Reliability assessment method and system for tissue elasticity measurement

Abstract

The present invention discloses a reliability assessment method for tissue elasticity measurement, including obtaining ultrasonic information of the measured tissue and obtaining a shear wave propagation trajectory map of the measured tissue based on the ultrasonic information; extracting features of the ultrasonic information and features of the shear wave propagation trajectory map; calculating an ultrasonic information feature score based on the features of the ultrasonic information and the weight of each feature of the ultrasonic information, and calculating a shear wave trajectory map feature score based on the features of the shear wave trajectory map and the weight of each feature of the shear wave trajectory map; and calculating a score representing the elasticity measurement result of the measured tissue based on the ultrasonic information feature score and the shear wave trajectory map feature score. By combining ultrasonic information and shear wave propagation features, a more comprehensive tissue elasticity assessment can be provided, providing a multi-dimensional evaluation, reducing the errors that may be caused by single feature characterization, improving the accuracy of the score, and thus being able to effectively reflect the measurement quality and credibility, helping doctors judge the reliability of the results.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a reliability assessment method and system for tissue elasticity measurement. Background Art

[0002] Liver fibrosis is a common histological feature of various chronic liver diseases in the pathological development process. In liver detection applications, elastography is a medical imaging method to measure the hardness of liver tissue to assess the severity of liver fibrosis. Transient elastography (TE) is a common method in the field of soft tissue elasticity measurement based on ultrasound. This method is easy to operate, fast, safe and non-invasive, and has high patient tolerance. It has been recommended by the World Health Organization (WHO) and many world-class clinical practice guidelines for liver diseases as an important means of clinical liver fibrosis assessment. From a technical point of view, this method uses in vitro excitation mechanical vibration to generate shear waves and transmit them into the tested tissue, and uses ultrasound to track the displacement state of the shear wave propagating in the body to obtain the propagation speed of the shear wave in the tissue, thereby calculating the tissue elasticity.

[0003] Generally speaking, transient elasticity measurement method displays and provides two types of information to clinical practitioners: one is the shear wave propagation trajectory map (also known as elasticity map), and the other is tissue elasticity parameters. Specifically, the shear wave propagation trajectory map usually visualizes the shear wave propagation process in a two-dimensional time-depth map. Tissue elasticity parameters are calculated based on the propagation trajectory of the wave in the elasticity map, using image processing technology or signal processing technology to detect its slope. Since the numerical value of liver elasticity provides important reference information in the diagnosis of fibrosis, how to ensure the quality of the elasticity map is particularly critical for subsequent elasticity calculations.

[0004] However, the pattern of the elastogram is very complex in real clinical situations. The shear wave propagation trajectory may be affected by the probe pressure signal, afterwave, reflected wave and background noise, making it difficult for clinical practitioners to understand its physical and biological meaning. In addition, in the examination of clinical elasticity measurement, the commonly used reliability index is based on statistics: that is, the numerical value of the interquartile range / median (IQR / median) in ten valid measurements is used to reflect the reliability of the elastic parameters. However, this statistical method is not direct enough to accurately evaluate the quality of the elastogram. Therefore, a method is needed to directly evaluate the quality of tissue elasticity measurement and / or elastogram to ensure the reliability of the calculated elastic parameters.

[0005] Based on this, a new solution is needed. Summary of the invention

[0006] The main purpose of the present invention is to provide a reliability assessment method and system for tissue elasticity measurement.

[0007] The present invention provides a reliability assessment method for tissue elasticity measurement, comprising the following steps:

[0008] Step S1, obtaining ultrasonic information of a measured tissue and obtaining a shear wave propagation trajectory diagram of the measured tissue based on the ultrasonic information;

[0009] Step S2, extracting the characteristics of the ultrasonic information and the characteristics of the shear wave propagation trajectory diagram;

[0010] Step S3, calculating an ultrasound information feature score based on the features of the ultrasound information and the weight of each feature of the ultrasound information, and calculating a shear wave trajectory feature score based on the features of the shear wave trajectory and the weight of each feature of the shear wave trajectory;

[0011] Step S4: Calculate a score representing the elasticity measurement result of the measured tissue based on the ultrasound information feature score and the shear wave trajectory feature score.

[0012] Preferably, the ultrasound information includes one or more of an original echo radio frequency signal, a one-dimensional ultrasound image, a two-dimensional ultrasound image and / or a three-dimensional ultrasound image.

[0013] Preferably, in step S2, the extracted features of the ultrasound information include the signal intensity of the ultrasound information and / or features of the measured tissue in the ultrasound information.

[0014] Preferably, the characteristics of the shear wave propagation trajectory diagram include one or more of the morphological parameters, linear parameters, continuity parameters, number and / or noise intensity of the background signal of the shear wave propagation trajectory. In step S2, extracting the characteristics of the shear wave propagation trajectory diagram includes:

[0015] Performing morphological operations on the shear wave propagation trajectory diagram of the measured tissue to determine the boundary of the shear wave propagation trajectory diagram;

[0016] A plurality of features of the shear wave propagation trajectory map are extracted from the boundary.

[0017] Preferably, the characteristics of the shear wave propagation trajectory diagram include one or more of the morphological parameters, linear parameters, continuity parameters, number and / or noise intensity of the background signal of the shear wave propagation trajectory. In step S2, extracting the characteristics of the shear wave propagation trajectory diagram includes:

[0018] Dividing and determining an area of ​​interest of the shear wave propagation trajectory diagram, wherein the area of ​​interest includes a plurality of detection sub-areas;

[0019] Performing morphological operations in each of the detection sub-regions to determine the boundaries of the shear wave propagation trajectory diagram in each of the detection sub-regions;

[0020] Extracting the features of the shear wave propagation trajectory diagram in each of the detection sub-regions from the boundary of each of the detection sub-regions;

[0021] Based on the preset weight of each detection sub-region and the characteristics of each detection sub-region, the characteristics of the shear wave propagation trajectory map are obtained by weighting.

