Method for evaluating acquisition quality of ultrasonic image and ultrasonic imaging equipment

Abstract

The embodiment of the invention discloses an ultrasonic image acquisition quality evaluation method and ultrasonic imaging equipment, and the method comprises the steps: obtaining an ultrasonic image which comprises an ultrasonic gray-scale image and a sampling image which is displayed in a sampling frame of the ultrasonic gray-scale image in an overlapping manner; the sampling image can comprise a color Doppler image, an elastic image, an energy Doppler image or a vector blood flow image, a focus area in the ultrasonic image can be determined, the coincidence degree of the focus area and the sampling frame is determined, and an evaluation result of the acquisition quality of the ultrasonic image is determined according to the coincidence degree. By providing the method for evaluating the acquisition quality of the ultrasonic image, the possibility that a doctor scans again is reduced, and the diagnosis efficiency and the correctness of a diagnosis result are improved.

Description

[0001] This application is a divisional application of the invention patent application filed on December 15, 2020, with application number 202011479395.X and invention title "A method for evaluating the acquisition quality of ultrasound images and an ultrasound imaging device".

[0002] This application claims priority to Chinese Patent Application No. 201911295919.7, filed on December 16, 2019, entitled "A Method for Evaluating the Acquisition Quality of Ultrasonic Images and an Ultrasonic Imaging Device", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of ultrasound technology, and in particular to a method for evaluating the acquisition quality of ultrasound images and an ultrasound imaging device. Background Technology

[0004] Ultrasound diagnosis is a diagnostic method that applies ultrasound technology to the human body. It obtains ultrasound images by scanning human tissues, and uses these images to understand the data and morphology of the tissues, thereby detecting diseases and providing warnings. Operators can use the probes of an ultrasound imaging system to scan areas of the patient's body (such as the breast, thyroid, and uterus). When a lesion is found, the ultrasound image of that lesion can be saved. Subsequently, doctors or intelligent analysis software can determine the ultrasound analysis results for that lesion based on the saved ultrasound image, such as the lesion's location, size, shape, and echogenicity.

[0005] During the scanning process, operators often acquire poor-quality ultrasound images of lesions due to lack of experience or operational errors. This affects the diagnostic analysis results of doctors or intelligent analysis software, increasing the likelihood of rescanning. Summary of the Invention

[0006] This application provides a method for evaluating the acquisition quality of ultrasound images and an ultrasound imaging device, which can improve diagnostic efficiency and the accuracy of diagnostic results.

[0007] A first aspect of this application provides a method for evaluating the acquisition quality of ultrasound images, comprising: acquiring an ultrasound image of a target tissue, the ultrasound image including an ultrasound grayscale image and a sampled image superimposed within a sampling frame of the ultrasound grayscale image, the sampled image including a color Doppler image, an elastography image, an energy Doppler image, or a vector blood flow image; determining a lesion region in the ultrasound image; determining the overlap between the lesion region and the sampling frame; and determining an evaluation result of the acquisition quality of the ultrasound image based on the overlap.

[0008] A second aspect of this application provides an ultrasound imaging device, comprising:

[0009] probe;

[0010] A transmitting circuit that excites the probe to emit ultrasonic waves toward the target tissue;

[0011] A receiving circuit that controls the probe to receive ultrasound echoes returned from the target tissue to obtain an ultrasound echo signal;

[0012] A processor that processes the ultrasound echo signal to obtain an ultrasound image of the target tissue;

[0013] A display showing the ultrasound image;

[0014] The processor is configured to perform the following steps: acquire an ultrasound image of a target tissue, the ultrasound image including an ultrasound grayscale image and a sampled image superimposed within a sampling frame of the ultrasound grayscale image, the sampled image including a color Doppler image, an elastography image, an energy Doppler image, or a vector blood flow image; determine a lesion region in the ultrasound image; determine the overlap between the lesion region and the sampling frame; and determine an evaluation result of the acquisition quality of the ultrasound image based on the overlap.

[0015] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the ultrasound image acquisition quality assessment method provided in the first aspect.

[0016] The method provided in the first aspect of this application's embodiments acquires an ultrasound image including an ultrasound grayscale image and a color Doppler image or elastic image superimposed within a sampling frame of the ultrasound grayscale image. This method can determine the lesion region in the ultrasound image, determine the overlap between the lesion region and the sampling frame, and determine the evaluation result of the ultrasound image acquisition quality based on the overlap. By providing a method for evaluating the acquisition quality of ultrasound images, this method helps reduce the likelihood of doctors re-scanning, improves diagnostic efficiency, and increases the accuracy of diagnostic results. Attached Figure Description

[0017] Figure 1 This is a schematic structural block diagram of the ultrasound imaging device according to an embodiment of this application;

[0018] Figure 2 This is a schematic diagram of an embodiment of the ultrasound image acquisition quality evaluation method of this application;

[0019] Figure 3 yes Figure 2A schematic diagram of a specific implementation of step 201 in the corresponding embodiment;

[0020] Figure 4 This is a schematic diagram of another embodiment of the ultrasound image acquisition quality evaluation method of this application;

[0021] Figure 5 This is a schematic diagram of another embodiment of the ultrasound image acquisition quality evaluation method of this application;

[0022] Figure 6 This is a schematic diagram of one embodiment of the ultrasound image processing apparatus of this application. Detailed Implementation

[0023] This application provides a method and apparatus for evaluating the acquisition quality of ultrasound images, which assists operators in evaluating the quality of acquired ultrasound images.

