Tissue state assessment method, ultrasound detection device, and storage medium

Abstract

Disclosed are a tissue state assessment method, an ultrasonic detection device, and a storage medium. The method comprises: acquiring a first ultrasonic echo signal of a target tissue (S2100); determining, according to the first ultrasonic echo signal, a first parameter value of at least one target ultrasonic quantitative parameter, the target ultrasonic quantitative parameter being an ultrasonic quantitative parameter related to a tissue state (S2200); obtaining, on the basis of a tissue state prediction model and according to the first parameter value of the at least one target ultrasonic quantitative parameter, a first index value of a target assessment index, wherein the target assessment index is an index representing that a target tissue state is normal or abnormal (S2300); and obtaining, according to the first index value of the target assessment index, a state assessment result of the target tissue (S2400).

Description

A method for assessing tissue condition, an ultrasonic testing device, and a storage medium.

[0001] This disclosure claims priority to Chinese Patent Application No. 202411603487.2, filed on November 11, 2024, entitled "A method for assessing tissue condition, an ultrasonic testing device and a storage medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of medical device technology, and more specifically, to a method for assessing tissue condition, an ultrasound testing device, and a storage medium. Background Technology

[0003] With the development of medical technology, ultrasound technology is playing an increasingly important role in medical assessment and treatment.

[0004] Ultrasound technology is not only applicable to the medical field but also widely used in industry, civil engineering, and other areas. In the medical field, quantitative parameters of ultrasound can be used to detect cells, tissues, and organs, providing important information for disease assessment and treatment. Moreover, ultrasound technology is non-invasive, painless, has no specific contraindications, and does not involve radiation damage. This makes ultrasound technology more readily accepted by doctors and patients, and it is particularly suitable for long-term monitoring and follow-up treatment.

[0005] Furthermore, ultrasound technology can acquire information about the internal structure, properties, and defects of tissues without damaging them. It can accurately reflect minute changes in tissue composition and structure, and can monitor tissue changes in real time during treatment, helping doctors adjust treatment plans promptly and improve treatment outcomes. Quantitative ultrasound parameters can provide a variety of information, such as sound velocity, frequency, attenuation coefficient, and time interval. These parameters can complement each other, providing comprehensive quantitative tissue analysis.

[0006] Therefore, the application of ultrasound quantitative parameters in clinical practice to help accurately perform quantitative tissue assessment is of great value. Summary of the Invention

[0007] One object of this disclosure is to provide a method for assessing tissue condition, an ultrasonic testing device, and a storage medium.

[0008] According to a first aspect of the present disclosure, a method for assessing organizational status is provided, comprising:

[0009] Acquire the first ultrasound echo signal of the target tissue;

[0010] A first parameter value of at least one target ultrasound quantitative parameter is determined based on the first ultrasound echo signal, wherein the target ultrasound quantitative parameter is an ultrasound quantitative parameter related to tissue state.

[0011] Based on the tissue state prediction model, a first index value of the target evaluation index is obtained according to the first parameter value of the at least one target ultrasound quantitative parameter; wherein, the target evaluation index includes at least an index characterizing whether the target tissue state is normal or abnormal.

[0012] The status assessment result of the target organization is obtained based on the first indicator value of the target assessment indicator.

[0013] Optionally, the method further includes:

[0014] Acquire multiple predefined quantitative ultrasound parameters;

[0015] Based on the multiple ultrasound quantitative parameters, multiple parameter combinations are obtained, and each parameter combination includes at least one ultrasound quantitative parameter;

[0016] Determine the correlation between each parameter combination and the target evaluation index;

[0017] One or more parameter combinations are selected based on the relevance, and the relevance of the selected parameter combination should be at least better than the relevance of the unselected parameter combination, or the selected parameter combination is the one with the highest relevance among all parameter combinations.

[0018] The target ultrasound quantitative parameters are determined based on one or more selected combinations of parameters.

[0019] Optionally, the at least one target ultrasound quantitative parameter includes at least one of tissue thickness, ultrasound scattering parameter, nonlinear parameter, attenuation parameter, texture parameter, motion parameter, and elastic parameter; wherein, the ultrasound scattering parameter includes at least one of average intensity parameter, scattering peak value, scatterer density, and scatterer distribution characteristics; the nonlinear characteristic parameter includes at least one of normalized nonlinear parameter, ultrasound nonlinear coefficient, second harmonic amplitude, phase matching, nonzero energy flow, and higher-order elastic constant; the motion parameter includes at least one of displacement and strain; and the elastic parameter includes parameters used to characterize the elastic coefficient and / or viscosity coefficient.