[0022] Preferably, when the number of shear wave trajectories in the shear wave trajectory diagram is greater than or equal to 2, the characteristics of the shear wave propagation trajectory diagram also include one or more of the positional relationship, adjacent relationship and / or hierarchical relationship between different shear wave trajectories.

[0023] Preferably, before step S3, the method further includes: acquiring auxiliary evaluation parameters, wherein the auxiliary evaluation parameters include at least one of human morphometric parameters, ultrasonic physical parameters, image feature parameters, pressure parameters and motion parameters;

[0024] In the step S3, it also includes calculating the auxiliary evaluation parameter score based on the auxiliary evaluation parameter and the weight of each auxiliary evaluation parameter;

[0025] In the step S4, a score characterizing the elasticity measurement result of the measured tissue is calculated based on the ultrasound information feature score, the shear wave trajectory feature score and the auxiliary evaluation parameter score.

[0026] Preferably, the anthropometric parameters include height, weight, body mass index, waist circumference, hip circumference, chest circumference and subcutaneous thickness reflecting body shape, and one or more of body fat percentage, visceral fat, muscle mass, bone density, basal metabolic rate and phase angle reflecting body composition analysis;

[0027] The image feature parameters include one or more of radiomics parameters, texture feature parameters, shape feature parameters, and spatial relationship feature parameters acquired from ultrasound images and representing different physiological and pathological states of tissues;

[0028] The ultrasonic physical parameters include one or more of ultrasonic attenuation coefficient, spectral parameters, scatterer characteristics and backscatter coefficient distribution model, Nakagami distribution model, acoustic structure quantification technology based on statistical model and Shannon entropy based on non-statistical model;

[0029] The pressure parameter includes the pressure applied by the probe to the measured tissue collected by the pressure detection element on the ultrasonic probe;

[0030] The motion parameters include one or more of angular velocity, acceleration, speed or angle acquired by a spatial sensor on the ultrasound probe.

[0031] Preferably, in step S4, a score characterizing the elasticity measurement result of the measured tissue is calculated by using a binary variable, a continuous variable or a discrete variable.

[0032] Preferably, in step S4, the score characterizing the elasticity measurement result of the measured tissue is calculated according to different preset weight combinations or preset mathematical statistical methods.

[0033] Preferably, the method further comprises: when the score is within a preset threshold range, outputting the shear wave trajectory diagram.

[0034] Preferably, it also includes:

[0035] Based on the shear wave trajectory diagram, calculating the slope of the shear wave propagation trajectory;

[0036] Based on the slope of the shear wave propagation trajectory and the shear wave trajectory feature score obtained in step S3, the shear wave trajectory feature score is calculated according to different preset weight combinations;

[0037] Based on the ultrasound information feature score, the recalculated shear wave trajectory map feature score and the auxiliary evaluation parameter score, a score characterizing the elasticity measurement result of the measured tissue is calculated.

[0038] Preferably, the method further comprises: when the score is outside a preset threshold range, outputting feedback prompt information based on the characteristics of the ultrasound information, the characteristics of the shear wave trajectory diagram and the auxiliary evaluation parameters.

[0039] In addition, to achieve the above-mentioned purpose, the present invention also proposes a reliability evaluation system for tissue elasticity measurement, comprising:

[0040] A parameter acquisition module, used for acquiring ultrasonic information of the measured tissue and acquiring a shear wave propagation trajectory diagram of the measured tissue based on the ultrasonic information;

[0041] A feature extraction module, used to extract features of the ultrasound information and features of the shear wave propagation trajectory diagram;

[0042] A feature score calculation module, used to calculate the ultrasound information feature score based on the feature of the ultrasound information and the weight of each feature of the ultrasound information, and to calculate the shear wave trajectory feature score based on the feature of the shear wave trajectory and the weight of each feature of the shear wave trajectory;

[0043] The measurement result evaluation module is used to calculate a score representing the elasticity measurement result of the measured tissue based on the ultrasound information feature score and the shear wave trajectory map feature score.

[0044] Preferably, the parameter acquisition module is further used to acquire auxiliary evaluation parameters, wherein the auxiliary evaluation parameters include at least one of human morphometric parameters, ultrasonic physical parameters, image feature parameters, pressure parameters and motion parameters;

[0045] The feature score calculation module is further used to calculate the auxiliary evaluation parameter score based on the auxiliary evaluation parameter and the weight of each auxiliary evaluation parameter;

[0046] The measurement result evaluation module is further used to calculate a score characterizing the elasticity measurement result of the measured tissue based on the ultrasound information feature score, the shear wave trajectory feature score and the auxiliary evaluation parameter score.

[0047] Preferably, it also includes a result output module, which is used to output the shear wave trajectory diagram when the score is within a preset threshold range, and output feedback prompt information based on the characteristics of the ultrasound information, the characteristics of the shear wave trajectory diagram and the auxiliary evaluation parameters when the score is outside the preset threshold range.

[0048] According to another aspect of the present invention, there is also provided a reliability assessment device for measuring tissue elasticity, comprising:

[0049] Memory for storing computer programs;

[0050] A processor is used to implement the steps of the reliability assessment method for tissue elasticity measurement as described above when executing the computer program.

[0051] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the reliability assessment method for measuring tissue elasticity as described above are implemented.

[0052] According to yet another aspect of the present invention, a computer data analysis method is provided, which implements the steps of the reliability assessment method for tissue elasticity measurement as described above by using an artificial intelligence model established through data training.

