Periodic parameter analysis method and ultrasonic imaging system

Abstract

A method (200) for analyzing periodic parameters and an ultrasonic imaging system (100), the method comprising: acquiring multiple frames of ultrasonic images acquired within a preset time (S210); determining the cardiac region of at least two frames of ultrasonic images in the multiple frames of ultrasonic images, and measuring the cardiac region to obtain a measurement result corresponding to each frame of ultrasonic images (S220); obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasonic images (S230); obtaining a first reliability evaluation result of the measurement result of each frame of ultrasonic images in at least two frames of ultrasonic images based on the image quality of each frame of ultrasonic images (S240); obtaining a second reliability evaluation result of the periodic parameters based on the first reliability evaluation results corresponding to the at least two frames of ultrasonic images, and displaying the periodic parameters and the second reliability evaluation results in a visual manner (S250). The method can automatically evaluate the reliability of the periodic parameters.

Description

[0001] manual Technical Field

[0002] The present application relates to the technical field of ultrasonic imaging, and more specifically to a periodic parameter analysis method and an ultrasonic imaging system. Background Art

[0003] Cardiopulmonary function assessment is the focus of the POC (point-of-care, bedside testing) field (including: critical care, emergency, anesthesia), and it is often necessary to dynamically monitor cardiac function parameters in real time to determine whether the drug or the strategy being implemented is effective or not. During real-time ultrasound scanning, the quality of ultrasound images varies, resulting in dynamic changes in the reliability of measurement parameters based on ultrasound images, which requires doctors to be distracted to screen the measurement parameters, and cannot achieve true fully automatic cardiopulmonary function assessment.

[0004] The heart is the power organ that maintains normal blood circulation in the human body. The left ventricular ejection fraction (EF) is the most commonly used and important indicator for evaluating left ventricular systolic function. It has important clinical value for the diagnosis, disease monitoring, efficacy evaluation and prognosis of many cardiovascular diseases (including ischemic heart disease, cardiomyopathy, valvular heart disease, congenital heart disease, etc.). The ejection fraction can be automatically calculated by automatically tracing the left ventricular endocardium. However, this is only applicable to ideal conditions where the image quality is stable and all cardiac cycles are relatively reliable. Therefore, it is only suitable for offline analysis scenarios and cannot meet the clinical needs of dynamic real-time monitoring in the POC field. Summary of the invention

[0005] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0006] In one embodiment, a method for analyzing periodic parameters is provided, the method comprising:

[0007] Acquire multiple frames of ultrasound images collected within a preset time;

[0008] Determine the heart region of at least two frames of ultrasound images among the multiple frames of ultrasound images, and measure the heart region to obtain a measurement result corresponding to each frame of the ultrasound image;

[0009] Obtaining a periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images;

[0010] In at least two frames of the ultrasound image, obtaining a first reliability evaluation result of the measurement result of each frame of the ultrasound image based on the image quality of each frame of the ultrasound image;

[0011] A second reliability evaluation result of the periodic parameter is obtained based on the first reliability evaluation result corresponding to the at least two frames of the ultrasound image, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner.

[0012] In one embodiment, a method for analyzing periodic parameters is provided, the method comprising:

[0013] Acquire multiple frames of ultrasound images collected within a preset time;

[0014] Determine a target area of ​​at least two frames of ultrasound images among the multiple frames of ultrasound images, and measure the target area to obtain a measurement result corresponding to each frame of the ultrasound image;

[0015] Obtaining a periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images;

[0016] In at least two frames of the ultrasound image, obtaining a first reliability evaluation result of the measurement result of each frame of the ultrasound image based on the image quality of each frame of the ultrasound image;

[0017] A second reliability evaluation result of the periodic parameter is obtained based on the first reliability evaluation result corresponding to the at least two frames of the ultrasound image, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner.

[0018] In one embodiment, an ultrasound imaging system is provided, the ultrasound imaging system comprising:

[0019] Ultrasound probe;

[0020] A transmitting circuit, used for stimulating the ultrasonic probe to transmit ultrasonic waves to the object under test;

[0021] A receiving circuit, used for controlling the ultrasonic probe to receive the ultrasonic echo to obtain an ultrasonic echo signal;

[0022] A processor is used to execute the periodic parameter analysis method as described above.

[0023] The periodic parameter analysis method and ultrasound imaging system according to the embodiments of the present application can realize automatic evaluation of the reliability of the periodic parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] In the attached picture:

[0026] Figure 1 A schematic block diagram of an ultrasound imaging system according to an embodiment of the present application is shown;

[0027] Figure 2 A schematic flow chart showing a method for analyzing periodic parameters according to an embodiment of the present invention;

[0028] Figure 3A and Figure 3B An example of periodic parameters according to an embodiment of the present invention is shown;

[0029] Figure 4 A schematic diagram showing a display interface according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present application more obvious, the example embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0031] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present application. However, it is apparent to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present application, some technical features well known in the art are not described.

[0032] It should be understood that the present application can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0033] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present application. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0034] In order to thoroughly understand the present application, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present application. The optional embodiments of the present application are described in detail as follows, but in addition to these detailed descriptions, the present application may also have other implementation methods.

[0035] The periodic parameter analysis method and ultrasound imaging system provided in the present application can be applied to the human body and various animals.

[0036] Next, first refer to Figure 1 An ultrasound imaging system according to an embodiment of the present application is described. Figure 1 A schematic structural block diagram of an ultrasound imaging system 100 according to an embodiment of the present application is shown.

[0037] like Figure 1 As shown, the ultrasound imaging system 100 includes an ultrasound probe 110, a transmitting circuit 112, a receiving circuit 114, a processor 116, and a display 118. Further, the ultrasound imaging system may also include a transmit / receive selection switch 120 and a beamforming module 122, and the transmitting circuit 112 and the receiving circuit 114 may be connected to the ultrasound probe 110 through the transmit / receive selection switch 120.

[0038] The ultrasonic probe 110 includes a plurality of transducer array elements, which can be arranged in a row to form a linear array, or arranged in a two-dimensional matrix to form a surface array, or a plurality of transducer array elements can also form a convex array. The transducer array element is used to transmit ultrasonic waves according to the excitation electrical signal, or to convert the received ultrasonic waves into electrical signals, so each transducer array element can be used to realize the mutual conversion between the electrical pulse signal and the ultrasonic wave, so as to realize the transmission of ultrasonic waves to the tissue of the target area of ​​the object to be tested, and can also be used to receive the ultrasonic wave echo reflected by the tissue. When performing ultrasonic testing, which transducer array elements are used to transmit ultrasonic waves and which transducer array elements are used to receive ultrasonic waves can be controlled by the transmission sequence and the reception sequence, or the transducer array elements can be controlled to transmit ultrasonic waves or receive ultrasonic wave echoes in time slots. The transducer array elements participating in ultrasonic wave transmission can be excited by electrical signals at the same time, so as to transmit ultrasonic waves at the same time; or, the transducer array elements participating in ultrasonic wave beam transmission can also be excited by several electrical signals with a certain time interval, so as to continuously transmit ultrasonic waves with a certain time interval.