[0020] Optionally, acquiring the first ultrasound echo signal of the target tissue includes:

[0021] The first ultrasound echo signal of the target tissue under different conditions was obtained.

[0022] Optionally, the different conditions include when the target tissue is subjected to different pressures, or when it actively contracts or relaxes, or at different times.

[0023] Optionally, the characterization of the target tissue state as normal or abnormal includes the risk level of at least one of sarcopenia, tissue fatty tissue, liver inflammation, portal hypertension, renal failure, pancreatic cancer, fatty pancreas, articular cartilage damage, local muscle damage, liver fibrosis, and cirrhosis.

[0024] The target tissue is a tissue associated with at least one of the following: sarcopenia, degree of tissue adipose tissue, liver inflammation, portal hypertension, renal failure, pancreatic cancer, fatty pancreas, articular cartilage damage, local muscle damage, liver fibrosis, and cirrhosis.

[0025] Optionally, acquiring the first ultrasound echo signal of the target tissue includes:

[0026] Acquire the second ultrasound echo signal of at least one test area of ​​the target tissue;

[0027] The target region in the area to be tested is determined based on the second ultrasonic echo signal;

[0028] An ultrasonic signal is emitted toward the target area, and the ultrasonic echo signal reflected by the target area is received as the first ultrasonic echo signal.

[0029] Optionally, when the at least one target ultrasound quantitative parameter includes a nonlinear parameter, determining the parameter value of the at least one target ultrasound quantitative parameter based on the ultrasound echo signal includes:

[0030] Wherein, β represents a nonlinear parameter, ρ0 represents the density of the target tissue, p2(x) represents the amplitude of the second harmonic in the first ultrasonic echo signal, p0 represents the amplitude of the first ultrasonic echo signal at the target tissue, c0 represents the propagation speed of the first ultrasonic echo signal in the target tissue, f represents the frequency of the first ultrasonic echo signal, α1 represents the attenuation coefficient of the fundamental wave in the first ultrasonic echo signal, α2 represents the attenuation coefficient of the second harmonic in the first ultrasonic echo signal, and x represents the propagation distance of the first ultrasonic echo signal.

[0031] Optionally, the organization status prediction model is obtained using the following method:

[0032] Obtain a training sample set, wherein each training sample in the training sample set includes a second parameter value of the at least one target ultrasound quantitative parameter and a second index value of the target evaluation index, wherein the second parameter value is determined based on the third ultrasound echo signal of the corresponding training sample;

[0033] The organization state prediction model is trained based on the training sample set.

[0034] According to a second aspect of this disclosure, an ultrasonic testing device is provided, including a processor and a memory, the memory for storing a computer program, and the processor for executing the method as described in the first aspect of this disclosure under the control of the computer program.

[0035] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect of this disclosure.

[0036] Through the embodiments of this disclosure, a first parameter value of at least one target ultrasound quantitative parameter is determined based on the first ultrasound echo signal of the target tissue, and a state assessment result of the target tissue is obtained based on the first parameter value of the at least one target ultrasound quantitative parameter. The quantitative, objective, and non-invasive technical advantages of ultrasound technology provide strong support for the state assessment of the target tissue.

[0037] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present disclosure and, together with their description, serve to explain the principles of the present disclosure.

[0039] Figure 1 is a block diagram of an example hardware configuration of an ultrasonic testing device that can be used to implement embodiments of the present disclosure;

[0040] Figure 2 is a flowchart of an organization status assessment method according to an embodiment of the present disclosure;

[0041] Figure 3 is a flowchart of an organization status assessment method according to another embodiment of the present disclosure;

[0042] Figure 4 is a flowchart of an example of an organization status assessment method according to an embodiment of the present disclosure;

[0043] Figure 5 is a block diagram of an ultrasonic testing apparatus according to an embodiment of the present disclosure. Detailed Implementation

[0044] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0045] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the scope of this disclosure or its application or use.

[0046] Techniques, methods, and apparatus known to those skilled in the art in the relevant field may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0047] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0048] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0049] <Hardware Configuration>

[0050] Figure 1 is a block diagram illustrating the hardware configuration of an ultrasonic testing device 1000 that can implement embodiments of the present disclosure.