[0024] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Figure 1 This is a schematic block diagram of the ultrasonic imaging device 10 according to an embodiment of this application. The ultrasonic imaging device 10 may include a probe 100, a transmitting circuit 101, a transmitting / receiving selection switch 102, a receiving circuit 103, a beamforming circuit 104, a processor 105, a display 106, and a memory 107. The transmitting circuit 101 can excite the probe 100 to emit ultrasonic waves towards a target area. The receiving circuit 103 can receive the ultrasonic echoes returning from the target area through the probe 100, thereby obtaining ultrasonic echo signals / data. The ultrasonic echo signals / data are processed by the beamforming circuit 104 and then sent to the processor 105. The processor 105 processes the ultrasonic echo signals / data to obtain an ultrasonic image of the target object or an invasive object. The ultrasonic images obtained by the processor 105 can be stored in the memory 107. These ultrasonic images can be displayed on the display 106.

[0026] In one embodiment of this application, the display 106 of the aforementioned ultrasonic imaging device 10 may be a touch screen, a liquid crystal display, or an independent display device such as a liquid crystal display or a television set, separate from the ultrasonic imaging device 10, or a display screen on an electronic device such as a mobile phone or tablet computer, etc.

[0027] In one embodiment of this application, the memory 107 of the aforementioned ultrasonic imaging device 10 may be a flash memory card, a solid-state memory, a hard disk, etc.

[0028] In one embodiment of this application, a computer-readable storage medium is also provided, which stores a plurality of program instructions. After being called and executed by the processor 105, the plurality of program instructions can execute some or all of the steps or any combination of the steps in the ultrasound imaging method in various embodiments of this application.

[0029] In one embodiment, the computer-readable storage medium may be a memory 107, which may be a non-volatile storage medium such as a flash memory card, a solid-state memory, or a hard disk.

[0030] In one embodiment of this application, the processor 105 of the aforementioned ultrasound imaging device 10 can be implemented by software, hardware, firmware, or a combination thereof. It can use circuits, one or more application-specific integrated circuits (ASICs), one or more general-purpose integrated circuits, one or more microprocessors, one or more programmable logic devices, or a combination of the aforementioned circuits or devices, or other suitable circuits or devices, so that the processor 105 can execute the corresponding steps of the ultrasound imaging methods in the various embodiments of this application.

[0031] The method for evaluating the acquisition quality of ultrasound images in this application is described below with reference to the accompanying drawings.

[0032] Combination Figure 1 The schematic diagram of the ultrasonic imaging device 10 shown is provided below. Figure 2 The method for evaluating the acquisition quality of ultrasound images provided in this application embodiment may include the following steps:

[0033] 201. Acquire ultrasound images of the target tissue;

[0034] Ultrasonic imaging equipment 10 generally supports multiple modes of ultrasound examination, such as B-mode, color Doppler mode, ultrasound elastography mode, energy Doppler mode, and vector flow mode. B-mode is used to acquire grayscale ultrasound images of human tissues; color Doppler mode is used to acquire color Doppler images of human tissues; and color Doppler, energy Doppler, and vector flow modes are generally used to analyze blood flow in human tissues. Ultrasonic elastography mode is used to acquire elastic images of human tissues, which are generally used to analyze strain information in human tissues.

[0035] In this embodiment, an ultrasound image of the target tissue can be acquired. This ultrasound image may include a grayscale ultrasound image and a sampled image superimposed within a sampling frame of the grayscale ultrasound image. The sampled image may include a color Doppler image, an elastography image, an energy Doppler image, or a vector blood flow image. The target tissue may be a portion of the patient's body tissue, such as the thyroid gland, breast, or uterus. The sampling frame can be manually selected, for example, by manually selecting the sampling frame on the displayed grayscale ultrasound image of the tissue; alternatively, the machine can automatically retrieve the sampling frame and then manually adjust it to a suitable position; or the machine can automatically retrieve the sampling frame and automatically adjust it to a suitable position, which may be the lesion area.

[0036] In one possible implementation, refer to Figure 3 In step 201, the ultrasound imaging device 10 may specifically perform the following steps:

[0037] 2011. The first ultrasonic wave is emitted towards the target tissue, and the ultrasonic echo returned from the target tissue is received to obtain the first ultrasonic echo signal;

[0038] In mode B, the ultrasound imaging device 10 can emit ultrasound waves (referred to as first ultrasound waves) toward the target tissue and receive ultrasound echoes returning from the target tissue to obtain a first ultrasound echo signal.

[0039] 2012. The first ultrasonic echo signal was processed to obtain an ultrasonic grayscale image;

[0040] By performing beamforming, image processing, and other techniques on the obtained first ultrasound echo signal, an ultrasound grayscale image can be obtained, which can characterize the B-image of the target tissue.

[0041] 2013. Receive the operation command to switch to sampling mode:

[0042] The ultrasound imaging device 10 can receive input mode switching commands and switch to the corresponding sampling mode. This sampling mode may include color Doppler mode, elastic mode, energy Doppler mode, or vector flow mode. For example, the ultrasound imaging device 10 generally has options corresponding to each mode; for instance, mode B generally corresponds to the option marked "B", color Doppler mode generally corresponds to the option marked "C", energy Doppler mode generally corresponds to the option marked "P", and elastic mode generally corresponds to the option marked "E". The input of these commands can be achieved through buttons, touch, voice, or gestures; no specific method is specified here.

[0043] 2014. In response to operation commands, a sampling frame is displayed on the ultrasound grayscale image;

[0044] The size and position of the sampling frame can be the default, or it can be adjusted by the user, or it can be intelligently set by the ultrasound imaging device 10 through the analysis of the ultrasound grayscale image (e.g., the analysis of lesions).