[0039] During the ultrasonic imaging process, the transmitting circuit 112 sends the delayed focused transmitting pulse to the ultrasonic probe 110 through the transmitting / receiving selection switch 120. The ultrasonic probe 110 is stimulated by the transmitting pulse to transmit an ultrasonic beam to the tissue of the target area of ​​the object to be measured, and receives the ultrasonic echo with tissue information reflected from the tissue of the target area after a certain delay, and reconverts the ultrasonic echo into an electrical signal. The receiving circuit 114 receives the electrical signal converted and generated by the ultrasonic probe 110, obtains the ultrasonic echo signal, and sends these ultrasonic echo signals to the beamforming module 122, which performs focusing delay, weighting and channel summing on the ultrasonic echo data, and then sends them to the processor 116. The processor 116 performs signal detection, signal enhancement, data conversion, logarithmic compression and other processing on the ultrasonic echo signal to form an ultrasonic image. The ultrasonic image obtained by the processor 116 can be displayed on the display 118 or stored in the memory 124.

[0040] Optionally, the processor 116 may be implemented as software, hardware, firmware or any combination thereof, and may use a single or multiple application specific integrated circuits (ASICs), a single or multiple general purpose integrated circuits, a single or multiple microprocessors, a single or multiple programmable logic devices, or any combination of the aforementioned circuits and / or devices, or other suitable circuits or devices. Furthermore, the processor 116 may control other components in the ultrasound imaging system 100 to perform the corresponding steps of the methods in various embodiments of the present specification.

[0041] The display 118 is connected to the processor 116, and the display 118 may be a touch screen, a liquid crystal display, etc.; or, the display 118 may be an independent display such as a liquid crystal display, a television, etc., which is independent of the ultrasound imaging system 100; or, the display 118 may be a display screen of an electronic device such as a smart phone or a tablet computer, etc. The number of the displays 118 may be one or more. For example, the display 118 may include a main screen and a touch screen, wherein the main screen is mainly used to display ultrasound images, and the touch screen is mainly used for human-computer interaction.

[0042] The display 118 can display the ultrasound image obtained by the processor 116. In addition, while displaying the ultrasound image, the display 118 can also provide the user with a graphical interface for human-computer interaction. One or more controlled objects are set on the graphical interface, and the user is provided with a human-computer interaction device to input operation instructions to control these controlled objects, thereby performing corresponding control operations. For example, an icon is displayed on the graphical interface, and the icon can be operated by the human-computer interaction device to perform a specific function, such as drawing a region of interest box on the ultrasound image.

[0043] Optionally, the ultrasound imaging system 100 may further include other human-machine interaction devices in addition to the display 118, which are connected to the processor 116. For example, the processor 116 may be connected to the human-machine interaction device via an external input / output port, and the external input / output port may be a wireless communication module, or a wired communication module, or a combination of the two. The external input / output port may also be implemented based on USB, bus protocols such as CAN, and / or wired network protocols.

[0044] The human-computer interaction device may include an input device for detecting user input information, which may be, for example, a control instruction for ultrasonic transmission / reception timing, an operation input instruction for drawing points, lines or frames on an ultrasonic image, or other instruction types. The input device may include a keyboard, a mouse, a scroll wheel, a trackball, a mobile input device (such as a mobile device with a touch screen, a mobile phone, etc.), a multi-function knob, etc., or a combination of multiple thereof. The human-computer interaction device may also include an output device such as a printer.

[0045] The ultrasound imaging system 100 may further include a memory 124 for storing instructions executed by the processor, storing received ultrasound echoes, storing ultrasound images, etc. The memory may be a flash memory card, a solid-state memory, a hard disk, etc. It may be a volatile memory and / or a non-volatile memory, a removable memory and / or a non-removable memory, etc.

[0046] It should be understood that Figure 1The components included in the ultrasonic imaging system 100 are only exemplary, and the ultrasonic imaging system 100 may include more or fewer components, and the present application is not limited thereto.

[0047] Next, we will refer to Figure 2 A method for analyzing periodic parameters according to an embodiment of the present application is described. Figure 2 It is a schematic flow chart of the method 200 for analyzing periodic parameters according to an embodiment of the present application.

[0048] like Figure 2 As shown, a method 200 for analyzing periodic parameters according to an embodiment of the present application includes the following steps:

[0049] In step S210, multiple frames of ultrasound images acquired within a preset time are acquired;

[0050] In step S220, a target region (for example, a heart region) of at least two frames of ultrasound images among the multiple frames of ultrasound images is determined, and the target region is measured to obtain a measurement result corresponding to each frame of the ultrasound image;

[0051] In step S230, a periodic parameter is obtained according to the measurement results corresponding to the multiple frames of ultrasound images;

[0052] In step S240, in at least two frames of the ultrasound image, a first reliability evaluation result of the measurement result of each frame of the ultrasound image is obtained based on the image quality of each frame of the ultrasound image;

[0053] In step S250, a second reliability evaluation result of the periodic parameter is obtained based on the first reliability evaluation result corresponding to the at least two frames of the ultrasound image, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner.

[0054] The analysis method 200 of periodic parameters of the embodiment of the present application can realize automatic evaluation of the reliability of periodic parameters, solving the problem that previous evaluation schemes can only evaluate the image quality of single-frame ultrasound images but cannot evaluate the reliability of periodic parameters obtained based on multiple-frame ultrasound images. It is convenient for users to determine the reliability of periodic parameters and select periodic parameters according to the second reliability evaluation result.

[0055] In step S210, the ultrasound image can be collected in real time, so that the reliability of the periodic parameters can be evaluated in real time while the periodic parameters are obtained in real time. Since the analysis method of periodic parameters according to an embodiment of the present invention can automatically analyze the reliability of the periodic parameters, there is no need for the user to select ultrasound images frame by frame, so it is suitable for the scene of real-time acquisition of periodic parameters. While generating periodic parameters in real time, the second reliability of the periodic parameters is determined in real time, so that the user can determine the reliability of the periodic parameters, and select a periodic parameter with higher reliability from multiple periodic parameters, so as to meet the clinical needs of dynamic real-time monitoring in the POC field. Of course, the ultrasound image can also be a pre-collected ultrasound image extracted from a storage medium or received through remote transmission, that is, the analysis method 200 of periodic parameters is also applicable to the scene of offline analysis.

[0056] Before executing step S210, the acquisition parameters may be preset. The acquisition parameters may include the acquisition duration (i.e., the length of the preset time in step S210), the frame rate (i.e., the number of ultrasound images acquired per second), the type of ultrasound image, etc. It should be noted that the acquisition duration needs to be greater than or equal to the length of a cycle, so as to ensure that the acquired multiple frames of ultrasound images can contain the ultrasound images necessary for calculating the periodic parameters, such as the ultrasound images of the end diastole and the ultrasound images of the end systole necessary for calculating the ejection fraction.

