WO2022141083A1 - Periodic parameter analysis method and ultrasonic imaging system - Google Patents

Abstract

A periodic parameter analysis method (200) and an ultrasonic imaging system (100). The analysis method comprises: acquiring a plurality of ultrasonic image frames collected within a preset period of time (S210); determining a heart area of at least two ultrasonic image frames in the plurality of ultrasonic image frames, and measuring the heart area to obtain the measurement result corresponding to each ultrasonic image frame (S220); obtaining a periodic parameter according to the measurement result corresponding to the plurality of ultrasonic image frames (S230); obtaining, in at least two ultrasonic image frames, a first reliability evaluation result of the measurement result of each ultrasonic image frame on the basis of the image quality of each ultrasonic image frame (S240); and obtaining a second reliability evaluation result of the periodic parameter on the basis of the first reliability evaluation result corresponding to the at least two ultrasonic image frames, and displaying the periodic parameter and the second reliability evaluation result in a visual manner (S250). The analysis method can automatically evaluate the reliability of the periodic parameter.

Description

Periodic parameter analysis method and ultrasonic imaging system

manual

technical field

The present application relates to the technical field of ultrasound imaging, and more particularly, to a method for analyzing periodic parameters and an ultrasound imaging system.

Background technique

Cardiopulmonary function assessment is the focus of the POC (point-of-care, bedside detection) 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 ineffective. In the process of real-time ultrasound scanning, the quality of ultrasound images is variable, resulting in dynamic changes in the reliability of measurement parameters based on ultrasound images. Doctors need to be distracted to screen measurement parameters, and true automatic cardiopulmonary function assessment cannot be achieved.

The heart is the power organ that maintains normal blood circulation in the human body. Left ventricular ejection fraction (EF) is the most commonly used and the most important index to evaluate left ventricular systolic function, and it is very important for many cardiovascular diseases (including ischemic heart disease, cardiomyopathy, valvular heart disease, congenital heart disease, etc.). It has important clinical value in diagnosis, condition monitoring, curative effect evaluation and prognosis determination. The ejection fraction can be calculated automatically by tracing the left ventricular endocardium. However, this is only suitable for the ideal situation where the image quality is stable and all cardiac cycles are reliable. Therefore, it is only suitable for offline analysis scenarios. Meet the clinical needs of dynamic real-time monitoring in the POC field.

SUMMARY OF THE INVENTION

A series of concepts in simplified form have been introduced in the Summary section, which are described in further detail in the Detailed Description section. The Summary of the Invention section of the present invention is not intended to attempt to limit the key features and essential technical features of the claimed technical solution, nor is it intended to attempt to determine the protection scope of the claimed technical solution.

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

Acquiring multiple frames of ultrasound images collected within a preset time;

determining the cardiac region of at least two ultrasound images in the multiple frames of ultrasound images, and measuring the cardiac region to obtain a measurement result corresponding to each frame of the ultrasound image;

obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images;

In at least two frames of the ultrasound images, a first reliability evaluation result of the measurement results of each frame of the ultrasound images is obtained based on the image quality of the ultrasound images of each frame;

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 images, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner. Reliability assessment results.

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

Acquiring multiple frames of ultrasound images collected within a preset time;

determining the target area of at least two frames of ultrasonic images in the multiple frames of ultrasonic images, and measuring the target area to obtain a measurement result corresponding to each frame of the ultrasonic images;

obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images;

In at least two frames of the ultrasound images, a first reliability evaluation result of the measurement results of each frame of the ultrasound images is obtained based on the image quality of the ultrasound images of each frame;

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 images, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner. Reliability assessment results.

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

Ultrasound probe;

a transmitting circuit, used to excite the ultrasonic probe to transmit ultrasonic waves to the measured object;

a receiving circuit, configured to control the ultrasonic probe to receive the echo of the ultrasonic wave to obtain an ultrasonic echo signal;

A processor for executing the above-mentioned method of analyzing periodic parameters.

The method for analyzing periodic parameters and the ultrasonic imaging system according to the embodiments of the present application can realize automatic evaluation of the reliability of periodic parameters.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative labor.

In the attached image:

FIG. 1 shows a schematic block diagram of an ultrasound imaging system according to an embodiment of the present application;

FIG. 2 shows a schematic flowchart of a method for analyzing periodic parameters according to an embodiment of the present invention;

3A and 3B illustrate examples of periodic parameters according to an embodiment of the present invention;

FIG. 4 shows a schematic diagram of a display interface according to an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present application more apparent, the exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a 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 by 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 efforts shall fall within the protection scope of the present application.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced without one or more of these details. In other instances, some technical features known in the art have not been described in order to avoid confusion with the present application.

It should be understood that the application may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this application to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a," "an," and "the/the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the terms "compose" and/or "include", when used in this specification, identify the presence of stated features, integers, steps, operations, elements and/or components, but do not exclude one or more other The presence or addition of features, integers, steps, operations, elements, parts and/or groups. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

For a thorough understanding of the present application, detailed structures will be presented in the following description in order to explain the technical solutions proposed by the present application. Alternative embodiments of the present application are described in detail below, however, the present application may have other embodiments in addition to these detailed descriptions.

The method for analyzing periodic parameters and the ultrasonic imaging system provided by the present application can be applied to the human body, and can also be applied to various animals.

In the following, an ultrasound imaging system according to an embodiment of the present application is first described with reference to FIG. 1 , which shows a schematic structural block diagram of an ultrasound imaging system 100 according to an embodiment of the present application.

As shown in FIG. 1 , 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 further include a transmit/receive selection switch 120 and a beam forming module 122 , and the transmit circuit 112 and the reception circuit 114 may be connected to the ultrasound probe 110 through the transmit/receive selection switch 120 .

The ultrasonic probe 110 includes a plurality of transducer array elements, and the plurality of transducer array elements can be arranged in a row to form a linear array, or arranged in a two-dimensional matrix to form an area array, and 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 convert the received ultrasonic waves into electrical signals, so each transducer array element can be used to realize the mutual conversion of electrical pulse signals and ultrasonic waves, so as to realize the transmission to the measured object. The tissue in the target area transmits ultrasonic waves, and can also be used to receive ultrasonic echoes reflected by the tissue. During 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 through the transmitting sequence and receiving sequence, or the transducer array elements can be controlled to divide time slots for transmitting ultrasonic waves Or receive echoes of ultrasonic waves. The transducer elements participating in ultrasonic emission can be excited by electrical signals at the same time, so as to emit ultrasonic waves at the same time; Ultrasound at certain time intervals.

