Ultrasound imaging apparatus, ultrasound imaging method, intelligent infusion system, and storage medium

Abstract

The application discloses an ultrasonic imaging device, an ultrasonic imaging method, an intelligent infusion system and a storage medium, and relates to the technical field of medical equipment.The application comprises a first memory and a first processor.The first memory stores a computer program.The first processor is used for executing the computer program to realize the following steps: obtaining ultrasonic echo data of a target region collected by an ultrasonic probe; performing image reconstruction processing on the ultrasonic echo data to obtain ultrasonic image data; performing blood vessel feature extraction processing on the ultrasonic image data to obtain a blood vessel position parameter and a blood vessel depth parameter; generating target display parameters of a guide mark according to the blood vessel position parameter and the blood vessel depth parameter; generating a target ultrasonic image with the guide mark superimposed based on the ultrasonic image data and the target display parameters, and sending the target ultrasonic image to a display device through a serial port and displaying the target ultrasonic image.The determination of the blood vessel positioning result obtained based on the target ultrasonic image is improved, and the success rate of subsequent interventional operations is improved.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to an ultrasound imaging device, an ultrasound imaging method, an intelligent infusion system, and a storage medium. Background Technology

[0002] In clinical procedures such as intravenous infusions that require percutaneous vascular intervention, ultrasound imaging equipment is often used to observe the distribution and morphology of subcutaneous blood vessels. Ultrasound imaging equipment emits an ultrasound beam towards the target area through an ultrasound probe and receives the echo signals reflected by the tissue. After beamforming and image reconstruction processing of the echo signals, an ultrasound image of the target area is generated and displayed.

[0003] Existing ultrasound imaging devices, after completing the aforementioned image reconstruction, directly generate and display conventional ultrasound images of the target area. Conventional ultrasound images present grayscale distribution information of tissue structures or blood flow distribution; the location and depth of blood vessels in the image are unclear. When obtaining vascular localization results based on these conventional ultrasound images, the lack of objective indications of vessel location and depth leads to uncertainty in the obtained results, thus affecting the success rate of subsequent interventional procedures. Therefore, reducing the uncertainty of vascular localization results obtained from conventional ultrasound images is an urgent problem to be solved. Summary of the Invention

[0004] This application provides an ultrasound imaging device, an ultrasound imaging method, an intelligent infusion system, and a storage medium to address the problem of high uncertainty in obtaining vascular localization results based on conventional ultrasound images.

[0005] In a first aspect, embodiments of this application provide an ultrasound imaging device, comprising: a first memory and a first processor, wherein the first memory stores a computer program, and the first processor is configured to execute the computer program to perform the following steps: Acquire ultrasonic echo data of the target area collected by the ultrasonic probe, wherein the ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe. The ultrasound echo data is subjected to image reconstruction processing to obtain ultrasound image data; The ultrasound image data is processed to extract vascular features, and vascular location parameters and vascular depth parameters are obtained. Based on the vessel location parameters and the vessel depth parameters, target display parameters for the guide marker are generated; Based on the ultrasound image data and the target display parameters, a target ultrasound image with the guide identifier superimposed is generated, and the target ultrasound image is sent to the display device via a serial port so that the display device displays the target ultrasound image.

[0006] Secondly, embodiments of this application provide an ultrasound imaging method, the method comprising: Acquire ultrasonic echo data of the target area collected by the ultrasonic probe, wherein the ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe. The ultrasound echo data is subjected to image reconstruction processing to obtain ultrasound image data; The ultrasound image data is processed to extract vascular features, and vascular location parameters and vascular depth parameters are obtained. Based on the vessel location parameters and the vessel depth parameters, target display parameters for the guide marker are generated; Based on the ultrasound image data and the target display parameters, a target ultrasound image with the guide identifier superimposed is generated, and the target ultrasound image is sent to the display device via a serial port so that the display device displays the target ultrasound image.

[0007] Thirdly, embodiments of this application provide an intelligent infusion system, including the ultrasound imaging device described in the first aspect above.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the ultrasound imaging method described in the second aspect above.

[0009] Based on this, one technical solution provided in this application, after acquiring ultrasound echo data and performing image reconstruction to obtain ultrasound image data, further performs vascular feature extraction processing on the ultrasound image data, automatically extracting vascular position parameters and vascular depth parameters from the image data. This ensures that the vascular positioning information no longer depends on the interpretation of unlabeled images, but is instead obtained directly from the image data by the device as objective parameters. Next, target display parameters for guidance icons are generated based on the extracted vascular position parameters and vascular depth parameters. The resulting guidance icons have a definite correspondence with the actual position and depth of the vascular vessels in the image in spatial coordinates. Subsequently, a target ultrasound image superimposed with the guidance icons is generated based on the ultrasound image data and the target display parameters, and the target ultrasound image is output to a display device for display, so that the position and depth of the vascular vessels are clearly represented in the form of icons in the final presented target ultrasound image. Therefore, the vascular localization results obtained based on the target ultrasound image are deterministic information provided by the device after automatic extraction and superposition of labels. This eliminates the uncertainty of localization results caused by the lack of labeling of vascular position and depth in conventional ultrasound images, solves the problem of high uncertainty in vascular localization results read from conventional ultrasound images, improves the determinism of vascular localization results obtained based on the target ultrasound image, and thus improves the success rate of subsequent interventional operations. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the structure of an ultrasound imaging device provided in one embodiment of this application; Figure 2 This is a schematic diagram of a data processing flow of an ultrasound imaging device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an intelligent infusion system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an intelligent infusion device provided in one embodiment of this application; Figure 5 This is a schematic diagram of a data processing flow of an intelligent infusion device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a power supply device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a display device provided in an embodiment of this application; Figure 8 This is another structural schematic diagram of the intelligent infusion device provided in one embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] Please see Figure 1This application provides an ultrasound imaging device 100, including a first memory and a first processor. The first processor may employ a ZTNQ-3CT2100 chip, which is a high-performance FPGA architecture with integrated rich logic resources and digital signal processing units, capable of meeting the needs of multi-channel parallel beamforming and real-time image processing. The first memory may include FLASH memory, DDR3 memory, and EMMC memory. The FLASH memory is used to store computer programs and FPGA configuration files, the DDR3 memory is used to cache ultrasound echo data and image data, and the EMMC memory is used to store system programs and historical image data; these are merely examples here.

[0014] The ultrasonic imaging device 100 also integrates a transmit beamsynthesizer, a multi-channel high-speed analog-to-digital converter (AFD), a high-voltage pulse generator, a transmit / receive transceiver switch, an analog receiver front-end (AFE), and a power management module. The transmit / receive transceiver switch connects to an external ultrasonic probe (piezoelectric transducer array). During transmission, it directs the transmit drive signal output from the high-voltage pulse generator to the ultrasonic probe; during reception, it directs the echo signal received by the ultrasonic probe to the AFE, thus isolating and switching the transmit and receive paths. The input of the AFE connects to the echo signal output of the transmit / receive transceiver switch, and the output connects to the input of the multi-channel high-speed AFD. This AFE performs low-noise amplification, time gain compensation, and filtering on the echo signal before outputting it to the multi-channel high-speed AFD. The output of the multi-channel high-speed AFD connects to the data input of a first processor, converting the received analog echo signal into a digital signal for processing by the first processor. The output of the transmit beam combiner is connected to the control terminal of the high-voltage pulse generator. Based on the transmission timing and waveform parameters provided by the transmit beam combiner, the high-voltage pulse generator generates a high-voltage pulse, which is then transmitted via a transmit / receive switch to the external ultrasonic probe, exciting each element of the ultrasonic probe to emit ultrasonic waves. The power management module manages and monitors the power supply to each module within the device.

[0015] The ultrasound imaging device 100 is also used to obtain operating voltage from the power supply device 300 and to receive operation commands and indicate operating status via buttons / indicator lights; it is connected to a display device via an externally configured serial port interface to upload the generated target ultrasound image with superimposed guide marks to the display device 400 for display.

[0016] Please see Figure 2 The first memory stores a computer program, and the first processor executes the computer program to perform the following steps: S101: Acquire ultrasonic echo data of the target area collected by the ultrasonic probe.

[0017] As an example, ultrasound echo data is a digital signal obtained by beamforming the echo signal received by the ultrasound probe. Specifically, a first processor controls a transmitting beam combiner to generate delay and apodization parameters for the transmitted beam, which is then driven by a high-voltage pulse generator to transmit a focused ultrasound beam towards the target area. During propagation, the ultrasound waves are reflected at tissue interfaces, generating echo signals. After each element of the ultrasound probe receives the echo signal, it is sent to a multi-channel high-speed analog-to-digital converter via a transmit / receive switch, converting it into a multi-channel digital signal sequence. The first processor reads the multi-channel digital signal sequence and performs receiving beamforming in the digital domain, applying delays and weights corresponding to the focal point position to each channel signal, causing the signals from each channel to be superimposed in phase at the target focal point, resulting in spatially synthesized ultrasound echo data in digital signal form.

