Ultrasonic imaging device and ultrasonic imaging method

Abstract

An ultrasonic imaging device and an ultrasonic imaging method, the device comprising a transmitting and receiving circuit, an ultrasonic probe, a processor, and a display, wherein the transmitting and receiving circuit controls the ultrasonic probe to transmit a first ultrasonic wave to a portion of a target object where nerve block is to be performed in a grayscale imaging mode; the processor generates an ultrasonic image of the portion where nerve block is to be performed based on the first ultrasonic echo data, identifies a neurovascular region in the ultrasonic image, and obtains the position of a sampling frame including the neurovascular region; the transmitting and receiving circuit also controls the ultrasonic probe to transmit a second ultrasonic wave to the position of the sampling frame in a Doppler imaging mode; the processor also obtains blood flow information based on the second ultrasonic echo data, obtains a vascular region in the ultrasonic image based on the blood flow information, obtains a nerve region in the ultrasonic image based on the neurovascular region and the vascular region, and controls the display to display the ultrasonic image, wherein the nerve region and the vascular region are highlighted so as to be distinguishable from each other on the ultrasonic image.

Description

Technical Field

[0001] The present application relates to the technical field of ultrasonic imaging, and more specifically to an ultrasonic imaging device and an ultrasonic imaging method. Background Art

[0002] Traditional nerve blocks often rely on surface landmarks, arterial pulsation, foreign body sensation during acupuncture, or the use of nerve stimulators to locate nerves, resulting in low block success rates and poor safety. In recent years, ultrasound-guided nerve blocks have been widely used and rapidly developed. Clinicians can observe nerves and surrounding structures through ultrasound imaging and, under real-time, dynamic ultrasound guidance, puncture the target nerve for precise nerve blockade. This effectively minimizes damage to surrounding vital structures, reduces the risk of complications, and improves block efficiency.

[0003] However, most nerve regions have numerous blood vessels distributed inside or around them (e.g., the axillary brachial plexus), and these blood vessels appear anechoic and often accompanied by artifacts under ultrasound imaging. Their characteristics are similar to those of nerves under ultrasound imaging, and veins are even more difficult to identify. As a result, both novice and experienced physicians often mistake blood vessels for nerves, leading to accidental puncture of blood vessels and causing additional damage to patients.

[0004] To address this issue, most current solutions involve enabling color Doppler imaging in questionable areas, observing the presence of blood flow to determine whether the area is a blood vessel. However, during the scan, the surgeon or assistant must enable color Doppler and adjust the Doppler sampling frame position and parameters, which increases the surgical workflow and affects the smoothness of the procedure. Furthermore, enabling color Doppler can cause a momentary drop in the image frame rate, creating a sensation of image lag and slowdown for the surgeon, impacting the surgeon's operating experience. Summary of the Invention

[0005] The present application is made in order to solve the above-mentioned problems. According to one aspect of the present application, an ultrasound imaging device is provided, comprising a transmitting and receiving circuit, an ultrasound probe, a processor, and a display, wherein: the transmitting and receiving circuit is configured to control the ultrasound probe to transmit a first ultrasound wave toward a portion of a target object where nerve block is to be performed, receive an echo of the first ultrasound wave, and obtain first ultrasound echo data from the echo of the first ultrasound wave in a grayscale imaging mode; the processor is configured to generate an ultrasound image of the portion of the target object where nerve block is to be performed based on the first ultrasound echo data, identify a neurovascular region in the ultrasound image, and obtain a position of a sampling frame including the neurovascular region; the transmitting and receiving circuit is further configured to control the ultrasound probe to transmit a second ultrasound wave toward the location of the sampling frame, receive an echo of the second ultrasound wave, and obtain second ultrasound echo data from the echo of the second ultrasound wave in a Doppler imaging mode; the processor is further configured to obtain blood flow information based on the second ultrasound echo data, obtain a blood vessel region in the ultrasound image based on the blood flow information, obtain a nerve region in the ultrasound image based on the neurovascular region and the blood vessel region, and control the display to display the ultrasound image, wherein the nerve region and the blood vessel region are highlighted so as to be distinguishable from each other on the ultrasound image.

[0006] According to another aspect of the present application, an ultrasound imaging device is provided, comprising a transmitting and receiving circuit, an ultrasound probe, a processor, and a display, wherein: the transmitting and receiving circuit is configured to control the ultrasound probe to transmit a first ultrasound wave toward a portion of a target object where a nerve block is to be performed, receive an echo of the first ultrasound wave, and obtain first ultrasound echo data from the echo of the first ultrasound wave in a grayscale imaging mode; the processor is configured to generate an ultrasound image of the portion where a nerve block is to be performed based on the first ultrasound echo data; the transmitting and receiving circuit is further configured to control the ultrasound probe to transmit a second ultrasound wave toward the portion where a nerve block is to be performed, receive an echo of the second ultrasound wave, and obtain second ultrasound echo data from the echo of the second ultrasound wave in a Doppler imaging mode; the processor is further configured to obtain a nerve region and a blood vessel region in the ultrasound image based on deep learning, machine learning, or an image processing algorithm based on the first ultrasound echo data and the second ultrasound echo data, and control the display to display the ultrasound image, wherein the nerve region and the blood vessel region are distinguishably highlighted on the ultrasound image, and the highlighted result is used to guide puncture of the portion where a nerve block is to be performed.

[0007] According to another aspect of the present application, an ultrasound imaging method is provided, the method comprising: controlling an ultrasound probe in a grayscale imaging mode to transmit a first ultrasound wave toward a portion of a target object where nerve block is to be performed, receiving an echo of the first ultrasound wave, and acquiring first ultrasound echo data from the echo of the first ultrasound wave; generating an ultrasound image of the portion where nerve block is to be performed based on the first ultrasound echo data, identifying a neurovascular region in the ultrasound image, and acquiring a position of a sampling frame including the neurovascular region; controlling the ultrasound probe in a Doppler imaging mode to transmit a second ultrasound wave toward the position of the sampling frame, receiving an echo of the second ultrasound wave, and acquiring second ultrasound echo data from the echo of the second ultrasound wave; acquiring blood flow information based on the second ultrasound echo data, acquiring a vascular region in the ultrasound image based on the blood flow information, and acquiring a nerve region in the ultrasound image based on the neurovascular region and the vascular region; and displaying the ultrasound image, highlighting the nerve region and the vascular region on the ultrasound image so as to be distinguishable from each other.