[0057] For example, it can be based on Figure 1 The ultrasonic imaging system 100 shown performs ultrasonic scanning to obtain multiple frames of ultrasonic images. Specifically, the processor 116 controls the transmitting circuit 112 to send the delayed focused transmitting pulse to the ultrasonic probe 110 through the transmitting / receiving selection switch 120. The ultrasonic probe 110 is stimulated by the transmitting pulse to transmit an ultrasonic beam to the target tissue (such as heart tissue, vascular tissue, etc.) of the object under test, and receives the ultrasonic echo with tissue information reflected from the tissue in the target area after a certain delay, and reconverts the ultrasonic echo into an electrical signal. The receiving circuit 114 receives the electrical signal converted and generated by the ultrasonic probe 110, obtains the ultrasonic echo signal, and sends these ultrasonic echo signals to the beamforming module 122, which performs focusing delay, weighting and channel summing on the ultrasonic echo data, and then sends them to the processor 116. The processor 116 performs signal detection, signal enhancement, data conversion, logarithmic compression and other processing on the ultrasonic echo signal to form different types of ultrasonic images, including but not limited to B-type ultrasonic images, C-type ultrasonic images, etc. The multiple frames of ultrasound images obtained by the processor 116 may be sent to the display 118 for display.

[0058] Taking the periodic parameters as cardiac function parameters as an example, the ultrasound image is an ultrasound image of the ventricular area collected for cardiac tissue. In the process of collecting ultrasound images, the ultrasonic sound beam generated by the ultrasound probe 110 enters the chest wall and scans in a fan shape. According to the position and angle of the ultrasound probe 110, cross-sectional images of different layers and orientations of cardiac tissue can be obtained. Usually, the heart sections included in the ultrasound image may include the apical four-chamber heart plane or the apical two-chamber heart plane. The multiple frames of ultrasound images collected within a preset time are a set of continuous ultrasound image sequences, and each ultrasound image corresponds to a time point in the cardiac cycle. The heart repeats the diastolic-contracting movement process, and a cardiac cycle refers to the time from one end of diastole to the next end of diastole, or from one end of systole to the next end of systole.

[0059] In step S220, the target area of ​​at least two frames of ultrasound images in the multiple frames of ultrasound images is first determined. The target area can be determined for at least two frames in the multiple frames of ultrasound images, or for each frame in the multiple frames of ultrasound images. Generally speaking, the at least two frames of ultrasound images include ultrasound images within at least one cycle (e.g., one cardiac cycle).

[0060] The target region determined in the ultrasound image depends on the type of periodic parameter to be obtained. For example, if the periodic parameter to be obtained is a cardiac function parameter, the target region is a ventricular region, specifically a left ventricular region, and the left ventricular region can be determined by identifying the left ventricular endocardial region in the ultrasound image. If the periodic parameter to be obtained is a respiratory parameter such as the inferior vena cava collapsibility index, the inferior vena cava expansion index, and the inferior vena cava variability, the target region can be a vascular region, and the vascular region can specifically be the inferior vena cava.

[0061] Exemplarily, a machine learning method can be used to determine the target area in the ultrasound image. Specifically, first, a machine learning method is used to extract features from the ultrasound image. The feature extraction method used can be traditional PCA (principal component analysis), LDA (linear discriminant analysis), Harr feature extraction, texture feature extraction, etc., or a deep neural network can be used for feature extraction; then, the extracted features are matched with the features in a pre-constructed database, and the extracted features are classified using classifiers such as KNN (K-nearest neighbor classifier), SVM (support vector machine), random forest, neural network, etc., to determine the category of the image features of each image block in the ultrasound image, and the target area is divided in the ultrasound image according to the category of the image block.

[0062] Alternatively, an end-to-end deep learning neural network based on deep learning can be used to learn features of a pre-built database by stacking convolutional layers and fully connected layers, and adding upsampling or deconvolution layers to make the input and output sizes the same, thereby directly obtaining the target area of ​​the input image and its corresponding category. The deep learning neural networks used include FCN (fully convolutional neural network), U-Net (U-net), Mask R-CNN (mask candidate region neural network), etc.

[0063] Optionally, a conventional image segmentation algorithm may be used to determine the target region of at least two frames of ultrasound images, wherein the image segmentation algorithm may include various applicable image segmentation algorithms such as Graph Cut algorithm, Level Set algorithm, Random Walker algorithm, etc.

[0064] In one embodiment, before identifying the target area, the ultrasound image may be subjected to denoising, enhancement and other processing to more accurately identify the target area.

[0065] After the target area is identified, the target area is measured to obtain a measurement result corresponding to each frame of the ultrasound image in at least two frames of ultrasound images. Exemplarily, if the periodic parameter is a cardiac function parameter, the measurement result is a ventricular measurement result. If the cardiac function parameter is the ejection fraction, the measurement result corresponding to each frame of the ultrasound image may be the ventricular volume obtained by measuring the ventricular area. If the cardiac function parameter is the short-axis shortening rate, the measurement result corresponding to each frame of the ultrasound image may be the ventricular short-axis length obtained by measuring the ventricular area. If the periodic parameter is a respiratory parameter, the measurement result corresponding to each frame of the ultrasound image may be the vascular diameter obtained by measuring the vascular area.

[0066] The calculation module for ventricular volume can be implemented based on the Simpson method. The Simpson method mainly includes the single-plane Simpson method and the double-plane Simpson method. The principle of the Simpson method is that the volume of an object is equal to the sum of the volumes of multiple equally divided sections of the object, and the volume of each section can be calculated based on the ellipsoid. When the Simpson method is used to calculate the left ventricular volume, an ultrasound image containing the apical four-chamber heart section and an ultrasound image containing the apical two-chamber heart section can be taken, and the left ventricle can be divided into twenty elliptical cylinders along the long axis of the left ventricle. The long axis diameter and short axis diameter of the elliptical cylinder can be obtained according to the diameter of the apical four-chamber heart section and the diameter of the apical two-chamber heart section, and then the cross-sectional area of ​​the elliptical cylinder can be obtained. The height of the elliptical cylinder is one twentieth of the length of the long axis of the heart. The volume of each elliptical cylinder can be calculated according to the cross-sectional area and height of the elliptical cylinder. The ventricular volume can be obtained by adding up the volumes of the twenty elliptical cylinders.

[0067] In step S230, periodic parameters are obtained according to the measurement results corresponding to the multiple frames of ultrasound images. The periodic parameters are physiological characteristic parameters with periodicity related to heartbeat, breathing, etc.

[0068] Among them, the periodic parameters are mainly divided into two categories. One type of periodic parameters characterizes the average level of the measurement results within a preset time, and specifically can characterize the average level of the measurement results within a period. For example, this type of periodic parameter can be the average value of the measurement results corresponding to each frame of ultrasound image within a period. It can be seen that this type of periodic parameter is closely related to the reliability of the measurement results of each frame of ultrasound image.