[0018] S102: Perform image reconstruction processing on the ultrasound echo data to obtain ultrasound image data.

[0019] As an example, the first processor performs image reconstruction processing on the ultrasound echo data acquired in S101, converting the ultrasound echo data into ultrasound image data. The image reconstruction processing may include envelope extraction and grayscale mapping of the ultrasound echo data, converting the echo intensity of the ultrasound echo data into corresponding grayscale values ​​to generate ultrasound image data. The ultrasound image data reflects the morphological information of the tissue structure within the target area.

[0020] S103: Perform vascular feature extraction processing on ultrasound image data to obtain vascular location parameters and vascular depth parameters.

[0021] As an example, the first processor performs vascular feature extraction processing on the ultrasound image data generated in S102, identifies vascular regions from the ultrasound image data, and extracts vascular location parameters and vascular depth parameters. The vascular location parameters characterize the lateral and longitudinal coordinate positions of the blood vessel within the target area, while the vascular depth parameters characterize the depth coordinate position of the blood vessel within the tissue. The first processor uses image processing algorithms to analyze the ultrasound image data, automatically determining the boundary and center position of the blood vessel based on the differences in image features between the vascular region and the surrounding tissue, thereby obtaining the vascular location parameters and vascular depth parameters.

[0022] S104: Generate target display parameters for the guide marker based on the vessel location parameters and vessel depth parameters.

[0023] As an example, the first processor generates target display parameters for the guide icon based on the vessel location parameters and vessel depth parameters obtained in S103. The target display parameters define the display position and presentation format of the guide icon in the ultrasound image to be generated, so that the guide icon maintains a correspondence with the actual position and depth of the vessel in the target area when it is subsequently overlaid.

[0024] S105: Based on ultrasound image data and target display parameters, generate a target ultrasound image with superimposed guide markers, and send the target ultrasound image to the display device via serial port so that the display device displays the target ultrasound image.

[0025] As an example, the first processor writes the ultrasound image data generated in S102 as a background layer into the frame buffer. The frame buffer is a storage area within the first processor used to temporarily store image data to be displayed. The background layer is an address space in the frame buffer used to store the ultrasound image data. The ultrasound image data is stored in the background layer in the form of a pixel matrix. Each pixel in the background layer has a corresponding grayscale value or color value, reflecting the morphological information of the tissue structure within the target area.

[0026] The first processor draws the guide icon graphic in the graphics rendering layer of the frame buffer according to the target display parameters generated in S104. The graphics rendering layer is a separate address space in the frame buffer, independent of the background layer, used to store the graphic elements to be superimposed. The target display parameters define the drawing attributes of the guide icon graphic in the graphics rendering layer, including display start coordinates and direction indicator parameters. The first processor determines the starting pixel coordinates of the guide icon graphic in the graphics rendering layer according to the display start coordinates, and determines the extension direction and ending pixel coordinates of the guide icon graphic from the starting pixel coordinates according to the direction indicator parameters. It then writes the pixel values ​​of the guide icon graphic pixel by pixel along the path from the starting pixel coordinates to the ending pixel coordinates in the graphics rendering layer, completing the drawing of the guide icon graphic. The pixel values ​​of the guide icon graphic are different from the grayscale or color values ​​of the tissue structures in the background layer, so that the guide icon graphic can be clearly identified in the subsequent superimposed and fused target ultrasound image.

[0027] The first processor overlays and fuses the background layer and the graphics rendering layer to form the target ultrasound image. The overlay and fusion process can be as follows: The first processor iterates through each pixel coordinate of the target ultrasound image. For each pixel coordinate, it determines whether the graphics rendering layer contains a guide marker pixel value at that coordinate. If the graphics rendering layer contains a guide marker pixel value at that coordinate, then that guide marker pixel value is used as the target ultrasound image pixel value at that coordinate. If the graphics rendering layer does not contain a guide marker pixel value at that coordinate, then the background layer pixel value at that coordinate is used as the target ultrasound image pixel value. In the resulting target ultrasound image after overlay and fusion, the pixels of the guide marker graphics overlap the corresponding pixels of the ultrasound image data, while the non-guide marker graphics areas retain the original pixel values ​​of the ultrasound image data. This target ultrasound image preserves both the original tissue structure and blood flow information, and also clearly and intuitively marks the location and depth of blood vessels.

[0028] The first processor sends the target ultrasound image data to the display device via a serial interface. The serial interface packages the pixel data of the target ultrasound image frame by frame according to the frame format of the serial communication protocol and transmits it to the serial interface receiver of the display device via a physical connection. The display device typically includes an LCD screen and a driving circuit. After receiving the pixel data of the target ultrasound image, the serial interface receiver of the display device writes the pixel data into the display buffer inside the display device. The driving circuit reads the pixel data from the display buffer according to the display refresh sequence and drives the LCD screen to scan and display the target ultrasound image line by line. By observing the ultrasound image with guiding markers on the display device, the operator can directly obtain the objective location and depth information of the blood vessels as a reference for subsequent interventional procedures.

[0029] As can be seen, the ultrasound image ultimately displayed to the operator in this embodiment directly includes the vascular location markers automatically extracted and superimposed by the device, realizing a fully automated processing flow from image acquisition to feature extraction and marker superimposition. The vascular location results obtained based on this image are deterministic, no longer relying on the subjective interpretation of different operators, thus solving the problem of high uncertainty in vascular location results based on conventional ultrasound image readings in the background art. This improves the objectivity and consistency of vascular location, thereby increasing the success rate of subsequent interventional procedures.

[0030] In summary, the technical solution provided in this application, after acquiring ultrasound echo data and reconstructing the image to obtain ultrasound image data, further performs vascular feature extraction processing on the ultrasound image data, automatically extracting vascular position parameters and vascular depth parameters from the image data. This ensures that the vascular positioning information no longer depends on the interpretation of unlabeled images, but is instead obtained directly from the image data by the device as objective parameters. Next, target display parameters for guidance icons are generated based on the extracted vascular position and depth parameters. The resulting guidance icons have a definite correspondence with the actual position and depth of the vascular vessels in the image in spatial coordinates. Subsequently, a target ultrasound image superimposed with the guidance icons is generated based on the ultrasound image data and the target display parameters, and the target ultrasound image is output to a display device for display, so that the position and depth of the vascular vessels are clearly represented in the form of icons in the final presented target ultrasound image. Therefore, the vascular localization results obtained based on the target ultrasound image are deterministic information provided by the device after automatic extraction and superposition of labels. This eliminates the uncertainty of localization results caused by the lack of labeling of vascular position and depth in conventional ultrasound images, solves the problem of high uncertainty in vascular localization results read from conventional ultrasound images, improves the determinism of vascular localization results obtained based on the target ultrasound image, and thus improves the success rate of subsequent interventional operations.

[0031] In one embodiment, acquiring the ultrasonic echo data of the target area collected by the ultrasonic probe includes the following steps: S111: Controls the ultrasonic probe to emit an ultrasonic beam toward the target area and receives the echo signal of the ultrasonic beam. S112: Perform dynamic focusing delay processing on the echo signal to obtain delay data for each channel; S113: Perform apodization weighting on the delay data of each channel to obtain the weighted composite channel data; S114: Perform quadrature demodulation processing on the channel composite data to obtain the baseband in-phase component and the baseband quadrature component; S115: Perform filtering and extraction processing on the baseband in-phase component and the baseband quadrature component to obtain downsampled in-phase data stream and downsampled quadrature data stream respectively, and use the downsampled in-phase data stream and downsampled quadrature data stream as ultrasonic echo data.

[0032] As an example, the first processor controls the transmit beam synthesizer to generate delay and apodization parameters for the transmit beam, which are then used by a high-voltage pulse generator to drive the ultrasound probe to emit a focused ultrasonic beam toward the target area. As the ultrasonic beam propagates within the target area, it is reflected at tissue interfaces, forming echo signals. Each element of the ultrasound probe receives the echo signals, which are then sent to a multi-channel high-speed analog-to-digital converter (ADC) via a transmit / receive switch. The ADC converts the echo signals received by each element into digital signals, forming a multi-channel digital signal sequence.