During ultrasound imaging, the transmit circuit 112 transmits the delayed-focused transmit pulses to the ultrasound probe 110 through the transmit/receive selection switch 120 . The ultrasonic probe 110 is stimulated by the transmission pulse to transmit an ultrasonic beam to the tissue in the target area of the object to be measured, and after a certain delay, receives the ultrasonic echo with tissue information reflected from the tissue in the target area, and sends the ultrasonic wave back to the target area. The waves are reconverted into electrical signals. The receiving circuit 114 receives the electrical signals converted and generated by the ultrasonic probe 110, obtains ultrasonic echo signals, and sends these ultrasonic echo signals to the beamforming module 122, and the beamforming module 122 performs focus delay, weighting and channeling on the ultrasonic echo data Summation, etc., are then sent to processor 116. The processor 116 performs signal detection, signal enhancement, data conversion, logarithmic compression, etc. on the ultrasonic echo signal to form an ultrasonic image. The ultrasound images obtained by the processor 116 may be displayed on the display 118 or stored in the memory 124 .

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

The display 118 is connected to the processor 116, and the display 118 may be a touch display screen, a liquid crystal display screen, etc.; or, the display 118 may be an independent display such as a liquid crystal display, a TV set, etc. independent of the ultrasound imaging system 100; or, the display 118 may It is the display screen of electronic devices such as smartphones, tablets, etc. The number of displays 118 may be one or more. For example, the display 118 may include a main screen and a touch screen, where the main screen is mainly used for displaying ultrasound images, and the touch screen is mainly used for human-computer interaction.

Display 118 may display ultrasound images obtained by processor 116 . In addition, the display 118 can also provide the user with a graphical interface for human-computer interaction while displaying the ultrasound image, set one or more controlled objects on the graphical interface, and provide the user with the human-computer interaction device to input operating instructions to control these objects. The controlled object, so as to perform the corresponding control operation. For example, an icon is displayed on the graphical interface, and the icon can be operated by using a human-computer interaction device to perform a specific function, such as drawing a region of interest frame on the ultrasound image.

Optionally, the ultrasound imaging system 100 may also include other human-computer interaction devices other than the display 118, which are connected to the processor 116. For example, the processor 116 may be connected to the human-computer interaction device through an external input/output port. / The output port can be a wireless communication module, a wired communication module, or a combination of the two. External input/output ports may also be implemented based on USB, bus protocols such as CAN, and/or wired network protocols, and the like.

Wherein, the human-computer interaction device may include an input device for detecting the user's input information, for example, the input information may be a control instruction for the ultrasonic transmission/reception sequence, or a point, line or frame drawn on the ultrasonic image. Manipulate input instructions, or may also include other instruction types. Input devices may include one or a combination of keyboards, mice, scroll wheels, trackballs, mobile input devices (eg, mobile devices with touch display screens, cell phones, etc.), multifunction knobs, and the like. The human-computer interaction apparatus may also include an output device such as a printer.

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

It should be understood that the components included in the ultrasound imaging system 100 shown in FIG. 1 are only illustrative, and may include more or less components. This application is not limited to this.

Hereinafter, an analysis method of a periodic parameter according to an embodiment of the present application will be described with reference to FIG. 2 . FIG. 2 is a schematic flowchart of a periodic parameter analysis method 200 according to an embodiment of the present application.

As shown in FIG. 2 , a method 200 for analyzing periodic parameters according to an embodiment of the present application includes the following steps:

In step S210, acquiring multiple frames of ultrasound images collected within a preset time;

In step S220, a target area (eg, a heart area) of at least two frames of ultrasonic images in the multiple frames of ultrasonic images is determined, and the target area is measured to obtain a measurement result corresponding to each frame of the ultrasonic image;

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

In step S240, in at least two frames of the ultrasonic images, a first reliability evaluation result of the measurement results of each frame of the ultrasonic images is obtained based on the image quality of the ultrasonic images of each frame;

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 images, and the periodic parameter and the second reliability evaluation result.

The periodic parameter analysis method 200 of the embodiment of the present application can realize automatic assessment of the reliability of periodic parameters, and solves the problem that the previous assessment solution can only assess the image quality of a single-frame ultrasound image, and cannot assess the image quality of a single-frame ultrasound image. The problem of evaluating the reliability of the periodic parameter obtained from the image is convenient for the user to determine the reliability of the periodic parameter and select the periodic parameter according to the second reliability evaluation result.

In step S210, the ultrasound image may be acquired in real time, so that the reliability evaluation of the periodic parameter can be performed in real time while acquiring the periodic parameter in real time. Since the method for analyzing periodic parameters according to the embodiment of the present invention can automatically analyze and obtain the reliability of periodic parameters without the need for the user to select ultrasound images frame by frame, it is suitable for the scenario of obtaining periodic parameters in real time. At the same time, the second reliability of the periodic parameter is determined in real time, so that the user can determine the reliability degree of the periodic parameter, and select the periodic parameter with higher reliability from multiple periodic parameters, so as to meet the dynamic real-time monitoring in the POC field. clinical needs. Of course, the ultrasound image may also be a pre-acquired ultrasound image extracted from a storage medium or received through remote transmission, that is, the periodic parameter analysis method 200 is also suitable for offline analysis scenarios.

Before performing step S210, acquisition parameters may be preset. Wherein, the acquisition parameters may include acquisition duration (that is, the length of the preset time in step S210), frame rate (that is, the number of ultrasound images collected per second), the type of ultrasound images, and the like. It should be noted that the acquisition time needs to be greater than or equal to the length of one cycle, so as to ensure that the acquired multi-frame ultrasonic images can include the ultrasonic images necessary to calculate the periodic parameters, such as the end-diastolic period necessary to calculate the ejection fraction. ultrasound images and end-systolic ultrasound images.