[0069] Another type of periodic parameter characterizes the degree of change of the measurement result within a preset time, and specifically can characterize the degree of change of the measurement result within a period. Taking the ejection fraction (EF) as an example, the calculation formula of EF is as follows:

[0070] EF = (EDV - ESV) / ​​EDV;

[0071] Among them, EDV (End-Diastolic Volume) is the left ventricular volume at the end of diastole, usually the maximum value of the ventricular volume in the cycle; ESV (End Systolic Volume) is the ventricular volume at the end of systole, usually the minimum value of the ventricular volume in the cycle. That is, EF is calculated based on the ventricular volume at the end of diastole and the ventricular volume at the end of systole. It can be seen that this type of periodic parameters is calculated based on the measurement results of key frame ultrasound images, which is closely related to the reliability of the measurement results of key frame ultrasound images. Since this type of ultrasound image is generally calculated based on the peak value of the measurement results, the key frame ultrasound image can include the ultrasound image corresponding to the peak value of the measurement results in multiple frames of ultrasound images. Among them, the peak value of the measurement result includes the maximum and minimum values ​​of the measurement result.

[0072] Continuing with the ejection fraction as an example, when calculating the ejection fraction, we first need to find all the ventricular volumes of a complete cardiac cycle, determine the maximum ventricular volume and the minimum ventricular volume in the same cardiac cycle, as the EDV value and ESV value, respectively, and calculate the ejection fraction according to the above ejection fraction calculation formula. Among them, the cardiac cycle refers to the process that the cardiovascular system goes through from the start of one heartbeat to the start of the next heartbeat.

[0073] In one example, finding all ventricular volumes of a complete cardiac cycle from the ventricular volumes measured according to each frame of ultrasound image includes: obtaining a curve of the ventricular volume changing with time according to the ventricular volumes corresponding to the multiple frames of ultrasound images; and determining the cardiac cycle according to the curve of the ventricular volume changing with time. Optionally, the peak interval of the curve of the ventricular volume changing with time can be measured to obtain the cardiac cycle, or the spectral analysis of the curve of the ventricular volume changing with time can be performed to obtain the cardiac cycle.

[0074] Optionally, other ventricular measurement results can be plotted over time to determine the cardiac cycle, and then all ventricular volumes in a complete cardiac cycle can be obtained according to the cardiac cycle, or the ventricular volume at the end of diastole and the ventricular volume at the end of systole can be directly obtained. The ventricular measurement results include but are not limited to the left ventricular long axis length, the left ventricular short axis length, the left ventricular area, etc.

[0075] Alternatively, a characteristic curve may be generated based on the characteristic value of each frame of ultrasound images in the multiple frames of ultrasound images continuously acquired within a preset time, and then the characteristic curve may be periodically analyzed to identify the cardiac cycle. The characteristic curve may be an image similarity curve. Specifically, a certain frame in the multiple frames of ultrasound images may be selected as a standard frame, and a similarity coefficient between each frame of ultrasound image and the standard frame may be calculated to generate an image similarity curve.

[0076] In step S240, in at least two frames of ultrasound images, a first reliability evaluation result of the measurement result of each frame of the ultrasound image is obtained based on the image quality of each frame of the ultrasound image.

[0077] Among them, at least two frames of ultrasound images are at least two frames of ultrasound images related to the measurement of periodic parameters in multiple frames of ultrasound images. When the periodic parameter represents the average level of the measurement results within a preset time, the reliability of the periodic parameter is related to the reliability of each measurement result, so the at least two frames of ultrasound images are multiple frames of ultrasound images continuously collected within the preset time, and at least include multiple frames of ultrasound images within one cycle. When the periodic parameter represents the degree of change of the measurement results within the preset time, the reliability of the periodic parameter is related to the reliability of the measurement results used to calculate the periodic parameter, and the at least two frames of ultrasound images at least include key frame ultrasound images used to calculate the periodic parameter. As described above, since such periodic parameters are generally calculated based on the peak value of the measurement results within the cycle, the key frame image includes the ultrasound image corresponding to the peak value of the measurement result. Since the reliability of the measurement results of adjacent frames of ultrasound images is generally correlated, in order to reduce errors, the key frame ultrasound image can also include at least one frame of ultrasound image in the neighborhood of the ultrasound image corresponding to the peak value of the measurement result.

[0078] Since the quality of the ultrasound image is a basic factor in determining whether the measurement result is reliable, if the quality of the ultrasound image is not good, the measurement result is unlikely to be reliable. Therefore, a first reliability evaluation result of the measurement result of each frame of the ultrasound image is obtained based at least on the image quality of each frame of the ultrasound image. In one embodiment, each of the at least two frames of ultrasound images can be input into a trained network model, and a first reliability evaluation result of the image quality of each frame of the ultrasound image is output. The network model is implemented by, for example, a VGG, ResNet or other network as the backbone, through a classification or regression task.

[0079] Alternatively, a conventional image processing method may be used to obtain a first reliability evaluation result on the image quality of each frame of ultrasound image according to the signal-to-noise ratio of each frame of ultrasound image in at least two frames of ultrasound image, wherein the higher the signal-to-noise ratio, the lower the score of the reliability evaluation result.

[0080] In one embodiment, the image quality of an ultrasound image can be determined based on at least one of the following: the grayscale of the ultrasound image, the image clarity of the ultrasound image, the proportion of an effective area in the ultrasound image, the proportion of spots, snowflakes or reticulations in the ultrasound image, and the ultrasound probe, probe parameters or imaging parameters used to acquire the ultrasound image.

[0081] As for the grayscale of the ultrasound image, it may include the overall grayscale of the ultrasound image, and may also include the grayscale of the ultrasound image within the effective area. The image quality may be determined based on at least one of whether the mean value of the image grayscale is within the threshold range, whether the image grayscale is uniform, and whether the extreme value of the image grayscale meets the standard of the grayscale extreme value. As for whether the grayscale of the ultrasound image is uniform, a grayscale histogram of the ultrasound image may be drawn, and by judging whether the grayscale in the grayscale histogram is uniformly distributed, it is ensured that the image grayscale will not be concentrated in a certain area and affect the image quality of the ultrasound image.

[0082] If the grayscale of the ultrasound image meets the grayscale standard, for example, the grayscale mean of the ultrasound image is appropriate and the image is uniform, then the ultrasound image can more accurately display the morphology of the tissue, and the quality of the ultrasound image is high; conversely, if the grayscale of the ultrasound image does not meet the grayscale standard, the quality of the ultrasound image is low. Therefore, the quality of the ultrasound image can be determined by the grayscale of the ultrasound image. For example, the grayscale standard of the ultrasound image quality such as the grayscale mean standard, the grayscale uniformity standard and the grayscale extreme value standard can be set. Further, the deviation between the grayscale of the ultrasound image and the grayscale standard can be calculated, and a functional relationship or other corresponding relationship between the deviation and the image quality can be established to determine the quality of the ultrasound image through the relationship between the grayscale of the ultrasound image and the grayscale standard. Of course, the deviation between the grayscale of the ultrasound image and the grayscale standard can be evaluated from one perspective, such as the grayscale uniformity dimension; it can also be evaluated from multiple dimensions, such as the grayscale mean, grayscale extreme value and grayscale uniformity dimensions, to comprehensively obtain the deviation between the grayscale of the ultrasound image and the grayscale standard.