[0033] After reading the multi-channel digital signal sequence from the multi-channel high-speed analog-to-digital converter, the first processor performs received beamforming processing in the digital domain. Due to differences in the acoustic path from different elements of the ultrasonic probe to the target focal point, the echoes from the same target focal point arrive at each element at different times, resulting in time differences between the channel signals. To superimpose the signals from each channel in phase at the target focal point, the first processor executes S112: performing dynamic focus delay processing on each channel signal in the multi-channel digital signal sequence. The dynamic focus delay processing applies a corresponding delay amount to the signal of each channel based on the distance difference from the target focal point to each element of the ultrasonic probe, aligning the echo components from the same target focal point in time, thus obtaining the delay data for each channel. For example, for a depth of... The target focal point, the first processor, based on the ultrasound probe's... Calculation of the acoustic path delay from each element to the target focal point , will the Each channel signal is shifted on the time axis , obtained the Individual channel delay data.

[0034] After completing the dynamic focusing delay processing, the echo components from the target focal point in each channel signal are aligned in time. However, when the signals from each channel are superimposed, sidelobes are generated, and the echo signals in the direction of the sidelobes interfere with the signal at the target focal point. To suppress sidelobes, the first processor executes S113: performing apodization weighting processing on the delay data of each channel. The apodization weighting processing multiplies the delay data of each channel by a preset weighting coefficient. The weighting coefficient is determined according to the position of the channel in the ultrasound probe array, with the weighting coefficient corresponding to the center array element being greater than that corresponding to the edge array elements. For example, the Hanning window function is used to generate the weighting coefficients corresponding to each channel. , will the Each channel delay data is multiplied by its corresponding weighting coefficient. The weighted number of the first The data from each channel is weighted, and the weighted data from all channels constitutes the weighted composite data of the channels.

[0035] The channel composite data obtained after apodization weighting processing has a center frequency of The radio frequency (RF) signal requires a high sampling rate and a large data volume for direct processing. To reduce the sampling rate while preserving the amplitude and phase information of the echo signal, the first processor executes S114: performing quadrature demodulation processing on the weighted channel composite data. The quadrature demodulation process multiplies the channel composite data with both the in-phase reference signal and the quadrature reference signal, and after low-pass filtering, obtains the baseband in-phase component. Orthogonal components of baseband The radio frequency signal is down-converted to baseband.

[0036] Baseband in-phase component after quadrature demodulation Orthogonal components of baseband The sampling rate remains the same as the RF sampling rate. To further reduce the data rate and thus decrease the amount of data processing required for subsequent image reconstruction, the first processor executes S115: [processes the baseband in-phase component]. Orthogonal components of baseband Perform filtering and decimation processing. The filtering and decimation process first processes the baseband in-phase components. Orthogonal components of baseband Low-pass filtering is performed to remove components in the signal with frequencies higher than the Nyquist frequency after decimation, preventing spectral aliasing after decimation; then, the decimation factor is preset. Perform downsampling, that is, every One sampling point is retained for each sampling point, reducing the data rate to the original level. The downsampled in-phase data stream and the downsampled quadrature data stream were obtained respectively. The downsampled in-phase data stream and the downsampled quadrature data stream were used together as ultrasound echo data.

[0037] As can be seen, through this embodiment, dynamic focusing delay processing ensures that the echo components from the target focal point in each channel signal are superimposed in phase, enhancing the signal at the target focal point. Echoes from non-focal points are weakened due to phase inconsistency, achieving spatial focusing. Apodization weighting processing suppresses beam sidelobes and reduces echo interference in the sidelobe direction by reducing the weighting coefficients of edge elements. Quadrature demodulation downconverts the RF signal to baseband, reducing the signal bandwidth while preserving signal amplitude and phase information. Filtering and decimation processing reduces the data rate while filtering out high-frequency components to avoid spectral aliasing, thus reducing the data processing load for subsequent image reconstruction.

[0038] In one embodiment, the ultrasound image data includes B-mode grayscale image data and color Doppler image data; This involves performing image reconstruction processing on ultrasound echo data to obtain ultrasound image data, including the following steps: S121: Perform envelope detection operation on the ultrasonic echo data to obtain the envelope signal value; S122: Perform logarithmic compression mapping on the envelope signal values ​​to generate B-mode grayscale image data; S123: Perform wall filtering on ultrasound echo data to extract the blood flow Doppler frequency shift component; S124: Perform autocorrelation estimation on the Doppler frequency shift component of blood flow to obtain blood flow velocity estimate and blood flow direction estimate; S125: Based on the estimated blood flow velocity and blood flow direction, map the color blood flow coding values ​​to generate color blood flow Doppler image data.

[0039] As an example, the first processor processes the downsampled in-phase data stream of the ultrasonic echo data acquired in S101. and downsampling orthogonal data stream Perform envelope detection. The formula for envelope detection is as follows: ,in The envelope signal value, To downsample the in-phase component values ​​at sampling points in the in-phase data stream, This refers to the orthogonal component values ​​at corresponding sampling points in the downsampled orthogonal data stream. Envelope signal value. It represents the amplitude of the echo signal at that sampling point.

[0040] Ultrasonic echo signals have a large dynamic range. When used directly for display, weak signals are overwhelmed by strong signals, resulting in unclear tissue structure layers in the image. To map the large dynamic range echo signal to a suitable grayscale range for display, the first processor executes S122: adjusting the envelope signal value... Perform a logarithmic compression mapping. The formula for calculating the logarithmic compression mapping is as follows: ,in These are the grayscale values ​​after logarithmic compression. This is the gain coefficient. The bias constant is This represents the envelope signal value. Logarithmic compression mapping will transform the envelope signal value with a large dynamic range. The image is compressed to the display grayscale range to generate B-mode grayscale image data. The B-mode grayscale image data reflects the echo intensity distribution of tissue structures within the target area in the form of pixel grayscale values ​​G.

[0041] In vascular detection, in addition to tissue structure information, blood flow information is also needed to assist in identifying vascular regions. The Doppler frequency shift generated by red blood cell movement in blood flow is relatively high, while the clutter generated by the movement of the vessel wall and tissue has a lower frequency. To extract the blood flow Doppler frequency shift component, the first processor executes S123: downsampling the in-phase data stream from the ultrasound echo data. and downsampling orthogonal data stream Wall filtering is performed. Wall filtering uses a high-pass filter to filter the ultrasound echo data. The cutoff frequency of the high-pass filter is set to remove low-frequency components generated by vessel wall and tissue movement, while retaining the higher-frequency blood flow Doppler shift components generated by red blood cell movement. The blood flow Doppler shift components are then treated as in-phase components. and orthogonal components Output in the form of .

[0042] To extract the velocity and direction information of blood flow from the blood flow Doppler frequency shift component, the first processor executes S124: performing an autocorrelation estimation operation on the blood flow Doppler frequency shift component. The autocorrelation estimation operation calculates the autocorrelation function between the blood flow Doppler frequency shift components corresponding to two adjacent transmitted pulses at the same sampling position. The calculation formula is: ,in Indicates the pulse emission sequence number. The imaginary unit. Based on the autocorrelation function. The phase angle is used to calculate the average Doppler angular frequency, and the phase angle is used to calculate the phase angle. ,in Autocorrelation function The imaginary part, Autocorrelation function The real part. Blood flow velocity estimate. The calculation formula is: ,in For the speed of sound in the organization, The ultrasonic transmission center angular frequency, The pulse emission interval is given. The blood flow direction estimate is derived from the autocorrelation function. phase angle The sign is determined. A positive value indicates that the blood flow is directed towards the ultrasound probe. A negative value indicates that the blood flow is moving away from the direction of the ultrasound probe.

[0043] To visualize blood flow velocity and direction information in an image, the first processor executes S125: based on the estimated blood flow velocity... The blood flow direction estimate is mapped to color flow encoded values ​​to generate color Doppler image data. The mapping method for the color flow encoded values ​​is as follows: a positive blood flow direction estimate is mapped to the red color space, and a negative blood flow direction estimate is mapped to the blue color space. The absolute value of the blood flow velocity estimate, |v|, is mapped to a color brightness value. The color flow encoded value of each pixel in the color Doppler image data simultaneously reflects the blood flow velocity and direction at that pixel location.

[0044] As can be seen, in this embodiment, the B-mode grayscale image data, generated through envelope detection and logarithmic compression of the ultrasound echo data, provides information on the echo intensity distribution of tissue structures within the target area; the color Doppler image data, generated through wall filtering, autocorrelation estimation, and color encoding of the ultrasound echo data, provides information on the velocity and direction distribution of blood flow within the target area. These two types of image data together constitute the ultrasound image data, providing a basis for discriminative analysis of vascular features based on both tissue structure and blood flow function.