[0008] According to another aspect of the present application, an ultrasound imaging method is provided, the method comprising: controlling an ultrasound probe to transmit a first ultrasound wave toward a portion of a target object where nerve block is to be performed in a grayscale imaging mode, receiving an echo of the first ultrasound wave, and acquiring first ultrasound echo data from the echo of the first ultrasound wave; generating an ultrasound image of the portion where nerve block is to be performed based on the first ultrasound echo data; controlling the ultrasound probe to transmit a second ultrasound wave toward the portion where nerve block is to be performed in a Doppler imaging mode, receiving an echo of the second ultrasound wave, and acquiring second ultrasound echo data from the echo of the second ultrasound wave; acquiring a nerve region and a blood vessel region in the ultrasound image based on deep learning, machine learning, or an image processing algorithm based on the first ultrasound echo data and the second ultrasound echo data; and displaying the ultrasound image, highlighting the nerve region and the blood vessel region on the ultrasound image so as to be distinguishable from each other, the highlighted result being used to guide puncture of the portion where nerve block is to be performed.

[0009] According to the embodiments of the present application, the ultrasound imaging device and ultrasound imaging method can automatically distinguish between nerve areas and blood vessel areas in ultrasound images, and use different highlighting methods for nerve areas and blood vessel areas respectively, which can give doctors different prompts to intuitively distinguish between nerves and blood vessels, thereby reducing the risk of accidental puncture of blood vessels during surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 Schematic diagram showing blood vessels and nerves under ultrasound imaging.

[0012] Figure 2 A schematic structural block diagram of an ultrasonic imaging device according to an embodiment of the present application is shown.

[0013] Figure 3 An exemplary schematic diagram illustrating a neurovascular region in an ultrasound image of a site to be subjected to nerve blockade, generated by an ultrasound imaging apparatus according to an embodiment of the present application.

[0014] Figure 4 A schematic diagram illustrating an example of a sampling frame including a neurovascular region acquired by an ultrasound imaging apparatus according to an embodiment of the present application.

[0015] Figure 5 A schematic diagram illustrating another example of a sampling frame including a neurovascular region acquired by an ultrasound imaging apparatus according to an embodiment of the present application.

[0016] Figure 6 An exemplary schematic diagram showing an ultrasonic imaging device according to an embodiment of the present application acquiring a neurovascular region, a vascular region, and a neural network.

[0017] Figure 7 A schematic diagram illustrating an example in which an ultrasonic imaging apparatus according to an embodiment of the present application highlights and distinguishes a nerve region and a blood vessel region from each other on an ultrasonic image.

[0018] Figure 8 A schematic diagram illustrating another example of highlighting a nerve region and a blood vessel region on an ultrasound image to distinguish them from each other by an ultrasound imaging apparatus according to an embodiment of the present application.

[0019] Figure 9 A schematic diagram illustrating yet another example of highlighting a nerve region and a blood vessel region on an ultrasound image to distinguish them from each other, using an ultrasound imaging apparatus according to an embodiment of the present application.

[0020] Figure 10 A schematic structural block diagram of an ultrasonic imaging device according to another embodiment of the present application is shown.

[0021] Figure 11 A schematic flowchart of an ultrasound imaging method according to an embodiment of the present application is shown.

[0022] Figure 12 A schematic flowchart of an ultrasound imaging method according to another embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present application more apparent, the following is a detailed description of example embodiments of the present application with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this application.

[0024] Most neural territories are densely populated with blood vessels (e.g., the axillary brachial plexus), which appear anechoic and often accompanied by artifacts on ultrasound imaging. Figure 1 Figure 2 shows a schematic diagram of blood vessels and nerves under ultrasound imaging. Figure 1 As shown, under ultrasound imaging, the characteristics of blood vessels and nerves appear close to each other, making it difficult to distinguish between the two. As a result, both novice and experienced doctors often mistake blood vessels for nerves, resulting in accidental puncture of blood vessels and causing additional damage to patients.

[0025] To address this issue, most current solutions involve enabling color Doppler imaging in questionable areas, observing the presence of blood flow to determine whether the area is a blood vessel. However, during the scan, the operator or assistant must enable color Doppler and adjust the Doppler sampling frame position and parameters, which increases the surgical workflow and affects the smoothness of the procedure. Furthermore, enabling color Doppler can cause a momentary drop in the image frame rate, creating a sensation of image lag and slowness for the surgeon, impacting the operator's experience.

[0026] Based on this, this application proposes an ultrasonic imaging solution. Figures 2 to 12 To describe.

[0027] Figure 2 FIG. 2 shows a schematic structural block diagram of an ultrasonic imaging device 200 according to an embodiment of the present application. Figure 2As shown, the ultrasonic imaging device 200 includes a transmitting and receiving circuit 220, an ultrasonic probe 210, a processor 230 and a display 240, wherein: the transmitting and receiving circuit 220 is used to control the ultrasonic probe 210 to transmit a first ultrasonic wave to the part of the target object to be subjected to nerve block in a grayscale imaging mode, receive the echo of the first ultrasonic wave, and obtain first ultrasonic echo data from the echo of the first ultrasonic wave; the processor 230 is used to generate an ultrasonic image of the part to be subjected to nerve block based on the first ultrasonic echo data, identify the neurovascular region in the ultrasonic image, and obtain a sample including the neurovascular region. The transmitting and receiving circuit 220 is further used to control the ultrasound probe 210 to transmit a second ultrasound wave to the location of the sampling frame in the Doppler imaging mode, receive the echo of the second ultrasound wave, and obtain second ultrasound echo data from the echo of the second ultrasound wave; the processor 230 is further used to obtain blood flow information based on the second ultrasound echo data, obtain a vascular area in the ultrasound image based on the blood flow information, obtain a nerve area in the ultrasound image based on the neurovascular area and the vascular area, and control the display 240 to display the ultrasound image, wherein the nerve area and the vascular area are highlighted so as to be distinguishable from each other on the ultrasound image.