[0083] As for the image clarity of an ultrasound image, if the image clarity is high, the quality of the ultrasound image is correspondingly high; if the ultrasound image clarity is low, the quality of the ultrasound image is correspondingly low. The clarity of an ultrasound image can be a specific value, and the clarity can be expressed in the form of a ten-point score, a hundred-point score, or a percentage; it can also be a qualitative standard, including clear, relatively clear, relatively fuzzy, fuzzy, etc. The calculation of image clarity can be calculated from dimensions such as whether the ultrasound image is too bright or too dark, or whether the resolution of the ultrasound image is high enough.

[0084] In one embodiment, the clarity of the ultrasound image can be calculated based on the gradient information. In general, the higher the gradient value, the richer the edge information of the picture, and the clearer the image. Exemplarily, a functional relationship or other corresponding relationship between the gradient information of the effective area and the image clarity can be established. For example, the image clarity can be calculated based on gradient information by using Brenner gradient function, Tenengrad gradient function, Laplacian gradient function, etc. In another embodiment, the artificial intelligence model can be trained by inputting two types of ultrasound images with clear and blurred images. Exemplarily, the artificial intelligence model can perform a binary classification problem of clarity and blur on the ultrasound image, and for the input ultrasound image to be tested, the artificial intelligence model can input a clear or blurred classification result. It should be emphasized that the artificial intelligence model can also grade the clarity of the ultrasound image into clear, relatively clear, relatively blurred, blurred, etc., so that the artificial intelligence model can output a clarity grade for the input ultrasound image to be tested.

[0085] For the proportion of effective areas in ultrasound images, the effective areas may be ultrasound image areas related to the acquisition of detection information. Exemplarily, for a certain tissue or organ, the effective area may be an area in the ultrasound image containing the image of the tissue or organ, or an image area of ​​a tissue or organ nodule, or other ultrasound image areas related to the acquisition of detection information. The main purpose of detecting the effective area proportion of ultrasound images is to ensure that the effective area of ​​the ultrasound image accounts for an appropriate proportion of the entire image, for example, the proportion should not be too small, but should be greater than 1 / 2. Exemplarily, a specific detection method is to obtain the effective area by threshold segmentation of image processing, calculate the proportion of the effective area to the overall image area, and determine whether the proportion meets the preset proportion requirement. Among them, the size or proportion of the effective area is related to parameters such as ultrasound scanning depth or magnification / reduction multiples. In one embodiment, it is possible to detect whether the ultrasound scanning depth meets the standard, for example, whether the ultrasound scanning depth is within the threshold range, so as to determine whether the effective area proportion of the ultrasound image is appropriate.

[0086] It is understandable that if the effective area ratio of an ultrasound image is too small, it will be difficult to accurately reflect the morphology of tissues and organs on the ultrasound image, which is not conducive to obtaining detection information based on the ultrasound image. Therefore, the quality of the ultrasound image can be determined by the effective area ratio of the ultrasound image. For example, the effective area ratio of the ultrasound image can be calculated, and a functional relationship or other corresponding relationship between the effective area ratio of the 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.

[0087] As for the proportion of spots, snowflakes or reticulation in the ultrasound image, the entire ultrasound image can be detected to determine whether there are spots, snowflakes or reticulation in the ultrasound image, as well as the proportion of spots, snowflakes or reticulation; or the effective area can be determined in the ultrasound image first, and then the ultrasound image in the effective area is detected. It can be understood that if there are spots, snowflakes or reticulation in the ultrasound image, the spots, snowflakes or reticulation in the ultrasound image may cover the heart or blood vessel area, affecting the quality of the image. Therefore, a functional relationship or other corresponding relationship between the presence or absence of spots, snowflakes or reticulation and the image validity can be established. The greater the proportion, the lower the image quality of the ultrasound image. Furthermore, different weights can be given to the three image defects of spots, snowflakes or reticulation according to the different degrees of influence of the three on tissue recognition in the image, so as to determine the quality of the ultrasound image according to whether there are spots, snowflakes or reticulation in the detected ultrasound image.

[0088] For the detection of spots, snowflakes or reticulation in ultrasound images, it is possible to detect whether the texture of the ultrasound image meets the preset image texture standard. Exemplarily, a detection model for image texture can be pre-trained, and the ultrasound image can be input into the detection model to obtain a detection result of whether the texture meets the preset image texture standard, wherein the image texture includes: whether the image has spots, snowflakes, and reticulation.

[0089] Taking the ultrasonic probe, probe parameters and / or imaging parameters as an example, the quality of the ultrasonic image can be determined by the correspondence between the ultrasonic probe, probe parameters and / or imaging parameters and the tissues and organs to be tested included in the ultrasonic image. When performing ultrasonic testing on a patient, different probe parameters and imaging parameters need to be selected according to different test sites so as to achieve the best imaging effect for different test sites. For example, a linear array probe with a high frequency of use for superficial tissues; a convex array probe with a low frequency of use for abdominal organs. If the ultrasonic probe and the corresponding probe parameters and imaging parameters are used incorrectly in actual operation, the quality of the ultrasonic image will be affected. Therefore, the tissue category contained in the ultrasonic image can be compared with one or more of the ultrasonic probe, probe parameters and imaging parameters used to scan the ultrasonic image. If the two match, the quality of the ultrasonic image is determined to be high, and if the two do not match, the quality of the ultrasonic image is determined to be low.

[0090] In addition to the quality of the ultrasound image, the first reliability evaluation result is also related to the characteristic parameters of the target area. The measurement results of the ultrasound image can be comprehensively evaluated based on the image quality of the ultrasound image and the characteristic parameters of the target area to obtain the first reliability evaluation result. The characteristic parameters of the target area include the shape of the target area or the contrast of the boundary of the target area, such as the shape of the heart area or the contrast of the boundary of the heart area. If the shape of the target area is unreasonable and does not conform to the actual situation, the first reliability result is low. If the contrast of the boundary of the target area is too low, it means that the segmentation effect is not good, and thus the first reliability result is correspondingly low. Exemplarily, a trained machine learning model can be used to analyze the characteristic parameters of the target area to obtain the first reliability evaluation result.

[0091] In some embodiments, the measurement results can be screened according to the first reliability assessment results, and the measurement results corresponding to the first reliability assessment results that do not meet the preset requirements are eliminated. The periodic parameters are obtained only based on the measurement results corresponding to at least two first reliability assessment results that meet the preset requirements to improve the accuracy of the periodic parameters.

[0092] Since the periodic parameters are calculated based on the measurement results of multiple frames of ultrasound images within one period, it is still impossible to evaluate whether the final measurement result is reliable based on the first reliability evaluation result of a single measurement result alone.

[0093] Therefore, in step S250, a second reliability evaluation result of the periodic parameter is obtained based on the first reliability evaluation result corresponding to at least two frames of ultrasound images. That is, the first reliability evaluation result is a reliability evaluation result that characterizes the reliability of a single measurement result, and the second reliability evaluation result is a reliability evaluation result that characterizes the reliability of the periodic parameter.