[0045] In one embodiment, the ultrasound image data includes B-mode grayscale image data and color Doppler image data; This involves performing vascular feature extraction processing on ultrasound image data to obtain vascular location parameters and vascular depth parameters, including the following steps: S131: Perform vessel wall edge detection on the B-mode grayscale image data to generate vessel contour mask data; S132: Perform blood flow velocity thresholding on the color Doppler image data to generate blood flow region mask data; S133: Perform mask overlay on the vascular contour mask data and the blood flow region mask data to obtain vascular fusion mask data; S134: Based on the vascular fusion mask data, extract the horizontal and vertical coordinates of the vascular centerline as vascular position parameters, and the depth coordinates of the vascular wall as vascular depth parameters.

[0046] As an example, the first processor performs vessel wall edge detection on the B-mode grayscale image data generated in S102. The vessel wall differs from the surrounding tissue in grayscale value, appearing as a region of drastic grayscale change in the B-mode grayscale image data. To extract the boundary position of the vessel wall, the first processor uses a gradient operator to calculate the gradient magnitude of each pixel in the B-mode grayscale image data. The gradient magnitude calculation formula is as follows: ,in The gradient is in the horizontal direction. This represents the gradient in the vertical direction. The first processor calculates the gradient magnitude. Pixels exceeding a preset edge threshold are marked as edge pixels with a value of 1; the gradient magnitude is... Pixels with a value less than or equal to a preset edge threshold are marked as non-edge pixels with a value of 0, and blood vessel contour mask data is generated. Pixel regions with a value of 1 in the blood vessel contour mask data correspond to the blood vessel wall boundary.

[0047] The detection results of blood vessel wall edges in B-mode grayscale images may be affected by interference from other tissue interfaces, making it difficult to accurately distinguish blood vessels from other tubular structures based solely on morphological boundaries. Since blood flow signals exist inside blood vessels, while other tubular structures do not, blood flow information can be used to further confirm the blood vessel region. Therefore, the first processor executes S132: performing blood flow velocity thresholding on the color Doppler image data. The first processor extracts the absolute value of the estimated blood flow velocity for each pixel from the color Doppler image data, and... Compare with a preset blood flow velocity threshold. When When the blood flow velocity exceeds a preset threshold, the pixel location is determined to have a blood flow signal and marked as a blood flow pixel with a value of 1; when... When the blood flow velocity is less than or equal to a preset blood flow velocity threshold, the pixel location is determined to have no blood flow signal, marked as a non-blood flow pixel with a value of 0, and blood flow region mask data is generated. Pixel regions with a value of 1 in the blood flow region mask data correspond to regions where blood flow signals are detected.

[0048] After obtaining vascular contour mask data and blood flow region mask data respectively, the vascular contour mask data provides morphological boundary information of the vascular wall, but may include the boundaries of non-vascular tubular structures; the blood flow region mask data provides blood flow filling information inside the vascular wall, but the boundary of the blood flow signal may be smaller than the actual vascular wall boundary. To combine the advantages of both types of information and reduce false positives and false negatives caused by a single information source, the first processor executes S133: performing mask overlay on the vascular contour mask data and the blood flow region mask data to obtain vascular fusion mask data. The mask overlay method can be as follows: performing a logical AND operation on the pixel values ​​at the same pixel coordinates in the two mask data; if the pixel value is 1 in both the vascular contour mask data and the blood flow region mask data, the pixel value in the vascular fusion mask data is set to 1; otherwise, it is set to 0. The pixel region with a value of 1 in the vascular fusion mask data simultaneously satisfies the two conditions of being located within the vascular wall boundary and detecting a blood flow signal; this region corresponds to the cross-sectional region of the vascular vessel.

[0049] After obtaining the vessel fusion mask data, the first processor executes S134: morphological thinning is performed on the pixel regions with a value of 1 in the vessel fusion mask data, peeling away the boundary pixels layer by layer until the remaining pixels form a skeleton line of single-pixel width, which is the vessel centerline. The first processor extracts the horizontal and vertical coordinate values ​​of each pixel on the vessel centerline in the image coordinate system, using these coordinate values ​​as vessel position parameters. Simultaneously, the first processor searches along the depth direction in the pixel regions with a value of 1 in the vessel fusion mask data, selecting the pixel with the smallest coordinate value in the depth direction, using the depth coordinate value of this pixel as the depth coordinate value of the vessel wall, and using the depth coordinate value of the vessel wall as the vessel depth parameter.

[0050] As can be seen, through this embodiment, the vascular contour mask data provides the morphological boundary information of the vascular wall, and the blood flow region mask data provides the blood flow filling information inside the vascular wall. The vascular fusion mask data obtained by superimposing and fusing the two types of mask data through logical AND operation combines morphological information and functional information. The pixel region with a value of 1 is located within the vascular wall boundary and a blood flow signal is detected. This reduces the false detection of non-vascular structures caused by a single information source and the missed detection caused by the lack of blood flow signal, and improves the accuracy of extracting vascular position parameters and vascular depth parameters.

[0051] In one embodiment, the generation of target display parameters for the guide marker based on vessel location parameters and vessel depth parameters includes the following steps: S141: Determine the starting coordinates for displaying the guide marker based on the blood vessel location parameters; S142: Determine the direction indication parameters of the guide marker based on the vessel depth parameters; S143: Generate target display parameters based on the starting coordinates and direction indicator parameters.

[0052] As an example, the first processor extracts the horizontal and vertical coordinate values ​​of the vessel centerline from the vessel location parameters obtained in S103. The vessel centerline consists of a sequence of coordinate points, each containing a horizontal coordinate value. and a vertical coordinate value The sequence of coordinate points is represented as The first processor iterates through all coordinate points along the centerline of the blood vessel, compares the depth coordinates of each point, and selects the point with the smallest depth coordinate as the target point. The lateral coordinate of this target point is then used as the target point. and vertical coordinate values These serve as the starting coordinates for display. The starting coordinates determine the initial drawing position of the guide marker within the framebuffer graphics drawing layer.

[0053] The first processor extracts the depth coordinates of the blood vessel wall from the blood vessel depth parameters obtained in S103. Depth coordinates These are the pixel coordinates of the blood vessel wall along the depth direction in the image coordinate system. The first processor will then process the depth coordinates... Multiply by the preset display scaling factor This yields the depth indication length value in pixels. The calculation formula is: Display scaling factor This is used to convert depth coordinate values ​​in the image coordinate system to pixel distances in the display coordinate system. The first processor uses depth to indicate length values. As a direction indicator parameter.

[0054] The first processor will display the starting coordinates. and direction indication parameters These are combined into a single data structure to generate the target display parameters. The target display parameters can contain two fields: a start coordinate field that stores the starting coordinates for display. The direction parameter field stores the direction indication parameters. When the target ultrasound image is generated by overlaying in step S105, the first processor reads the starting coordinate field of the target display parameters and positions the pixel drawing pointer to the coordinates in the graphics drawing layer of the frame buffer. At; read the direction parameter field of the target display parameters from the coordinates. Begin writing the guide marker pixel values ​​pixel by pixel along the preset depth direction, the number of pixels written equal to the direction indicator parameter. Draw a line of length in the graphics drawing layer. The guide marker line segment for each pixel. After the first processor overlays the graphics rendering layer with the background layer, the guide marker line segment appears at the corresponding position in the target ultrasound image.

[0055] As can be seen, through this embodiment, the starting coordinates are determined by the point with the smallest depth coordinate value on the blood vessel centerline, the direction indication parameter is obtained by proportional conversion of the blood vessel wall depth coordinate value, the starting point of the guide marker line segment is aligned with the shallowest position of the blood vessel centerline in the image, and the length of the line segment corresponds to the depth of the blood vessel in the tissue, providing the operator with an objective indication of the blood vessel position.

[0056] In one embodiment, sending the target ultrasound image to the display device via a serial port includes the following steps: S151: Perform image compression encoding on the target ultrasound image to obtain compressed image data; S152: Perform serial port data frame packing on the compressed image data to obtain a serial port data packet. The serial port data packet includes a frame header identifier, image data payload, and a check field. S153: Sends serial port data packets to the display device via the serial interface.

[0057] As an example, the first processor performs image compression encoding on the target standard acoustic image superimposed in S105. The image compression encoding uses the JPEG compression algorithm. The first processor can divide the target standard acoustic image into multiple 8-pixel × 8-pixel image blocks, perform a discrete cosine transform on each image block to obtain a transform coefficient matrix; perform quantization processing on the transform coefficient matrix, using a preset quantization table to quantize each transform coefficient, compressing high-frequency coefficients to zero or small values; perform zigzag scanning and entropy encoding on the quantized coefficients to obtain compressed image data. The amount of compressed image data is less than the amount of original pixel data in the target standard acoustic image. Next, serial port data frame packaging is performed on the compressed image data. The serial port data frame packaging method can be as follows: add a frame header identifier to the front of the compressed image data. The frame header identifier is a preset fixed byte sequence used to identify the start position of a frame of data; add a check field to the back of the compressed image data. The check field is a checksum obtained by performing cyclic redundancy check calculation on the compressed image data, used by the receiving end to verify whether errors have occurred during data transmission. The frame header identifier, compressed image data, and check field together constitute the serial port data packet. Finally, the serial port data packets are sent byte by byte to the display device via the serial interface. After receiving the serial port data packets, the display device detects the frame header identifier to determine the start position of the data frame, extracts the compressed image data from the serial port data packets, performs cyclic redundancy check calculation on the compressed image data, and compares the calculation result with the check field; if the comparison matches, the compressed image data is decompressed to restore the target standard audio image and drives the display screen to display it.