[0045] 2015. A second ultrasonic wave is emitted toward the target tissue, and the ultrasonic echo returned from the target tissue is received to obtain a second ultrasonic echo signal;

[0046] 2016. The second ultrasonic echo signal is processed to obtain a sampled image superimposed within the sampling frame of the ultrasonic grayscale image;

[0047] The sampled image can be one or more of the following: color Doppler image, elastic image, energy Doppler image, and vector blood flow image.

[0048] In one possible implementation, an ultrasound image, comprising an ultrasound grayscale image and a sampled image superimposed within a sampling frame of the ultrasound grayscale image, can also be read from a storage medium.

[0049] 202. Identify the lesion area in the ultrasound image;

[0050] After step 201, the ultrasound imaging device 10 can determine the lesion area in the ultrasound image.

[0051] In one possible implementation, after step 201, the ultrasound imaging device 10 may, in response to receiving a command to save the ultrasound image, determine the lesion region in the ultrasound image.

[0052] In one possible implementation, the user can select the lesion area in the ultrasound image based on experience, and the ultrasound imaging device 10 can determine the lesion area in the ultrasound image based on the user's selection.

[0053] Alternatively, in one possible implementation, the ultrasound imaging device 10 can analyze the ultrasound image and automatically determine the lesion area in the ultrasound image.

[0054] Regarding methods for determining the lesion region, in one possible implementation, the lesion region can be determined from an ultrasound grayscale image; in another possible implementation, the lesion region can be determined from a sampled image; in yet another possible implementation, the lesion region can be determined based on an ultrasound grayscale image and a color Doppler image; or, in yet another possible implementation, the lesion region can be determined based on an ultrasound grayscale image and an elastic image.

[0055] It should be noted that the determination of the aforementioned lesion regions can utilize traditional image processing boundary segmentation or object detection algorithms, or it can employ machine learning or deep learning algorithms. Taking breast lesions as an example, machine learning or deep learning algorithms involve feeding the image of the breast lesion boundaries, already labeled by the doctor, along with the coordinates of the boundaries or regions of interest (ROIs), into a deep learning segmentation or object detection network for training, such as a convolutional neural network. During training, the error between the predicted value and the labeled location is calculated, iteratively approximating the model to obtain a reference model for lesion segmentation or location detection. For breast images in different modes (e.g., color Doppler mode, elastic mode, energy Doppler mode, or vector blood flow mode), different models or algorithms can be selected to determine the lesion regions.

[0056] 203. Determine the overlap between the lesion area and the sampling frame;

[0057] For example, the degree of overlap can be determined based on the ratio of the intersection and union of the lesion area and the sampling frame, or based on the distance between the center of the lesion area and the center of the sampling frame.

[0058] 204. Determine the evaluation results of ultrasound image acquisition quality based on overlap;

[0059] In one possible implementation, in step 204, the ultrasound imaging device 10 can determine the acquisition quality level or acquisition quality score of the ultrasound image based on the overlap determined in step 203, and then display the acquisition quality level or acquisition quality score of the ultrasound image.

[0060] A higher degree of overlap indicates a better quality level or score for the ultrasound image acquisition. For example, a pre-defined correspondence between overlap and acquisition quality level or score can be established. Specifically, an overlap between 0 and 0.3 corresponds to a poor quality level; an overlap between 0.3 and 0.6 corresponds to a good quality level; and an overlap between 0.6 and 1 corresponds to a superior quality level. Different acquisition quality levels or scores can be distinguished using different text, graphics, or colors.

[0061] In one possible implementation, the overlap ratio can be used to determine whether the ultrasound image meets a preset condition (referred to as the first preset condition). For example, in step 204, when the overlap ratio is greater than or equal to a preset threshold, the ultrasound imaging device 10 can determine that the acquisition quality of the ultrasound image meets the first preset condition; when the overlap ratio is less than the preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet the first preset condition. If the acquisition quality of the ultrasound image meets the first preset condition, it can be considered that the acquisition quality of the ultrasound image is qualified, or the acquisition quality meets the requirements, reaches a preset level, reaches a preset score, etc., and the ultrasound image has a high degree of reliability as a basis for disease diagnosis, and the ultrasound image can be saved. If the acquisition quality of the ultrasound image does not meet the first preset condition, it can be considered that the acquisition quality of the ultrasound image is unqualified, or the acquisition quality is insufficient, does not reach a preset level, does not reach a preset score, etc., and the ultrasound image has a low degree of reliability as a basis for disease diagnosis, and a rescan can be requested.

[0062] In one possible implementation, the acquisition quality of the ultrasound image can be further comprehensively evaluated by combining the image quality of the ultrasound image itself. The evaluation method for the acquisition quality of the ultrasound image also includes:

[0063] Determine the image quality of the ultrasound images;

[0064] The evaluation results of determining the acquisition quality of ultrasound images based on the degree of overlap in step 204 further include:

[0065] The assessment results of ultrasound image acquisition quality are determined based on the overlap and image quality of the ultrasound images.

[0066] In one possible implementation, determining the image quality of an ultrasound image includes determining the image quality of the ultrasound image based on at least one of the following: image grayscale, image sharpness, effective area ratio of the image, presence of spots, snowflake-like particles or meshes in the image, and the probe used, probe parameters or imaging parameters.