[0094] As described above, the periodicity parameter includes two types, one characterizing the average level of the measurement result within a preset time, and the other characterizing the degree of change of the measurement result within the preset time. For the first type of periodicity parameter, the second reliability evaluation result can be calculated according to the first reliability evaluation result corresponding to the multiple frames of ultrasound images continuously acquired within the preset time, wherein the multiple frames of ultrasound images continuously acquired within the preset time can be all ultrasound images within a complete cycle.

[0095] Furthermore, the second reliability evaluation result of this type can be the average value of the first reliability evaluation result within a complete cycle. Assuming that there are Frames ultrasound images in a cycle, scorei is the first reliability evaluation result of the i-th frame ultrasound image, and the value range is [0, MAXSCORE]. Then the second reliability evaluation result Qcycle representing the average level of the measurement results within the preset time is expressed as:

[0096]

[0097] When the periodic parameter characterizes the degree of change of the measurement result within a preset time, the second reliability evaluation result can be calculated based on the first reliability evaluation result of the measurement result corresponding to the key frame ultrasound image used to calculate the periodic parameter. The key frame ultrasound image includes the ultrasound image corresponding to the peak value of the measurement result. The key frame ultrasound image also includes at least one frame of ultrasound image in the neighborhood of the ultrasound image corresponding to the peak value of the measurement result.

[0098] Continuing with the ejection fraction (EF) as an example, the reliability of EF is mainly affected by the reliability of EDV (End-Diastolic Volume) and ESV (End Systolic Volume). The reliability of EDV and ESV is mainly determined by the first reliability evaluation result of the measurement results of the peak frame and at least one frame of ultrasound image in its neighborhood. If the neighborhood range is r, the peak frame is p eak, the value range of the key frame is [peak-r, peak+r], so the reliability evaluation result of EF is expressed as:

[0099]

[0100] Wherein, Qpeak is the first reliability evaluation result corresponding to the key frame ultrasound image,

[0101] The above two second reliability evaluation results can also be combined with each other. For example, for a periodic parameter that characterizes the degree of change of the measurement result within a preset time, such as the ejection fraction, its reliability can also be evaluated by referring to the first type of second reliability evaluation result Qcycle. For example, if the Qef of two ejection fractions is the same, it is considered that the ejection fraction with a higher Qcycle has a higher reliability.

[0102] An example of calculating the second reliability evaluation result is as follows Figure 3A and Figure 3B shown. Figure 3A and Figure 3B The curve represents the change of the actual ventricular volume in a cardiac cycle (note that it is not the change of the ventricular volume measurement result). The first reliability of the ventricular volume measured based on different frames of ultrasound images is different. Figure 3A and Figure 3B The first reliability is displayed on the curve in different filling modes. The value range of the first reliability is 0-2 points. Figure 3A and Figure 3B The unfilled circle represents 3 points, the dot-filled circle represents 1 point, and the black-filled circle represents 0 points. The ventricular volume with the first reliability of 0 points cannot be used because of its low reliability. The key frame is the ultrasound image corresponding to the peak value of the measurement result and a frame within its neighborhood, that is, r = 1, then Figure 3A The calculation results of Qcycle, Qed, Qes and Qef are: Qcycle = 0.615, Qes = 0.5, Qed = 1, Qef = 0.7; then Figure 3B The calculation results of Qcycle, Qed, Qes and Qef are: Qcycle=0.615, Qes=0.83, Qed=1, Qef=0.9.

[0103] for Figure 3A In the case shown in FIG. 1 , the measured value corresponding to the true ES value (i.e., the measurement result of the sixth frame) has very low reliability and cannot be used. In other words, although the true ventricular volume corresponding to the ultrasound image of this frame is Figure 3A The minimum values ​​shown are not necessarily Figure 3A As shown in the figure, on the contrary, the measurement result of this frame may be very large or much smaller than the actual result. The ES value extracted from the acceptable measurement result (i.e., the SE value measured by the ultrasound image of the 5th frame) will be greater than the actual ESV. Figure 3A The calculation method will underestimate the EF value. Figure 3A The Qef measured from the data is consistent with the actual situation.

[0104] And in Figure 3B In the case shown, the first reliability of the measured values ​​corresponding to the true ED and ES is high, and the EF value can be accurately evaluated. Figure 3B The data measured Qef = 0.9, which is higher than Figure 3A Qef = 0.7, which also shows that Figure 3B The EF parameters are more reliable and consistent with the actual situation. It can be seen that the second reliability evaluation result obtained according to the first reliability evaluation result corresponding to the key frame in the embodiment of the present invention can well reflect the real reliability of EF.

[0105] Afterwards, the periodic parameter and the second reliability evaluation result obtained above are displayed in a visual manner, so that the user can judge the reliability of the periodic parameter according to the second reliability evaluation result, and select a more reliable periodic parameter from multiple periodic parameters.

[0106] In one embodiment, the second reliability evaluation result can be indicated by the color of the mark representing the periodic parameter while displaying the periodic parameter. The mark representing the periodic parameter can be text, graphics, or symbols. Figure 4 In the display interface shown, still taking the ejection fraction as an example of the periodic parameter, the display interface displays a mark 403 representing the periodic parameter, i.e., EF=55%. If the second reliability evaluation result of the ejection fraction is high, for example, the second reliability evaluation result exceeds a preset threshold, the color of the mark 403 can be displayed as green to indicate that the periodic parameter is reliable; similarly, if the second reliability evaluation result of the ejection fraction is low, the color of the mark 403 can be displayed as red. Of course, in addition to this, other methods can also be used to display the second reliability evaluation result, such as directly displaying the value of the second reliability evaluation result near the mark 403, or displaying reliable or unreliable.

[0107] In some embodiments, a curve showing the variation of the measurement results over time may be obtained according to the measurement results corresponding to the multiple frames of ultrasound images, and the second reliability evaluation result may be represented by different colors in units of cycles on the curve showing the variation of the measurement results over time. Figure 4 , wherein a curve 402 showing the change of ventricular volume over time is shown, and the curve 402 is divided into a plurality of periods, and the color of each period of the curve 402 can be determined according to the second reliability evaluation result of the periodic parameter corresponding to each period, and the user can select a periodic parameter with a higher reliability according to the color of each period of the curve 402 as the final selected periodic parameter. Optionally, the period corresponding to the currently displayed identifier 403 can also be marked on the curve 402.

[0108] Furthermore, the period in which the second reliability evaluation result meets the preset requirements (i.e., the period within the vertical line range on curve 402) can be marked on the curve of the measurement result changing with time for user reference. The user can directly select the periodic parameter corresponding to the period marked on the volume curve.

[0109] In addition to displaying the second reliability evaluation result, the first reliability evaluation result may also be displayed for user reference. Exemplarily, the corresponding first reliability evaluation result may be displayed while displaying each frame of ultrasound image. For example, the first reliability evaluation result corresponding to the current frame of ultrasound image may be indicated by the color of the identification of the image type of the current frame of ultrasound image or the color of the identification of the target area in the current frame of ultrasound image.