[0058] As can be seen, through this embodiment, image compression encoding reduces the amount of transmitted data, and serial port data frame packaging provides a frame synchronization verification mechanism, ensuring the integrity and reliability of the target standard sound image transmitted on the serial communication link.

[0059] In one embodiment, the first processor is configured to execute a computer program to further perform the following steps: S106: In response to an operation that adjusts the position of the ultrasound probe, acquire updated ultrasound echo data; S107: Generate updated ultrasound image data and updated vessel location parameters based on the updated ultrasound echo data; S108: Generate updated target display parameters for the guide marker based on the updated vessel location parameters; S109: Based on the updated ultrasound image data and the updated target display parameters of the guide marker, generate an updated target ultrasound image with the updated guide marker superimposed, and send the updated target ultrasound image to the display device.

[0060] As an example, when the ultrasonic probe moves across the surface of the target area, the relative position of the ultrasonic probe and the target area changes, and the echo signal acquired by the ultrasonic probe changes accordingly. In response to the ultrasonic probe's position adjustment operation, the first processor re-executes S101 to acquire updated ultrasonic echo data. The updated ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe at the new position.

[0061] The first processor re-executes step S102 on the updated ultrasound echo data to obtain updated ultrasound image data. The updated ultrasound image data reflects the morphological information of the tissue structure within the target area at the new location.

[0062] The first processor re-executes step S103 on the updated ultrasound image data to obtain updated vessel position parameters and updated vessel depth parameters. The updated vessel position parameters and updated vessel depth parameters reflect the position of the vessel in the image and its depth within the tissue at the new location.

[0063] The first processor re-executes S104 based on the updated vessel location parameters and updated vessel depth parameters to generate updated target display parameters for the guide identifier.

[0064] The first processor re-executes S105 based on the updated ultrasound image data and the updated target display parameters of the guide marker, generates an updated target ultrasound image superimposed with the updated guide marker, and sends the updated target ultrasound image to the display module for display through the serial communication interface.

[0065] As can be seen, through this embodiment, when the position of the ultrasound probe changes, the first processor automatically re-executes S101 to S105 to update the ultrasound echo data, ultrasound image data, blood vessel position parameters, target display parameters, and target ultrasound image, so that the display module always displays the target ultrasound image and guide mark corresponding to the current position of the ultrasound probe, and the position and form of the guide mark correspond to the actual position and depth of the blood vessel under the current ultrasound probe position.

[0066] Secondly, please refer to Figure 2 This application provides an ultrasound imaging method applicable to the ultrasound imaging apparatus of the first aspect described above. The ultrasound imaging apparatus may include a first memory and a first processor. The first memory stores a computer program, and the first processor executes the computer program to perform the following steps: S101: Acquire ultrasonic echo data of the target area collected by the ultrasonic probe. The ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe. S102: Perform image reconstruction processing on the ultrasound echo data to obtain ultrasound image data; S103: Perform vascular feature extraction processing on ultrasound image data to obtain vascular location parameters and vascular depth parameters; S104: Generate target display parameters for the guide marker based on the vessel location parameters and vessel depth parameters; S105: Based on ultrasound image data and target display parameters, generate a target ultrasound image with superimposed guide markers, and send the target ultrasound image to the display device via serial port so that the display device displays the target ultrasound image.

[0067] In summary, the technical solution provided in this application, after acquiring ultrasound echo data and reconstructing the image to obtain ultrasound image data, further performs vascular feature extraction processing on the ultrasound image data, automatically extracting vascular position parameters and vascular depth parameters from the image data. This ensures that the vascular positioning information no longer depends on the interpretation of unlabeled images, but is instead obtained directly from the image data by the device as objective parameters. Next, target display parameters for guidance icons are generated based on the extracted vascular position and depth parameters. The resulting guidance icons have a definite correspondence with the actual position and depth of the vascular vessels in the image in spatial coordinates. Subsequently, a target ultrasound image superimposed with the guidance icons is generated based on the ultrasound image data and the target display parameters, and the target ultrasound image is output to a display device for display, so that the position and depth of the vascular vessels are clearly represented in the form of icons in the final presented target ultrasound image. Therefore, the vascular localization results obtained based on the target ultrasound image are deterministic information provided by the device after automatic extraction and superposition of labels. This eliminates the uncertainty of localization results caused by the lack of labeling of vascular position and depth in conventional ultrasound images, solves the problem of high uncertainty in vascular localization results read from conventional ultrasound images, improves the determinism of vascular localization results obtained based on the target ultrasound image, and thus improves the success rate of subsequent interventional operations.

[0068] In one embodiment, acquiring the ultrasonic echo data of the target area collected by the ultrasonic probe includes the following steps: S111: Controls the ultrasonic probe to emit an ultrasonic beam toward the target area and receives the echo signal of the ultrasonic beam. S112: Perform dynamic focusing delay processing on the echo signal to obtain delay data for each channel; S113: Perform apodization weighting on the delay data of each channel to obtain the weighted composite channel data; S114: Perform quadrature demodulation processing on the channel composite data to obtain the baseband in-phase component and the baseband quadrature component; S115: Perform filtering and extraction processing on the baseband in-phase component and the baseband quadrature component to obtain downsampled in-phase data stream and downsampled quadrature data stream respectively, and use the downsampled in-phase data stream and downsampled quadrature data stream as ultrasonic echo data.

[0069] In one embodiment, the ultrasound image data includes B-mode grayscale image data and color Doppler image data; This involves performing image reconstruction processing on ultrasound echo data to obtain ultrasound image data, including the following steps: S121: Perform envelope detection operation on the ultrasonic echo data to obtain the envelope signal value; S122: Perform logarithmic compression mapping on the envelope signal values ​​to generate B-mode grayscale image data; S123: Perform wall filtering on ultrasound echo data to extract the blood flow Doppler frequency shift component; S124: Perform autocorrelation estimation on the Doppler frequency shift component of blood flow to obtain blood flow velocity estimate and blood flow direction estimate; S125: Based on the estimated blood flow velocity and blood flow direction, map the color blood flow coding values ​​to generate color blood flow Doppler image data.

[0070] In one embodiment, the ultrasound image data includes B-mode grayscale image data and color Doppler image data; This involves performing vascular feature extraction processing on ultrasound image data to obtain vascular location parameters and vascular depth parameters, including the following steps: S131: Perform vessel wall edge detection on the B-mode grayscale image data to generate vessel contour mask data; S132: Perform blood flow velocity thresholding on the color Doppler image data to generate blood flow region mask data; S133: Perform mask overlay on the vascular contour mask data and the blood flow region mask data to obtain vascular fusion mask data; S134: Based on the vascular fusion mask data, extract the horizontal and vertical coordinates of the vascular centerline as vascular position parameters, and the depth coordinates of the vascular wall as vascular depth parameters.

[0071] In one embodiment, the generation of target display parameters for the guide marker based on vessel location parameters and vessel depth parameters includes the following steps: S141: Determine the starting coordinates for displaying the guide marker based on the blood vessel location parameters; S142: Determine the direction indication parameters of the guide marker based on the vessel depth parameters; S143: Generate target display parameters based on the starting coordinates and direction indicator parameters.

[0072] It should be noted that each step of the ultrasound imaging method corresponds one-to-one with the function or step of the ultrasound imaging device in the above embodiments. To avoid repetition, they are not listed here. The specific implementation method and the technical effect achieved by the ultrasound imaging method can be referred to the relevant description of the ultrasound imaging device embodiment in the first aspect. To avoid repetition, they will not be repeated here.

[0073] Thirdly, please refer to Figure 3 This application provides an intelligent infusion system, including the ultrasound imaging device 100 described in the first aspect above.

[0074] It should be noted that the functions or steps that the ultrasound imaging device in the intelligent infusion system can achieve can be referred to the relevant descriptions of the aforementioned ultrasound imaging device embodiments. To avoid repetition, they will not be described one by one here.