Exemplarily, ultrasound scanning may be performed based on the ultrasound imaging system 100 shown in FIG. 1 to acquire multiple frames of ultrasound images. Specifically, the processor 116 controls the transmit circuit 112 to transmit the delayed-focused transmit pulses to the ultrasound probe 110 through the transmit/receive selection switch 120 . The ultrasound probe 110 is excited by the transmitting pulse to transmit an ultrasound beam to the target tissue (such as heart tissue, blood vessel tissue, etc.) of the measured object, and after a certain delay, receives the ultrasound echo with tissue information reflected from the tissue in the target area. wave and reconvert this ultrasonic echo into an electrical signal. The receiving circuit 114 receives the electrical signals converted and generated by the ultrasonic probe 110, obtains ultrasonic echo signals, and sends these ultrasonic echo signals to the beamforming module 122, and the beamforming module 122 performs focus delay, weighting and channeling on the ultrasonic echo data Summation, etc., are then sent to processor 116. The processor 116 performs signal detection, signal enhancement, data conversion, logarithmic compression, etc. on the ultrasonic echo signals to form different types of ultrasonic images, including but not limited to B-mode ultrasonic images, C-mode ultrasonic images, and the like. The multiple frames of ultrasound images obtained by the processor 116 may be sent to the display 118 for display.

Taking the periodic parameter as the cardiac function parameter as an example, the ultrasound image is an ultrasound image including the ventricular region collected for cardiac tissue. In the process of collecting ultrasonic images, the ultrasonic sound beam generated by the ultrasonic probe 110 enters the chest wall and scans in a fan shape. According to the different positions and angles of the ultrasonic probe 110 , slice views of different levels and orientations of the cardiac tissue can be obtained. Usually, the cardiac section included in the ultrasound image may include the apical four-chamber plane or the apical two-chamber plane, etc. The multi-frame ultrasound images acquired 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-systolic movement process, and a cardiac cycle refers to the time from one end-diastole to the next end-diastole, or from one end-systole to the next end-systole.

In step S220, the target area of at least two frames of ultrasound images in the multiple frames of ultrasound images is first determined. Wherein, the target area may be determined for at least two frames of the multiple frames of ultrasound images, or the target area may be determined for each frame of the multiple frames of ultrasound images. Generally, the at least two frames of ultrasound images include at least one cycle (eg, one cardiac cycle) of the ultrasound images.

The target area determined in the ultrasound image depends on the type of periodic parameter to be acquired. For example, if the periodic parameter to be acquired is a cardiac function parameter, the target area is the ventricular area, specifically the left ventricle area, and the left ventricular area can be determined by identifying the left ventricular endocardium area in the ultrasound image. If the periodic parameters to be acquired are respiratory parameters such as the inferior vena cava collapse index, the inferior vena cava dilation index, and the inferior vena cava variation rate, the target area may be the blood vessel area, and the blood vessel area may specifically be the inferior vena cava.

Illustratively, machine learning methods may be employed to determine target regions in ultrasound images. Specifically, first, a machine learning method is used to extract features from an ultrasound image, and the feature extraction methods used can be traditional PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), Harr feature (Harr feature) extraction, texture feature extraction Extraction, etc., deep neural network can also be used for feature extraction; then, the extracted features are matched with the features in the pre-built database, using KNN (K-nearest neighbor classifier), SVM (support vector machine), random Classifiers such as forests and neural networks classify the extracted features to determine the category of image features of each image block in the ultrasound image, and divide the target area in the ultrasound image according to the category of the image block.

Alternatively, an end-to-end deep learning neural network based on deep learning can also be used to learn features from a pre-built database by stacking convolutional layers and fully connected layers, and adding upsampling or deconvolution layers to make the input and output The size of the input image is the same, so that the target area of the input image and its corresponding category can be directly obtained. The deep learning neural network used includes FCN (full convolutional neural network), U-Net (U-shaped network), Mask R-CNN (mask candidate region neural network), etc.

Optionally, a traditional image segmentation algorithm can also be used to determine the target area of the at least two frames of ultrasound images. The image segmentation algorithm may include various applicable image segmentation algorithms such as Graph Cut (graph cut) algorithm, Level Set (level set) algorithm, and Random Walker (random walk) algorithm.

In one embodiment, before identifying the target area, the ultrasonic image may also be processed by denoising, enhancement, etc., to more accurately identify the target area.

After the target area is identified, the target area is measured to obtain a measurement result corresponding to each frame of the at least two frames of ultrasonic 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 region. 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 short-axis length of the ventricle obtained by measuring the ventricular region. If the periodic parameter is a breathing parameter, the measurement result corresponding to each frame of the ultrasound image may be the blood vessel diameter obtained by measuring the blood vessel area.

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

In step S230, periodic parameters are obtained according to the measurement results corresponding to the multiple frames of ultrasound images. The periodic parameter is a periodic physiological characteristic parameter related to heartbeat, respiration and the like.

Among them, periodic parameters are mainly divided into two categories. A type of periodic parameter represents the average level of the measurement results within a preset time period, and may specifically represent the average level of the measurement results in the period. For example, this type of periodic parameter may be the average level of the measurement results corresponding to each frame of ultrasound images in a period. It can be seen that such periodic parameters are closely related to the reliability of the measurement results of each frame of ultrasound images.

Another type of periodic parameter represents the degree of change of the measurement result within a preset time period, and may specifically represent the degree of change of the measurement result within the period. Taking ejection fraction (EF) as an example, the formula for calculating EF is as follows:

EF=(EDV-ESV)/EDV;

Among them, EDV (End-Diastolic Volume, end-diastolic volume) is the left ventricular volume at the end of diastole, usually the maximum ventricular volume in the cycle; ESV (End-Systolic Volume, end-systolic volume) is the ventricular volume at the end of systole, Usually the minimum value of the ventricular volume during the cycle. That is, EF is calculated from the ventricular volume at the end of diastole and the ventricular volume at the end of systole. It can be seen that such periodic parameters are calculated according to the measurement results of the key frame ultrasound images, and are mainly closely related to the reliability of the measurement results of the key frame ultrasound images. Since this type of ultrasound image is generally calculated according to the peak value of the measurement result, the key frame ultrasound image may include ultrasound images corresponding to the peak value of the measurement result in the multiple frames of ultrasound images. The peak value of the measurement result includes the maximum value and the minimum value of the measurement result.