[0028] In the embodiment of the present application, the ultrasound imaging device 200 first generates a grayscale image of the site to be subjected to nerve blockade, and identifies the neurovascular region in the grayscale image, such as Figure 3 shown. Figure 3 FIG2 shows an exemplary schematic diagram of a neurovascular region in an ultrasound image of a site to be subjected to nerve block generated by the ultrasound imaging device 200 according to an embodiment of the present application. Figure 3 As shown in FIG, the neurovascular region is a region including the neural region and the vascular region. Since it is difficult to distinguish the neural region from the vascular region in grayscale images, the region including both regions is identified first. Then, based on the neural network region, a sampling frame including the neural network region is placed (manually or automatically) (later combined with Figure 4 and Figure 5 For example, the sampling frame is used for Doppler imaging to obtain blood flow information. Based on the blood flow information, the vascular region in the grayscale image can be obtained. Then, based on the previously obtained neurovascular region and vascular region, the neural region can be obtained, thereby distinguishing the neural region from the vascular region (later combined with Figure 6 In order to enable the user (doctor) to clearly distinguish the neural area and the blood vessel area, a grayscale image is finally displayed on the display 240, and the neural network and the blood vessel area are highlighted on the grayscale image so that they can be distinguished from each other (later combined with Figures 7 to 9 to exemplarily describe), thereby enabling users to intuitively distinguish nerves and blood vessels.

[0029] Therefore, in general, the ultrasound imaging device 200 according to the embodiment of the present application utilizes the difference between nerves and blood flow under the Doppler effect to automatically distinguish nerves and blood vessels in the neurovascular area, and uses different highlighting methods for each of them, which can give doctors different prompts to intuitively distinguish nerves and blood vessels, thereby reducing the risk of accidental puncture of blood vessels during surgery.

[0030] In an embodiment of the present application, after generating an ultrasound image of the site where a nerve block is to be performed, processor 230 can automatically identify the neurovascular region in the ultrasound image. This can be achieved by performing segmentation, edge detection, or target tracking on the ultrasound image. This can be achieved through deep learning or other machine learning methods, traditional image processing, and other methods. Several exemplary methods are described below.

[0031] In one example, processor 230 can use a deep learning-based image segmentation method to automatically identify neurovascular regions in ultrasound images. Specifically, the constructed database can be trained by stacking convolutional and deconvolutional layers to learn features and anatomical structure boundaries. For the input ultrasound image, the network can directly generate an image output of the same size, representing the specific boundary range of the key anatomical structure (neurovascular region). Common networks include: FCN, Unet, SegNet, DeepLab, Mask RCNN, etc.

[0032] In another example, the processor 230 can use other machine learning-based image segmentation methods to automatically identify neurovascular regions in ultrasound images. Specifically, the image can be pre-segmented using methods such as threshold segmentation, Snake, level set, GraphCut, ASM, and AAM to obtain a set of candidate anatomical structure (neurovascular region) boundary ranges in the ultrasound image; then, feature extraction is performed on each candidate boundary range. The feature extraction method can be traditional features such as PCA, LDA, HOG, Harr, and LBP, or features extracted by a neural network; then, the extracted features are matched with the features extracted from the marked boundary ranges in the database, and classified using a discriminator such as KNN, SVM, random forest, or neural network to determine whether the current candidate boundary range contains key anatomical structures and obtain its corresponding category.

[0033] In another example, processor 230 can automatically identify neurovascular regions in ultrasound images based on traditional image processing edge detection methods. Specifically, the ultrasound image can be smoothed and de-noised using a smoothing filter such as a mean filter, a Gaussian filter, or a bilateral filter. Edges can then be extracted from the de-noised image using edge detection operators such as Roberts, Sobel, Kirsch, Canny, and Laplacian.

[0034] In another example, the processor 230 can automatically identify the neurovascular region in the ultrasound image based on a target tracking method. Specifically, the neurovascular region in the first frame of the ultrasound image can be obtained using the methods described in the above examples or other methods. Then, a discriminative model method (such as Struck, TLD, DCF, etc.) or a deep learning network (such as SiamFC++, SiamMask, etc.) is used to extract features of the target region in the current frame. Then, the target region in the next frame is extracted using the constructed database model to generate the specific boundary range of the key anatomical structure (neurovascular region).

[0035] In an embodiment of the present application, after identifying the neurovascular region in the ultrasound image, the processor 230 may obtain the position of the sampling frame including the neurovascular region. For example, the processor 230 may automatically generate a sampling frame including the neurovascular region to obtain the position of the sampling frame; or, the processor 230 may receive user input to obtain the position of the sampling frame including the neurovascular region. The automatic generation of the sampling frame including the neurovascular region by the processor 230 may include: generating a sampling frame with a fixed size and position for each frame of ultrasound image and capable of at least including the neurovascular region; or generating a sampling frame including the neurovascular region for each frame of ultrasound image, and the position of the sampling frame moves as the position of the neurovascular region moves. The following is combined with Figure 4 and Figure 5 To describe.

[0036] Figure 4 FIG. 2 is a schematic diagram showing an example of a sampling frame including a neurovascular region acquired by the ultrasonic imaging device 200 according to an embodiment of the present application. Figure 4 As shown, the white box is the Doppler sampling box of fixed size and position. In this example, a sampling box of fixed size and position is generated for each ultrasound image frame, which at least includes the neurovascular region. The sampling box may also include other vascular regions in addition to the neurovascular region. This method of generating the sampling box is simple and easy to implement.