[0051] As shown in Figure 1, the ultrasonic testing device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a probe handle 1700, etc. The processor 1100 may be a CPU, a microprocessor (MCU), etc. The memory 1200 may include, for example, ROM (Read-Only Memory), RAM (Random Access Memory), or non-volatile memory such as a hard disk. The interface device 1300 may include, for example, a USB interface, a headphone jack, etc. The communication device 1400 may be capable of wired or wireless communication, specifically including WiFi communication, Bluetooth communication, 2G / 3G / 4G / 5G communication, etc. The display device 1500 may be, for example, an LCD screen, a touch screen, etc. The input device 1600 may include, for example, a touch screen, a keyboard, motion input, etc. The probe handle 1700 can be used to emit and receive ultrasonic waves.

[0052] The ultrasonic testing device shown in Figure 1 is merely illustrative and does not imply any limitation on this disclosure, its application, or use. In embodiments applied to this disclosure, the memory 1200 of the ultrasonic testing device 1000 is used to store instructions for controlling the processor 1100 to operate in order to perform any of the methods provided in the embodiments of this disclosure. Those skilled in the art will understand that although multiple devices are shown in Figure 1 for the ultrasonic testing device 1000, this disclosure may relate only to a portion of these devices; for example, the ultrasonic testing device 1000 may only relate to the processor 1100, the memory 1200, and the probe handle 1700. Those skilled in the art can design instructions based on the schemes disclosed herein. How the instructions control the processor to operate is well known in the art and will not be described in detail here.

[0053] <Method Implementation>

[0054] This disclosure provides a method for assessing tissue condition, which can be performed by an ultrasonic testing device, such as the aforementioned ultrasonic testing device 1000.

[0055] Figure 2 is a flowchart of an organization status assessment method according to an embodiment of the present disclosure.

[0056] As shown in Figure 2, the method includes the following steps S2100 to S2400:

[0057] Step S2100: Acquire the first ultrasound echo signal of the target tissue.

[0058] In this embodiment, the target tissue can be any one of the following: muscle, liver, kidney, pancreas, or blood vessels.

[0059] In one embodiment of this disclosure, an ultrasonic signal may be emitted only to the target tissue, and the ultrasonic echo signal reflected by the target tissue may be received, which is the first ultrasonic echo signal.

[0060] In one embodiment of this disclosure, acquiring a first ultrasound echo signal of a target tissue includes: acquiring a first ultrasound echo signal of the target tissue under different conditions; wherein, the different conditions include when the target tissue is subjected to different pressures, or when it actively contracts or relaxes, or at different times.

[0061] In one embodiment of this disclosure, acquiring the first ultrasound echo signal of the target tissue includes the following steps S2110 to S2130:

[0062] Step S2110: Acquire a second ultrasonic echo signal of at least one area to be tested.

[0063] In this embodiment, the ultrasonic testing device can be a parallel ultrasonic testing device, which can transmit and receive ultrasonic signals in parallel to at least one area to be tested through multiple channels to obtain a second ultrasonic echo signal.

[0064] Furthermore, it can transmit an ultrasonic signal to at least one area to be tested and receive a second ultrasonic echo signal reflected by at least one area to be tested.

[0065] Step S2120: Determine the target area in the region to be measured based on the second ultrasonic echo signal.

[0066] In this embodiment, the target area is the area where the target tissue is located in the area to be tested.

[0067] In this embodiment, a first ultrasound image of at least one test area can be generated based on the second ultrasound echo signal reflected from at least one test area. The obtained first ultrasound image is unaffected by distortion caused by traditional scan-line transmission and reception imaging, has a high pulse repetition frequency, and acquires the target area in a very short time, not exceeding 100ms. Its advantages are speed and absence of distortion artifacts. The obtained first ultrasound image is derived from the average of second ultrasound echo signals obtained from a single transmission or multiple transmissions. Generating the first ultrasound image based on the average of second ultrasound echo signals obtained from multiple transmissions can improve the signal-to-noise ratio of the first ultrasound image.

[0068] Furthermore, the target area can be precisely delineated based on the first ultrasound image using image recognition algorithms or machine learning models.

[0069] In one embodiment of this disclosure, determining the target region within the region to be tested based on the first ultrasound image may include: preprocessing the first ultrasound image; extracting feature values ​​of selected features in the first ultrasound image using an image processing algorithm; and classifying the region in the first ultrasound image based on the feature values ​​of the selected features using a classification algorithm to obtain the target region.

[0070] Specifically, preprocessing can include denoising, enhancement, smoothing, and other processes to improve the quality of the first ultrasound image, facilitating subsequent feature extraction and classification.

[0071] Furthermore, the image processing algorithm can be any one or more of edge detection algorithms, corner detection algorithms, and texture analysis algorithms. The selected features can be any one or more of shape, contour, and texture.

[0072] Furthermore, the classification algorithm can be any one or more of support vector machines, decision trees, and neural networks.

