A liver ultrasound image acquisition system and method based on an ultrasound imaging device

Abstract

The application is suitable for the field of ultrasonic imaging technology, and provides a liver ultrasonic image acquisition system based on an ultrasonic imaging device.The liver ultrasonic image acquisition system comprises an ultrasonic imaging device and a host computer connected to the ultrasonic imaging device.The ultrasonic imaging device is used for transmitting ultrasonic signals according to acquisition parameters, generating liver ultrasonic images according to echo signals of the ultrasonic signals, and transmitting the liver ultrasonic images to the host computer through a communication interface.The host computer is used for compressing the liver ultrasonic images when a quality evaluation value of the liver ultrasonic images is greater than a preset evaluation value, generating compressed liver ultrasonic images, establishing a transmission link between the host computer and an image server, and uploading the compressed liver ultrasonic images to the image server through the transmission link.The application can reduce the storage of low-quality liver ultrasonic images in the image server and reduce the capacity pressure of the image server during the acquisition process of the liver ultrasonic images.

Description

Technical Field

[0001] This application belongs to the field of ultrasound imaging technology, and in particular relates to a liver ultrasound imaging acquisition system and method for liver imaging data. Background Technology

[0002] In the clinical acquisition process of liver ultrasound, ultrasound imaging equipment generally acquires and uploads all images generated during the scan without discrimination. Due to the influence of probe operation stability, ultrasound imaging equipment will inevitably produce low-quality liver ultrasound images.

[0003] Current acquisition systems lack liver ultrasound image quality assessment capabilities and cannot intercept low-quality liver ultrasound images during the acquisition phase. This results in low-quality images being directly stored on the image server, wasting storage resources and increasing capacity pressure. Therefore, minimizing the storage of low-quality liver ultrasound images on the image server during acquisition is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a liver ultrasound image acquisition system for liver imaging data, aiming to solve the technical problem of how to reduce the storage of low-quality liver ultrasound images on the image server during the acquisition process.

[0005] In a first aspect, embodiments of this application provide a liver ultrasound image acquisition system based on an ultrasound imaging device, the liver ultrasound image acquisition system including an ultrasound imaging device and a host computer connected to the ultrasound imaging device; Ultrasonic imaging equipment is used to acquire the current status. When the current status is normal, it acquires the working mode and sends the working mode to the host computer. The host computer, when the working mode is Doppler mode, encapsulates the Doppler sampling frequency, pulse repetition frequency, wall filter frequency, sampling volume, scanning angle, imaging depth, frame rate, gain parameter, dynamic range, and number of sampling points in JSON format to generate acquisition parameters, and then transmits the acquisition parameters to the ultrasound imaging device. An ultrasound imaging device is used to transmit ultrasound signals according to the acquisition parameters, generate liver ultrasound images based on the echo signals of the ultrasound signals, and transmit the liver ultrasound images to a host computer through a communication interface. The host computer is used to input liver ultrasound images into a deep learning segmentation model. The deep learning segmentation model segments the spots and artifacts in the liver ultrasound images, generating regions containing spots and artifacts. Pixels within the spot regions are selected as spot pixels, and the number of spot pixels in the liver ultrasound image is calculated. Pixels within the artifact regions are selected as artifact pixels, and the number of artifact pixels in the liver ultrasound image is calculated. Based on the number of artifact pixels, the number of spot pixels, the resolution of the liver ultrasound image, and a preset quality evaluation model, a quality evaluation value for the liver ultrasound image is generated. When the quality evaluation value is greater than the preset value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through this link.

[0006] In one possible implementation of the first aspect, a transmission link is established between a host computer and an image server, through which compressed liver ultrasound images are uploaded to the image server, including: A transmission link is established between the host computer and the image server. Based on the transmission rate, packet loss rate, transmission delay, data volume of the compressed liver ultrasound image, and a preset time model, the estimated transmission time for the compressed liver ultrasound image to be transmitted from the host computer to the image server is generated. When the estimated transmission time is less than the preset time threshold, the compressed liver ultrasound image is uploaded to the image server through the transmission link.