[0053] The beneficial effects brought about by the technical solution provided by the present invention are: the reliability assessment method and system for tissue elasticity measurement provided by the present invention, by processing and analyzing the features obtained from ultrasound information and the features obtained from the shear wave propagation trajectory diagram, obtain a score used to characterize the tissue elasticity measurement results, and the score can accurately evaluate the quality and credibility of the tissue elasticity measurement, thereby ensuring the measurement reliability; by combining ultrasound information and shear wave propagation characteristics, a more comprehensive tissue elasticity assessment can be provided; through the fusion and optimization of multiple feature weights, a multi-dimensional evaluation is provided, which can reduce the errors that may be caused by single feature characterization and improve the accuracy of the score, thereby effectively reflecting the measurement quality and credibility, and helping doctors judge the reliability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings in the following description are only embodiments of the invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without creative work:

[0055] Figure 1 FIG. 1 is a flow chart of a reliability assessment method for measuring tissue elasticity provided by an embodiment of the present invention;

[0056] Figure 2-Figure 7 Shown are the shear wave propagation trajectories of the liver acquired under different conditions;

[0057] Figure 8-Figure 11 Shown is a B-mode ultrasound image of the liver. DETAILED DESCRIPTION

[0058] In order to facilitate understanding of the invention, the invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the invention more thorough and comprehensive.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the invention belongs. The terms used in the specification of the invention herein are only for the purpose of describing specific embodiments and are not intended to limit the invention.

[0060] Embodiment 1

[0061] To achieve the above object, the present invention provides a reliability assessment method for tissue elasticity measurement, referring to Figure 1, the reliability assessment method for tissue elasticity measurement comprises the following steps:

[0062] Step S1, obtaining ultrasonic information of a measured tissue and obtaining a shear wave propagation trajectory diagram of the measured tissue based on the ultrasonic information;

[0063] Specifically, in this embodiment, the measured tissue is the liver, and the ultrasound information is the structural imaging information of the measured tissue, including: ultrasound signals and ultrasound images, that is, the ultrasound information includes one or more of the original echo radio frequency signal, one-dimensional ultrasound image (such as A-type image and M-type image), two-dimensional ultrasound image (such as B-type image, Doppler image, elasticity image) and / or three-dimensional ultrasound image. Without limitation, the original echo radio frequency signal and A-type ultrasound image can reflect the one-dimensional morphology of the measured tissue, the M-type ultrasound image can reflect the dynamic information of the one-dimensional morphology of the measured tissue, the B-type ultrasound image can reflect the two-dimensional morphology in the measured tissue, and the three-dimensional ultrasound image can reflect the three-dimensional spatial morphology of the measured tissue.

[0064] Specifically, in this embodiment, after the ultrasonic information of the measured tissue is obtained, the shear wave propagation trajectory diagram of the measured tissue is obtained based on the ultrasonic information of the measured tissue. It can be understood by those skilled in the art that obtaining the shear wave propagation trajectory diagram of the measured tissue by using the ultrasonic information of the measured tissue is a prior art in the art, and the present invention will not be repeated here.

[0065] Step S2, extracting the characteristics of the ultrasonic information and the characteristics of the shear wave propagation trajectory diagram;

[0066] Specifically, in one embodiment of the present invention, the signal strength of the ultrasound information can directly affect the imaging result. Therefore, the elasticity measurement result of the measured tissue can be reflected by the signal strength of the ultrasound information. Take the measurement of liver elasticity as an example: in B-type ultrasound images, the quality of the acoustic window directly affects the ultrasound imaging effect. A high-quality acoustic window can provide a clear and strong echo signal, which is also helpful for accurately measuring the elasticity of the tissue. Therefore, if the acoustic window quality is high, it will be manifested as a strong echo signal in the liver area and a bright image (such as Fig.11 If the acoustic window quality is low, the liver area will show weak echo signals, dark images, and low echo acoustic shadowing (such as Figure 8-10 as shown).

[0067] Specifically, in another embodiment of the present invention, the propagation characteristics of ultrasound in different tissues are closely related to the physical properties of the tissues (such as elasticity, density, etc.). The various parameters and attributes related to the tissues obtained by ultrasound imaging technology can reflect the physical properties, structure and function of the tissues. Therefore, the elasticity measurement results of the measured tissue can be reflected by the characteristics of the measured tissue in the ultrasound information. Taking the measurement of liver elasticity as an example: in a three-dimensional ultrasound image, the characteristics of the measured tissue in the ultrasound information may be the proportion of liver tissue and non-liver tissue (such as intestine, lung, kidney); in a B-type ultrasound image, the characteristics of the measured tissue in the ultrasound information may be the proportion of intrahepatic blood vessels and bile ducts compared to liver parenchyma; in an A-type ultrasound image or a B-type ultrasound image, the characteristics of the measured tissue in the ultrasound information may also be the gray value ratio of liver parenchyma to liver capsule; in an M-type ultrasound image, the characteristics of the measured tissue in the ultrasound information may be the amplitude, speed, and frequency of the liver area signal changing with time, which characterizes the stability of the liver before and after measurement and can reflect the measurement reliability to a certain extent; in a B-type ultrasound image, the characteristics of the measured tissue in the ultrasound information may also be the proportion of acoustic shadowing caused by lung gas or ribs to liver tissue.

[0068] Specifically, in one embodiment of the present invention, in step S2, extracting features of the shear wave propagation trajectory diagram includes: performing morphological operations on the shear wave propagation trajectory diagram of the measured tissue to determine the boundary of the shear wave propagation trajectory diagram; and extracting multiple features of the shear wave propagation trajectory diagram from the boundary.

[0069] Furthermore, in one embodiment of the present invention, a morphological operation is performed on the acquired shear wave propagation trajectory map of the measured tissue to extract the boundary, that is, the edge of the shear wave. Specifically, an opening operation is used for boundary extraction, and the opening operation mainly consists of two steps: expansion and erosion. The expansion operation will expand the object area, restore the eroded image, expand the boundary of the object, and usually increase the pixels of the boundary; the erosion operation will shrink the object area, reduce the boundary of the object, and make the boundary thinner or disappear. By comparing the difference between the expanded image and the eroded image, the boundary of the object can be obtained.