[0073] Step S2130: Transmit an ultrasonic signal to the target area and receive the ultrasonic echo signal reflected by the target area as the first ultrasonic echo signal.

[0074] In one embodiment, ultrasonic signals may be emitted only towards the target area, and the received ultrasonic echo signals may be used as the first ultrasonic echo signals of the target tissue.

[0075] In another embodiment, an ultrasonic signal may be emitted to at least one area to be tested, and an ultrasonic echo signal reflected by at least one area to be tested may be received. Based on a sampling frame corresponding to the target area, the ultrasonic echo signal reflected by the target tissue may be extracted from the received ultrasonic echo signal as a first ultrasonic echo signal.

[0076] Step S2200: Determine the first parameter value of at least one target ultrasound quantitative parameter based on the first ultrasound echo signal. The target ultrasound quantitative parameter is an ultrasound quantitative parameter related to the tissue state.

[0077] In this embodiment, the characterization of the target tissue state as normal or abnormal includes the risk level of at least one of sarcopenia, the degree of tissue fatty tissue, liver inflammation, portal hypertension, renal failure, pancreatic cancer, fatty pancreas, articular cartilage damage, local muscle damage, liver fibrosis, and cirrhosis.

[0078] Furthermore, the target tissue is a tissue associated with at least one of the following: sarcopenia, degree of tissue adipose tissue, liver inflammation, portal hypertension, renal failure, pancreatic cancer, fatty pancreas, articular cartilage damage, local muscle damage, liver fibrosis, and cirrhosis.

[0079] When the characteristics of a normal or abnormal target tissue include the risk level of at least one of sarcopenia, degree of muscle fat accumulation, or local muscle injury, the target tissue is muscle; when the characteristics of a normal or abnormal target tissue include the risk level of at least one of liver inflammation, liver fibrosis, or cirrhosis, the target tissue is liver; when the characteristics of a normal or abnormal target tissue include the risk level of at least one of pancreatic cancer or fatty pancreas, the target tissue is pancreas; when the characteristics of a normal or abnormal target tissue include the risk level of kidney failure, the target tissue is kidney.

[0080] Furthermore, when the characteristics of the target tissue state being normal or abnormal include the risk level of the target condition being sarcopenia, the target tissue can be the forearm muscles, specifically the superficial flexor and / or extensor muscles of the forearm.

[0081] In one embodiment of this disclosure, at least one target ultrasound quantitative parameter includes at least one of tissue thickness, ultrasound scattering parameter, nonlinear parameter, attenuation parameter, texture parameter, motion parameter, and elastic parameter; the ultrasound scattering parameter includes at least one of normalized average intensity parameter, scattering peak value, scatterer density, and scatterer distribution characteristics; the nonlinear characteristic parameter includes at least one of normalized nonlinear parameter, ultrasound nonlinear coefficient, second harmonic amplitude, phase matching, nonzero energy flow, and higher-order elastic constant; the motion parameter includes at least one of displacement and strain; and the elastic parameter includes parameters characterizing the elastic coefficient and / or viscosity coefficient.

[0082] Furthermore, the motion parameters specifically refer to the changes in displacement, strain, etc., of the target tissue caused by force (external pressure or active contraction and relaxation).

[0083] Studies have found that the L3 pyramidal plane skeletal muscle region is the most commonly used site for assessing overall muscle condition. However, due to limited field of view, ultrasound cannot be used to measure the area of ​​all skeletal muscles within the lumbar spine plane. Therefore, the forearm muscles, upper arm muscles, thigh muscles, and calf muscles are the most commonly used areas for ultrasound assessment of muscle condition, but there is currently no universally accepted optimal measurement site that reflects overall muscle condition. Considering the moderate to high effectiveness of muscle thickness at various locations in diagnosing sarcopenia and the imaging quality of ultrasound, the forearm muscles can be selected as the target muscle tissue for ultrasound assessment because they are more superficial and provide high-quality imaging, making them suitable for diagnosing muscle atrophy. Since flexor and extensor muscles have different functions, the order of atrophy also differs. Therefore, the superficial flexor digitorum (FDS) and extensor digitorum (ED) of the forearm muscles can be selected to represent forearm flexion and extension, respectively, to assess low muscle content and low muscle mass. Furthermore, the volume of muscle under contraction is more strongly correlated with muscle contraction potential.

[0084] In one embodiment of this disclosure, the superficial flexor and extensor muscles of the forearm may be selected as target tissues to assess the condition of the muscles.