[0007] In one possible implementation of the first aspect, the quality assessment model is defined as follows: ; The quality assessment value of the ultrasound image indicates that the liver ultrasound image performs better in terms of reducing the number of artifact pixels, reducing the number of speckle pixels, and improving resolution. Conversely, a higher quality assessment value indicates that the liver ultrasound image performs worse in terms of reducing the number of artifact pixels, reducing speckle pixels, and improving resolution. This indicates the number of artifact pixels in a liver ultrasound image. Indicates the preset maximum number of artifact pixels; This indicates the number of pixels in a liver ultrasound image. This indicates the preset maximum number of speckle pixels; This indicates the resolution of the liver ultrasound image. Indicates the maximum resolution supported by the ultrasound imaging equipment; Indicates the first weighting coefficient. This represents the second weighting coefficient. Indicates the third weighting coefficient. , , The sum of is 1.

[0008] In one possible implementation of the first aspect, the time model is defined as follows: ; It is the estimated transmission time of compressed liver ultrasound images from the host computer to the image server; This indicates the data volume of the compressed liver ultrasound image. Indicates the transmission rate of the transmission link; This indicates the packet loss rate of the transmission link; This indicates the transmission delay of the transmission link.

[0009] In one possible implementation of the first aspect, the ultrasound imaging device includes a desktop ultrasound imaging device and a portable ultrasound imaging device.

[0010] In one possible implementation of the first aspect, the image server includes a NAS server and an FTP server.

[0011] In one possible implementation of the first aspect, the communication interface is an Ethernet interface.

[0012] In one possible implementation of the first aspect, the communication interface is a USB interface.

[0013] Secondly, embodiments of this application provide an acquisition method based on the aforementioned liver ultrasound imaging system, including: The host computer receives the uploaded results of compressed liver ultrasound images returned by the image server; When the host computer receives a successful upload result, it generates a first recording instruction and a second recording instruction. The first recording instruction records the number of the compressed liver ultrasound image in the log, and the second recording instruction records the upload time of the compressed liver ultrasound image in the log.

[0014] In one possible implementation of the second aspect, when the host computer receives a successful upload result, it generates a first recording instruction and a second recording instruction. The first recording instruction records the number of the compressed liver ultrasound image in the log, and the second recording instruction records the upload time of the compressed liver ultrasound image in the log. Afterwards, the acquisition method includes: Create a display window that shows the number of the compressed liver ultrasound image and the upload time of the compressed liver ultrasound image.

[0015] The beneficial effects of the embodiments of this application are as follows: Firstly, based on the number of artifact pixels, the number of speckle pixels, the resolution, and the preset quality evaluation model of the liver ultrasound image, a quality evaluation value for the liver ultrasound image is generated. When the quality evaluation value of the liver ultrasound image is greater than the preset evaluation value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through the transmission link. This reduces the number of low-quality liver ultrasound images stored on the image server during the acquisition process, thereby saving storage resources on the image server and significantly reducing the capacity pressure on the image server. Secondly, a higher quality evaluation value for liver ultrasound images indicates that the liver ultrasound images perform better in terms of reducing the number of artifact pixels, reducing the number of speckle pixels, and improving resolution. If the quality evaluation value of liver ultrasound images is greater than the preset evaluation value, it means that the liver ultrasound images meet the preset standards, which helps to improve the reliability of liver ultrasound images. Attached Figure Description

[0016] Figure 1 is a structural block diagram of the liver ultrasound imaging acquisition system provided in an embodiment of this application; Figure 2 This is an application flowchart of the liver ultrasound image acquisition system provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the implementation of the data acquisition method provided in this application embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] Example One Referring to Figure 1, which is a structural block diagram of the liver ultrasound image acquisition system provided in an embodiment of this application, detailed below: A liver ultrasound image acquisition system based on an ultrasound imaging device, the liver ultrasound image acquisition system comprising an ultrasound imaging device and a host computer connected to the ultrasound imaging device; Ultrasonic imaging equipment is used to acquire the current status. When the current status is normal, it acquires the working mode and sends the working mode to the host computer. The host computer, when the working mode is Doppler mode, encapsulates the Doppler sampling frequency, pulse repetition frequency, wall filter frequency, sampling volume, scanning angle, imaging depth, frame rate, gain parameter, dynamic range, and number of sampling points in JSON format to generate acquisition parameters, and then transmits the acquisition parameters to the ultrasound imaging device. An ultrasound imaging device is used to transmit ultrasound signals according to the acquisition parameters, generate liver ultrasound images based on the echo signals of the ultrasound signals, and transmit the liver ultrasound images to a host computer through a communication interface. The host computer is used to input liver ultrasound images into a deep learning segmentation model. The deep learning segmentation model segments the spots and artifacts in the liver ultrasound images, generating regions containing spots and artifacts. Pixels within the spot regions are selected as spot pixels, and the number of spot pixels in the liver ultrasound image is calculated. Pixels within the artifact regions are selected as artifact pixels, and the number of artifact pixels in the liver ultrasound image is calculated. Based on the number of artifact pixels, the number of spot pixels, the resolution of the liver ultrasound image, and a preset quality evaluation model, a quality evaluation value for the liver ultrasound image is generated. When the quality evaluation value is greater than the preset value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through this link.