[0070] Furthermore, in one embodiment of the present invention, the dilation operation Dilate(I, SE) and the erosion operation Erode(I, SE) can be expressed as follows:

[0071]

[0072]

[0073] Where I is the input binary image and SE is the structural element.

[0074] Furthermore, in another embodiment of the present invention, in addition to the opening operation, other methods may be used for edge detection and extraction, such as Sobel operator, Laplacian operator, Prewitt operator, Roberts operator, Canny edge detection algorithm and the like.

[0075] Further, in one embodiment of the present invention, the characteristics of the shear wave propagation trajectory map include the morphological parameters, linear parameters (such as Figure 2 ), continuity parameters (such as Figure 3 as shown), number (as shown Figure 7 ) and / or the noise intensity of the background signal (e.g. Figure 6 The morphological parameters include the length parameter, area parameter, shape classification (such as strip, rectangular, cylindrical, conical) of the propagation trajectory (such as Figure 5 As shown in the figure), the area ratio of the propagation trajectory to the elastic map, and at least one characteristic of the contrast relative to the background noise; the linear parameters include quantitative irregularity values ​​or linear values. Specifically, the length and shape of the propagation trajectory will affect the propagation speed and attenuation characteristics of the wave. Different forms may lead to different measurement results, affecting the accuracy of elastic evaluation; the linearity of the propagation trajectory is related to the uniformity of the tissue. Nonlinear or irregular trajectories may indicate structural differences within the tissue, which will directly affect the reliability of elastic calculations; the continuity of the propagation trajectory reflects the overall consistency and uniformity of the tissue. If the propagation trajectory is interrupted or obvious gaps appear, it may cause incomplete data, thereby affecting the accurate measurement of elasticity; high noise levels will mask weak signals, making it difficult to accurately identify and analyze the propagation trajectory, which will interfere with the measurement results and reduce the accuracy of elasticity measurement. Therefore, changes in these parameters directly affect the propagation characteristics of shear waves in tissues, and thus affect the accuracy and reliability of their elasticity measurement results.

[0076] Therefore, after extracting the boundary, for each boundary, appropriate features can be extracted from the boundary, such as the boundary contour, angle information, curvature, slope of the fitted line, etc., using methods such as Hough transform, straight line fitting, angle measurement, direction histogram, principal component analysis, etc. Specifically, Hough transform is a technology used to detect geometric shapes such as straight lines in image processing. This method can be used to identify straight lines in an image, and then evaluate and extract features such as parallelism and distortion of the boundary based on the slope of the detected straight line. The fitting straight line method uses the least squares method and other methods to fit a straight line for the point set on the boundary, and then calculates the slope of these fitted straight lines to evaluate and extract features such as parallelism and distortion of the boundary. The angle measurement method calculates the angles between each line segment on the boundary, and evaluates and extracts features such as parallelism and distortion of the boundary based on the distribution of these angles. The direction histogram method can perform a direction histogram analysis on the boundary, find the main directions in the boundary, and then observe whether these main directions are similar to evaluate and extract features such as parallelism and distortion of the boundary. For point sets on the boundary, the principal component analysis method can also be used to find the main feature directions, and then compare these feature directions to evaluate and extract features such as parallelism and distortion of the boundary.

[0077] Furthermore, in one embodiment of the present invention, when the number of shear wave trajectories in the shear wave trajectory diagram is greater than or equal to 2, the characteristics of the shear wave propagation trajectory diagram also include one or more of the positional relationship between different shear wave trajectories (such as the difference in distance between adjacent trajectories), the adjacency relationship (such as the difference in intensity signals, morphology, linearity, and continuity between adjacent trajectories) and / or the hierarchical relationship (such as the angle between adjacent trajectories). Changes in these positional, adjacency, and hierarchical relationships will directly affect the propagation characteristics of the shear wave and the signal quality of the measurement, thereby affecting the accurate assessment of tissue elasticity. Specifically, the characteristics of the shear wave propagation trajectory diagram can be extracted by matching the acquired shear wave propagation trajectory diagram with the preset shear wave propagation trajectory diagram.

[0078] Furthermore, in one embodiment of the present invention, the ultrasonic signal and the shear wave propagation trajectory diagram may be paired or unpaired, that is, the ultrasonic signal may be an ultrasonic signal before, after, or during acquisition of the elasticity diagram, but the present invention is not limited thereto.

[0079] Step S3, calculating the ultrasound information feature score based on the features of the ultrasound information and the weight of each feature of the ultrasound information, and calculating the shear wave trajectory feature score based on the features of the shear wave trajectory and the weight of each feature of the shear wave trajectory.

[0080] Specifically, in one embodiment of the present invention, first, based on the characteristics of the ultrasound information and the weights of the characteristics of each ultrasound information, the ultrasound information feature score is calculated according to preset weights or preset mathematical and statistical methods, wherein the characteristics of the ultrasound information are the signal strength of the above-mentioned ultrasound information and / or the characteristics of the measured tissue in the ultrasound information. Secondly, based on the characteristics of the shear wave trajectory graph and the weights of the characteristics of each shear wave trajectory graph, the shear wave trajectory graph feature score is calculated according to preset weights or preset mathematical and statistical methods, wherein the characteristics of the shear wave propagation trajectory graph include one or more of the morphological parameters, linear parameters, continuity parameters, number and / or noise intensity of the background signal of the shear wave propagation trajectory.

[0081] Step S4: Calculate a score representing the elasticity measurement result of the measured tissue based on the ultrasound information feature score and the shear wave trajectory feature score.