[0037] Figure 5 FIG. 2 is a schematic diagram showing another example of a sampling frame including a neurovascular region acquired by the ultrasonic imaging device 200 according to an embodiment of the present application. Figure 5As shown in the figure, the small black box surrounding the neurovascular region and moving with the position of the neurovascular region (in different frames) is the Doppler sampling frame. In this example, a sampling frame including the neurovascular region is generated for each frame of the ultrasound image. The position of the sampling frame moves with the position of the neurovascular region. This method of generating the sampling frame is more accurate. The sampling frame generally only includes the neurovascular region and excludes other vascular regions, which can reduce the computational complexity of subsequent blood flow information acquisition.

[0038] exist Figure 4 and Figure 5 In the examples shown, the sampling frame is shown as a rectangle, which is only exemplary. In other examples, the sampling frame can also be other shapes, such as a parallelogram, a fan, etc., and different shapes can be adaptively adopted according to different probes.

[0039] In an embodiment of the present application, after obtaining the sampling frame, Doppler imaging (such as color Doppler imaging (C mode), pulsed Doppler imaging (PW mode), continuous Doppler imaging (CW mode), power Doppler imaging (POWER mode), etc.) can be performed on the location of the sampling frame to obtain blood flow information, and the vascular area in the grayscale image can be obtained based on the blood flow information. In general, the vascular area is obtained by using the Doppler effect. Among them, the Doppler effect is: the frequency shift caused by the movement of the wave source or receiver relative to the medium. The function of Doppler ultrasound is to obtain the velocity v of the tissue or blood flow, and its calculation formula is:

[0040]

[0041] Among them, f o is the transmission frequency, which depends on the transmission parameters of the ultrasonic imaging device 200; d is the frequency difference, extracted by the ultrasonic imaging device 200 according to the received signal, and f o c is the speed of sound, which is a fixed value; θ is the angle between the speed of sound and the direction of target movement.

[0042] Depending on the user's needs, the user can adjust the Doppler blood flow imaging parameters to achieve the desired effect. Adjustable Doppler imaging parameters may include, for example, the Doppler sampling frame (region of interest) position, Doppler pulse repetition frequency (PRF), blood flow velocity range (Scale), Doppler signal gain (Gain), ultrasound beam emission direction, and wall filter parameters.

[0043] In an embodiment of the present application, blood flow information within the sampling frame can be obtained through Doppler parameters, and the frame rate can be maintained at a high level to avoid the user from feeling image freeze, thereby improving the user operation experience.

[0044] After obtaining the blood flow information within the sampling frame, the blood vessel area within the neurovascular area may be obtained, and then the nerve area within the neurovascular area may be obtained based on the neurovascular area and the blood vessel area.

[0045] In one embodiment of the present application, when the sampling frame also includes other vascular regions in the ultrasound image in addition to the neurovascular region, the processor 230 obtains the vascular region in the ultrasound image based on the blood flow information, and obtains the nerve region in the ultrasound image based on the neurovascular region and the vascular region. This may include: obtaining all vascular regions in the sampling frame based on the blood flow information; determining the vascular region within the neurovascular region based on all vascular regions; and obtaining the nerve region within the neurovascular region based on the vascular region within the neurovascular region (for example, the nerve region is obtained by subtracting the vascular region from the neurovascular region). This embodiment is generally applicable to Figure 4 The sampling frame shown.

[0046] In another embodiment of the present application, when the sampling frame only includes the neurovascular region, the processor 230 obtains the vascular region in the ultrasound image based on the blood flow information, and obtains the nerve region in the ultrasound image based on the neurovascular region and the vascular region, which may include: obtaining the vascular region within the neurovascular region in the ultrasound image based on the blood flow information; obtaining the nerve region within the neurovascular region based on the vascular region within the neurovascular region (for example, the nerve region is obtained by subtracting the vascular region from the neurovascular region). This embodiment is generally applicable to Figure 5 The sampling frame shown.

[0047] Figure 6 FIG. 2 shows an exemplary schematic diagram of an ultrasonic imaging device 200 according to an embodiment of the present application acquiring a neurovascular region, a vascular region, and a neural network. Figure 6 As shown, Figure 6 The area selected by the white dotted line in the left figure is the neurovascular area. Figure 6 The area selected by the white dotted line in the middle figure is the vascular area in the neurovascular area. Figure 6 The area between the two white dotted lines in the right figure is the nerve area in the neurovascular area.

[0048] After obtaining the blood vessel region and the nerve region in the neurovascular region, an ultrasound image may be displayed on the display 240, wherein the nerve region and the blood vessel region are highlighted so as to be distinguishable from each other on the ultrasound image. Figures 7 to 9An exemplary schematic diagram for highlighting a nerve region and a blood vessel region so as to be distinguishable from each other will be exemplarily described.

[0049] like Figure 7 As shown in FIG, in this example, different pseudo colors are added to the nerve region and the blood vessel region (because the figure is a grayscale image, it is not reflected, but there are pseudo colors in actual applications), so that they can be distinguished from each other intuitively. Figure 8 As shown in FIG, in this example, the nerve region and the blood vessel region are each outlined so that they can be distinguished from each other intuitively. Figure 9 As shown in the figure, in this example, the nerve area is enhanced and the blood vessel area is displayed with a color Doppler effect (because it is a grayscale image in the figure, it is not reflected, but in actual application, the blood vessel area has a color effect), so that they can be distinguished from each other intuitively. It should be understood that Figures 7 to 9 The display shown is merely an exemplary distinguishing and highlighting manner. In other examples, any other suitable display manner may be used as long as the nerve region and the blood vessel region can be visually distinguished and displayed.

[0050] The above exemplifies an ultrasonic imaging device 200 according to one embodiment of the present application. Based on the above description, the ultrasonic imaging device 200 according to the embodiment of the present application utilizes the difference between nerves and blood flow under the Doppler effect to automatically distinguish nerves from blood vessels in the neurovascular region, and uses different highlighting methods for the nerve region and the blood vessel region, respectively, to provide different prompts to the doctor to intuitively distinguish nerves from blood vessels, thereby reducing the risk of accidental puncture of blood vessels during surgery.