[0019] When the quality evaluation value of a liver ultrasound image is not greater than the preset evaluation value, the liver ultrasound image is removed, and a prompt message to reacquire the liver ultrasound image is displayed.

[0020] The Doppler sampling frequency, measured in Hz, is the sampling rate at which an ultrasound imaging device acquires Doppler signals. The Doppler sampling frequency directly determines the accuracy of signal acquisition; a higher frequency results in better reproduction of the ultrasound signal, effectively reducing signal distortion and providing accurate raw data support for subsequent signal analysis and data processing.

[0021] The pulse repetition frequency (PRF) is the number of times the ultrasonic probe emits an ultrasonic pulse signal per unit time, and it is a key parameter for controlling the signal detection range in ultrasonic imaging. Its value directly affects the speed range of the detectable signal; if it is too low, signal aliasing and measurement errors may occur, while if it is too high, the signal penetration effect may be affected. It needs to be adjusted reasonably according to the imaging requirements.

[0022] The wall filter frequency is a core parameter in ultrasound signal processing. Its main function is to filter out irrelevant interference components in the ultrasound signal and retain the effective signal from the target area. Properly setting the wall filter frequency can reduce the impact of clutter and artifacts on imaging, improve the clarity and signal purity of ultrasound images, and provide a reliable foundation for subsequent image analysis.

[0023] The sampling volume is a specific range within the detection area selected by the ultrasound imaging equipment for signal acquisition and parameter measurement. Its size can be flexibly adjusted according to actual detection needs. Properly setting the sampling volume ensures accurate capture of ultrasound signals from the target area, avoids interference from irrelevant areas, and guarantees the relevance and accuracy of the acquired data.

[0024] The scanning angle, which is the angular range at which the ultrasound probe emits ultrasound waves, directly determines the size of the field of view in ultrasound imaging. A larger scanning angle covers a wider detection area and can capture ultrasound signals from more areas simultaneously; however, an excessively large angle can lead to signal dispersion and reduced image resolution, requiring a balance between the field of view and image clarity.

[0025] Imaging depth refers to the maximum depth to which ultrasound waves can penetrate the object being detected and form an effective image. Its value is directly related to the frequency of the ultrasound probe. The greater the imaging depth, the wider the internal range of the object that can be detected, but this will lead to reduced image brightness, a lower signal-to-noise ratio, and blurred details. Conversely, the smaller the imaging depth, the higher the image clarity, but the detection range is limited. Therefore, it needs to be set reasonably according to the detection requirements.

[0026] The frame rate is the number of ultrasound image frames generated and displayed per second by the ultrasound imaging equipment. The frame rate directly affects the smoothness of dynamic ultrasound images. The higher the frame rate, the clearer the dynamic changes of the object being detected can be captured, avoiding image stuttering and blurring. A frame rate that is too low will result in discontinuous dynamic images, affecting the integrity of signal observation and data acquisition.

[0027] The gain parameter is used to adjust the amplification of the ultrasonic echo signal, and is divided into total gain, near-field gain, and far-field gain, which can be flexibly adjusted according to the detection scenario. Appropriately increasing the gain can enhance weak echo signals and make the image clearer; if the gain is too high, it will amplify clutter signals and produce artifacts; if the gain is too low, the echo signal will be indistinct, resulting in blurred images and affecting subsequent analysis.

[0028] Dynamic range, the amplitude difference between the strongest and weakest echo signals in an ultrasound image, directly determines the image's tonal richness. A larger dynamic range allows for clearer differentiation of signal differences between different regions of the object being examined, revealing more details; a smaller dynamic range results in a monotonous image, losing some signal details and affecting image quality and data accuracy.