[0110] Continue to refer to Figure 4 , a boundary line 404 is drawn in the ventricular region of the currently displayed ultrasound image 401. If the currently displayed ultrasound image corresponds to a high first reliability evaluation result, the boundary line 404 can be displayed in green; the image type 405 of the currently displayed ultrasound image is A4C (Apical 4-Chamber View). If the first reliability evaluation result of the measurement result of the currently displayed ultrasound image is high, for example, exceeding a preset threshold, the "A4C" mark in the ultrasound image can be displayed in green to indicate that the measurement result of the current frame ultrasound image is reliable. Thus, no other text or symbols are added, and no additional occlusion is generated for the ultrasound image. The first reliability evaluation result can be indicated only by the original mark on the ultrasound image.

[0111] Of course, in addition to the above two methods, the first reliability evaluation result can also be represented by other marks on or outside the ultrasound image. Alternatively, on the curve of the measurement result changing over time, the first reliability evaluation result can also be represented by different colors in units of frames.

[0112] In summary, the periodic parameter analysis method 200 of the embodiment of the present application can realize automatic evaluation of the reliability of the periodic parameters, which is convenient for users to judge the reliability of the periodic parameters.

[0113] The embodiment of the present application also provides an ultrasonic imaging system for implementing the above-mentioned periodic parameter analysis method 200. The ultrasonic imaging system includes an ultrasonic probe, a transmitting circuit, a receiving circuit, a processor and a display. The transmitting circuit is used to stimulate the ultrasonic probe to transmit ultrasonic waves to the object under test; the receiving circuit is used to control the ultrasonic probe to receive the echo of the ultrasonic wave to obtain an ultrasonic echo signal; the processor is used to: obtain multiple frames of ultrasonic images collected within a preset time; determine the target area of ​​at least two frames of ultrasonic images in the multiple frames of ultrasonic images, and measure the target area to obtain the measurement results corresponding to each frame of the ultrasonic image; obtain the periodic parameter according to the measurement results corresponding to the multiple frames of ultrasonic images; obtain the first reliability evaluation result of the measurement result of each frame of the ultrasonic image based on the image quality of each frame of the ultrasonic image in at least two frames of the ultrasonic image; obtain the second reliability evaluation result of the periodic parameter based on the first reliability evaluation result corresponding to the at least two frames of the ultrasonic image, and display the periodic parameter and the second reliability evaluation result in a visual manner.

[0114] In one embodiment, the first reliability evaluation result of the measurement result of each frame of the ultrasound image in at least two frames of the ultrasound image is obtained based on the image quality of each frame of the ultrasound image, including: inputting each frame of the at least two frames of the ultrasound image into a trained network model, and outputting the first reliability evaluation result of the image quality of each frame of the ultrasound image, or, obtaining the first reliability evaluation result of the image quality of each frame of the ultrasound image based on the signal-to-noise ratio of each frame of the ultrasound image in the at least two frames of the ultrasound image.

[0115] In one embodiment, the image quality of the ultrasound image is determined based on at least one of the following: the grayscale of the ultrasound image, the image clarity of the ultrasound image, the proportion of the effective area in the ultrasound image, the proportion of spots, snowflakes or reticulations in the ultrasound image, and the ultrasound probe, probe parameters or imaging parameters used to acquire the ultrasound image.

[0116] In one embodiment, the first reliability evaluation result is also related to a characteristic parameter of the target area, and the characteristic parameter of the target area includes: a shape of the target area or a contrast of a boundary of the target area.

[0117] In one embodiment, the periodic parameter represents an average level of the measurement result within the preset time, and the at least two frames of ultrasound images are multiple frames of ultrasound images acquired continuously within the preset time.

[0118] In one embodiment, the periodic parameter represents a degree of change of the measurement result within the preset time, and the at least two frames of ultrasound images are key frame ultrasound images in the multiple frames of ultrasound images.

[0119] In one embodiment, the key frame ultrasound image includes an ultrasound image corresponding to a peak value of the measurement results in the multiple frames of ultrasound images.

[0120] In one embodiment, the key frame ultrasound image further includes at least one frame of ultrasound image in the neighborhood of the ultrasound image corresponding to the peak value of the measurement result.

[0121] In one embodiment, obtaining the periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images includes: obtaining the periodic parameter according to the measurement results corresponding to at least two of the first reliability evaluation results that meet preset requirements.

[0122] In one embodiment, the processor is also used to: obtain a curve showing the measurement results changing over time based on the measurement results corresponding to the multiple frames of ultrasound images; represent the second reliability evaluation result by different colors in units of cycles on the curve showing the measurement results changing over time, or represent the first reliability evaluation result by different colors in units of frames on the curve showing the measurement results changing over time.

[0123] In one embodiment, the processor is further configured to: mark, on a curve showing changes in the measurement results over time, a period during which the second reliability evaluation result meets a preset requirement.

[0124] In one embodiment, displaying the second reliability evaluation result in a visual manner includes: representing the second reliability evaluation result by the color of the identifier representing the periodic parameter.

[0125] In one embodiment, the processor is also used to control the display to display the first reliability assessment result, and the display of the first reliability assessment result includes: representing the first reliability assessment result corresponding to the current frame ultrasound image by the color of the image type identification of the current frame ultrasound image or the color of the identification of the target area in the current frame ultrasound image.

[0126] In one embodiment, the target region comprises a ventricular region, the measurement results comprise ventricular measurement results, and the periodic parameters comprise cardiac function parameters.

[0127] In one embodiment, the ventricular measurement results include ventricular volume and the cardiac function parameter includes ejection fraction.

[0128] In one embodiment, the periodic parameters are obtained according to the measurement results corresponding to the multiple frames of ultrasound images, including: obtaining a curve of the ventricular volume changing with time according to the ventricular volume corresponding to the multiple frames of ultrasound images; determining the cardiac cycle according to the curve of the ventricular volume changing with time; determining the maximum ventricular volume and the minimum ventricular volume in the same cardiac cycle, and obtaining the ejection fraction according to the maximum ventricular volume and the minimum ventricular volume.

[0129] In one embodiment, determining the cardiac cycle based on the curve of the ventricular volume changing with time includes: measuring the peak spacing of the curve of the ventricular volume changing with time to obtain the cardiac cycle, or performing spectral analysis on the curve of the ventricular volume changing with time to obtain the cardiac cycle.

[0130] In one embodiment, the target area includes a blood vessel area, the measurement result includes a blood vessel diameter, and the periodic parameter includes at least one of the following: an inferior vena cava collapsibility index, an inferior vena cava expansion index, and an inferior vena cava variability rate.

[0131] Re-reference Figure 1 , the ultrasound imaging system can be implemented as follows Figure 1 The ultrasound imaging system 100 shown may include an ultrasound probe 110, a transmitting circuit 112, a receiving circuit 114, a processor 116 and a display 118. Optionally, the ultrasound imaging system 100 may also include a transmit / receive selection switch 120 and a beamforming module 122. The transmitting circuit 112 and the receiving circuit 114 may be connected to the ultrasound probe 110 via the transmit / receive selection switch 120. The relevant description of each component may refer to the relevant description above and will not be repeated here.

[0132] The above only describes the main functions of the components of the ultrasound imaging system. For more details, please refer to the related description of the periodic parameter analysis method 200. The ultrasound imaging system of the embodiment of the present application can realize the automatic evaluation of the reliability of the periodic parameters.