[0051] Figure 10 FIG. 1 shows a schematic structural block diagram of an ultrasonic imaging device 1000 according to an embodiment of the present application. Figure 10As shown, the ultrasonic imaging device 1000 includes a transmitting and receiving circuit 1020, an ultrasonic probe 1010, a processor 1030 and a display 1040, wherein: the transmitting and receiving circuit 1020 is used to control the ultrasonic probe 1010 to transmit a first ultrasonic wave to the part of the target object to be subjected to nerve block in the grayscale imaging mode, receive the echo of the first ultrasonic wave, and obtain first ultrasonic echo data from the echo of the first ultrasonic wave; the processor 1030 is used to generate an ultrasonic image of the part to be subjected to nerve block based on the first ultrasonic echo data; the transmitting and receiving circuit 1020 is also used to control the ultrasonic probe 1010 in the Doppler imaging mode. The ultrasound probe 1010 is controlled to transmit a second ultrasound wave to the site where nerve block is to be performed, receive an echo of the second ultrasound wave, and obtain second ultrasound echo data from the echo of the second ultrasound wave; the processor 1030 is further used to obtain the nerve area and the blood vessel area in the ultrasound image based on the first ultrasound echo data and the second ultrasound echo data according to deep learning, machine learning or image processing algorithm, and control the display 1040 to display the ultrasound image, wherein the nerve area and the blood vessel area are highlighted on the ultrasound image so as to be distinguishable from each other, and the highlighted result is used to guide puncture of the site where nerve block is to be performed.

[0052] The ultrasonic imaging device 1000 according to the embodiment of the present application is similar in function to the ultrasonic imaging device 200 described above, and both can realize the distinction and highlighting of nerve areas and blood vessel areas on ultrasonic images (the display effect is similar to the above-mentioned image). Figures 7 to 9 The difference is that the ultrasound imaging device 1000 according to the embodiment of the present application does not obtain the nerve region after obtaining the neurovascular region and the blood vessel region, but directly obtains both the nerve region and the blood vessel region in the ultrasound image based on the ultrasound echo data in the grayscale imaging mode and the ultrasound echo data in the Doppler imaging mode according to deep learning, machine learning or traditional image processing algorithms.

[0053] Therefore, the ultrasonic imaging device 1000 according to the embodiment of the present application automatically obtains nerve areas and vascular areas by combining ultrasonic two-dimensional images with one or more other modal information (such as ultrasound data under imaging modes such as color Doppler imaging, pulsed Doppler imaging, continuous Doppler imaging, and power Doppler imaging). In the embodiment of the present application, the method for automatically identifying nerve areas and vascular areas by combining ultrasonic two-dimensional images and other multimodal information can be: by classifying the ultrasonic image, target detection, and other algorithms to identify the ultrasonic image category and the key structural information contained therein, and its implementation method can be achieved through deep learning or other machine learning, traditional image processing, and other methods. The following is a description with reference to several examples.

[0054] In one example, an ultrasound image generated in grayscale imaging mode and ultrasound data generated in color Doppler imaging mode for the target area of ​​a target subject where a nerve block is to be performed are input into a processor 1030, which then outputs the nerve and vascular regions in the ultrasound image generated in grayscale imaging mode. Processor 1030 may include an analysis unit, which may include a pre-segmentation unit, a feature extraction unit, and a discrimination unit. The pre-segmentation unit pre-segments the image using methods such as threshold segmentation, Snake, level set, GraphCut, ASM, and AAM to obtain a set of candidate anatomical structure boundary regions from the ultrasound image. The feature extraction unit extracts features from each candidate boundary region, including texture, spatial, and grayscale information from the two-dimensional ultrasound image. The discrimination unit matches the extracted features with features extracted from labeled boundary regions in a database and classifies them using a discriminator such as KNN, SVM, random forest, or neural network to determine whether the candidate boundary region contains a critical anatomical structure and obtain its corresponding category. The analysis unit can also include a deep learning neural network, which uses stacked convolutional and deconvolutional layers to learn features and anatomical structure boundaries from the constructed database. For input ultrasound images generated in grayscale imaging mode and ultrasound data in color Doppler imaging mode, the network can directly generate an image output of the same size, indicating the specific boundaries of key anatomical structures. Common networks include: FCN, Unet, SegNet, DeepLab, Mask RCNN, etc.

[0055] In another example, an ultrasound image generated in grayscale imaging mode and an ultrasound image generated in power Doppler imaging mode for the target subject's area to undergo nerve blockade are input to processor 1030, which then outputs the nerve and vascular regions in the ultrasound image generated in grayscale imaging mode. Processor 1030 may include an analysis unit, which may include a pre-segmentation unit, a feature extraction unit, and a discrimination unit. The pre-segmentation unit pre-segments the image using methods such as threshold segmentation, Snake, level set, GraphCut, ASM, and AAM to obtain a set of candidate anatomical structure boundary regions from the ultrasound image. The feature extraction unit extracts features from each candidate boundary region, including velocity, energy, and variance from color Doppler or spectral Doppler data. The discrimination unit matches the extracted features with features extracted from labeled boundary regions in a database and classifies them using a discriminator such as KNN, SVM, random forest, or neural network to determine whether the candidate boundary region contains a critical anatomical structure and obtain its corresponding category. The analysis unit can also include a deep learning neural network, which uses stacked convolutional and deconvolutional layers to learn features and anatomical structure boundaries from the constructed database. For both the grayscale ultrasound image and the power Doppler ultrasound data, the network can directly generate an image output of the same size, indicating the specific boundaries of key anatomical structures. Common networks include FCN, Unet, SegNet, DeepLab, Mask RCNN, and others.

[0056] Based on the above description, the ultrasonic imaging device 1000 according to an embodiment of the present application directly obtains the nerve area and blood vessel area in the ultrasound image based on the ultrasonic echo data in the grayscale imaging mode and the ultrasonic echo data in the Doppler imaging mode according to deep learning, machine learning or traditional image processing algorithms, and uses different highlighting methods for the nerve area and the blood vessel area respectively, which can give different prompts to the doctor to intuitively distinguish between nerves and blood vessels, thereby reducing the risk of accidental puncture of blood vessels during surgery.