Continuing to take the ejection fraction as an example, when calculating the ejection fraction, we first need to find out all the ventricular volumes of a complete cardiac cycle, determine the maximum and minimum ventricular volumes in the same cardiac cycle, and use them as the EDV value and the ESV value, respectively. Ejection fraction was calculated according to the above formula. Among them, the cardiac cycle refers to the process experienced by the cardiovascular system from the beginning of one heartbeat to the beginning of the next heartbeat.

In an example, finding all ventricular volumes of a complete cardiac cycle from the ventricular volumes measured according to each frame of ultrasound images, including: obtaining a curve of the ventricular volume with time according to the ventricular volumes corresponding to the multiple frames of ultrasound images; The cardiac cycle is determined from the curve of the ventricular volume versus time. Alternatively, the peak spacing of the ventricular volume versus time curve can be measured to obtain the cardiac cycle, or a spectral analysis of the ventricular volume versus time curve can be performed to obtain the cardiac cycle.

Optionally, other ventricular measurements can be plotted over time to determine the cardiac cycle, and then obtain all ventricular volumes in a complete cardiac cycle from the cardiac cycle, or directly obtain the ventricular volume at end-diastole and end-systole. ventricular volume. Wherein, the measurement results of the ventricle include but are not limited to the long-axis length of the left ventricle, the short-axis length of the left ventricle, and the area of the left ventricle.

Alternatively, a characteristic curve may also be generated according to the characteristic value of each frame of ultrasonic images in multiple frames of ultrasonic images continuously collected within a preset time period, and then the above characteristic curve is 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 the similarity coefficient between each frame of ultrasound images and the standard frame may be calculated to generate an image similarity curve.

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

Wherein, the at least two frames of ultrasound images are at least two frames of ultrasound images related to the measurement of periodic parameters among the 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 at least two frames of ultrasound images are multiple frames continuously collected within the preset time. Ultrasound images, and at least include multiple frames of ultrasound images in one cycle. When the periodic parameter represents the degree of change of the measurement result within a preset time, the reliability of the periodic parameter is related to the reliability of the measurement result used to calculate the periodic parameter, and at least two frames of ultrasound images include at least the measurement results used to calculate the periodic parameter. keyframe ultrasound images. As mentioned above, since such periodic parameters are generally calculated based on the peak value of the measurement result in 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 images may further include at least one frame of ultrasound images in the neighborhood of the ultrasound images corresponding to the peaks of the measurement results.

Because the quality of the ultrasound image is the basic factor to determine whether the measurement result is reliable, if the quality of the ultrasound image is not good, the measurement result is difficult to be reliable. Therefore, a first reliability evaluation result of the measurement result of each frame of ultrasound image is obtained based on at least the image quality of each frame of ultrasound image. In one embodiment, each of the at least two frames of ultrasonic images may be input into the trained network model, and a first reliability evaluation result regarding the image quality of each frame of ultrasonic images may be output. For example, the network model uses VGG, ResNet and other networks as the backbone, and is implemented through classification or regression tasks.

Alternatively, a traditional image processing method may also be used to obtain the first reliability evaluation result regarding the image quality of each frame of ultrasonic images according to the signal-to-noise ratio of each frame of the at least two frames of ultrasonic images. Among them, the higher the signal-to-noise ratio, the lower the score of the reliability evaluation result.

In one embodiment, the image quality of the ultrasound image may be determined according to 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 speckles and snowflakes in the ultrasound image or the proportion of the texture, the ultrasound probe, probe parameters or imaging parameters used to acquire the ultrasound image.

As for the grayscale of the ultrasound image, it may include the overall grayscale of the ultrasound image, or may include the grayscale of the ultrasound image in the effective area. The image quality may be determined according to at least one of whether the average value of the image grayscales is within a threshold range, whether the image grayscales are uniform, and whether the extreme values of the image grayscales satisfy the grayscale extreme value criteria. As to whether the grayscale of the ultrasound image is uniform, the grayscale histogram of the ultrasound image can be drawn, and by judging whether the grayscale in the grayscale histogram is uniformly distributed, it is ensured that the grayscale of the image will not be concentrated in a certain area and affect the image quality of the ultrasound image. .

If the grayscale of the ultrasound image satisfies the grayscale standard, for example, the mean value of the grayscale of the ultrasound image is appropriate and the image is uniform, the ultrasound image can more accurately display the morphology of the tissue, and the quality of the ultrasound image is high; on the contrary, if the ultrasound image is of high quality When the gray scale does not meet the gray scale standard, the quality of the ultrasound image is low, therefore, the quality of the ultrasound image can be determined by the gray scale of the ultrasound image. For example, the grayscale standard of the ultrasound image quality, such as the standard of grayscale mean value, the standard of grayscale uniformity, and the standard of grayscale extreme value of the ultrasound image, can be set. Further, the grayscale and grayscale standard of the ultrasound image can be calculated. The deviation between the deviation and the image quality is established to establish a functional relationship or other corresponding relationship, so as 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 an ultrasound image and the grayscale standard can be evaluated from one angle, such as the grayscale uniformity dimension; it can also be evaluated from multiple dimensions, such as grayscale mean, grayscale extreme, and grayscale uniformity Sex and other dimensions, comprehensively obtain the deviation between the grayscale of the ultrasound image and the grayscale standard.

Regarding the image definition of the 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 expression of clarity can be expressed in the form of a ten-point scale, a hundred-point scale, or a percentage; it can also be a qualitative standard, including clear, clear, blurry, blur, etc. The calculation of the image sharpness can be calculated from whether the ultrasound image is too bright or too dark, or whether the resolution of the ultrasound image is high enough.

In one embodiment, the sharpness of the ultrasound image may be calculated from 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 sharpness may be established. For example, the image sharpness can be calculated based on the gradient information through a Brenner gradient function, a Tenengrad gradient function, a Laplacian gradient function, or the like. In another embodiment, the artificial intelligence model can be trained by inputting two types of ultrasound images with sharpness and blurring. Exemplarily, the artificial intelligence model can perform a clear and fuzzy binary classification problem on the ultrasound image, and for the input ultrasound image to be tested, the artificial intelligence model can input clear or fuzzy classification results. It should be emphasized that the artificial intelligence model can also grade the clarity of the ultrasound image, such as clear, clear, blurred, and fuzzy, so that the artificial intelligence model can grade the output clarity of the input ultrasound image to be tested.