[0075] In one embodiment, please refer to Figure 3 The intelligent infusion system also includes an intelligent infusion device 200; please refer to [link / reference]. Figure 4 The intelligent infusion device 200 includes a second memory and a second processor. The second processor can employ a GD32F303VET6 chip, which is a microcontroller based on an ARM Cortex-M4 core, integrating analog peripherals and digital interface resources for multi-channel sensor data acquisition, real-time motor control, and serial communication. The second memory includes FLASH memory and EMMC memory. The FLASH memory stores the computer program and configuration parameters, while the EMMC memory caches sensor detection data and intermediate data during processing.

[0076] The intelligent infusion device 200 also integrates a TMC2209 stepper motor driver chip, an infrared drop count sensor, a pressure sensor, an ultrasonic bubble sensor, a serial interface, and a power management module. The output of the TMC2209 stepper motor driver chip connects to the control terminal of the drive motor, generating a corresponding drive signal based on the speed control value provided by the second processor. This signal controls the drive motor to operate at the target speed, driving the infusion actuator to act on the infusion tubing and change the flow rate of the liquid within the tubing. The output of the infrared drop count sensor connects to the data input terminal of the second processor, collecting and transmitting pulse signals of droplets in the infusion tubing. The output of the pressure sensor connects to the data input terminal of the second processor, collecting and transmitting pressure detection values ​​from the infusion tubing. The output of the ultrasonic bubble sensor connects to the data input terminal of the second processor, collecting and transmitting bubble detection values ​​from the infusion tubing. The power management module manages and monitors the power supply to each module within the device.

[0077] The intelligent infusion device 200 is also used to obtain working voltage from the power supply device 300 and receive operation commands and indicate working status through buttons / indicator lights; it is connected to the display device 400 through an external serial port interface to send the drip rate detection value and alarm signal to the display device 400 for display.

[0078] Please see Figure 5 The second memory stores a computer program, and the second processor executes the computer program to perform the following steps: S201: Acquire the droplet pulse signal output by the infrared droplet count sensor; S202: Calculate the droplet rate detection value based on the number of droplet pulse signals per unit time; S203: Generate a rotation speed compensation value based on the drip rate detection value and the preset drip rate reference value; S204: Determine the speed control quantity based on the speed compensation value; S205: Generates an injection drive signal based on the speed control value; S206: Based on the infusion drive signal, control the drive motor of the infusion actuator to operate according to the speed control amount, so as to drive the infusion actuator to act on the infusion pipeline and change the flow rate of the liquid in the infusion pipeline; S207: Return to the execution of acquiring fluid state detection data collected by the sensing component until the drop rate detection value is within the target drop rate reference range constructed based on the preset drop rate reference value.

[0079] As an example, the second processor can read the droplet pulse signal from the output of the infrared droplet count sensor. The infrared droplet count sensor can be installed at the drip chamber of the infusion line. The sensor includes an infrared emitting tube and an infrared receiving tube, which are positioned opposite each other on either side of the drip chamber. When a droplet falls from above the drip chamber and passes through the infrared light path between the emitting and receiving tubes, the droplet blocks the infrared beam. The light intensity received by the receiving tube changes from strong to weak and then back to strong. The infrared droplet count sensor converts this light intensity change into a pulse signal, which is output to the second processor as the droplet pulse signal.

[0080] After receiving the droplet pulse signal, the second processor calculates the drop rate detection value based on the number of pulses per unit time. The unit time is a pre-set statistical time window. The second processor counts the received droplet pulse signals periodically within this unit time, calculating the number of pulses per unit time. The second processor divides the counted pulse count by the unit time to obtain the drop rate detection value in drops per minute. For example, if the unit time is set to 10 seconds, and the second processor counts 12 droplet pulse signals within 10 seconds, the drop rate detection value is calculated as 12 ÷ 10 seconds × 60 seconds = 72 drops per minute.

[0081] The second processor generates a speed compensation value based on the detected drip rate and a preset drip rate reference value. The second processor calculates the difference between the detected drip rate and the preset drip rate reference value and inputs this difference into a proportional-integral-derivative (PID) controller. The PID controller multiplies the proportional, integral, and derivative terms of the difference by their respective coefficients and then sums them to generate the speed compensation value. A positive speed compensation value indicates that the detected drip rate is lower than the preset drip rate reference value, requiring an increase in the drive motor speed to improve the liquid flow rate; a negative speed compensation value indicates that the detected drip rate is higher than the preset drip rate reference value, requiring a decrease in the drive motor speed to reduce the liquid flow rate. For example, if the preset drip rate reference value is 60 drops per minute and the currently calculated drip rate is 50 drops per minute, the difference is 10 drops per minute, and the PID controller calculates and outputs a positive speed compensation value.

[0082] The second processor determines the speed control quantity based on the speed compensation value. The second processor reads the speed control quantity at the current moment, adds the current speed control quantity to the speed compensation value, and obtains the updated speed control quantity.

[0083] The second processor generates an injection drive signal based on the updated speed control value. The injection drive signal is a pulse width modulation (PWM) signal, and the duty cycle of the PWM signal is positively correlated with the speed control value. The second processor converts the updated speed control value into a corresponding PWM duty cycle value according to a preset mapping relationship, and generates the injection drive signal based on this duty cycle value.

[0084] The second processor outputs the infusion drive signal to the TMC2209 stepper motor driver chip. The TMC2209 stepper motor driver chip generates a drive current based on the pulse width modulation duty cycle of the infusion drive signal, driving the drive motor of the infusion actuator to operate at the speed corresponding to the speed control value. When the drive motor rotates, it drives the infusion actuator to move via a transmission mechanism. The infusion actuator periodically squeezes and releases the infusion tubing in a peristaltic manner, changing the flow rate of the liquid in the infusion tubing.

[0085] After completing one speed adjustment, the second processor returns to the step of acquiring the droplet pulse signal output by the infrared droplet count sensor. It re-acquires the droplet pulse signal and calculates a new droplet rate detection value. This new droplet rate detection value is then compared with the preset droplet rate reference value to generate a new speed compensation value and update the speed control quantity, forming a closed-loop feedback control cycle from droplet rate detection to speed adjustment. This closed-loop feedback control cycle continues until the droplet rate detection value enters the target droplet rate reference range. The target droplet rate reference range is defined by subtracting a preset allowable negative deviation value from the preset droplet rate reference value as the lower limit, and adding a preset allowable positive deviation value to the preset droplet rate reference value as the upper limit. For example, if the preset droplet rate reference value is 60 drops per minute, the preset allowable negative deviation value is 2 drops per minute, and the preset allowable positive deviation value is 2 drops per minute, then the target droplet rate reference range is 58 drops per minute to 62 drops per minute. When the droplet rate detection value is within the range of 58 drops per minute to 62 drops per minute, the second processor maintains the current speed control quantity unchanged, does not generate a new speed compensation value, and drives the motor to maintain the current speed.

[0086] As can be seen, in this embodiment, the second processor uses the droplet pulse signal collected by the infrared droplet count sensor to calculate the droplet rate detection value, and uses the difference between the droplet rate detection value and the preset droplet rate reference value to generate a speed compensation value through a proportional-integral-derivative controller. The speed control quantity is then adjusted in a closed loop, so that the speed of the drive motor is dynamically adjusted to follow the droplet rate deviation. The liquid flow rate in the infusion pipeline changes accordingly, and the droplet rate detection value gradually approaches the preset droplet rate reference value and stabilizes within the target droplet rate reference range.

[0087] It should be noted that the second processor, used to execute the computer program, also performs the following steps: acquiring the stepping pulse feedback signal of the drive motor; determining the actual number of rotation steps of the drive motor based on the stepping pulse feedback signal; generating a motor step loss detection result based on the actual number of rotation steps and the target number of rotation steps corresponding to the speed control quantity; and generating a motor abnormality alarm signal when the motor step loss detection result indicates a step loss. Thus, the second processor can acquire the stepping pulse feedback signal to count the actual number of rotation steps of the drive motor, compare the actual number of rotation steps with the target number of rotation steps corresponding to the speed control quantity, and determine that the motor has lost step and generate a motor abnormality alarm signal when the difference between the two exceeds a preset step loss threshold, thereby realizing the detection of the actual operating status of the drive motor and step loss alarm.

[0088] In one embodiment, the second processor is further configured to execute a computer program to perform the following steps: S208: Acquire the pipeline pressure detection value collected by the pressure sensor and the bubble detection value collected by the ultrasonic bubble sensor; S209: An alarm signal is generated when the drip rate detection value exceeds the target drip rate reference range, or when the pipeline pressure detection value exceeds the preset pressure reference range, or when the bubble detection value exceeds the preset bubble threshold. S2010: Sends the alarm signal to the display device via the serial port so that the display device can display the alarm information.