[0057] The following combination Figure 11 The ultrasonic imaging method 1100 according to an embodiment of the present application is described. The method 1100 can be implemented by the ultrasonic imaging device 200 described above. In the above description, the imaging method of the ultrasonic imaging device 200 has been described. Therefore, the method 1100 is only briefly described below. Figure 11 As shown, the ultrasound imaging method 1100 may include the following steps:

[0058] In step S1110 , the ultrasound probe is controlled in grayscale imaging mode to transmit a first ultrasound wave to a portion of the target object where nerve block is to be performed, an echo of the first ultrasound wave is received, and first ultrasound echo data is acquired from the echo of the first ultrasound wave.

[0059] In step S1120 , an ultrasound image of the site to be subjected to nerve block is generated based on the first ultrasound echo data, a neurovascular region in the ultrasound image is identified, and a position of a sampling frame including the neurovascular region is acquired.

[0060] In step S1130 , the ultrasound probe is controlled to transmit a second ultrasound wave toward the location of the sampling frame in the Doppler imaging mode, an echo of the second ultrasound wave is received, and second ultrasound echo data is acquired from the echo of the second ultrasound wave.

[0061] In step S1140, blood flow information is acquired based on the second ultrasonic echo data, a blood vessel region in the ultrasonic image is acquired based on the blood flow information, and a nerve region in the ultrasonic image is acquired based on the neurovascular region and the blood vessel region.

[0062] In step S1150 , the ultrasound image is displayed, and the nerve region and the blood vessel region are highlighted so as to be distinguishable from each other on the ultrasound image.

[0063] In an embodiment of the present application, the nerve area and the blood vessel area are highlighted on the ultrasound image in a manner that can distinguish between them, including any of the following: adding different pseudo colors to the nerve area and the blood vessel area respectively; outlining the nerve area and the blood vessel area respectively; image enhancement of the nerve area and display of color Doppler effect on the blood vessel area.

[0064] In an embodiment of the present application, the Doppler imaging mode includes any one of the following: a color Doppler imaging mode, a pulsed Doppler imaging mode, a continuous Doppler imaging mode, and a power Doppler imaging mode.

[0065] In an embodiment of the present application, obtaining the position of a sampling frame including a neurovascular region includes: automatically generating a sampling frame including a neurovascular region to obtain the position of the sampling frame; or receiving user input to obtain the position of the sampling frame including the neurovascular region.

[0066] In an embodiment of the present application, a sampling frame including a neurovascular region is automatically generated, including: generating a sampling frame with a fixed size and position and capable of at least including the neurovascular region for each frame of ultrasound image; or generating a sampling frame including the neurovascular region for each frame of ultrasound image, and the position of the sampling frame moves following the position of the neurovascular region.

[0067] In an embodiment of the present application, the sampling frame also includes other vascular regions in the ultrasound image except the neurovascular region, the vascular region in the ultrasound image is obtained based on the blood flow information, and the nerve region in the ultrasound image is obtained based on the neurovascular region and the vascular region, including: obtaining all vascular regions in the sampling frame based on blood flow information; determining the vascular region within the neurovascular region based on all vascular regions; and obtaining the nerve region within the neurovascular region based on the vascular region within the neurovascular region.

[0068] In an embodiment of the present application, the sampling frame only includes the neurovascular region, and the vascular region in the ultrasound image is acquired based on the blood flow information. The nerve region in the ultrasound image is acquired based on the neurovascular region and the vascular region, including: acquiring the vascular region within the neurovascular region in the ultrasound image based on the blood flow information; and obtaining the nerve region within the neurovascular region based on the vascular region within the neurovascular region.

[0069] In an embodiment of the present application, identifying a neurovascular region in an ultrasound image includes: performing segmentation, edge detection, or target tracking on the ultrasound image to obtain the neurovascular region.

[0070] According to the ultrasound imaging method 1100 of the embodiment of the present application, the difference between nerves and blood flow under the Doppler effect is used to automatically distinguish nerves from blood vessels in the neurovascular area, and different highlighting methods are used for the nerve area and the blood vessel area respectively, which can give doctors different prompts to intuitively distinguish nerves from blood vessels, thereby reducing the risk of accidental puncture of blood vessels during surgery.

[0071] The following combination Figure 12 The ultrasonic imaging method 1200 according to an embodiment of the present application is described. The method 1200 can be implemented by the ultrasonic imaging device 1000 described above. In the above description, the imaging method of the ultrasonic imaging device 1000 has been described. Therefore, the method 1200 is only briefly described below. Figure 12 As shown, the ultrasound imaging method 1200 may include the following steps:

[0072] In step S1210, the ultrasound probe is controlled in a grayscale imaging mode to transmit a first ultrasound wave to a portion of the target object where nerve block is to be performed, an echo of the first ultrasound wave is received, and first ultrasound echo data is acquired from the echo of the first ultrasound wave.

[0073] In step S1220 , an ultrasound image of the site where nerve block is to be performed is generated based on the first ultrasound echo data.

[0074] In step S1230, the ultrasound probe is controlled in the Doppler imaging mode to transmit a second ultrasound wave to the site where nerve block is to be performed, an echo of the second ultrasound wave is received, and second ultrasound echo data is acquired from the echo of the second ultrasound wave.

[0075] In step S1240, based on the first ultrasonic echo data and the second ultrasonic echo data, a nerve area and a blood vessel area in the ultrasonic image are acquired according to deep learning, machine learning, or an image processing algorithm.

[0076] In step S1250 , the ultrasound image is displayed, and the nerve region and the blood vessel region are highlighted on the ultrasound image so as to be distinguishable from each other. The highlighted result is used to guide puncture of the site to be subjected to nerve block.

[0077] In an embodiment of the present application, the nerve area and the blood vessel area are highlighted on the ultrasound image in a manner that can distinguish between them, including any of the following: adding different pseudo colors to the nerve area and the blood vessel area respectively; outlining the nerve area and the blood vessel area respectively; image enhancement of the nerve area and display of color Doppler effect on the blood vessel area.