Regarding the proportion of the effective area in the ultrasound image, the effective area may be the area of the ultrasound image related to the acquisition of detection information. Exemplarily, for a certain tissue or organ, the effective area may be an area of the ultrasound image that includes the image of the tissue or organ, or an ultrasound image area related to the acquisition of detection information, such as an image area of a tissue or organ nodule. The main purpose of detecting the proportion of the effective area of the ultrasound image is to ensure that the proportion of the effective area of the ultrasound image to the entire image is appropriate, for example, the proportion should not be too small, but should be greater than 1/2. Exemplarily, a specific detection method is to obtain an effective area through threshold segmentation of image processing, etc., calculate the ratio of the effective area to the overall image area, and determine whether the ratio meets a preset ratio requirement. Wherein, the size or proportion of the effective area is related to parameters such as the ultrasonic scanning depth or the magnification/reduction multiple. In one embodiment, it may be detected whether the ultrasound scanning depth meets the standard, for example, whether the ultrasound scanning depth is within a threshold range, so as to determine whether the proportion of the effective area of the ultrasound image is appropriate.

It can be understood that if the proportion of the effective area of the ultrasound image is too small, it is difficult to accurately reflect the shape of the tissues and organs on the ultrasound image, which is not conducive to obtaining detection information based on the ultrasound image. Therefore, the effective area proportion of the ultrasound image can be used. To determine the quality of the ultrasound image, for example, the effective area proportion of the ultrasound image can be calculated, and a functional relationship or other correspondence between the effective area proportion of the ultrasound image and the image quality can be established, so as to determine the ultrasound image through the effective area proportion of the ultrasound image. quality.

Regarding the proportion of speckles, snowflakes, or reticles in the ultrasound image, the entire ultrasound image can be inspected to determine whether there are speckles, snowflakes, or reticles in the ultrasound image, and whether speckles, snowflakes, or patterns are present in the ultrasound image. The proportion of the texture; it is also possible to first determine the effective area in the ultrasonic image, and then detect the ultrasonic image in the effective area. It can be understood that if there are speckles, snowflakes or reticles in the ultrasound image, the speckles, snowflakes or reticles in the ultrasound image may cover the heart or blood vessel area and affect the quality of the image. Therefore, it can be established whether there is The functional relationship or other corresponding relationship between speckle, snowflake grain or texture and image validity, the larger the proportion, the lower the image quality of the ultrasound image. Further, according to the different degrees of influence of the three on the identification of tissues in the image, different weights can be assigned to the three image defects of spots, fine snowflakes or reticles, etc., so as to determine whether there are spots, Snowflakes or textures determine the quality of an ultrasound image.

For the detection of spots, snowflakes or nets in an ultrasound image, it can be detected whether the texture of the ultrasound image conforms to a preset image texture standard. Exemplarily, a detection model for image texture can be pre-trained, and the ultrasound image is input into the detection model to obtain a detection result of whether the texture meets a preset image texture standard, wherein the image texture includes: whether the image has spots, whether there is snowflakes or not. Fine grain, with or without reticulation.

Taking the ultrasound probe, probe parameters and/or imaging parameters as an example, the quality of the ultrasound image can be determined by the correspondence between the ultrasound probe, the probe parameters and/or the imaging parameters and the tissues and organs to be tested included in the ultrasound image. When performing ultrasound inspection on a patient, it is necessary to select different probe parameters and imaging parameters according to different inspection parts, so as to achieve the best imaging effect for different inspection parts. For example, superficial tissues use high-frequency linear array probes; abdominal organs use low-frequency convex array probes. If the ultrasound probe and the corresponding probe parameters and imaging parameters are incorrectly used in actual operation, the quality of the ultrasound image will be affected. Therefore, the type of tissue contained in the ultrasound image can be compared with one or more of the ultrasound probe, probe parameters and imaging parameters used to scan the ultrasound image, and if the two match, it is determined that the quality of the ultrasound image is high , if the two do not match, it is determined that the quality of the ultrasound image is low.

In addition to the quality of the ultrasonic image, the first reliability evaluation result is also related to the characteristic parameters of the target area. The measurement result of the ultrasonic image can be comprehensively evaluated according to the image quality of the ultrasonic image and the characteristic parameters of the target area to obtain the first reliability evaluation result. Sexual assessment results. 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 real 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 accordingly the first reliability result is also low. Exemplarily, the trained machine learning model may be used to analyze the characteristic parameters of the target area to obtain the first reliability evaluation result.

In some embodiments, the measurement results may be screened according to the first reliability evaluation results, and the measurement results corresponding to the first reliability evaluation results that do not meet the preset requirements are excluded, and only the at least two first reliability evaluation results that meet the preset requirements Periodic parameters are obtained from the measurement results corresponding to the reliability evaluation results, so as to improve the accuracy of the periodic parameters.

Since the periodic parameter is calculated based on the measurement results of multiple frames of ultrasound images in one cycle, it is still impossible to evaluate whether the final measurement result is reliable only by the first reliability evaluation result of a single measurement result.

Therefore, 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 ultrasound images. That is, the first reliability evaluation result is a reliability evaluation result characterizing the reliability degree of a single measurement result, and the second reliability evaluation result is a reliability evaluation result characterizing the reliability degree of the periodic parameter.

As mentioned above, the periodic parameter includes two types, one representing the average level of the measurement results within a preset time, and the other representing the degree of variation of the measurement results within the preset time. For the first periodic parameter, the second reliability evaluation result may be calculated according to the first reliability evaluation result corresponding to the multiple frames of ultrasound images continuously collected within the preset time, wherein the multiple frames of ultrasound images continuously collected within the preset time may be are all ultrasound images in a complete cycle.