[0067] It should be noted that the acquisition quality of the ultrasound image can be comprehensively evaluated based on the aforementioned overlap ratio and image quality. For example, when the overlap ratio is between 0 and 0.3, the acquisition quality level is poor regardless of whether the image quality is high or low; when the overlap ratio is between 0.3 and 0.6, the image quality is high and the acquisition quality level is good, while the image quality is low and the acquisition quality level is poor; when the overlap ratio is between 0.6 and 1, the image quality is high and the acquisition quality level is excellent, while the image quality is low and the acquisition quality level is poor or good. Furthermore, when the overlap ratio is greater than or equal to a preset threshold and the image quality is high, it can be determined that the acquisition quality of the ultrasound image meets the first preset condition; when the overlap ratio is greater than or equal to the preset threshold and the image quality is low, it can be determined that the acquisition quality of the ultrasound image does not meet the first preset condition; and when the overlap ratio is less than the preset threshold, it can be determined that the acquisition quality of the ultrasound image does not meet the first preset condition.

[0068] Taking image grayscale as an example, image grayscale can include the overall grayscale of the ultrasound image or the grayscale of the ultrasound image within the effective region. Image quality can be determined based on at least one of the following: whether the mean of the image grayscale is within a threshold range, whether the image grayscale is uniform, and whether the extreme values ​​of the image grayscale meet the criteria for extreme grayscale values. To determine whether the grayscale of the ultrasound image is uniform, a grayscale histogram of the ultrasound image can be plotted. By judging whether the grayscale is uniformly distributed in the grayscale histogram, it can be ensured that the image grayscale does not concentrate in a certain area and affect the image quality of the ultrasound image.

[0069] If the grayscale of an ultrasound image meets the grayscale standards—for example, if the average grayscale value is appropriate and the image is uniform—then the ultrasound image can accurately display the morphology of the thyroid or breast, and the quality of the ultrasound image is high. Conversely, if the grayscale of an ultrasound image does not meet the grayscale standards, the quality of the ultrasound image is low. Therefore, the quality of an ultrasound image can be determined by its grayscale. For example, standards for the average grayscale value, grayscale uniformity, and grayscale extreme values ​​can be set to define the grayscale quality of the ultrasound image. Furthermore, the deviation between the grayscale value of the ultrasound image and the grayscale standards can be calculated, and a functional relationship or other correspondence between this deviation and image quality can be established to determine the quality of the ultrasound image through the relationship between the grayscale value and the grayscale standards. Of course, the deviation between the grayscale value of an ultrasound image and the grayscale standards can be evaluated from one perspective, such as the grayscale uniformity dimension; or it can be evaluated from multiple dimensions, such as the average grayscale value, grayscale extreme values, and grayscale uniformity, to comprehensively obtain the deviation between the grayscale value of the ultrasound image and the grayscale standards.

[0070] Taking image sharpness as an example, high ultrasound image sharpness corresponds to high ultrasound image quality, while low ultrasound image sharpness corresponds to low ultrasound image quality. Ultrasound image sharpness can be a specific value, expressed as a score out of ten, a percentage, or a fraction; it can also be a qualitative standard, including terms like sharp, relatively sharp, somewhat blurry, or blurry. Image sharpness can be calculated from dimensions such as whether the ultrasound image is too bright or too dark, or whether the ultrasound image resolution is high enough.

[0071] In one embodiment, the sharpness of an ultrasound image can be calculated based on gradient information. Generally, the higher the gradient value, the richer the edge information of the image, and the sharper the image. For example, a functional relationship or other correspondence between the gradient information of the effective region and image sharpness can be established. For instance, image sharpness can be calculated based on gradient information using the Brenner gradient function, Tenengrad gradient function, Laplacian gradient function, etc. In another embodiment, an artificial intelligence model can be trained by inputting two types of thyroid or breast ultrasound images: sharp and blurry. For example, the artificial intelligence model can perform a binary classification problem of sharp and blurry ultrasound images; for the input ultrasound image to be tested, the artificial intelligence model can input a classification result of sharpness or blurriness. It should be emphasized that the artificial intelligence model can also classify the sharpness of ultrasound images into levels such as sharp, relatively sharp, relatively blurry, and blurry, so that the artificial intelligence model can output a sharpness classification for the input ultrasound image to be tested.

[0072] Take the presence of spots, snowflake-like patterns, or reticular patterns in an image as an example. Detecting the presence of spots, snowflake-like patterns, or reticular patterns in an ultrasound image can be done by inspecting the entire ultrasound image; alternatively, a valid region can be identified first, and then the ultrasound image within that valid region can be inspected. Understandably, if spots, snowflake-like patterns, or reticular patterns are present in an ultrasound image, they may obscure key structures of the thyroid or breast, affecting image quality. Therefore, a functional relationship or other correspondence can be established between the presence of spots, snowflake-like patterns, or reticular patterns and image validity. For example, the larger the area of ​​spots, snowflake-like patterns, or reticular patterns in an ultrasound image, the lower the image quality; conversely, the smaller the area of ​​spots, snowflake-like patterns, or reticular patterns, the higher the image quality; when there are no spots, snowflake-like patterns, or reticular patterns in an ultrasound image, the image quality is the highest in the evaluation dimension of image defects. Furthermore, based on the different degrees of influence of the three factors on the identification of the thyroid or breast in the image, different weights can be assigned to the three image defects of spots, snowflakes, or reticular patterns, etc., so as to determine the quality of the ultrasound image based on whether spots, snowflakes, or reticular patterns exist in the detected ultrasound image.

[0073] For the detection of spots, snowflake-like textures, or mesh-like patterns in ultrasound images, the texture of the ultrasound image can be checked to see if it conforms to a preset image texture standard. For example, an image texture detection model can be pre-trained, and the ultrasound image can be input into the detection model to obtain the detection result of whether the texture conforms to the preset image texture standard. The image texture includes: whether the image has spots, snowflake-like textures, or mesh-like patterns.