[0085] In this embodiment, the subject can be positioned supine with palms facing upwards, and continuous short-axis scanning can be performed on the flexor muscles on the forearm side to ensure that the forearm muscles are not compressed, in order to obtain the first ultrasound echo signal of the superficial flexor muscles of the fingers in a relaxed state. Alternatively, the subject can be positioned supine with palms facing upwards, and then instructed to clench their fist with maximum force to obtain the first ultrasound echo signal of the superficial flexor muscles of the fingers in a contracted state. Another option is to position the subject supine with palms facing downwards and continuously scan the extensor muscles to obtain the first ultrasound echo signal of the extensor muscles of the forearm in a relaxed state. Finally, the subject can be positioned supine with palms facing downwards and then instructed to lift their fingers with maximum force to obtain the first ultrasound echo signal of the extensor muscles of the forearm in a contracted state.

[0086] In an embodiment where the target ultrasound quantitative parameter includes tissue thickness, determining a first parameter value of at least one target ultrasound quantitative parameter based on a first ultrasound echo signal may include: determining the reception time of the first ultrasound echo signal corresponding to the boundary of the target tissue based on the parameter value of the target characteristic parameter of the first ultrasound echo signal and a first threshold value of the target characteristic parameter corresponding to the target tissue; and determining the thickness value of the target tissue based on the reception time of the first ultrasound echo signal corresponding to the boundary of the target tissue.

[0087] The target characteristic parameters include reflection characteristic parameters and / or scattering characteristic parameters. Reflection characteristic parameters include reflection values ​​and / or reflection value distribution parameters. Scattering characteristic parameters include at least one of scattering peak value, scatterer density, and scatterer distribution characteristics.

[0088] In this embodiment, the tissue thickness of the target tissue is an effective indicator for assessing tissue quality. With age, tissues gradually shrink, leading to a decrease in the tissue thickness of the target tissue. Therefore, by measuring the tissue thickness of the target tissue, its quality can be intuitively understood. In the assessment of the target tissue's condition, when the tissue thickness is below the corresponding normal range, it may indicate an abnormal condition of the target tissue.

[0089] Since different tissues have different reflection or transmission characteristics of ultrasound, the strong fascial reflection signals on the upper and lower surfaces of the target tissue can be identified based on the target characteristic parameters of the first ultrasound echo signal, and the muscle thickness can be calculated based on the time difference of receiving the strong fascial reflection signals on the upper and lower surfaces of the muscle.

[0090] In embodiments where the target tissue is muscle, the tissue thickness of the target tissue may include at least one of the following: the tissue thickness of the flexor digitorum superficialis in a contracted forearm state, the tissue thickness of the extensor digitorum superficialis in a contracted forearm state, the tissue thickness of the flexor digitorum superficialis in a relaxed forearm state, and the tissue thickness of the extensor digitorum superficialis in a relaxed forearm state. Ultrasound propagation is a nonlinear process, and the impact of nonlinear effects cannot be ignored in clinical diagnosis. The fundamental wave image of traditional B-mode ultrasound lacks a comprehensive description of biological tissue and cannot reflect the nonlinear changes in ultrasound waves propagating within the human body, thus affecting the accuracy of diagnostic results. Ultrasound nonlinear parameters are parameters used to describe the nonlinear effects generated when ultrasound waves propagate in a medium.

[0091] The nonlinear parameter is a parameter corresponding to the nonlinear effect generated by the propagation of the ultrasonic signal in the tissue under test. In some embodiments, the nonlinear parameter is determined based on the correspondence between the second harmonic and the nonlinear parameter when the ultrasonic signal propagates in the tissue under test, and the attenuation coefficient of the ultrasonic signal propagating in the tissue under test.

[0092] The mathematical relationship between the second harmonic and the nonlinear parameters is as follows:

[0093] Where β represents the nonlinear parameter, ρ0 represents the density of the target tissue, p2(x) represents the amplitude of the second harmonic in the first ultrasonic echo signal, p0 represents the amplitude of the first ultrasonic echo signal at the target tissue, c0 represents the propagation speed of the first ultrasonic echo signal in the target tissue, f represents the frequency of the first ultrasonic echo signal, and x represents the propagation distance of the first ultrasonic echo signal. When ultrasound propagates in the tissue under test, the attenuation of the ultrasound in the tissue under test needs to be considered, and corresponding compensation should be made based on this attenuation to improve the accuracy of the nonlinear parameter determination. Combining the above mathematical relationship and the attenuation coefficient of the ultrasonic signal propagating in the tissue under test, the nonlinear parameter β is obtained based on the following calculation formula:

[0094] Where α1 represents the attenuation coefficient of the fundamental wave in the first ultrasonic echo signal, and α2 represents the attenuation coefficient of the second harmonic wave in the first ultrasonic echo signal, when x approaches 0... Substitute the value of x into the above formula. When x approaches 0... The value is determined by: determining the second harmonic amplitude at different propagation distances, and plotting... For the curve of x, and through linear derivation, we can obtain the result when x approaches 0. The value of .