[0029] The number of sampling points refers to the total number of signal data points collected by the ultrasound imaging equipment during a single scan. The more sampling points there are, the richer the details of the acquired ultrasound signals, and the higher the resolution and clarity of the image, which can more accurately reflect the actual state of the object being inspected. However, too many sampling points will increase the processing pressure on the equipment and the amount of data stored, so a reasonable balance needs to be struck between detail accuracy and equipment load.

[0030] Among them, the deep learning segmentation model is an artificial intelligence model built on deep learning algorithms to achieve accurate segmentation of images or data. Its core logic is to classify and divide the input liver ultrasound image at the pixel level by simulating the process of human visual perception and feature recognition, thereby extracting the area where the spots are located and the area where the artifacts are located from the liver ultrasound image.

[0031] The quality evaluation model is defined as follows: ; The quality assessment value of the ultrasound image indicates that the liver ultrasound image performs better in terms of reducing the number of artifact pixels, reducing the number of speckle pixels, and improving resolution. Conversely, a higher quality assessment value indicates that the liver ultrasound image performs worse in terms of reducing the number of artifact pixels, reducing speckle pixels, and improving resolution. This indicates the number of artifact pixels in a liver ultrasound image. Indicates the preset maximum number of artifact pixels; This indicates the number of pixels in a liver ultrasound image. This indicates the preset maximum number of speckle pixels; This indicates the resolution of the liver ultrasound image. Indicates the maximum resolution supported by the ultrasound imaging equipment; Indicates the first weighting coefficient. This represents the second weighting coefficient. Indicates the third weighting coefficient. , , The sum of is 1.

[0032] in, , , The sum of is 1, for example, =0.4, =0.3, =0.3.

[0033] Ultrasonic imaging equipment includes desktop ultrasonic imaging equipment and portable ultrasonic imaging equipment.

[0034] The image server includes a NAS server and an FTP server.

[0035] The Chinese name for NAS server is Network Attached Storage Server.

[0036] The full English name of NAS server is Network Attached Storage Server.

[0037] Among them, a NAS server is a dedicated network storage device that connects to the network via a local area network or a wide area network. It can provide centralized and shareable storage services without relying on other computing servers and has independent storage management capabilities.

[0038] The Chinese name for an FTP server is File Transfer Protocol Server. The full English name of an FTP server is File Transfer Protocol Server.

[0039] An FTP server is a server-side device built on the File Transfer Protocol. Its core function is to enable file transfer operations such as uploading, downloading, deleting, and modifying between different devices over a network. It is a core tool for cross-device and cross-network file communication.

[0040] Among them, JSON format is a lightweight plain text data exchange format derived from JavaScript syntax. In data transmission scenarios, JSON format does not depend on a specific programming language, and both host computers and ultrasound imaging equipment can read, write and parse it, enabling efficient transmission of structured information.

[0041] The full English name for JSON format is JavaScript Object Notation. The full Chinese name for JSON format is JavaScript Object Representation. Optionally, the communication interface is an Ethernet interface.

[0042] Optionally, the communication interface is a USB interface.

[0043] When the quality evaluation value of a liver ultrasound image exceeds the preset evaluation value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through the transmission link. This ensures that the quality of the liver ultrasound images uploaded to the server meets the standards, avoids low-quality and interfering images from occupying network bandwidth and server storage resources, improves the storage efficiency of liver ultrasound images, and ensures the standardization and reliability of the uploaded liver ultrasound images.

[0044] For ease of explanation, the following example is provided: For example, if the preset evaluation value is 0.6, and the quality evaluation value of the liver ultrasound image is 0.8, then the overall quality of the liver ultrasound image is at a qualified level and can be uploaded to the image server. If the quality evaluation value of the liver ultrasound image is 0.5, then the overall quality of the liver ultrasound image is at a substandard level and will not be uploaded to avoid consuming bandwidth and storage.

[0045] The preset evaluation value is a threshold for judging whether a liver ultrasound image is acceptable. By using the preset evaluation value, liver ultrasound images with a quality evaluation value greater than the preset evaluation value can be selected without manual judgment.

[0046] For ease of explanation, the following example is provided: Assuming that 200 liver ultrasound images were originally required to be uploaded, after quality screening, only 70 liver ultrasound images with a quality rating greater than the preset rating value will be uploaded. Uploading only these 70 liver ultrasound images with a quality rating greater than the preset rating value reduces the amount of data uploaded, significantly reduces network bandwidth usage, and avoids resource waste caused by invalid data transmission. At the same time, the image server stores only liver ultrasound images with a quality rating greater than the preset rating value, eliminating the need to occupy additional storage space for liver ultrasound images with a quality rating less than the preset rating value. This greatly improves the storage efficiency of the image server and facilitates quick retrieval and viewing of liver ultrasound images with a quality rating greater than the preset rating value.