Further, the second reliability evaluation result of this type may be an average value of the first reliability evaluation results in a complete cycle. Assuming that there are Frames frames of ultrasound images in a cycle, scorei is the first reliability evaluation result of the ith frame of ultrasound images, and the value range is [0, MAXSCORE]. Then the second reliability evaluation result Qcycle, which represents the average level of the measurement results within the preset time, is expressed as:

When the periodic parameter represents the degree of change of the measurement result within a preset time, the second reliability assessment result may be calculated according to the first reliability assessment result of the measurement result corresponding to the key frame ultrasound image used to calculate the periodic parameter. Wherein, the key frame ultrasound images include ultrasound images corresponding to the peaks of the measurement results. 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 of the measurement result.

Continuing to take ejection fraction (EF) as an example, the reliability of EF is mainly affected by the reliability of EDV (End-Diastolic Volume, end-diastolic volume) and ESV (End-Systolic Volume, end-systolic volume). The reliability of EDV and ESV is mainly determined by the first reliability evaluation result of the measurement result of the peak frame and at least one frame of ultrasound images in its neighborhood. If the neighborhood range is r and the peak frame is peak, then the value range of the key frame is [peak-r, peak+r], so the reliability evaluation result of EF is expressed as:

Among them, Qpeak is the first reliability evaluation result corresponding to the key frame ultrasound image,

The above two second reliability evaluation results may also be combined with each other. For example, for a periodic parameter representing the degree of change of the measurement result within a preset time, such as ejection fraction, the reliability can also be evaluated with reference to the second reliability evaluation result Qcycle of the first type. For example, if the Qef of the two ejection fractions are the same, then the ejection fraction with the higher Qcycle is considered to be more reliable.

An example of the calculation of the second reliability evaluation result is shown in FIGS. 3A and 3B . The curves in FIG. 3A and FIG. 3B represent the change of the real ventricular volume in a cardiac cycle (it should be noted that it is not the change of the measurement result of the ventricular volume), the first reliability of the ventricular volume measured based on different frames of ultrasound images is not the same, 3A and 3B show the first reliability on the curve according to different filling methods. Given that the value range of the first reliability is 0-2 points, Figures 3A and 3B use unfilled circles to represent 3 points, dot-filled circles to represent 1 point, and black filled circles to represent 0 points. The ventricular volume with a first reliability score of 0 cannot be used because the reliability is too low. Taking the key frame as the ultrasound image corresponding to the peak of the measurement result and a frame in its neighborhood, that is, r=1, the calculation results of Qcycle, Qed, Qes, and Qef in FIG. 3A are respectively: Qcycle=0.615, Qes= 0.5, Qed=1, Qef=0.7; then the calculation results of Qcycle, Qed, Qes and Qef in FIG. 3B are: Qcycle=0.615, Qes=0.83, Qed=1, Qef=0.9.

For the situation shown in Figure 3A, the measurement value corresponding to the real ES value (ie, the measurement result of the sixth frame) is very low in reliability and cannot be used. That is to say, although the real ventricular volume corresponding to this frame of ultrasound image is shown in Figure 3A The minimum value shown, but the measurement result is not necessarily as shown in Figure 3A, on the contrary, the measurement result of this frame may be very large, or may be much smaller than the actual result. The ES value extracted from the acceptable measurement results (ie, the SE value measured in the fifth frame of ultrasound image) will be larger than the true ESV, so the calculation method of Figure 3A will underestimate the EF value. The measured Qef based on the data in Figure 3A is in good agreement with the ground truth.

However, in the case shown in FIG. 3B , the first reliability of the measured values corresponding to the real ED and ES is high, and the EF value can be accurately evaluated. The measured Qef=0.9 based on the data in FIG. 3B is higher than the Qef=0.7 in FIG. 3A , which also indicates that the EF parameter in FIG. 3B is more reliable and is 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 the EF.

After that, 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 the plurality of periodic parameters. is a reliable periodic parameter.

In one embodiment, while displaying the periodic parameter, the second reliability evaluation result may be represented by the color of the logo representing the periodic parameter. The identifiers that characterize the periodic parameters can be words, graphics or symbols, and the like. For example, referring to the display interface shown in Fig. 4 , still taking the periodic parameter as ejection fraction as an example, the display interface displays a sign 403 representing the periodic parameter, that is, EF=55%. If the second reliability evaluation result of the ejection fraction is high, for example, for example, the second reliability evaluation result exceeds a certain preset threshold, the color of the indicator 403 may be displayed in green to indicate that the periodic parameter is reliable; similar Alternatively, if the second reliability evaluation result of the ejection fraction is low, the color of the indicator 403 may be displayed in red. Of course, in addition to this, the second reliability evaluation result can also be displayed in other ways, for example, directly displaying the value of the second reliability evaluation result near the mark 403, or displaying whether it is reliable or unreliable.

In some embodiments, a curve of the measurement results changing with time can also be obtained according to the measurement results corresponding to the multiple frames of ultrasound images, and the second reliability evaluation result is represented by different colors in units of cycles on the curve of the measurement results changing with time. . Referring to FIG. 4 , a curve 402 of ventricular volume changing with time is shown. The curve 402 is divided into a plurality of cycles. The value of each cycle of the curve 402 can be determined according to the second reliability evaluation result of the periodic parameter corresponding to each cycle. Color, the user can select a periodic parameter with higher reliability according to the color of each cycle of the curve 402 as the final selected periodic parameter. Optionally, the period corresponding to the currently displayed logo 403 may also be marked on the curve 402 .

Further, the period in which the second reliability evaluation result satisfies the preset requirement (that is, the period within the range of the vertical line on the curve 402 ) can also be marked on the curve of the change of the measurement result over time, so as to facilitate the user’s reference, and the user can directly select the volume. Periodic parameter corresponding to the period marked on the curve.

In addition to displaying the second reliability evaluation result, the first reliability evaluation result may also be displayed, so as to facilitate the user's reference. Exemplarily, the corresponding first reliability evaluation result can be displayed while displaying each frame of ultrasound image, for example, 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 can be used. Indicates the first reliability evaluation result corresponding to the ultrasound image of the current frame.

Continuing to refer to FIG. 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 higher first reliability evaluation result, the boundary line 404 may be displayed in green; The image type 405 of the ultrasound image is A4C (Apical4-Chamber View, apical four-chamber view). If the first reliability evaluation result of the measurement result of the currently displayed ultrasound image is high, for example, it exceeds a certain preset threshold, it can be The "A4C" mark in the ultrasound image is displayed in green to indicate that the measurement result of the current frame ultrasound image is reliable. Therefore, no other characters or symbols are added, and no additional occlusion is generated on the ultrasound image, and the first reliability evaluation result can be represented only by the original mark on the ultrasound image.

