiving unit 402, a display recorder 403, and a digital scanning converter 300 as described above.

[0190] The digital scanning converter 300 is communicatively connected to the display recorder 403 and the transmitting and receiving unit 402, respectively, and is used to receive the ultrasound echo data sent by the transmitting and receiving unit 402, and send the generated blood flow imaging image data to the display recorder 403 for display.

[0191] The digital scan converter is essentially a digital computer system with an image memory, capable of displaying clear dynamic images using standard television methods and providing powerful image processing capabilities. The digital scan converter in the ultrasound imaging system provided in this application can perform blood flow imaging methods as described in any of the above embodiments on the received ultrasound echo data, which can then be further imaged and displayed by a display recorder.

[0192] For details regarding the ultrasound imaging system and digital scan converter mentioned above, please refer to the aforementioned detailed introduction to blood flow imaging methods; they will not be repeated here.

[0193] The various embodiments in this application are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0194] It should also be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0195] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this application. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method of blood flow visualization, characterized by, Applied to an ultrasonic imaging system, comprising: acquiring real-time collected ultrasonic echo data; the ultrasonic echo data comprises blood flow signal data and noise signal data, and the numerical average level of the blood flow signal data is higher than that of the noise signal data; performing gain amplification processing on the ultrasonic echo data; performing numerical inversion processing on the ultrasonic echo data, so that the numerical average level of the blood flow signal data is lower than that of the noise signal data; through data compensation processing, the ultrasonic echo data of each pixel point is compensated by the same amplitude and then moved to the positive number field; mapping the numerical value of the compensated ultrasonic echo data in the chromatogram to generate a blood flow imaging graph; wherein, before the gain amplification processing on the ultrasonic echo data, it further comprises: performing wall filtering processing on the ultrasonic echo data to filter out tissue signal data in the ultrasonic echo data; wherein, before the numerical inversion processing on the ultrasonic echo data, it further comprises: performing frame correlation processing between consecutive multiple frames of the ultrasonic echo data; wherein, before the data compensation processing, it further comprises: performing numerical transformation on the ultrasonic echo data of each pixel point, so that the absolute value of the noise signal data is smaller and the absolute value of the blood flow signal data is larger, so as to expand the numerical difference between the blood flow signal data and the noise signal data.

2. The method of claim 1, wherein, The numerical transformation on the ultrasonic echo data of each pixel point comprises: for any one frame of ultrasonic echo data: using short-time integration method to identify the type of ultrasonic echo data of each pixel point in the current frame, the type including blood flow signal data and noise signal data; calculating the first numerical average of each blood flow signal data and the second numerical average of each noise signal data; determining the initial linear transformation relationship meeting the target numerical value range requirement according to the first numerical average and the second numerical average; adjusting the initial linear transformation relationship so that the absolute value of the transformed value of the first numerical average increases after adjustment and the absolute value of the transformed value of the second numerical average decreases after adjustment; using the adjusted initial linear transformation relationship as the target linear transformation relationship to perform numerical transformation on the ultrasonic echo data of each pixel point in the current frame.

3. The method of claim 2, wherein, The adjustment of the initial linear transformation relationship so that the absolute value of the transformed value of the first numerical average increases after adjustment and the absolute value of the transformed value of the second numerical average decreases after adjustment comprises: determining an initial transformation curve corresponding to the initial linear transformation relationship in a two-dimensional plane; determining a blood flow average point on the initial transformation curve corresponding to the first numerical average and a noise average point on the initial transformation curve corresponding to the second numerical average; determining a point between the blood flow average point and the noise average point on the initial transformation curve as a rotation fulcrum point; rotating the initial transformation curve around the rotation fulcrum point in a target rotation direction to obtain a target transformation curve; the target rotation direction is a direction in which the absolute value of the transformed value of the blood flow average point increases after rotation. Determine the corresponding target linear transformation relationship based on the target transformation curve.

4. The method of claim 2, wherein, The type of the ultrasonic echo data of each pixel point in the current frame is identified by using a short-time integral method, and the type of the ultrasonic echo data of each pixel point in the current frame is identified by using a short-time integral method, and the type of the ultrasonic echo data of each pixel point in the current frame is identified by using a short-time integral method. For any one pixel point: Integrating and calculating the ultrasonic echo data of the pixel point in the continuous multiple frames before and after the current frame; Determine whether the slope of the integral curve changes linearly; If yes, the ultrasonic echo data of the pixel point in the current frame is identified as noise signal data; If not, the ultrasonic echo data of the pixel point in the current frame is identified as blood flow signal data.

5. A blood flow visualization device, characterized by, Applied to an ultrasonic imaging system, comprising: An acquisition module is configured to acquire real-time collected ultrasonic echo data; the ultrasonic echo data includes blood flow signal data and noise signal data, and the numerical average level of the blood flow signal data is higher than that of the noise signal data; A processing module is configured to perform gain amplification processing on the ultrasonic echo data and numerical inversion processing on the ultrasonic echo data, so that the numerical average level of the blood flow signal data is lower than that of the noise signal data; A compensation module is configured to move the ultrasonic echo data of each pixel point to the positive number domain by the same amplitude of data compensation after data compensation processing; A mapping module is configured to map the numerical value of the compensated ultrasonic echo data in a chromatogram to generate a blood flow visualization graph; The processing module is further configured to perform wall filtering processing on the ultrasonic echo data before performing gain amplification processing on the ultrasonic echo data, so as to filter out tissue signal data in the ultrasonic echo data; The processing module is further configured to perform frame correlation processing between the continuous multiple frames of the ultrasonic echo data before performing numerical inversion processing on the ultrasonic echo data; The processing module is further configured to perform numerical transformation on the ultrasonic echo data of each pixel point before the compensation module moves the ultrasonic echo data of each pixel point to the positive number domain by the same amplitude of data compensation after data compensation processing, so that the absolute value of the noise signal data is smaller and the absolute value of the blood flow signal data is larger, so as to expand the numerical difference between the blood flow signal data and the noise signal data.

6. A digital scan converter characterized by Comprising: A memory is configured to store a computer program; A processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1 to 4.

7. An ultrasound imaging system, characterized by The digital scan converter, the display recorder, the transmitting and receiving unit, and the ultrasonic transducer are connected in communication, the digital scan converter receives the ultrasonic echo data sent by the transmitting and receiving unit, generates image data of the blood flow visualization graph and sends the image data to the display recorder for display. The digital scan converter, the display recorder, the transmitting and receiving unit, and the ultrasonic transducer are connected in communication, the digital scan converter receives the ultrasonic echo data sent by the transmitting and receiving unit, generates image data of the blood flow visualization graph and sends the image data to the display recorder for display.

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