[0074] Taking the effective region ratio of an image as an example, the quality of an ultrasound image can be determined by the effective region ratio. The effective region of an ultrasound image can be any ultrasound image region related to the acquisition of detection information. For example, for the thyroid gland, the effective region can be any ultrasound image region containing the thyroid gland image, or an image region containing thyroid nodules, or other ultrasound image regions related to the acquisition of detection information. Detecting the effective region ratio of an ultrasound image is mainly to ensure that the effective region occupies an appropriate proportion of the overall image; for example, the proportion should not be too small, but should be greater than 1 / 2. For example, a specific detection method involves obtaining the effective region through image processing threshold segmentation, calculating the proportion of the effective region to the overall image region, and determining whether this proportion meets a preset requirement. The size or proportion of the effective region is related to parameters such as the ultrasound scanning depth or magnification / reduction factor. In one embodiment, it is possible to detect whether the ultrasound scanning depth meets a standard, for example, whether the ultrasound scanning depth is within a threshold range, thereby determining whether the effective region ratio of the ultrasound image is appropriate.

[0075] Understandably, if the effective area of ​​an ultrasound image is too small, it will be difficult to accurately reflect the morphology of the thyroid or breast on the ultrasound image, which is not conducive to obtaining detection information based on the ultrasound image. Therefore, the quality of an ultrasound image can be determined by the effective area ratio. For example, the effective area ratio of an ultrasound image can be calculated, and a functional relationship or other correspondence between the effective area ratio of an ultrasound image and the image quality can be established to determine the quality of the ultrasound image by the effective area ratio of the ultrasound image.

[0076] Taking probes, probe parameters, and / or imaging parameters as examples, the quality of an ultrasound image can be determined by the correspondence between the probe, probe parameters, and / or imaging parameters and the thyroid or breast tissue being examined in the ultrasound image. When performing ultrasound examinations on patients, different probe parameters and imaging parameters need to be selected according to different examination sites to achieve the best imaging effect for each site. For example, a high-frequency linear array probe is used for superficial thyroid and breast tissues; a low-frequency convex array probe is used for abdominal organs. However, in practice, users may, due to lack of experience or negligence, incorrectly use an ultrasound probe and corresponding probe parameters suitable for the abdomen, as well as the corresponding imaging parameters for the abdomen, during thyroid or breast ultrasound imaging. This will result in a failure to obtain high-quality thyroid or breast ultrasound images, affecting the overall quality of the ultrasound image. Similarly, users may incorrectly use imaging parameters suitable for the breast during thyroid ultrasound imaging, which will also result in a failure to obtain high-quality thyroid ultrasound images, affecting the overall quality of the ultrasound image.

[0077] The system can identify the tissue types contained in an ultrasound image and compare them with the probe, probe parameters, and imaging parameters used to scan the image. When the tissue types in the ultrasound image correspond to the probe, probe parameters, and imaging parameters used, the ultrasound image quality is determined to be high; when the tissue types in the ultrasound image do not correspond to the probe, probe parameters, and imaging parameters used, the ultrasound image quality is determined to be low. Specifically, the tissue types in the ultrasound image can be compared with all of the probe, probe parameters, and imaging parameters used to scan the image, or only one or two of the probe, probe parameters, and imaging parameters used to scan the ultrasound image can be compared to determine the correspondence, thereby determining the image quality. Furthermore, a functional relationship or other correspondence can be established between the type of probe, probe parameters, and / or imaging parameters, the type of thyroid or breast tissue included in the ultrasound image, and the image quality, to determine the ultrasound image quality through this correspondence.

[0078] When using ultrasound imaging equipment 10 to examine a patient's target tissue, quality assessment of the acquired ultrasound images helps operators obtain high-quality ultrasound images, thereby reducing the probability of misdiagnosis and the likelihood of re-scanning the patient.

[0079] Alternatively, in one possible implementation, the ultrasound image processing method provided in this application can be applied not only to the ultrasound imaging device 10, but also to other computer devices (referred to as the target computer device), such as laptops, tablets, and desktop computers. After acquiring the ultrasound image of the target tissue, the ultrasound imaging device 10 can transmit it to the target computer device, where it is stored in a storage medium. Step 201 can specifically be: the target computer device reads the ultrasound image from the storage medium.

[0080] refer to Figure 4 In one possible implementation, the method of this application embodiment may further include the following steps:

[0081] 401. Acquire at least two frames of ultrasound grayscale images of the target tissue;

[0082] 402. Identify the lesion area in at least two frames of ultrasound grayscale images;

[0083] 403. Determine the lesion grade of the lesion region in at least two frames of ultrasound grayscale images;

[0084] In one possible implementation, the lesion grade of the lesion region in at least two frames of ultrasound grayscale images can be determined according to the grading corresponding to the BI-RADS breast imaging report and data system. This lesion grade can characterize the benign or malignant nature of the lesion. The ultrasound image can be judged to meet preset conditions (referred to as the second preset condition) based on the lesion grade; specifically, refer to steps 404 and 405.

[0085] 404. The lesion level of the lesion area based on at least two frames of ultrasound grayscale images meets the similarity condition, indicating that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition.

[0086] 405. The lesion level of the lesion area based on at least two frames of ultrasound grayscale images does not meet the similarity condition, indicating that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition.

[0087] If the acquisition quality of at least two ultrasound grayscale images meets the second preset condition, the ultrasound image can be considered to have high reliability as a basis for disease diagnosis; conversely, if the acquisition quality does not meet the second preset condition, the ultrasound image can be considered to have low reliability as a basis for disease diagnosis. Whether the acquisition quality of at least two ultrasound grayscale images meets the second preset condition can be indicated through text, graphics, or other means. The second preset condition can be referred to in the relevant explanation of the first preset condition mentioned above, and will not be repeated here.