[0082] Specifically, in one embodiment of the present invention, based on the ultrasound information feature score and the shear wave trajectory map feature score, a score characterizing the elasticity measurement result of the measured tissue is calculated by using a binary variable, a continuous variable, or a discrete variable. Specifically, the ultrasound information feature score and the shear wave trajectory map feature score can be input into a qualitative binary classifier to obtain a score of the elasticity measurement result characterizing the measured tissue represented by a binary variable, for example, normal or abnormal. The binary variable can be used to quickly evaluate the reliability of the elasticity measurement result and provide a simple classification. The ultrasound information feature score and the shear wave trajectory map feature score can also be input into a quantitative scorer to obtain a score of the elasticity measurement result characterizing the measured tissue represented by a discrete variable, for example, 1, 2, 3, 4, 5. The discrete variable can be used to achieve a clear graded evaluation of the reliability of the elasticity measurement result. The score of the elasticity measurement result characterizing the measured tissue can also be calculated based on the ultrasound information feature score and the shear wave trajectory map feature score according to different preset weight combinations or preset mathematical and statistical methods. The scoring result is a continuous variable, and the range is usually an interval (for example, an elasticity score between 0 and 100). The continuous variable can be used to accurately measure the reliability of the elasticity measurement result, which can provide richer information and details.

[0083] In this embodiment, by processing and analyzing the features obtained from the ultrasound information and the features obtained from the shear wave propagation trajectory diagram, a score is obtained to characterize the tissue elasticity measurement results. The score can accurately evaluate the quality and credibility of the tissue elasticity measurement, thereby ensuring the reliability of the measurement. By combining ultrasound information and shear wave propagation features, a more comprehensive tissue elasticity assessment can be provided; by fusion and optimization of multiple weighted features, a multi-dimensional evaluation can be provided, which can reduce the errors that may be caused by single feature characterization, improve the accuracy of the score, and effectively reflect the measurement quality and credibility, helping doctors judge the reliability of the results.

[0084] Embodiment 2

[0085] The difference between this embodiment and embodiment one is that, in step S2, extracting the features of the shear wave propagation trajectory map includes: dividing and determining the area of ​​interest of the shear wave propagation trajectory map, wherein the area of ​​interest includes a plurality of detection sub-regions; performing morphological operations on each of the detection sub-regions to determine the boundaries of the shear wave propagation trajectory map in each of the detection sub-regions; extracting the features of the shear wave propagation trajectory map in each of the detection sub-regions from the boundaries of each of the detection sub-regions; and obtaining the features of the shear wave propagation trajectory map by weighting based on the preset weights of each of the detection sub-regions and the features of each of the detection sub-regions.

[0086] In this embodiment, by dividing the shear wave propagation trajectory map and determining multiple detection sub-regions, the characteristics of different regions can be analyzed more carefully, which helps to capture local feature changes. By extracting features from the boundaries of each sub-region, feature extraction is more accurate and can reflect the specific conditions of each sub-region, rather than simply processing the overall image. According to the preset weights of each detection sub-region, the extracted features are weighted, which can better reflect the importance of different regions in the overall assessment, thereby improving the rationality and effectiveness of the scoring. This embodiment can improve the accuracy and credibility of tissue elasticity measurement through more detailed analysis and feature extraction of the shear wave propagation trajectory map, providing more reliable data support for clinical use.

[0087] Embodiment 3

[0088] The quality evaluation of the current elastic measurement is only based on the acquired shear wave propagation trajectory diagram, and the information used is limited and monotonous, which cannot fully reflect the quality of the elastic measurement. Therefore, the difference between this embodiment and the first embodiment is that before step S3, it also includes: obtaining auxiliary evaluation parameters, the auxiliary evaluation parameters include at least one of human morphometric parameters, ultrasonic physical parameters, image feature parameters, pressure parameters and motion parameters; in the step S3, it also includes calculating the auxiliary evaluation parameter score based on the auxiliary evaluation parameters and the weight of each auxiliary evaluation parameter; in the step S4, based on the ultrasonic information feature score, the shear wave trajectory feature score and the auxiliary evaluation parameter score, calculate the score that characterizes the elastic measurement result of the measured tissue.

[0089] Specifically, taking the liver as an example, common causes and conditions that lead to low reliability or even failure of liver elasticity measurement include obesity (caused by excessive fat deposition in subcutaneous tissue and liver), narrow intercostal space (caused by ribs affecting the propagation of ultrasound and shear waves, i.e. the influence of bony acoustic shadowing), incorrect positioning to non-liver tissue (caused by shear waves being unable to propagate in certain biological tissues or physical states), and abnormal contact between the detection probe and the measured tissue (caused by too little or too much force applied by the probe, which manifests as ultrasound and shear waves being unable to properly act on the measured tissue; if the probe is not stable enough during elasticity measurement, which also manifests as ultrasound and shear waves being unable to properly act on the measured tissue). The above examples have completely different physiological and physical manifestations, and elasticity diagrams alone cannot accurately characterize the so-called root causes of low measurement reliability or even failure. Therefore, in this embodiment, by introducing auxiliary evaluation parameters, such as human morphometric parameters, ultrasonic physical parameters, etc., more comprehensive tissue characteristic information can be obtained. This multi-dimensional data integration provides a richer background and helps to more accurately characterize the measurement differences in tissue elasticity between different individuals. Incorporating multiple auxiliary parameters into the scoring calculation can reduce the deviation that may be caused by a single parameter, thereby improving the accuracy and reliability of the final elasticity measurement results.