[0089] The alarm signals include drip rate abnormality alarm signals, pressure abnormality alarm signals, and bubble alarm signals. A drip rate abnormality alarm signal is generated when the detected drip rate exceeds the target drip rate reference range; a pressure abnormality alarm signal is generated when the detected pipeline pressure exceeds the preset pressure reference range; and a bubble alarm signal is generated when the detected bubble value exceeds the preset bubble threshold.

[0090] As an example, the second processor acquires the pipeline pressure detection value from the output of the pressure sensor via an analog-to-digital converter interface. The pressure sensor is installed on the wall of the infusion pipeline. The pressure sensor converts the pressure of the liquid within the pipeline against the pipe wall into a voltage signal. The second processor reads this voltage signal through the analog-to-digital converter interface and converts it into a pipeline pressure detection value. For example, the pressure sensor outputs a voltage signal from 0.5V to 4.5V, corresponding to a pressure range of 0kPa to 300kPa. The second processor converts the read voltage value using a linear relationship to obtain the pipeline pressure detection value.

[0091] The second processor acquires bubble detection values ​​from the output of the ultrasonic bubble sensor via an input / output interface. The ultrasonic bubble sensor is installed on the outer wall of the infusion tubing and includes an ultrasonic transmitter and an ultrasonic receiver, positioned opposite each other on either side of the tubing. The transmitter emits ultrasonic waves into the tubing, and the receiver receives the waves after they have passed through the liquid. When bubbles are present in the liquid, they reflect and scatter the ultrasonic waves, causing a change in the intensity of the received waves. The ultrasonic bubble sensor outputs a bubble detection value based on this change in intensity. The bubble detection value includes bubble volume parameters and bubble timestamp parameters, which the second processor reads from the output of the ultrasonic bubble sensor.

[0092] The second processor compares the detected drip rate value with the upper and lower limits of the target drip rate reference range. The target drip rate reference range is, for example, 58 drops per minute to 62 drops per minute. When the detected drip rate value is greater than 62 drops per minute or less than 58 drops per minute, the second processor determines that the detected drip rate value exceeds the target drip rate reference range and generates a drip rate anomaly alarm signal.

[0093] The second processor compares the detected pipeline pressure value with the upper and lower limits of a preset pressure reference range. The preset pressure reference range is, for example, 50 kPa to 150 kPa. When the detected pipeline pressure value is greater than 150 kPa, the second processor determines that the pipeline is blocked and generates a pressure anomaly alarm signal; when the detected pipeline pressure value is less than 50 kPa, the second processor determines that the pipeline is leaking and generates a pressure anomaly alarm signal.

[0094] The second processor compares the bubble detection value with a preset bubble threshold. The preset bubble threshold could be, for example, a single bubble volume threshold of 0.1 mL and a bubble count threshold of 3 per minute. The second processor extracts the single bubble volume value and the bubble count value per minute from the bubble detection value. When the single bubble volume value is greater than 0.1 mL, the second processor determines that there is a risk of large-volume bubbles and generates a bubble alarm signal; when the bubble count value per minute is greater than 3 per minute, the second processor determines that there is a risk of continuous bubbles and generates a bubble alarm signal.

[0095] The second processor sends the generated alarm signal to the display device via a serial interface. After receiving the alarm signal, the display device displays the corresponding alarm information on the display screen according to the type of alarm signal.

[0096] It should be noted that the second processor also sends the drip rate detection value, tubing pressure detection value, and bubble detection value to the display device via a serial interface. The display device displays the values ​​of the drip rate detection value, tubing pressure detection value, and bubble detection value on the display screen in real time. For example, the display device displays the current drip rate detection value as "60 drops / min" and the tubing pressure detection value as "100kPa" in digital form in the infusion status display area of ​​the display screen, and displays the current bubble detection result in the bubble status display area.

[0097] As can be seen, through this embodiment, the second processor collects the pipeline pressure detection value and the bubble detection value, and compares them with the corresponding threshold ranges in combination with the drip rate detection value. For abnormal drip rate, abnormal pressure and abnormal bubble, corresponding alarm signals are generated and sent to the display device. At the same time, each detection value is sent to the display device in real time for numerical display, realizing real-time monitoring of multiple parameters, classification of abnormality detection and alarm during infusion.

[0098] In one embodiment, please refer to Figure 3The intelligent infusion system provided in this application embodiment also includes a power supply device 300 and a display device 400. The power supply device 300 is connected to the ultrasound imaging device 100, the intelligent infusion device 200, and the display device 400, respectively, and is used to provide operating voltage to the ultrasound imaging device 100, the intelligent infusion device 200, and the display device 400, and to manage and monitor the power supply of each module. The display device 400 is connected to the ultrasound imaging device 100 and the intelligent infusion device 200, respectively, and is used to receive and display the target ultrasound image sent by the ultrasound imaging device 100, as well as the drip rate detection value and alarm signal sent by the intelligent infusion device 200; this is only an example.

[0099] Please see Figure 6 The power supply unit 300 includes a GD32F303VET6 chip, a power management module, a serial interface, and a power on / off button. The GD32F303VET6 chip serves as the main power management control chip, executing power management logic control. The power management module works with the GD32F303VET6 chip to complete power distribution and voltage conversion, regulate and filter the output voltage, and collect the power supply voltages of the intelligent infusion device 200, display device 400, and ultrasound imaging device 100 to monitor the power supply status of each device in real time. The serial interface is used for data communication with external devices. The power on / off button is used to power on and off the intelligent infusion system. The power supply unit 300 connects to an external DC power supply via an externally configured DC power interface to obtain the input voltage, and provides uninterrupted power to the system when the external DC power supply is disconnected via an externally configured battery.

[0100] Please see Figure 7The display device 400 includes a third processor, a third memory, a power management module, a serial port interface, an LVDS interface, an I2C interface, an external USB interface, a USB 3.0 interface, an HDMI interface, a DVP interface, an RGMII / Ethernet interface, a WIFI / Bluetooth module, a 5G / 4G module, a CAN interface, a MIPI interface, and buttons / indicator lights. The third processor uses a ZTNQ-3CT2100 chip to execute computer programs for image decoding, interface rendering, and audio / video output control. The third memory includes FLASH memory, DDR5 memory, and eMMC memory; the FLASH memory stores computer programs and system configuration parameters, the DDR5 memory caches image and interface data, and the eMMC memory stores the operating system and application programs. The power management module manages and monitors the power supply to the various modules within the display device 400. The LVDS interface is used to connect to an external display to transmit display signals; the I2C interface is used to connect to an external touch module to transmit touch data; the external USB interface is used to connect to an external USB device (such as a USB flash drive); the USB 3.0 interface is used to connect to the ultrasound imaging device 100; the HDMI and DVP interfaces are used to connect to an external display; the RGMII / Ethernet interface is used to connect to a wired network; the WIFI / Bluetooth module is used to connect to a wireless network; the 5G / 4G module is used to connect to a mobile communication network; the CAN interface is used to connect to an external CAN bus device; the MIPI interface is used to connect to an external image input device; the buttons / indicator lights are used to receive operation commands and indicate the working status; and the serial port interface is used to connect to the power supply device 300 and the intelligent infusion device 200.

[0101] The third memory stores a computer program, and the third processor executes the computer program to achieve the following steps: receiving the target ultrasound image sent by the ultrasound imaging device 100 through the USB 3.0 interface and controlling the external display to display the target ultrasound image; receiving the drip rate detection value and alarm signal sent by the intelligent infusion device 200 through the serial port interface and controlling the external display to display the drip rate detection value and alarm signal; when an alarm signal is received, controlling the voice module to output the corresponding voice alarm prompt.

[0102] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the ultrasound imaging method described in the second aspect; or, when executed by a processor, implements the steps executed by the first processor in the ultrasound imaging device described in the first aspect; or, when executed by a processor, implements the steps executed by the second processor in the intelligent infusion device described in the third aspect.

[0103] It should be noted that the functions or steps that the computer-readable storage medium can achieve are described in the foregoing embodiments. To avoid repetition, they will not be described one by one here.

[0104] Fifthly, this application provides an ultrasound imaging device, which corresponds one-to-one with the ultrasound imaging methods described above. Please refer to... Figure 8 The ultrasound imaging device includes an acquisition module 301, a first processing module 302, a second processing module 303, a first generation module 304, and a second generation module 305. Detailed descriptions of each functional module are as follows: The acquisition module 301 is used to acquire ultrasonic echo data of the target area collected by the ultrasonic probe. The ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe. The first processing module 302 is used to perform image reconstruction processing on the ultrasound echo data to obtain ultrasound image data; The second processing module 303 is used to perform vascular feature extraction processing on the ultrasound image data to obtain vascular location parameters and vascular depth parameters. The first generation module 304 is used to generate target display parameters for the guide marker based on the blood vessel location parameters and the blood vessel depth parameters. The second generation module 305 is used to generate a target ultrasound image with the guide identifier superimposed on it based on the ultrasound image data and the target display parameters, and send the target ultrasound image to the display device through a serial port so that the display device displays the target ultrasound image.