[0047] In addition, uploading high-quality liver ultrasound images can prevent low-quality images from interfering with teaching, ensure that the liver ultrasound images used in teaching meet the standards, help students establish a correct understanding of liver ultrasound images, cultivate a rigorous professional attitude, facilitate the orderly development of ultrasound teaching, effectively leverage the practical value of ultrasound images in teaching, make the teaching process more targeted and effective, and promote the high-quality development of ultrasound teaching.

[0048] For ease of explanation, the following example is provided: For example, in medical ultrasound teaching scenarios, when teachers conduct instruction on liver ultrasound, qualified liver ultrasound images can be directly used as core teaching materials. Students can access liver ultrasound images at any time through the image server. They can not only intuitively observe the core knowledge points such as the ultrasound morphology and echo characteristics of the liver, but also learn how to interpret liver ultrasound images by comparing image details, and quickly master the key points of liver ultrasound observation and judgment criteria. For example, in medical training courses, students can view liver ultrasound images after meeting the standards through a server, and personally perform training tasks such as image interpretation and parameter adjustment, combining theoretical knowledge with practical operation to improve their ultrasound interpretation skills.

[0049] The beneficial effects of the embodiments of this application are as follows: Firstly, based on the number of artifact pixels, the number of speckle pixels, the resolution, and the preset quality evaluation model of the liver ultrasound image, a quality evaluation value for the liver ultrasound image is generated. When the quality evaluation value of the liver ultrasound image is greater than the preset evaluation value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through the transmission link. This reduces the number of low-quality liver ultrasound images stored on the image server during the acquisition process, thereby saving storage resources on the image server and significantly reducing the capacity pressure on the image server. Secondly, a higher quality evaluation value for liver ultrasound images indicates that the liver ultrasound images perform better in terms of reducing the number of artifact pixels, reducing the number of speckle pixels, and improving resolution. If the quality evaluation value of liver ultrasound images is greater than the preset evaluation value, it means that the liver ultrasound images meet the preset standards, which helps to improve the reliability of liver ultrasound images.

[0050] Example 2 refer to Figure 2 , Figure 2 The following is a flowchart illustrating the application of the liver ultrasound imaging acquisition system provided in this embodiment of the application, detailed below: S201, The ultrasonic imaging device is used to obtain the current status. When the current status is normal, it obtains the working mode and sends the working mode to the host computer. S202, the host computer is used to encapsulate the Doppler sampling frequency, pulse repetition frequency, wall filter frequency, sampling volume, scanning angle, imaging depth, frame rate, gain parameter, dynamic range, and number of sampling points in JSON format when the working mode is Doppler mode, generate the acquisition parameters, and transmit the acquisition parameters to the ultrasound imaging device. S203, the ultrasound imaging device is used to transmit ultrasound signals according to the acquisition parameters, generate liver ultrasound images based on the echo signals of the ultrasound signals, and transmit the liver ultrasound images to the host computer through the communication interface. S204, the host computer is used to input liver ultrasound images into a deep learning segmentation model. The deep learning segmentation model segments the spots and artifacts in the liver ultrasound images, generating regions where spots and artifacts are located. Pixels within the regions where spots are located are selected as spot pixels, and the number of spot pixels in the liver ultrasound image is generated by counting the spot pixels. Pixels within the regions where artifacts are located are selected as artifact pixels, and the number of artifact pixels in the liver ultrasound image is generated by counting the artifact pixels. Based on the number of artifact pixels, the number of spot pixels, the resolution of the liver ultrasound image, and a preset quality evaluation model, a quality evaluation value for the liver ultrasound image is generated. When the quality evaluation value of the liver ultrasound image is greater than the preset evaluation value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through the transmission link.

[0051] This includes establishing a transmission link between the host computer and the image server, and uploading compressed liver ultrasound images to the image server via this link, including: A transmission link is established between the host computer and the image server. Based on the transmission rate, packet loss rate, transmission delay, data volume of the compressed liver ultrasound image, and a preset time model, the estimated transmission time for the compressed liver ultrasound image to be transmitted from the host computer to the image server is generated. When the estimated transmission time is less than the preset time threshold, the compressed liver ultrasound image is uploaded to the image server through the transmission link.