[0090] Furthermore, in one embodiment of the present invention, the human morphometric parameters include height, weight, body mass index, waist circumference, hip circumference, chest circumference and subcutaneous thickness reflecting body shape, and one or more of body fat percentage, visceral fat, muscle mass, bone density, basal metabolic rate, and phase angle reflecting body composition analysis; the image feature parameters include one or more of radiomics parameters, texture feature parameters, shape feature parameters, and spatial relationship feature parameters obtained from ultrasound images to characterize different physiological and pathological states of tissues; the ultrasound physical parameters include one or more of ultrasound attenuation coefficient, spectral parameters, scatterer characteristics and backscatter coefficient distribution model, Nakagami distribution model, acoustic structure quantification technology based on statistical model, and Shannon entropy based on non-statistical model; the pressure parameters include the pressure applied by the probe on the measured tissue collected by the pressure detection component on the ultrasound probe; the motion parameters include one or more of angular velocity, acceleration, velocity or angle collected by the spatial sensor on the ultrasound probe.

[0091] Embodiment 4

[0092] The difference between this embodiment and the third embodiment is that, after step S3, it also includes: when the score is within a preset threshold range, outputting the shear wave trajectory diagram; when the score is outside the preset threshold range, outputting feedback prompt information based on the characteristics of the ultrasound information, the characteristics of the shear wave trajectory diagram and the auxiliary evaluation parameters.

[0093] In this embodiment, when the score reaches a preset threshold or is within a threshold range, a shear wave trajectory diagram is output for visual display or further calculation processing, including calculating the slope of the shear wave propagation trajectory through image segmentation or detection. Furthermore, after calculating the slope of the shear wave propagation trajectory, it also includes calculating a new shear wave trajectory diagram feature score based on the slope of the shear wave propagation trajectory and the shear wave trajectory diagram feature score obtained in step S3 according to different preset weight combinations; step S4 then calculates the score that characterizes the elastic measurement results of the measured tissue based on the ultrasonic information feature score, the new shear wave trajectory diagram feature score and the auxiliary evaluation parameter score. By recalculating the shear wave trajectory diagram feature score in combination with the slope of the shear wave propagation trajectory, the elastic properties of the tissue can be more comprehensively reflected, thereby improving the reliability of the measurement result evaluation. The combined score can effectively reduce errors and improve the credibility of the results.

[0094] In this embodiment, when the score is outside the preset threshold range, it means that the reliability of the measured shear wave trajectory diagram characterizing tissue elasticity is low, and there may be problems in the measurement process. Therefore, based on the characteristics of the ultrasound information, the characteristics of the shear wave trajectory diagram, and the reasons for the strong correlation between the auxiliary evaluation parameter output and the quality score, for example, obesity, narrow intercostal space, wrong positioning to non-tested tissue, abnormal contact state between the detection probe and the tested tissue, etc. Therefore, the operator can be guided in real time to make relevant adjustments to ensure the quality of elasticity measurement.

[0095] Furthermore, the feedback prompt information may be expressed in a form of at least one of a tactile prompt, an auditory prompt, and a visual prompt.

[0096] This embodiment infers the reasons for low reliability or even failure of elasticity measurement based on information from different dimensions, and guides clinical workers to make corresponding adjustments based on the root causes through prompts to improve the reliability and success rate of elasticity measurement.

[0097] Embodiment 5

[0098] Furthermore, the present invention also proposes a reliability assessment system for tissue elasticity measurement, comprising: a parameter acquisition module, used to acquire ultrasonic information of the measured tissue and to acquire a shear wave propagation trajectory diagram of the measured tissue based on the ultrasonic information; a feature extraction module, used to extract features of the ultrasonic information and features of the shear wave propagation trajectory diagram; a feature score calculation module, used to calculate an ultrasonic information feature score based on features of the ultrasonic information and weights of each feature of the ultrasonic information, and to calculate a shear wave trajectory diagram feature score based on features of the shear wave trajectory diagram and weights of the features of each shear wave trajectory diagram; and a measurement result evaluation module, used to calculate a score characterizing the elasticity measurement result of the measured tissue based on the ultrasonic information feature score and the shear wave trajectory diagram feature score.

[0099] Furthermore, in another embodiment of the present invention, the parameter acquisition module is also used to acquire auxiliary evaluation parameters, and the auxiliary evaluation parameters include at least one of human morphometric parameters, ultrasonic physical parameters, image feature parameters, pressure parameters and motion parameters; the feature score calculation module is also used to calculate the auxiliary evaluation parameter score based on the auxiliary evaluation parameters and the weight of each auxiliary evaluation parameter; the measurement result evaluation module is also used to calculate the score characterizing the elasticity measurement result of the measured tissue based on the ultrasonic information feature score, the shear wave trajectory map feature score and the auxiliary evaluation parameter score.

[0100] Furthermore, in another embodiment of the present invention, it also includes a result output module, which is used to output the shear wave trajectory diagram when the score is within a preset threshold range, and output feedback prompt information based on the characteristics of the ultrasound information, the characteristics of the shear wave trajectory diagram and the auxiliary evaluation parameters when the score is outside the preset threshold range.

[0101] The functions of the various functional modules described in the device embodiment of the present invention can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0102] It should be noted that the description of various modules above is divided into these modules for the purpose of clarity. However, in actual implementation, the boundaries of various modules may be fuzzy. For example, any or all functional modules herein may share various hardware and / or software elements. For another example, any and / or all functional modules herein may be implemented in whole or in part by a shared processor executing software instructions. In addition, various software submodules executed by one or more processors may be shared between various software modules. Accordingly, unless explicitly required, the scope of the present invention is not limited by the mandatory boundaries between various hardware and / or software elements.