[0105] In one embodiment, the acquisition module 301 is further configured to: The ultrasonic probe is controlled to emit an ultrasonic beam toward the target area and to receive the echo signal of the ultrasonic beam. Dynamic focusing delay processing is performed on the echo signal to obtain delay data for each channel; Apodization weighting processing is performed on the delay data of each channel to obtain weighted composite channel data; The synthesized data from the channels is subjected to quadrature demodulation to obtain baseband in-phase components and baseband quadrature components; The baseband in-phase component and the baseband quadrature component are subjected to filtering and extraction processing to obtain downsampled in-phase data stream and downsampled quadrature data stream, respectively. The downsampled in-phase data stream and the downsampled quadrature data stream are used as the ultrasonic echo data.

[0106] In one embodiment, the ultrasound image data includes B-mode grayscale image data and color Doppler image data; In one embodiment, the first processing module 302 is further configured to: An envelope detection operation is performed on the ultrasonic echo data to obtain the envelope signal value; Logarithmic compression mapping is performed on the envelope signal values ​​to generate the B-mode grayscale image data; Wall filtering was performed on the ultrasound echo data to extract the blood flow Doppler frequency shift component; Autocorrelation estimation is performed on the blood flow Doppler frequency shift component to obtain blood flow velocity estimates and blood flow direction estimates; Based on the estimated blood flow velocity and the estimated blood flow direction, color blood flow coding values ​​are mapped to generate the color blood flow Doppler image data.

[0107] In one embodiment, the ultrasound image data includes B-mode grayscale image data and color Doppler image data; In one embodiment, the second processing module 303 is further configured to: Perform vessel wall edge detection on the B-mode grayscale image data to generate vessel contour mask data; Perform blood flow velocity threshold segmentation on the color Doppler image data to generate blood flow region mask data; The vascular contour mask data and the blood flow region mask data are overlaid to obtain vascular fusion mask data. Based on the vascular fusion mask data, the lateral and longitudinal coordinates of the vascular centerline are extracted as the vascular position parameters, and the depth coordinates of the vascular wall are extracted as the vascular depth parameters.

[0108] In one embodiment, the first generation module 304 is further configured to: Based on the blood vessel location parameters, determine the starting coordinates for displaying the guide marker; Based on the blood vessel depth parameters, determine the direction indication parameters of the guide marker; The target display parameters are generated based on the display start coordinates and the direction indication parameters.

[0109] For specific limitations regarding the ultrasound imaging device described in the fifth aspect, please refer to the limitations of the ultrasound imaging device in the first aspect above, which will not be repeated here. Each module in the ultrasound imaging device described in the fifth aspect can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0110] In this application, "multiple" refers to two or more.

[0111] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0112] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0113] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0114] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if a method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0115] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An ultrasonic imaging device, characterized in that, include: A first memory and a first processor, the first memory storing a computer program, the first processor executing the computer program to perform the following steps: Acquire ultrasonic echo data of the target area collected by the ultrasonic probe, wherein the ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe. The ultrasound echo data is subjected to image reconstruction processing to obtain ultrasound image data; The ultrasound image data is processed to extract vascular features, and vascular location parameters and vascular depth parameters are obtained. Based on the vessel location parameters and the vessel depth parameters, target display parameters for the guide marker are generated; Based on the ultrasound image data and the target display parameters, a target ultrasound image with the guide identifier superimposed is generated, and the target ultrasound image is sent to the display device via a serial port so that the display device displays the target ultrasound image.

2. The ultrasonic imaging device according to claim 1, characterized in that, The acquisition of ultrasonic echo data of the target area collected by the ultrasonic probe includes: The ultrasonic probe is controlled to emit an ultrasonic beam toward the target area and to receive the echo signal of the ultrasonic beam. Dynamic focusing delay processing is performed on the echo signal to obtain delay data for each channel; Apodization weighting processing is performed on the delay data of each channel to obtain weighted composite channel data; The synthesized data from the channels is subjected to quadrature demodulation to obtain baseband in-phase components and baseband quadrature components; The baseband in-phase component and the baseband quadrature component are subjected to filtering and extraction processing to obtain downsampled in-phase data stream and downsampled quadrature data stream, respectively. The downsampled in-phase data stream and the downsampled quadrature data stream are used as the ultrasonic echo data.

3. The ultrasonic imaging device according to claim 1, characterized in that, The ultrasound image data includes B-mode grayscale image data and color Doppler image data. The process of performing image reconstruction processing on the ultrasound echo data to obtain ultrasound image data includes: An envelope detection operation is performed on the ultrasonic echo data to obtain the envelope signal value; Logarithmic compression mapping is performed on the envelope signal values ​​to generate the B-mode grayscale image data; Wall filtering was performed on the ultrasound echo data to extract the blood flow Doppler frequency shift component; Autocorrelation estimation is performed on the blood flow Doppler frequency shift component to obtain blood flow velocity estimates and blood flow direction estimates; Based on the estimated blood flow velocity and the estimated blood flow direction, color blood flow coding values ​​are mapped to generate the color blood flow Doppler image data.

4. The ultrasonic imaging device according to claim 1, characterized in that, The ultrasound image data includes B-mode grayscale image data and color Doppler image data. The process of performing vascular feature extraction on the ultrasound image data to obtain vascular location parameters and vascular depth parameters includes: Perform vessel wall edge detection on the B-mode grayscale image data to generate vessel contour mask data; Perform blood flow velocity thresholding on the color Doppler image data to generate blood flow region mask data; The vascular contour mask data and the blood flow region mask data are overlaid to obtain vascular fusion mask data. Based on the vascular fusion mask data, the lateral and longitudinal coordinates of the vascular centerline are extracted as the vascular position parameters, and the depth coordinates of the vascular wall are extracted as the vascular depth parameters.

5. The ultrasonic imaging device according to claim 1, characterized in that, The step of generating target display parameters for the guide marker based on the vessel location parameters and the vessel depth parameters includes: Based on the blood vessel location parameters, determine the starting coordinates for displaying the guide marker; Based on the blood vessel depth parameters, determine the direction indication parameters of the guide marker; The target display parameters are generated based on the display start coordinates and the direction indication parameters.

6. An ultrasound imaging method, characterized in that, The method includes: Acquire ultrasonic echo data of the target area collected by the ultrasonic probe, wherein the ultrasonic echo data is a digital signal obtained by beamforming the echo signal received by the ultrasonic probe. The ultrasound echo data is subjected to image reconstruction processing to obtain ultrasound image data; The ultrasound image data is processed to extract vascular features, and vascular location parameters and vascular depth parameters are obtained. Based on the vessel location parameters and the vessel depth parameters, target display parameters for the guide marker are generated; Based on the ultrasound image data and the target display parameters, a target ultrasound image with the guide identifier superimposed is generated, and the target ultrasound image is sent to the display device via a serial port so that the display device displays the target ultrasound image.

7. An intelligent infusion system, characterized in that, Includes the ultrasound imaging device as described in any one of claims 1-5.

8. The intelligent infusion system according to claim 7, characterized in that, The intelligent infusion system further includes an intelligent infusion device; the intelligent infusion device includes: a second memory and a second processor, the second memory storing a computer program, and the second processor executing the computer program to perform the following steps: Acquire the droplet pulse signal output by the infrared droplet count sensor; The droplet rate detection value is calculated based on the number of pulses in the droplet pulse signal per unit time. Based on the detected drip rate value and the preset drip rate reference value, a rotation speed compensation value is generated; Based on the speed compensation value, determine the speed control quantity; Based on the speed control value, an infusion drive signal is generated; According to the infusion drive signal, the drive motor of the infusion actuator is controlled to operate according to the speed control amount, so as to drive the infusion actuator to act on the infusion pipeline and change the flow rate of the liquid in the infusion pipeline. Return to the process of acquiring the fluid state detection data collected by the sensing component until the drop rate detection value is within the target drop rate reference range constructed based on the preset drop rate reference value.

9. The intelligent infusion system according to claim 8, characterized in that, The second processor is further configured to execute the computer program to perform the following steps: Acquire pipeline pressure detection values ​​from pressure sensors and bubble detection values ​​from ultrasonic bubble sensors; An alarm signal is generated when the drip rate detection value exceeds the target drip rate reference range, or when the pipeline pressure detection value exceeds the preset pressure reference range, or when the bubble detection value exceeds the preset bubble threshold. The alarm signal is sent to the display device via a serial port so that the display device displays the alarm information.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the ultrasound imaging method as described in claim 6.

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