[0095] Ultrasonic scattering parameters can be used to evaluate the scattering characteristics of tissues. They describe the scattering phenomenon that occurs when ultrasonic waves encounter stray particles or interfaces in a medium. Ultrasonic scattering parameters can characterize the energy distribution of incident ultrasonic waves in various directions after scattering.

[0096] The scattering distribution parameters can include at least one of the K-distribution and the Nakagami distribution. In this embodiment, the Nakagami distribution statistical model is used to image the ultrasonic echo signal. The generated Nakagami image is the m-parameter mapping of the model, which is a shape parameter used to describe the statistical distribution of the backscattered signal envelope. The change of m from 0 to 1 indicates that the statistical distribution of the signal envelope changes from the "pre-Rayleigh distribution" (generally the K-distribution) to the Rayleigh distribution, and a value greater than 1 indicates that it follows the "post-Rayleigh distribution" (generally the Rician distribution).

[0097] The probability density function of the ultrasonic backscatter signal envelope R in the Nakagami statistical model can be expressed as:

[0098] Where Γ(·) represents the gamma function, U(·) represents the unit step function, r represents a specific abscissa value of the function, f(r) represents the numerical value of the scatterer distribution characteristics, Ω represents the scale parameter related to the Nakagami distribution, and m represents the Nakagami parameter.

[0099] Furthermore, the scale parameter Ω associated with the Nakagami distribution can be calculated using the following formula: Ω = E(R 2 )

[0100] Furthermore, the Nakagami parameter m can be calculated using the following formula:

[0101] Among them, E(R) 2 ) represents the mean square value of variable r.

[0102] In one embodiment of this disclosure, determining a first parameter value of at least one target ultrasound quantitative parameter includes: performing signal reconstruction processing on a first ultrasound echo signal; and performing signal analysis processing on the reconstructed first ultrasound echo signal to obtain the first parameter value of the target ultrasound quantitative parameter, wherein the signal analysis includes at least one of time domain analysis, frequency domain analysis, and time-frequency analysis.

[0103] In this embodiment, signal processing techniques such as Fourier transform, wavelet transform, or sparse representation can be used to reconstruct the first ultrasonic echo signal.

[0104] The signal reconstruction process includes signal denoising, signal enhancement, and feature extraction.

[0105] This embodiment can restore the characteristics of the original signal and improve the signal quality through the signal reconstruction process, providing a basis for subsequent analysis.

[0106] In this embodiment, the reconstructed first ultrasonic echo signal can be subjected to multi-dimensional signal analysis processing, including at least one of time-domain analysis (such as waveform analysis, statistical analysis), frequency-domain analysis (such as spectrum analysis, power spectrum analysis), and time-frequency analysis (such as short-time Fourier transform, wavelet transform).

[0107] By performing signal analysis on the reconstructed first ultrasonic echo signal, key feature information such as frequency, amplitude, phase, and energy can be extracted. This feature extraction process may involve steps such as feature selection and feature dimensionality reduction.

[0108] In one embodiment of this disclosure, where the target ultrasound quantitative parameter includes a texture parameter, determining a first parameter value for at least one target ultrasound quantitative parameter based on the first ultrasound echo signal may include steps S3100–S3500 as shown in FIG3:

[0109] Step S3100: Obtain a first ultrasound image of the area to be tested based on the first ultrasound echo signal, and obtain mask data that identifies the location of the target tissue in the area to be tested.

[0110] In this embodiment, the ultrasonic testing device can be a parallel ultrasonic testing device, which can transmit and receive ultrasonic signals in parallel through multiple channels. The resulting first ultrasonic image is unaffected by the distortion caused by traditional scan-line transmission and reception imaging. It has a high pulse repetition frequency and acquires the target area in a very short time, not exceeding 100ms, offering the advantages of speed and the absence of distortion artifacts. The obtained first ultrasonic image is either a single transmission or an average of multiple transmissions; averaging multiple transmissions can improve the signal-to-noise ratio.

[0111] Furthermore, it may involve transmitting an ultrasonic signal to at least one area to be tested and receiving an ultrasonic echo signal reflected by at least one area to be tested, and generating a first ultrasonic image based on the ultrasonic echo signal reflected by at least one area to be tested, wherein the target tissue is located within at least one area to be tested.