[0103] The embodiment of the present invention further provides an ultrasonic measurement device for liver fatty degeneration based on image processing, which may include:

[0104] Memory for storing computer programs;

[0105] The processor can implement the following steps when executing the computer program stored in the above memory:

[0106] Acquire ultrasonic information of the measured tissue and acquire a shear wave propagation trajectory map of the measured tissue based on the ultrasonic information; extract features of the ultrasonic information and features of the shear wave propagation trajectory map; calculate an ultrasonic information feature score based on features of the ultrasonic information and weights of each feature of the ultrasonic information, and calculate a shear wave trajectory map feature score based on features of the shear wave trajectory map and weights of features of each shear wave trajectory map; calculate a score characterizing the elasticity measurement results of the measured tissue based on the ultrasonic information feature score and the shear wave trajectory map feature score.

[0107] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps can be implemented;

[0108] Acquire ultrasonic information of the measured tissue and acquire a shear wave propagation trajectory map of the measured tissue based on the ultrasonic information; extract features of the ultrasonic information and features of the shear wave propagation trajectory map; calculate an ultrasonic information feature score based on features of the ultrasonic information and weights of each feature of the ultrasonic information, and calculate a shear wave trajectory map feature score based on features of the shear wave trajectory map and weights of features of each shear wave trajectory map; calculate a score characterizing the elasticity measurement results of the measured tissue based on the ultrasonic information feature score and the shear wave trajectory map feature score.

[0109] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0110] The embodiment of the present invention further provides a computer data analysis method, which implements the following steps through an artificial intelligence model established through data training:

[0111] Acquire ultrasonic information of the measured tissue and acquire a shear wave propagation trajectory map of the measured tissue based on the ultrasonic information; extract features of the ultrasonic information and features of the shear wave propagation trajectory map; calculate an ultrasonic information feature score based on features of the ultrasonic information and weights of each feature of the ultrasonic information, and calculate a shear wave trajectory map feature score based on features of the shear wave trajectory map and weights of features of each shear wave trajectory map; calculate a score characterizing the elasticity measurement results of the measured tissue based on the ultrasonic information feature score and the shear wave trajectory map feature score.

[0112] Those skilled in the art will appreciate that the specific method for establishing an artificial intelligence model based on data training adopts the existing technology in the field and will not be described in detail here.

[0113] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention 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.

[0114] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0115] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition they can be divided into multiple submodules or subunits or subassemblies. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification and all processes or units of any method or device disclosed in this manner can be combined in any combination. Unless otherwise clearly stated, each feature disclosed in this specification can be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0116] 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 invention and form different embodiments. For example, any one of the claimed embodiments may be used in any combination.

[0117] The various component embodiments of the present invention may 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 a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0118] It should be noted that the above embodiments illustrate the present invention rather than limit it, 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 word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware including a number of different elements and by means of a suitably programmed computer. 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.

Claims

1. A reliability assessment method for tissue elasticity measurement, characterized in that: The following steps are involved: Step S1, obtaining ultrasonic information of the measured tissue and obtaining a shear wave propagation trajectory diagram of the measured tissue based on the ultrasonic information, wherein the ultrasonic information includes one or more of an original echo radio frequency signal, a one-dimensional ultrasonic image, a two-dimensional ultrasonic image and / or a three-dimensional ultrasonic image; Step S2, extracting the features of the ultrasound information and the features of the shear wave propagation trajectory diagram, wherein the extracted features of the ultrasound information include the signal strength of the ultrasound information and / or the features of the measured tissue in the ultrasound information, and the features of the shear wave propagation trajectory diagram include one or more of the morphological parameters, linear parameters, continuity parameters, number and / or noise intensity of the background signal of the shear wave propagation trajectory; obtaining auxiliary evaluation parameters, wherein the auxiliary evaluation parameters include at least one of human morphometric parameters, ultrasound physical parameters, image feature parameters, pressure parameters and motion parameters; Step S3, based on the characteristics of the ultrasound information and the weight of each characteristic of the ultrasound information, calculate the ultrasound information characteristic score; based on the characteristics of the shear wave propagation trajectory map and the weight of the characteristics of each shear wave propagation trajectory map, calculate the shear wave propagation trajectory map characteristic score; based on the auxiliary evaluation parameters and the weight of each auxiliary evaluation parameter, calculate the auxiliary evaluation parameter score; Step S4, calculating a score representing the elasticity measurement result of the measured tissue based on the ultrasound information feature score, the shear wave propagation trajectory feature score and the auxiliary evaluation parameter score; The anthropometric parameters include height, weight, body mass index, waist circumference, hip circumference, chest circumference and subcutaneous thickness reflecting body shape, and one or more of body fat percentage, visceral fat, muscle mass, bone density, basal metabolic rate and phase angle reflecting body composition analysis; The image feature parameters include one or more of radiomics parameters, texture feature parameters, shape feature parameters, and spatial relationship feature parameters acquired from ultrasound images and representing different physiological and pathological states of tissues; The ultrasonic physical parameters include one or more of ultrasonic attenuation coefficient, spectral parameters, scatterer characteristics and backscatter coefficient distribution model, Nakagami distribution model, acoustic structure quantification technology based on statistical model and Shannon entropy based on non-statistical model; The pressure parameter includes the pressure applied by the probe to the measured tissue collected by the pressure detection element on the ultrasonic probe; The motion parameter includes one or more of angular velocity, acceleration, speed or angle acquired by a spatial sensor on the ultrasound probe.

2. The reliability assessment method for tissue elasticity measurement according to claim 1, characterized in that: In step S2, extracting the features of the shear wave propagation trajectory diagram includes: Performing morphological operations on the shear wave propagation trajectory diagram of the measured tissue to determine the boundary of the shear wave propagation trajectory diagram; A plurality of features of the shear wave propagation trajectory map are extracted from the boundary.