Of course, in addition to the above two ways, the first reliability evaluation result may also be represented by other identifiers on the ultrasound image or other than the ultrasound image. Alternatively, on the curve of the above-mentioned measurement result changing with time, the first reliability evaluation result may also be represented by different colors in units of frames.

To sum up, the method 200 for analyzing periodic parameters in this embodiment of the present application can realize automatic evaluation of the reliability of periodic parameters, which is convenient for users to judge the reliability of periodic parameters.

Embodiments of the present application further provide an ultrasound imaging system, which is used to implement 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 excite the ultrasonic probe to transmit ultrasonic waves to the measured object; the receiving circuit is used to control the ultrasonic probe to receive the echoes of the ultrasonic waves to obtain ultrasonic echo signals; the processor is used to: obtain preset multiple frames of ultrasound images collected in time; determine the target area of at least two frames of ultrasound images in the multiple frames of ultrasound images, and measure the target area to obtain a measurement result corresponding to each frame of the ultrasound image; The periodic parameters are obtained from the measurement results corresponding to the multiple frames of ultrasonic images; in at least two frames of the ultrasonic images, the first number of the measurement results of each frame of the ultrasonic images is obtained based on the image quality of the ultrasonic images of each frame. a reliability evaluation result; obtain a second reliability evaluation result of the periodicity parameter based on the first reliability evaluation result corresponding to the at least two frames of the ultrasound images, and display the periodicity in a visual manner parameters and the results of the second reliability assessment.

In one embodiment, obtaining a 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 the at least two frames of the ultrasonic image includes: inputting each of the at least two frames of the ultrasonic images into a trained network model, and outputting the first reliability assessment result regarding the image quality of each frame of the ultrasonic images, or , obtaining the first reliability evaluation result about the image quality of each frame of the ultrasonic image according to the signal-to-noise ratio of each frame of the ultrasonic image in the at least two frames of the ultrasonic image.

In one embodiment, the image quality of the ultrasound image is determined according to 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 The proportion of speckles, snowflakes, or reticles in the ultrasonic image, and the ultrasonic probe, probe parameters or imaging parameters used to collect the ultrasonic image.

In one embodiment, the first reliability evaluation result is further related to a characteristic parameter of the target area, where the characteristic parameter of the target area includes: the shape of the target area or the contrast of the boundary of the target area.

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

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

In one embodiment, the key frame ultrasound images include ultrasound images corresponding to peaks of measurement results in the multiple frames of ultrasound images.

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.

In one embodiment, the obtaining the periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images includes: according to the measurement corresponding to at least two of the first reliability evaluation results that meet a preset requirement As a result, the periodic parameter is obtained.

In one embodiment, the processor is further configured to: obtain a curve of the measurement result over time according to the measurement results corresponding to the multiple frames of ultrasound images; and on the curve of the measurement result over time, the period is The unit represents the second reliability evaluation result by different colors, or the first reliability evaluation result is represented by different colors in frame units on the curve of the change of the measurement result over time.

In one embodiment, the processor is further configured to: mark a period during which the second reliability evaluation result meets a preset requirement on a curve of the measurement result changing with time.

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 logo representing the periodic parameter.

In one embodiment, the processor is further configured to control the display to display the first reliability evaluation result, and the displaying the first reliability evaluation result includes: using the color of the image type of the current frame of ultrasound image or the current The color of the identification of the target area in the frame of ultrasound image represents the first reliability evaluation result corresponding to the current frame of ultrasound image.

In one embodiment, the target region includes a ventricular region, the measurement includes a ventricular measurement, and the periodic parameter includes a cardiac function parameter.

In one embodiment, the ventricular measurement includes ventricular volume and the cardiac function parameter includes ejection fraction.

In an embodiment, the obtaining the periodic parameter according to the measurement results corresponding to the multiple frames of ultrasound images includes: obtaining the time-varying ventricular volume according to the ventricular volume corresponding to the multiple frames of ultrasound images curve; determine the cardiac cycle according to the curve of the ventricular volume changing with time; determine the maximum ventricular volume and the minimum ventricular volume in the same cardiac cycle, and obtain the ejection fraction according to the maximum ventricular volume and the minimum ventricular volume.

In one embodiment, the determining the cardiac cycle according to the curve of the ventricular volume changing with time includes: measuring the peak interval of the curve of the ventricular volume changing with time to obtain the cardiac cycle, or, for the The ventricular volume versus time curve is spectrally analyzed to obtain the cardiac cycle.

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

Referring back to FIG. 1 , the ultrasound imaging system can be implemented as the ultrasound imaging system 100 shown in FIG. 1 . The ultrasound imaging system 100 may include an ultrasound probe 110 , a transmitting circuit 112 , a receiving circuit 114 , a processor 116 and a display 118 , optionally Ground, the ultrasound imaging system 100 may further include a transmit/receive selection switch 120 and a beam forming module 122, and the transmit circuit 112 and the reception circuit 114 may be connected to the ultrasound probe 110 through the transmit/receive selection switch 120. The relevant description of each component can be referred to above. The relevant description of the article will not be repeated here.

Only the main functions of the components of the ultrasound imaging system are described above, and for more details, please refer to the related description of the method 200 for analyzing periodic parameters. The ultrasound imaging system of the embodiment of the present application can realize automatic evaluation of the reliability of periodic parameters.

In addition, according to an embodiment of the present application, a computer storage medium is also provided, where program instructions are stored on the computer storage medium, and when the program instructions are run by a computer or a processor, a cycle for executing the embodiments of the present application is provided Corresponding steps of the method 200 for analyzing sexual parameters. The storage medium may include, for example, a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, read only memory (ROM), erasable programmable read only memory (EPROM), portable compact disk read only memory (CD-ROM), USB memory, or any combination of the above storage media. The computer-readable storage medium can be any combination of one or more computer-readable storage media.

In addition, according to the embodiments of the present application, a computer program is also provided, and the computer program can be stored in the cloud or on a 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 embodiments of the present application.

Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above-described example embodiments are exemplary only, and are not intended to limit the scope of the application thereto. Various changes and modifications may be made therein by those of ordinary skill in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by 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. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented.

In the description provided herein, numerous specific details are set forth. It will be understood, however, that the embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it is to be understood that in the description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, FIG. , or in its description. However, this method of application should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as the corresponding claims reflect, the invention lies in the fact that the corresponding technical problem may be solved with less than all features of a single disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this application.

It will be understood by those skilled in the art that all features disclosed in this specification (including the accompanying claims, abstract and drawings) and any method or apparatus so disclosed may be used in any combination, except that the features are mutually exclusive. Processes or units are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

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

Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules according to the embodiments of the present application. The present application can also be implemented as a program of apparatus (eg, computer programs and computer program products) for performing part or all of the methods described herein. Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.

It should be noted that the above-described embodiments illustrate rather than limit the application, and alternative embodiments may be devised by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. do not denote any order. These words can be interpreted as names.

The above are only specific embodiments of the present application or descriptions of the specific embodiments, and the protection scope of the present application is not limited thereto. Any changes or substitutions should be included within the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims (20)

1. A method for analyzing periodic parameters, characterized in that the method comprises:

   Acquiring multiple frames of ultrasound images collected within a preset time;

   determining the cardiac region of at least two ultrasound images in the multiple frames of ultrasound images, and measuring the cardiac region to obtain a measurement result corresponding to each frame of the ultrasound image;

   obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images;

   In at least two frames of the ultrasound images, a first reliability evaluation result of the measurement results of each frame of the ultrasound images is obtained based on the image quality of the ultrasound images of each frame;

   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 images, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner. Reliability assessment results.
2. The method according to claim 1, characterized in that, in the at least two frames of the ultrasonic images, a first measurement result of the measurement results of each frame of the ultrasonic images is obtained based on the image quality of the ultrasonic images of each frame. Reliability assessment results, including:

   inputting each of the at least two frames of the ultrasonic images into a trained network model, and outputting the first reliability assessment result regarding the image quality of each frame of the ultrasonic images, or ,

   The first reliability evaluation result regarding the image quality of each frame of the ultrasonic image is obtained according to the signal-to-noise ratio of each frame of the ultrasonic image in the at least two frames of the ultrasonic image.
3. The method of claim 1, wherein the image quality of the ultrasound image is determined according to 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 reticles in the ultrasound image, and the ultrasound image is collected. The ultrasound probe, probe parameters or imaging parameters used.
4. The method of claim 1, wherein the first reliability evaluation result is further related to the shape of the heart region or the contrast of the border of the heart region.
5. The method according to claim 1, wherein the periodic parameter represents the average level of the measurement result within the preset time, and the at least two frames of the ultrasound images are continuous in the preset time Acquired multiple frames of ultrasound images.
6. The method according to claim 1, wherein the periodic parameter represents the degree of change of the measurement result within the preset time period, and the at least two frames of the ultrasound images are among the multiple frames of ultrasound images. keyframe ultrasound images.
7. The method according to claim 6, wherein the key frame ultrasound images include ultrasound images corresponding to peaks of measurement results in the multiple frames of ultrasound images.
8. The method according to claim 7, wherein the key frame ultrasound image further comprises 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, wherein the obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images comprises:

   The periodic parameter is obtained according to the measurement results corresponding to at least two of the first reliability evaluation results that meet the preset requirements.
10. The method of claim 1, further comprising:

    obtaining a curve of the measurement result 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 the unit of cycles on the curve of the measurement result over time, or the second reliability evaluation result is represented by different colors in the unit of frame on the curve of the measurement result over time. The first reliability evaluation result is described.
11. The method of claim 10, further comprising:

    The period in which the second reliability evaluation result meets the preset requirement is marked on the curve of the change of the measurement result with time.
12. The method according to claim 1, wherein displaying the second reliability evaluation result in a visual manner comprises:

    The second reliability evaluation result is represented by the color of the logo representing the periodic parameter.
13. The method according to claim 1, further comprising displaying the first reliability evaluation result, and the displaying the first reliability evaluation result comprises:

    The first reliability evaluation result corresponding to the current frame of ultrasound image is represented 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.
14. 14. The method of any one of claims 1-13, wherein the cardiac region comprises a ventricular region, the measurement comprises a ventricular measurement, and the periodic parameter comprises a cardiac function parameter.
15. 15. The method of claim 14, wherein the ventricular measurement comprises a ventricular volume and the cardiac function parameter comprises an ejection fraction.
16. The method according to claim 15, wherein the obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images comprises:

    obtaining a curve of the ventricular volume 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 versus 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, wherein the determining the cardiac cycle according to the curve of the ventricular volume over time comprises:

    Measuring the peak spacing of the ventricular volume versus time curve to obtain the cardiac cycle, or performing spectral analysis of the ventricular volume versus time curve to obtain the cardiac cycle.
18. A method for analyzing periodic parameters, characterized in that the method comprises:

    Acquiring multiple frames of ultrasound images collected within a preset time;

    determining the target area of at least two frames of ultrasonic images in the multiple frames of ultrasonic images, and measuring the target area to obtain a measurement result corresponding to each frame of the ultrasonic images;

    obtaining periodic parameters according to the measurement results corresponding to the multiple frames of ultrasound images;

    In at least two frames of the ultrasound images, a first reliability evaluation result of the measurement results of each frame of the ultrasound images is obtained based on the image quality of the ultrasound images of each frame;

    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 images, and the periodic parameter and the second reliability evaluation result are displayed in a visual manner. Reliability assessment results.
19. The method of claim 18, wherein the target area comprises a blood vessel area, the measurement result comprises a blood vessel diameter, and the periodic parameter comprises at least one of the following: inferior vena cava collapse index, inferior vena cava dilation Index and inferior vena cava variability.
20. An ultrasonic imaging system, characterized in that the ultrasonic imaging system comprises:

    Ultrasound probe;

    a transmitting circuit, used to excite the ultrasonic probe to transmit ultrasonic waves to the measured object;

    a receiving circuit, configured to control the ultrasonic probe to receive the echo of the ultrasonic wave to obtain an ultrasonic echo signal;

    A processor for executing the method for analyzing periodic parameters according to any one of claims 1-19.

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