[0088] It should be noted that step 404 only limits the similarity condition of lesion grades to a necessary condition for the acquisition quality of at least two frames of ultrasound grayscale images to meet the second preset condition, and does not limit the similarity condition of lesion grades to a necessary and sufficient condition for the acquisition quality of at least two frames of ultrasound grayscale images to meet the second preset condition; the same explanation applies to step 405, and will not be repeated here. It should be noted that the similarity condition can be that the lesion grades are the same or nearly the same. Nearly the same can be considered as lesion grades being very similar, for example, a difference of one grade can be considered nearly the same. The dissimilarity condition can be that the lesion grades are different or significantly different, for example, a difference of two or more grades can be considered significantly different.

[0089] In one possible implementation, the target tissue may include breast tissue, and the at least two grayscale ultrasound images may include a cross-sectional image and a longitudinal cross-sectional image of the breast tissue. Step 403 may specifically include: determining the lesion grade of the lesion region in the cross-sectional image and the lesion grade of the lesion region in the longitudinal cross-sectional image according to the grading corresponding to the Breast Imaging Reporting and Data System (BI-RADS). It should be noted that the cross-sectional image is generally also called a transverse section image, and the longitudinal cross-sectional image is generally also called a longitudinal section image. The transverse section is generally a section with the maximum diameter of the lesion or close to the maximum diameter, and the longitudinal section is a section perpendicular to or approximately perpendicular to the transverse section.

[0090] In one possible implementation, after step 401 and before steps 404 and 405, the acquisition quality of the ultrasound image can be further comprehensively evaluated in conjunction with the aforementioned image quality. For example, the resolution and / or sharpness of at least two frames of ultrasound grayscale images can be determined, and whether the resolution and / or sharpness meet a third preset condition (e.g., a preset resolution and / or sharpness) can be used as a criterion for determining whether the acquisition quality of at least two frames of ultrasound grayscale images meets a second preset condition. For example, step 404 can specifically be: if the resolution and / or sharpness meet the third preset condition, and the lesion level meets a similar condition, it indicates that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition. Step 405 can specifically be: if the resolution and / or sharpness do not meet the third preset condition, or the lesion level does not meet a similar condition, it indicates that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition. The third preset condition can be that the resolution and / or sharpness is greater than a certain threshold. The similarity condition can be understood with reference to the foregoing explanation and will not be repeated here.

[0091] In one possible implementation, the ultrasound grayscale image and the sampled image can be displayed as two independent images, rather than superimposed, as shown in the reference. Figure 5 The method in this application embodiment may further include the following steps:

[0092] 501. Acquire at least one frame of ultrasound grayscale image and at least one frame of sampled image of the target tissue;

[0093] The sampled images include color Doppler images, elastic images, energy Doppler images, or vector blood flow images;

[0094] 502. Determine the lesion region in at least one frame of ultrasound grayscale image and at least one frame of sampled image;

[0095] 503. Determine the lesion grade of at least one frame of ultrasound grayscale image and the lesion grade of the lesion region of at least one sampled image;

[0096] 504. The lesion level of the lesion region based on at least one frame of ultrasound grayscale image and the lesion level of the lesion region based on at least one frame of sampled image meet similar conditions, indicating that the acquisition quality of at least one frame of ultrasound grayscale image and at least one frame of sampled image meets the second preset condition.

[0097] 505. If the lesion grade of the lesion region based on at least one frame of ultrasound grayscale image does not meet the similarity condition with the lesion grade of the lesion region based on at least one frame of sampled image, it indicates that the acquisition quality of at least one frame of ultrasound grayscale image and at least one frame of sampled image does not meet the second preset condition. This similarity condition can be understood with reference to the foregoing explanation and will not be repeated here.

[0098] It is understood that this embodiment can further combine the aforementioned image quality to comprehensively evaluate the acquisition quality of the ultrasound image. The relevant content can be understood by referring to the foregoing embodiments, and will not be repeated here.

[0099] In one possible implementation, Figure 5 In the corresponding embodiment, the target tissue may include breast tissue, and step 503 may specifically include: determining the lesion grade of at least one frame of ultrasound grayscale image and the lesion grade of at least one frame of sampled image according to the grading corresponding to the breast imaging report and data system BI-RADS.

[0100] The foregoing has provided a detailed description of the ultrasound image acquisition quality assessment method provided in this application. This application also provides an ultrasound image acquisition quality assessment device. (Reference) Figure 6 The ultrasound image acquisition quality assessment device of this application can be a computer device, including a processor 601 and a storage medium 602. In one possible implementation, the two can be connected via a bus. The storage medium 602 stores computer instructions. By calling the computer instructions, the processor 601 performs the following steps:

[0101] Acquire ultrasound images of the target tissue. The ultrasound images include ultrasound grayscale images and sampled images superimposed within the sampling frame of the ultrasound grayscale images. The sampled images include color Doppler images, elastography images, energy Doppler images, or vector blood flow images.

[0102] Identify the lesion area in the ultrasound image;

[0103] Determine the overlap between the lesion area and the sampling frame;

[0104] The assessment results of the acquisition quality of ultrasound images are determined based on the degree of overlap.

[0105] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0106] Read ultrasound images from the storage medium.

[0107] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0108] The first ultrasonic wave is emitted toward the target tissue, and the ultrasonic echo returned from the target tissue is received to obtain the first ultrasonic echo signal;

[0109] The first ultrasonic echo signal is processed to obtain an ultrasonic grayscale image.