[0052] For example, a transmission link is established between the host computer and the image server. Based on the transmission link's transmission rate, packet loss rate, transmission delay, the data volume of the compressed liver ultrasound image, and a preset time model, an estimated transmission time for the compressed liver ultrasound image to be transmitted from the host computer to the image server is generated, including: The host computer initiates a link establishment request to the image server, receives a response message from the image server based on the link establishment request, establishes a transmission link between the host computer and the image server based on the network address and image server identifier in the response message, sends a preset test data packet to the image server through the transmission link, and receives a response data packet from the image server based on the test data packet. Subtract the sending time of the test data packet from the receiving time of the response data packet to generate the transmission duration of the test data packet. Select the transmission duration of the test data packet as the transmission delay of the transmission link. Divide the number of lost test data packets by the total number of test data packets sent to obtain the packet loss rate of the transmission link. The transmission rate is obtained by dividing the total data volume of the test data packet by the transmission duration of the test data packet. Based on the transmission rate of the transmission link, the packet loss rate of the transmission link, the transmission delay of the transmission link, the data volume of the compressed liver ultrasound image, and the preset time model, the estimated transmission time of the compressed liver ultrasound image from the host computer to the image server is generated.

[0053] The time model is defined as follows: ; It is the estimated transmission time of compressed liver ultrasound images from the host computer to the image server; This indicates the data volume of the compressed liver ultrasound image. Indicates the transmission rate of the transmission link; This indicates the packet loss rate of the transmission link; This indicates the transmission delay of the transmission link.

[0054] in, This represents the base transmission time in a no-packet-loss scenario; adding 1 and Loss will give the retransmission amplification factor caused by packet loss. This quantifies the additional impact of packet loss and retransmission on the overall transmission time. After adding the transmission delay, it can generate the estimated transmission time of compressed liver ultrasound images from the host computer to the image server. The estimated transmission time provides a precise quantitative basis for the quality assessment and performance optimization of the transmission link between the host computer and the image server.

[0055] In this embodiment, when the estimated transmission time is less than a preset time threshold, the compressed liver ultrasound image is uploaded to the image server through the transmission link. This effectively avoids transmission delays, interruptions, and retransmissions caused by network fluctuations, insufficient bandwidth, and other factors, ensuring that the compressed liver ultrasound image is uploaded to the image server.

[0056] Example 3 refer to Figure 3 , Figure 3 The following is a flowchart illustrating the implementation of the data acquisition method provided in this application embodiment, detailed below: Step S301: The host computer receives the uploaded result of the compressed liver ultrasound image returned by the image server; In step S302, when the upload result is successful, the host computer generates a first recording instruction and a second recording instruction. The first recording instruction records the number of the compressed liver ultrasound image in the log, and the second recording instruction records the upload time of the compressed liver ultrasound image in the log.

[0057] In this embodiment of the application, the number of the compressed liver ultrasound image is recorded in the log by the first recording instruction, and the upload time of the compressed liver ultrasound image is recorded in the log by the second recording instruction, so as to realize the whole process of uploading the compressed liver ultrasound image, which is convenient for subsequent verification and positioning.

[0058] Specifically, when the host computer receives a successful upload result, it generates a first recording instruction and a second recording instruction. The first recording instruction records the number of the compressed liver ultrasound image in the log, and the second recording instruction records the upload time of the compressed liver ultrasound image in the log. The acquisition method then includes: Create a display window that shows the number of the compressed liver ultrasound image and the upload time of the compressed liver ultrasound image.

[0059] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and sub-samples of some embodiments may be included in or replace parts and sub-samples of other embodiments.

[0060] In this document, each embodiment focuses on describing the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0061] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. Furthermore, any software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