[0078] In an embodiment of the present application, the Doppler imaging mode includes any one of the following: a color Doppler imaging mode, a pulsed Doppler imaging mode, a continuous Doppler imaging mode, and a power Doppler imaging mode.

[0079] According to the ultrasound imaging method 1200 of the embodiment of the present application, based on the ultrasound echo data in the grayscale imaging mode and the ultrasound echo data in the Doppler imaging mode, the nerve area and the blood vessel area in the ultrasound image are directly acquired according to deep learning, machine learning or traditional image processing algorithms, and different highlighting methods are used for the nerve area and the blood vessel area respectively, which can give different prompts to the doctor to intuitively distinguish between nerves and blood vessels, thereby reducing the risk of accidental puncture of blood vessels during surgery.

[0080] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0081] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0083] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0084] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach of the present application should not be interpreted as reflecting the intention that the application claimed for protection requires more features than those explicitly recited in each claim. More precisely, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with fewer features than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0085] It will be understood by those skilled in the art that, except where mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus disclosed herein may be combined in any combination. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0086] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0087] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules in the article analysis device according to the embodiment of the present application. The present application can also be implemented as an ultrasonic blood flow imaging device program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0088] It should be noted that the above embodiments illustrate rather than limit the present application, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim listing several ultrasonic blood flow imaging devices, several of these ultrasonic blood flow imaging devices may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0089] The above description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An ultrasonic imaging device, characterized in that: The device includes a transmitting and receiving circuit, an ultrasound probe, a processor and a display, wherein: The transmitting and receiving circuit is used to control the ultrasound probe to transmit a first ultrasound wave to a part of the target object where nerve block is to be performed in a grayscale imaging mode, receive an echo of the first ultrasound wave, and obtain first ultrasound echo data from the echo of the first ultrasound wave; The processor is configured to generate an ultrasound image of the site to be subjected to nerve block based on the first ultrasound echo data, identify a neurovascular region in the ultrasound image, and obtain a position of a sampling frame including the neurovascular region; The transmitting and receiving circuit is further configured to control the ultrasonic probe to transmit a second ultrasonic wave toward the location of the sampling frame in a Doppler imaging mode, receive an echo of the second ultrasonic wave, and obtain second ultrasonic echo data from the echo of the second ultrasonic wave; The processor is also used to acquire blood flow information based on the second ultrasonic echo data, acquire a vascular area in the ultrasonic image based on the blood flow information, acquire a nerve area in the ultrasonic image based on the neurovascular area and the vascular area, and control the display to display the ultrasonic image, wherein the nerve area and the vascular area are highlighted on the ultrasonic image so as to be distinguishable from each other.

2. The device according to claim 1, characterized in that The nerve region and the blood vessel region are highlighted so as to be distinguishable from each other on the ultrasound image, including any one of the following: adding different pseudo colors to the nerve region and the blood vessel region respectively; Performing stroke display on the nerve region and the blood vessel region respectively; Image enhancement is performed on the nerve region, and a color Doppler effect is displayed on the blood vessel region.

3. The device according to claim 1, characterized in that The Doppler imaging mode includes any one of the following: color Doppler imaging mode, pulsed Doppler imaging mode, continuous Doppler imaging mode, and power Doppler imaging mode.

4. The device according to claim 1, characterized in that The processor obtains a position of a sampling frame including the neurovascular region, including: automatically generating a sampling frame including the neurovascular region to obtain the position of the sampling frame; or A user input is received to obtain a position of a sampling frame including the neurovascular region.

5. The device according to claim 4, characterized in that The processor automatically generates a sampling frame including the neurovascular region, including: generating, for each frame of the ultrasound image, a sampling frame with a fixed size and position and capable of at least including the neurovascular region; or A sampling frame including the neurovascular region is generated for each frame of the ultrasound image, and the position of the sampling frame moves in accordance with the position of the neurovascular region.

6. The device according to claim 1, characterized in that The sampling frame further includes other vascular regions in the ultrasound image except the neurovascular region, the processor acquiring the vascular region in the ultrasound image based on the blood flow information, and acquiring the nerve region in the ultrasound image based on the neurovascular region and the vascular region, including: acquiring all blood vessel regions in the sampling frame based on the blood flow information; determining a blood vessel region within the neurovascular region based on all the blood vessel regions; Based on the blood vessel area within the neurovascular area, a nerve area within the neurovascular area is obtained.

7. The device according to claim 1, characterized in that The sampling frame only includes the neurovascular region, the processor acquires the vascular region in the ultrasound image based on the blood flow information, and acquires the nerve region in the ultrasound image based on the neurovascular region and the vascular region, comprising: acquiring a blood vessel region within the neurovascular region in the ultrasound image based on the blood flow information; Based on the blood vessel area within the neurovascular area, a nerve area within the neurovascular area is obtained.

8. The device according to claim 1, characterized in that The processor identifies a neurovascular region in the ultrasound image, comprising: Segmentation, edge detection, or target tracking is performed on the ultrasound image to obtain the neurovascular region.

9. An ultrasonic imaging device, characterized in that: The device includes a transmitting and receiving circuit, an ultrasound probe, a processor and a display, wherein: The transmitting and receiving circuit is used to control the ultrasound probe to transmit a first ultrasound wave to a part of the target object where nerve block is to be performed in a grayscale imaging mode, receive an echo of the first ultrasound wave, and obtain first ultrasound echo data from the echo of the first ultrasound wave; The processor is configured to generate an ultrasound image of the site to be subjected to nerve block based on the first ultrasound echo data; The transmitting and receiving circuit is further configured to control the ultrasound probe to transmit a second ultrasound wave to the site to be subjected to nerve blockade in a Doppler imaging mode, receive an echo of the second ultrasound wave, and obtain second ultrasound echo data from the echo of the second ultrasound wave; The processor is also used to obtain the nerve area and the blood vessel area in the ultrasound image based on the first ultrasound echo data and the second ultrasound echo data according to deep learning, machine learning or image processing algorithm, and control the display to display the ultrasound image, wherein the nerve area and the blood vessel area are highlighted on the ultrasound image so as to be distinguishable from each other, and the highlighted result is used to guide puncture of the site to be subjected to nerve blockade.