[0110] Receive an operation command to switch to the sampling mode, which includes color Doppler mode, elastic mode, energy Doppler mode, or vector blood flow mode.

[0111] In response to an operation command, a sampling frame is displayed on the ultrasound grayscale image;

[0112] A second ultrasonic wave is emitted toward the target tissue, and the ultrasonic echo returned from the target tissue is received to obtain a second ultrasonic echo signal;

[0113] The second ultrasonic echo signal is processed to obtain a sampled image superimposed within the sampling frame of the ultrasonic grayscale image.

[0114] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0115] Receive instructions to save ultrasound images;

[0116] In response to the save command, the lesion area in the ultrasound image is identified.

[0117] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0118] Determine the lesion region in the ultrasound grayscale image, and / or, determine the lesion region in the sampled image.

[0119] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0120] The acquisition quality level or acquisition quality score of the ultrasound images is determined based on the degree of overlap.

[0121] Displays the acquisition quality level or acquisition quality score of the ultrasound image.

[0122] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0123] When the overlap is greater than or equal to a preset threshold, the acquisition quality of the ultrasound image is determined to meet the first preset condition.

[0124] When the overlap is less than a preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet the first preset condition.

[0125] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0126] The ultrasound image is saved once the acquisition quality meets the first preset condition.

[0127] The ultrasound image acquisition quality does not meet the first preset condition, prompting a rescan.

[0128] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0129] Acquire at least two frames of ultrasound grayscale images of the target tissue;

[0130] Identify the lesion region using at least two frames of ultrasound grayscale images;

[0131] Determine the lesion grade of the lesion region in at least two frames of ultrasound grayscale images;

[0132] The lesion level of the lesion region based on at least two frames of ultrasound grayscale images meets the similarity condition, indicating that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition.

[0133] The lesion level of the lesion region based on at least two frames of ultrasound grayscale images does not meet the similarity condition, indicating that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition.

[0134] In one possible implementation, the target tissue includes breast tissue, and at least two frames of ultrasound grayscale images include a cross-sectional image and a longitudinal cross-sectional image of the breast tissue.

[0135] Processor 601 is specifically used to perform the following steps:

[0136] The lesion grade of the lesion area in the cross-sectional image and the lesion grade of the lesion area in the longitudinal section image are determined according to the classification of the BI-RADS breast imaging reporting and data system.

[0137] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0138] Determine whether the resolution and / or sharpness of at least two frames of ultrasound grayscale images meet the third preset condition;

[0139] Based on the resolution and / or clarity meeting the third preset condition, and the lesion level meeting the similar condition, it is indicated that the acquisition quality of at least two frames of ultrasound grayscale images meets the second preset condition.

[0140] If the resolution and / or clarity do not meet the third preset condition, or the lesion grade does not meet the similarity condition, it indicates that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition.

[0141] In one possible implementation, processor 601 is specifically used to perform the following steps:

[0142] Acquire at least one frame of ultrasound grayscale image and at least one frame of sampled image of the target tissue, including color Doppler image, elastography image, energy Doppler image or vector blood flow image;

[0143] Identify the lesion region in at least one frame of ultrasound grayscale image and the lesion region in at least one frame of sampled image;

[0144] Determine the lesion grade of at least one frame of ultrasound grayscale image and the lesion grade of the lesion region of at least one sampled image;

[0145] The lesion level of the lesion region based on at least one frame of ultrasound grayscale image and the lesion level of the lesion region based on at least one frame of sampled image meet similar conditions, indicating that the acquisition quality of at least one frame of ultrasound grayscale image and at least one frame of sampled image meets the second preset condition.

[0146] The lesion level of the lesion region based on at least one frame of ultrasound grayscale image does not meet the similarity condition with the lesion level of the lesion region based on at least one frame of sampled image, indicating that the acquisition quality of at least one frame of ultrasound grayscale image and at least one frame of sampled image does not meet the second preset condition.

[0147] In one possible implementation, the target tissue includes breast tissue, and the processor 601 is specifically configured to perform the following steps:

[0148] Based on the classification corresponding to the BI-RADS breast imaging report and data system, determine the lesion grade of at least one frame of ultrasound grayscale image and the lesion grade of the lesion region of at least one sampled image.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] It should be noted that, in practical applications, the target tissue can be human, animal, etc. The target tissue can be the face, spine, heart, uterus, thyroid, or pelvic floor, or other parts of the human body, such as the brain, bones, liver, or kidneys, etc. This application does not limit the specific target tissue.

[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the acquisition quality of ultrasound images, characterized in that, include: Acquire ultrasound images of the target tissue, the ultrasound images including an ultrasound grayscale image and a sampled image superimposed within the sampling frame of the ultrasound grayscale image, the sampled image including a color Doppler image, an elastography image, an energy Doppler image or a vector blood flow image; Identify the lesion region in the ultrasound image; Determine the degree of overlap between the lesion region in the ultrasound image and the sampling frame; The assessment result of the acquisition quality of the ultrasound image is determined based on the degree of overlap.

2. The method according to claim 1, characterized in that, The acquisition of ultrasound images of the target tissue includes: reading the ultrasound images from a storage medium; Alternatively, acquiring ultrasound images of the target tissue includes: A first ultrasonic wave is emitted toward the target tissue, and the ultrasonic echo returned from the target tissue is received to obtain a first ultrasonic echo signal; The first ultrasonic echo signal is processed to obtain the ultrasonic grayscale image; Receive an operation command to switch to a sampling mode, wherein the sampling mode includes color Doppler mode, elastic mode, energy Doppler mode, or vector blood flow mode. In response to the operation command, the sampling frame is displayed on the ultrasound grayscale image; A second ultrasonic wave is emitted toward the target tissue, and the ultrasonic echo returned from the target tissue is received to obtain a second ultrasonic echo signal; The second ultrasonic echo signal is processed to obtain the sampled image superimposed within the sampling frame of the ultrasonic grayscale image.