3. The reliability assessment method for tissue elasticity measurement according to claim 1, characterized in that: In step S2, extracting the features of the shear wave propagation trajectory diagram includes: Dividing and determining an area of ​​interest of the shear wave propagation trajectory diagram, wherein the area of ​​interest includes a plurality of detection sub-areas; Performing morphological operations in each of the detection sub-regions to determine the boundaries of the shear wave propagation trajectory diagram in each of the detection sub-regions; Extracting the features of the shear wave propagation trajectory diagram in each of the detection sub-regions from the boundary of each of the detection sub-regions; Based on the preset weight of each detection sub-region and the characteristics of each detection sub-region, the characteristics of the shear wave propagation trajectory map are obtained by weighting.

4. The reliability assessment method for tissue elasticity measurement according to claim 2 or 3, characterized in that: When the number of shear wave trajectories in the shear wave propagation trajectory map is greater than or equal to 2, the characteristics of the shear wave propagation trajectory map further include one or more of positional relationships, adjacent relationships and / or hierarchical relationships between different shear wave trajectories.

5. The reliability assessment method for tissue elasticity measurement according to claim 1, characterized in that: In step S4, a score characterizing the elasticity measurement result of the measured tissue is calculated by using a binary variable, a continuous variable, or a discrete variable.

6. The reliability assessment method for tissue elasticity measurement according to claim 1, characterized in that: In step S4, a score characterizing the elasticity measurement result of the measured tissue is calculated according to different preset weight combinations or preset mathematical statistical methods.

7. The reliability assessment method for tissue elasticity measurement according to claim 1, characterized in that: Also includes: When the score is within a preset threshold range, the shear wave propagation trajectory diagram is output.

8. The reliability assessment method for tissue elasticity measurement according to claim 7, characterized in that: Also includes: Based on the shear wave propagation trajectory diagram, calculating the slope of the shear wave propagation trajectory; Based on the slope of the shear wave propagation trajectory and the shear wave propagation trajectory feature score obtained in step S3, the shear wave propagation trajectory feature score is calculated according to different preset weight combinations; Based on the ultrasound information feature score, the recalculated shear wave propagation trajectory map feature score and the auxiliary evaluation parameter score, a score characterizing the elasticity measurement result of the measured tissue is calculated.

9. The reliability assessment method for tissue elasticity measurement according to claim 1, characterized in that: Also includes: When the score is outside a preset threshold range, feedback prompt information is output based on the characteristics of the ultrasound information, the characteristics of the shear wave propagation trajectory diagram, and the auxiliary evaluation parameters.

10. A reliability assessment system for tissue elasticity measurement, characterized in that: include: a parameter acquisition module, used to acquire ultrasonic information of the measured tissue and acquire a shear wave propagation trajectory diagram of the measured tissue based on the ultrasonic information, wherein the ultrasonic information includes one or more of an original echo radio frequency signal, a one-dimensional ultrasonic image, a two-dimensional ultrasonic image and / or a three-dimensional ultrasonic image; and to acquire auxiliary evaluation parameters, wherein the auxiliary evaluation parameters include at least one of a human morphometric parameter, an ultrasonic physical parameter, an image characteristic parameter, a pressure parameter and a motion parameter; A feature extraction module, used for extracting features of the ultrasound information and features of the shear wave propagation trajectory diagram, wherein the extracted features of the ultrasound information include the signal strength of the ultrasound information and / or features of the measured tissue in the ultrasound information, and the features of the shear wave propagation trajectory diagram include one or more of the morphological parameters, linear parameters, continuity parameters, number of shear wave propagation trajectories and / or noise intensity of the background signal; A feature score calculation module, used to calculate the ultrasound information feature score based on the features of the ultrasound information and the weight of each feature of the ultrasound information, and to calculate the shear wave propagation trajectory map feature score based on the features of the shear wave propagation trajectory map and the weight of each feature of the shear wave propagation trajectory map; used to calculate the auxiliary evaluation parameter score based on the auxiliary evaluation parameter and the weight of each auxiliary evaluation parameter; A measurement result evaluation module, configured to calculate a score representing the elasticity measurement result of the measured tissue based on the ultrasound information feature score, the shear wave propagation trajectory map feature score and the auxiliary evaluation parameter score; The anthropometric parameters include height, weight, body mass index, waist circumference, hip circumference, chest circumference and subcutaneous thickness reflecting body shape, and one or more of body fat percentage, visceral fat, muscle mass, bone density, basal metabolic rate and phase angle reflecting body composition analysis; The image feature parameters include one or more of radiomics parameters, texture feature parameters, shape feature parameters, and spatial relationship feature parameters acquired from ultrasound images and representing different physiological and pathological states of tissues; The ultrasonic physical parameters include one or more of ultrasonic attenuation coefficient, spectral parameters, scatterer characteristics and backscatter coefficient distribution model, Nakagami distribution model, acoustic structure quantification technology based on statistical model and Shannon entropy based on non-statistical model; The pressure parameter includes the pressure applied by the probe to the measured tissue collected by the pressure detection element on the ultrasonic probe; The motion parameter includes one or more of angular velocity, acceleration, speed or angle acquired by a spatial sensor on the ultrasound probe.

11. The reliability evaluation system for tissue elasticity measurement according to claim 10, characterized in that: It also includes a result output module, which is used to output the shear wave propagation trajectory diagram when the score is within a preset threshold range, and output feedback prompt information based on the characteristics of the ultrasound information, the characteristics of the shear wave propagation trajectory diagram and the auxiliary evaluation parameters when the score is outside the preset threshold range.

12. A reliability assessment device for measuring tissue elasticity, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the reliability assessment method for tissue elasticity measurement as claimed in any one of claims 1 to 9 when executing the computer program.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the reliability assessment method for measuring tissue elasticity according to any one of claims 1 to 9 are implemented.

14. A computer data analysis method, characterized in that: The computer data analysis method implements the steps of the reliability assessment method for tissue elasticity measurement as claimed in any one of claims 1 to 9 by using an artificial intelligence model established through data training.

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