[0133] In addition, according to an embodiment of the present application, a computer storage medium is also provided, on which program instructions are stored, and when the program instructions are run by a computer or a processor, the corresponding steps of the analysis method 200 for periodic parameters of the embodiment of the present application are used to execute. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0134] In addition, according to an embodiment of the present application, a computer program is also provided, which can be stored in a cloud or local storage medium. When the computer program is run by a computer or a processor, it is used to execute the corresponding steps of the method for analyzing periodic parameters of the embodiment of the present application.

[0135] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application to this. Those of ordinary skill in the art may make various changes and modifications therein without departing from the scope and spirit of the present application. All these changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0136] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0138] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0139] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present application should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with features less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby explicitly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0140] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0141] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0142] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some modules according to the embodiments of the present application. The application can also be implemented as a device program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0143] It should be noted that the above embodiments illustrate rather than limit the present application, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0144] The above is only a specific implementation or description of a specific implementation of the present application, and the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for analyzing periodic parameters, characterized in that: The method comprises: Acquire multiple frames of ultrasound images collected within a preset time; Determine the heart region of at least two frames of ultrasound images among the multiple frames of ultrasound images, and measure the heart region to obtain a measurement result corresponding to each frame of the ultrasound image; Obtaining a periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images; In at least two frames of the ultrasound image, obtaining a first reliability evaluation result of the measurement result of each frame of the ultrasound image based on the image quality of each frame of the ultrasound image; A second reliability evaluation result reflecting the reliability of the periodic parameter is obtained based on the first reliability evaluation result corresponding to the at least two frames of the ultrasound image, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner.

2. The method according to claim 1, characterized in that The step of obtaining a first reliability evaluation result of the measurement result of each frame of the ultrasound image based on the image quality of each frame of the ultrasound image in at least two frames of the ultrasound image comprises: Inputting each of the at least two frames of the ultrasound images into the trained network model, and outputting the first reliability evaluation result on the image quality of each frame of the ultrasound image, or, The first reliability evaluation result on the image quality of each frame of the ultrasound image is obtained according to the signal-to-noise ratio of each frame of the ultrasound image in the at least two frames of the ultrasound image.

3. The method according to claim 1, characterized in that The image quality of the ultrasound image is determined according to at least one of the following: The grayscale of the ultrasonic image, the image clarity of the ultrasonic image, the proportion of the effective area in the ultrasonic image, the proportion of spots, snowflakes or reticulations in the ultrasonic image, and the ultrasonic probe, probe parameters or imaging parameters used to collect the ultrasonic image.

4. The method according to claim 1, characterized in that: The first reliability evaluation result is also related to the shape of the heart region or the contrast of the boundary of the heart region.

5. The method according to claim 1, characterized in that The periodic parameter represents an average level of the measurement result within the preset time, and the at least two frames of ultrasound images are multiple frames of ultrasound images acquired continuously within the preset time.

6. The method according to claim 1, characterized in that The periodic parameter represents the degree of change of the measurement result within the preset time, and the at least two frames of ultrasound images are key frame ultrasound images in the multiple frames of ultrasound images.

7. The method according to claim 6, characterized in that The key frame ultrasound image includes an ultrasound image corresponding to a peak value of the measurement results in the multiple frames of ultrasound images.

8. The method according to claim 7, characterized in that The key frame ultrasound image also includes at least one frame of ultrasound image in the neighborhood of the ultrasound image corresponding to the peak value of the measurement result.

9. The method according to claim 1, characterized in that: The obtaining of periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images includes: The periodic parameter is obtained according to the measurement results corresponding to at least two of the first reliability evaluation results that meet preset requirements.

10. The method according to claim 1, characterized in that Also includes: Obtaining a curve of the measurement results changing over time according to the measurement results corresponding to the multiple frames of ultrasound images; The second reliability evaluation result is represented by different colors in units of cycles on a curve showing the measurement result changing over time, or the first reliability evaluation result is represented by different colors in units of frames on a curve showing the measurement result changing over time.

11. The method according to claim 10, characterized in that Also includes: The period in which the second reliability evaluation result meets the preset requirement is marked on the curve of the measurement result changing with time.

12. The method according to claim 1, characterized in that Displaying the second reliability evaluation result in a visual manner includes: The second reliability evaluation result is represented by the color of the mark representing the periodic parameter.

13. The method according to claim 1, characterized in that The method further includes displaying the first reliability evaluation result, wherein the displaying the first reliability evaluation result includes: The first reliability evaluation result corresponding to the current frame ultrasound image is represented by the color of the identification of the image type of the current frame ultrasound image or the color of the identification of the target area in the current frame ultrasound image.

14. The method according to any one of claims 1 to 13, characterized in that The heart region includes a ventricular region, the measurement results include ventricular measurement results, and the periodic parameters include cardiac function parameters.

15. The method according to claim 14, characterized in that The ventricular measurement results include ventricular volume, and the cardiac function parameters include ejection fraction.

16. The method according to claim 15, characterized in that The obtaining of periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images includes: Obtaining a curve of the ventricular volume changing with time according to the ventricular volume corresponding to the multiple frames of ultrasound images; Determining the cardiac cycle according to the curve of the ventricular volume changing with time; The maximum ventricular volume and the minimum ventricular volume in the same cardiac cycle are determined, and the ejection fraction is obtained according to the maximum ventricular volume and the minimum ventricular volume.

17. The method according to claim 16, characterized in that Determining the cardiac cycle according to the curve of the ventricular volume changing with time includes: The peak interval of the curve of the ventricular volume changing with time is measured to obtain the cardiac cycle, or the spectrum analysis is performed on the curve of the ventricular volume changing with time to obtain the cardiac cycle.

18. A method for analyzing periodic parameters, characterized in that: The method comprises: Acquire multiple frames of ultrasound images collected within a preset time; Determine a target area of ​​at least two frames of ultrasound images among the multiple frames of ultrasound images, and measure the target area to obtain a measurement result corresponding to each frame of the ultrasound image; Obtaining a periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images; In at least two frames of the ultrasound image, obtaining a first reliability evaluation result of the measurement result of each frame of the ultrasound image based on the image quality of each frame of the ultrasound image; A second reliability evaluation result reflecting the reliability of the periodic parameter is obtained based on the first reliability evaluation result corresponding to the at least two frames of the ultrasound image, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner.

19. The method according to claim 18, characterized in that The target area includes a blood vessel area, the measurement result includes a blood vessel diameter, and the periodic parameter includes at least one of the following: an inferior vena cava collapse index, an inferior vena cava expansion index, and an inferior vena cava variation rate.

20. An ultrasonic imaging system, characterized in that: The ultrasonic imaging system comprises: Ultrasound probe; A transmitting circuit, used for stimulating the ultrasonic probe to transmit ultrasonic waves to the object under test; A receiving circuit, used for controlling the ultrasonic probe to receive the ultrasonic echo to obtain an ultrasonic echo signal; A processor, configured to execute the method for analyzing periodic parameters as described in any one of claims 1 to 19.

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