3. The method according to claim 1, characterized in that, The evaluation result of determining the acquisition quality of the ultrasound image based on the overlap degree includes: The acquisition quality level or acquisition quality score of the ultrasound image is determined based on the degree of overlap. The acquisition quality level or acquisition quality score of the ultrasound image is displayed.

4. The method according to claim 1, characterized in that, The evaluation result of determining the acquisition quality of the ultrasound image based on the overlap degree includes: When the overlap is greater than or equal to a preset threshold, it is determined that the acquisition quality of the ultrasound image meets a first preset condition. The first preset condition is used to indicate that the acquisition quality of the ultrasound image is qualified, the acquisition quality of the ultrasound image meets the requirements, the acquisition quality of the ultrasound image reaches a preset registration, or the acquisition quality of the ultrasound image reaches a preset score. When the overlap is less than a preset threshold, it is determined that the acquisition quality of the ultrasound image does not meet the first preset condition.

5. The method according to claim 4, characterized in that, The method further includes: Based on the fact that the acquisition quality of the ultrasound image meets the first preset condition, the ultrasound image is saved; If the quality of the ultrasound image acquisition does not meet the first preset condition, a rescan is prompted.

6. The method according to any one of claims 1 to 5, characterized in that, The target tissue includes breast tissue, and the method further includes: Acquire at least two frames of ultrasound grayscale images of the target tissue; To determine the lesion region in the at least two frames of ultrasound grayscale images; The lesion grade of the lesion region in the at least two frames of ultrasound grayscale images is determined according to the classification corresponding to the breast imaging report and data system BI-RADS. The lesion levels of the lesion regions based on the at least two frames of ultrasound grayscale images meet the similarity condition, indicating that the acquisition quality of the at least two frames of ultrasound grayscale images meets the second preset condition, and the similarity condition means that the lesion levels are the same or nearly the same. The lesion level of the lesion area in the at least two frames of ultrasound grayscale images does not meet the similarity condition, indicating that the acquisition quality of the at least two frames of ultrasound grayscale images does not meet the second preset condition.

7. The method according to claim 6, characterized in that, The at least two frames of ultrasound grayscale images include a cross-sectional image and a longitudinal cross-sectional image of the breast tissue; The determination of the lesion grade of the lesion region in the at least two frames of ultrasound grayscale images according to the grading corresponding to the BI-RADS breast imaging report and data system includes: The lesion grade of the lesion region in the cross-sectional image and the lesion grade of the lesion region in the longitudinal cross-sectional image are determined according to the classification corresponding to the BI-RADS breast imaging report and data system.

8. The method according to claim 6, characterized in that, The method further includes: Determine whether the resolution and / or sharpness of the at least two frames of ultrasound grayscale images meet the third preset condition; Based on the resolution and / or clarity meeting the third preset condition, and the lesion level meeting the similarity condition, it is indicated that the acquisition quality of the at least two frames of ultrasound grayscale images meets the second preset condition; If the resolution and / or clarity do not meet the third preset condition, or the lesion level does not meet the similarity condition, it indicates that the acquisition quality of at least two frames of ultrasound grayscale images does not meet the second preset condition.

9. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Acquire at least one frame of ultrasound grayscale image and at least one frame of sampled image of the target tissue, wherein the sampled image includes color Doppler image, elastography image, energy Doppler image or vector blood flow image; Determine the lesion region of the at least one frame of ultrasound grayscale image and the lesion region of the at least one frame of sampled image; Determine the lesion level of the lesion region in the at least one frame of ultrasound grayscale image and the lesion level of the lesion region in the at least one frame of sampled image; The lesion level of the lesion region in the at least one frame of ultrasound grayscale image and the lesion level of the lesion region in the at least one frame of sampled image meet similar conditions, indicating that the acquisition quality of the at least one frame of ultrasound grayscale image and the at least one frame of sampled image meets the second preset condition. The similarity condition means that the lesion levels are the same or nearly the same. If the lesion level of the lesion region in the at least one frame of the ultrasound grayscale image does not meet the similarity condition with the lesion level of the lesion region in the at least one frame of the sampled image, it indicates that the acquisition quality of the at least one frame of the ultrasound grayscale image and the at least one frame of the sampled image does not meet the second preset condition.

10. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Determine the image quality of the ultrasound image; The evaluation result of determining the acquisition quality of the ultrasound image based on the overlap degree includes: The evaluation result of the acquisition quality of the ultrasound image is determined based on the overlap and the image quality of the ultrasound image; The determination of the image quality of the ultrasound image includes determining the image quality of the ultrasound image based on at least one of the following: image grayscale, image sharpness, effective area ratio of the image, presence of spots, snowflake-like particles or mesh patterns in the image, and the probe used, probe parameters or imaging parameters.

11. An ultrasonic imaging device, characterized in that, include: probe; A transmitting circuit that excites the probe to emit ultrasonic waves toward the target tissue; A receiving circuit that controls the probe to receive ultrasound echoes returned from the target tissue to obtain an ultrasound echo signal; A processor that processes the ultrasound echo signal to obtain an ultrasound image of the target tissue; A display showing the ultrasound image; The processor is used to perform the steps of the method as described in any one of claims 1 to 10.

Images