10. The device according to claim 9, characterized in that The nerve region and the blood vessel region are highlighted so as to be distinguishable from each other on the ultrasound image, including any one of the following: adding different pseudo colors to the nerve region and the blood vessel region respectively; Performing stroke display on the nerve region and the blood vessel region respectively; Image enhancement is performed on the nerve region, and a color Doppler effect is displayed on the blood vessel region.

11. The device according to claim 9, characterized in that The Doppler imaging mode includes any one of the following: color Doppler imaging mode, pulsed Doppler imaging mode, continuous Doppler imaging mode, and power Doppler imaging mode.

12. An ultrasonic imaging method, characterized in that: The method comprises: controlling the ultrasound probe to transmit a first ultrasound wave toward a portion of the target object where nerve block is to be performed in a grayscale imaging mode, receiving an echo of the first ultrasound wave, and acquiring first ultrasound echo data from the echo of the first ultrasound wave; generating an ultrasound image of the site to be subjected to nerve block based on the first ultrasound echo data, identifying a neurovascular region in the ultrasound image, and acquiring a position of a sampling frame including the neurovascular region; In a Doppler imaging mode, controlling the ultrasound probe to transmit a second ultrasound wave toward the location of the sampling frame, receiving an echo of the second ultrasound wave, and acquiring second ultrasound echo data from the echo of the second ultrasound wave; acquiring blood flow information based on the second ultrasonic echo data, acquiring a blood vessel region in the ultrasonic image based on the blood flow information, and acquiring a nerve region in the ultrasonic image based on the neurovascular region and the blood vessel region; The ultrasound image is displayed, and the nerve region and the blood vessel region are highlighted so as to be distinguishable from each other on the ultrasound image.

13. The method according to claim 12, characterized in that The highlighting of the nerve region and the blood vessel region on the ultrasound image so as to be distinguishable from each other includes any one of the following: adding different pseudo colors to the nerve region and the blood vessel region respectively; Performing stroke display on the nerve region and the blood vessel region respectively; Image enhancement is performed on the nerve region, and a color Doppler effect is displayed on the blood vessel region.

14. The method according to claim 12, characterized in that The Doppler imaging mode includes any one of the following: color Doppler imaging mode, pulsed Doppler imaging mode, continuous Doppler imaging mode, and power Doppler imaging mode.

15. The method according to claim 12, characterized in that The acquiring the position of the sampling frame including the neurovascular region comprises: automatically generating a sampling frame including the neurovascular region to obtain the position of the sampling frame; or A user input is received to obtain a position of a sampling frame including the neurovascular region.

16. The method according to claim 15, characterized in that The automatically generating a sampling frame including the neurovascular region comprises: generating, for each frame of the ultrasound image, a sampling frame with a fixed size and position and capable of at least including the neurovascular region; or A sampling frame including the neurovascular region is generated for each frame of the ultrasound image, and the position of the sampling frame moves in accordance with the position of the neurovascular region.

17. The method according to claim 12, wherein: The sampling frame also includes other vascular regions in the ultrasound image except the neurovascular region, and acquiring the vascular region in the ultrasound image based on the blood flow information, and acquiring the nerve region in the ultrasound image based on the neurovascular region and the vascular region, comprises: acquiring all blood vessel regions in the sampling frame based on the blood flow information; determining a blood vessel region within the neurovascular region based on all the blood vessel regions; Based on the blood vessel area within the neurovascular area, a nerve area within the neurovascular area is obtained.

18. The method according to claim 12, wherein: The sampling frame only includes the neurovascular region, the acquiring of the vascular region in the ultrasound image based on the blood flow information, and the acquiring of the nerve region in the ultrasound image based on the neurovascular region and the vascular region include: acquiring a blood vessel region within the neurovascular region in the ultrasound image based on the blood flow information; Based on the blood vessel area within the neurovascular area, a nerve area within the neurovascular area is obtained.

19. The method according to claim 12, wherein: The identifying of the neurovascular region in the ultrasound image includes: Segmentation, edge detection, or target tracking is performed on the ultrasound image to obtain the neurovascular region.

20. An ultrasonic imaging method, characterized in that: The method comprises: controlling the ultrasound probe to transmit a first ultrasound wave toward a portion of the target object where nerve block is to be performed in a grayscale imaging mode, receiving an echo of the first ultrasound wave, and acquiring first ultrasound echo data from the echo of the first ultrasound wave; generating an ultrasound image of the site to be subjected to nerve blockade based on the first ultrasound echo data; controlling the ultrasound probe to transmit a second ultrasound wave toward the site to be subjected to nerve block in a Doppler imaging mode, receiving an echo of the second ultrasound wave, and acquiring second ultrasound echo data from the echo of the second ultrasound wave; Based on the first ultrasonic echo data and the second ultrasonic echo data, acquiring a nerve area and a blood vessel area in the ultrasonic image according to deep learning, machine learning, or an image processing algorithm; The ultrasound image is displayed, and the nerve region and the blood vessel region are highlighted on the ultrasound image so as to be distinguishable from each other. The result of the highlighting is used to guide puncture of the site where nerve block is to be performed.

21. The method according to claim 20, characterized in that The highlighting of the nerve region and the blood vessel region on the ultrasound image so as to be distinguishable from each other includes any one of the following: adding different pseudo colors to the nerve region and the blood vessel region respectively; Performing stroke display on the nerve region and the blood vessel region respectively; Image enhancement is performed on the nerve region, and a color Doppler effect is displayed on the blood vessel region.

22. The method according to claim 20, characterized in that The Doppler imaging mode includes any one of the following: color Doppler imaging mode, pulsed Doppler imaging mode, continuous Doppler imaging mode, and power Doppler imaging mode.

Images