[0112] This embodiment improves the speed and efficiency of data acquisition by introducing a parallel transmission and reception method for ultrasonic signals, thereby enhancing the efficiency of tissue texture analysis. Furthermore, the parallelized ultrasonic testing equipment employs intelligent frequency allocation and management algorithms, as well as high-precision synchronization and calibration technologies, ensuring the accuracy and stability of data acquisition. This provides a solid foundation for signal reconstruction, analysis, and model training, further improving the overall performance of the signal analysis and model training system.

[0113] The first ultrasound image obtained in this embodiment can be an ultrasound image that includes all tissues in the area to be tested.

[0114] In this embodiment, the mask data may be determined based on a second ultrasound image of the area to be tested of a reference human body obtained in advance.

[0115] Furthermore, it can be based on image recognition algorithms or machine learning models to accurately define the extent of the target tissue in the area to be tested, using it as mask data.

[0116] In one embodiment of this disclosure, mask data may be obtained in advance by methods such as manual sketching, semi-automatic segmentation, or fully automatic segmentation.

[0117] In another embodiment of this disclosure, obtaining mask data that identifies the location of the target tissue in the region to be tested may include: preprocessing the second ultrasound image; extracting feature values ​​of selected features in the second ultrasound image using an image processing algorithm; classifying the region in the second ultrasound image based on the feature values ​​of the selected features in the second ultrasound image using a classification algorithm to obtain the target region as mask data.

[0118] Specifically, preprocessing can include denoising, enhancement, smoothing, and other processes to improve the quality of the first ultrasound image, facilitating subsequent feature extraction and classification.

[0119] Furthermore, the image processing algorithm can be any one or more of edge detection algorithms, corner detection algorithms, and texture analysis algorithms. The selected features can be any one or more of shape, contour, and texture.

[0120] Furthermore, the classification algorithm can be any one or more of support vector machines, decision trees, and neural networks.

[0121] Step S3200: The first ultrasound image is divided into multiple image blocks.

[0122] In one embodiment, the first ultrasound image can be segmented into multiple image blocks of the same size using an image scaling method based on a set size, with each image block having a set size.

[0123] Specifically, the dimensions can be set according to the target tissue, the resolution of the ultrasonic testing equipment, and the specific analysis requirements. For example, the dimensions can be set to 20 pixels * 20 pixels. This reduces interference from invalid data and improves the accuracy of texture analysis results.

[0124] In another embodiment, the first ultrasound image can be segmented into a set number of image blocks using an image scaling method, based on a predetermined number.

[0125] Specifically, the number can be set according to the target tissue, the resolution of the ultrasound testing equipment, and the specific analysis requirements. For example, the number can be set to 10*20.

[0126] Step S3300: Based on the grayscale information of the pixels in each image block, obtain the parameter values ​​of the corresponding texture parameters for each image block.

[0127] In this embodiment, the texture parameters include at least one of mean, variance, energy, entropy, contrast, correlation, and homogeneity.

[0128] In this embodiment, the gray-level co-occurrence matrix method can be used to calculate the parameter values ​​of the texture parameters corresponding to each image block.

[0129] In embodiments where the texture parameters include the mean, the parameter values ​​for the texture parameters corresponding to the image patch can be determined using the following formula:

[0130] Where μ represents the mean, N represents the size of the image patch, and P ij This represents the grayscale information of the pixel in the i-th row and j-th column of an image block.

[0131] In embodiments where the texture parameters include variance, the parameter values ​​for the texture parameters corresponding to the image patch can be determined using the following formula:

[0132] Where, σ 2 P represents the variance, and N represents the size of the image patch. ij μ represents the grayscale information of the pixel in the i-th row and j-th column of an image block, where μ represents the mean.

[0133] In embodiments where texture parameters include energy, the parameter values ​​for texture parameters corresponding to image patches can be determined using the following formula:

[0134] Where Energy represents energy, P ij This represents the grayscale information of the pixel in the i-th row and j-th column of an image block, where N represents the size of the image block.

[0135] In embodiments where the texture parameter includes entropy, the parameter value of the texture parameter corresponding to the image patch can be determined by the following formula:

[0136] Where Energy represents entropy, P ij This represents the grayscale information of the pixel in the i-th row and j-th column of an image block, where N represents the size of the image block.

[0137] In embodiments where contrast is included as a texture parameter, the parameter value of the texture parameter corresponding to the image patch can be determined by the following formula:

[0138] Where Contrast represents contrast, P ij This represents the grayscale information of the pixel in the i-th row and j-th column of an image block, where N represents the size of the image block.