Claims

1. A liver ultrasound image acquisition system based on an ultrasound imaging device, characterized in that, The liver ultrasound imaging acquisition system includes an ultrasound imaging device and a host computer connected to the ultrasound imaging device. Ultrasonic imaging equipment is used to acquire the current status. When the current status is normal, it acquires the working mode and sends the working mode to the host computer. The host computer, when the working mode is Doppler mode, encapsulates the Doppler sampling frequency, pulse repetition frequency, wall filter frequency, sampling volume, scanning angle, imaging depth, frame rate, gain parameter, dynamic range, and number of sampling points in JSON format to generate acquisition parameters, and then transmits the acquisition parameters to the ultrasound imaging device. An ultrasound imaging device is used to transmit ultrasound signals according to the acquisition parameters, generate liver ultrasound images based on the echo signals of the ultrasound signals, and transmit the liver ultrasound images to a host computer through a communication interface. The host computer is used to input liver ultrasound images into a deep learning segmentation model. The deep learning segmentation model segments the spots and artifacts in the liver ultrasound images, generating regions containing spots and artifacts. Pixels within the spot regions are selected as spot pixels, and the number of spot pixels in the liver ultrasound image is calculated. Pixels within the artifact regions are selected as artifact pixels, and the number of artifact pixels in the liver ultrasound image is calculated. Based on the number of artifact pixels, the number of spot pixels, the resolution of the liver ultrasound image, and a preset quality evaluation model, a quality evaluation value for the liver ultrasound image is generated. When the quality evaluation value is greater than the preset value, the liver ultrasound image is compressed to generate a compressed liver ultrasound image. A transmission link is established between the host computer and the image server, and the compressed liver ultrasound image is uploaded to the image server through this link.

2. The liver ultrasound imaging acquisition system according to claim 1, characterized in that, Establish a transmission link between the host computer and the image server, and upload compressed liver ultrasound images to the image server through the transmission link, including: A transmission link is established between the host computer and the image server. Based on the transmission rate, packet loss rate, transmission delay, data volume of the compressed liver ultrasound image, and a preset time model, the estimated transmission time for the compressed liver ultrasound image to be transmitted from the host computer to the image server is generated. When the estimated transmission time is less than the preset time threshold, the compressed liver ultrasound image is uploaded to the image server through the transmission link.

3. The liver ultrasound imaging acquisition system according to claim 1, characterized in that, The quality assessment model is defined as follows: ； The quality assessment value of the ultrasound image indicates that the liver ultrasound image performs better in terms of reducing the number of artifact pixels, reducing the number of speckle pixels, and improving resolution. Conversely, a higher quality assessment value indicates that the liver ultrasound image performs worse in terms of reducing the number of artifact pixels, reducing speckle pixels, and improving resolution. This indicates the number of artifact pixels in a liver ultrasound image. Indicates the preset maximum number of artifact pixels; This indicates the number of pixels in a liver ultrasound image. This indicates the preset maximum number of speckle pixels; This indicates the resolution of the liver ultrasound image. Indicates the maximum resolution supported by the ultrasound imaging equipment; Indicates the first weighting coefficient. This represents the second weighting coefficient. Indicates the third weighting coefficient. , , The sum of is 1.

4. The liver ultrasound imaging acquisition system according to claim 2, characterized in that, The time model is defined as follows: ； It is the estimated transmission time of compressed liver ultrasound images from the host computer to the image server; This indicates the data volume of the compressed liver ultrasound image. Indicates the transmission rate of the transmission link; This indicates the packet loss rate of the transmission link; This indicates the transmission delay of the transmission link.

5. The liver ultrasound imaging acquisition system according to claim 1, characterized in that, Ultrasound imaging equipment includes desktop ultrasound imaging equipment and portable ultrasound imaging equipment.

6. The liver ultrasound imaging acquisition system according to claim 1, characterized in that, The image server includes a NAS server and an FTP server.

7. The liver ultrasound imaging acquisition system according to claim 1, characterized in that, The communication interface is an Ethernet interface.

8. The liver ultrasound imaging acquisition system according to claim 1, characterized in that, The communication interface is a USB interface.

9. A method for acquiring liver ultrasound images based on the liver ultrasound image acquisition system of claim 1, characterized in that, include: The host computer receives the uploaded results of compressed liver ultrasound images returned by the image server; When the host computer receives a successful upload result, it generates a first recording instruction and a second recording instruction. The first recording instruction records the number of the compressed liver ultrasound image in the log, and the second recording instruction records the upload time of the compressed liver ultrasound image in the log.

10. The data acquisition method according to claim 1, characterized in that, When the host computer receives a successful upload result, it generates a first recording instruction and a second recording instruction. The first recording instruction records the number of the compressed liver ultrasound image in the log, and the second recording instruction records the upload time of the compressed liver ultrasound image in the log. The acquisition method then includes: Create a display window that shows the number of the compressed liver ultrasound image and the upload time of the compressed liver ultrasound image.

Images