[0139] In embodiments where texture parameters include correlation, the parameter values ​​for texture parameters corresponding to image patches can be determined using the following formula:

[0140] Where Correlation represents correlation, P ij This represents the grayscale information of the pixel in the i-th row and j-th column of an image patch, where N represents the size of the image patch, μ represents the mean, and σ represents the mean. 2 Indicates variance.

[0141] In embodiments where texture parameters include homogeneity, the parameter values ​​for texture parameters corresponding to image patches can be determined using the following formula:

[0142] Where Homogeneity represents homogeneity, P ij This represents the grayscale information of the pixel in the i-th row and j-th column of an image block, where N represents the size of the image block.

[0143] These texture parameters can comprehensively describe the texture characteristics of the target organization, such as its thickness, depth, uniformity, and complexity, from multiple dimensions.

[0144] When calculating the gray-level co-occurrence matrix, it is also necessary to select appropriate distance and orientation parameters based on clinical symptom characteristics to reflect the different texture features of the target tissue.

[0145] Step S3400: The multiple image blocks are stitched together according to the parameter values ​​of the texture parameters corresponding to the multiple image blocks to obtain the first stitched image.

[0146] After processing each small block, the results are stitched together to form an image of the original size for further analysis. This method reduces computational complexity and improves processing efficiency.

[0147] When stitching images together, bilinear interpolation or other interpolation methods can be used to interpolate multiple image blocks, such as nearest neighbor interpolation and bicubic interpolation, to ensure the smoothness and continuity of the image. This helps reduce artifacts and distortions caused by image segmentation and stitching.

[0148] The size of the first stitched image is the same as the size of the first ultrasound image.

[0149] In this embodiment, each pixel in the first stitched image has a parameter value corresponding to each texture parameter.

[0150] When the texture parameters include multiple of the following: mean, variance, energy, entropy, contrast, correlation, and homogeneity, the parameter value of each pixel in the first stitched image can be obtained separately for each texture parameter, based on the parameter values ​​of multiple image patches corresponding to the texture parameter.

[0151] Step S3500: Based on the mask data and the parameter values ​​of the texture parameters corresponding to the pixels in the first stitched image, obtain the parameter values ​​of the texture parameters corresponding to the target tissue.

[0152] When the texture parameters include multiple of the following: mean, variance, energy, entropy, contrast, correlation, and homogeneity, the parameter value of the target tissue corresponding to the texture parameter can be obtained separately for each texture parameter, based on the mask data and the parameter value of each pixel in the first stitched image corresponding to the texture parameter.

[0153] In one embodiment of this disclosure, the parameter values ​​of the texture parameters corresponding to the target tissue are obtained based on the mask data and the parameter values ​​of the pixels in the first stitched image, including the following steps S3510 to S3530:

[0154] Step S3510: Extract the target image region representing the target tissue from the first stitched image based on the mask data.

[0155] In this embodiment, the target image region extracted based on the mask data can be the image region representing the target tissue in the first stitched image.

[0156] Step S3520: Determine the first statistical value of the texture parameter value corresponding to each pixel in the target image region.

[0157] In one embodiment of this disclosure, the first statistical value is at least one of the maximum value, minimum value, mean value, and median value.

[0158] In this embodiment, the first statistical value is used to determine the significant difference in the first ultrasound image.

[0159] Step S3530: Obtain the parameter value of the corresponding texture parameter of the target organization based on the first statistical value of the corresponding texture parameter.

[0160] In this embodiment, for each texture parameter, the first statistical value corresponding to that texture parameter can be used as the parameter value of the target organization corresponding to that texture parameter.

[0161] In one embodiment of this disclosure, obtaining the parameter value of the texture parameter corresponding to the target tissue based on the first statistical value of the corresponding texture parameter may further include: determining the second statistical value of the texture parameter corresponding to the pixel in the target image region; and obtaining the parameter value of the texture parameter corresponding to the target tissue based on the first statistical value only when the second statistical value meets a preset condition.

[0162] In one embodiment of this disclosure, the second statistic includes at least one of variance, lower quartile, and upper quartile.

[0163] In this embodiment, the second statistical value corresponding to the texture parameter can be used to help determine the stability of the parameter value of the texture parameter corresponding to the target tissue.

[0164] In this embodiment, a corresponding acceptable range can be set in advance for each texture parameter. The acceptable ranges for different texture parameters can be the same or different, and this is not limited here.

[0165] If the second statistical value for all texture parameters is within the corresponding acceptable range, it can be determined that the second statistical value meets the preset conditions; if the second statistical value for any texture parameter exceeds the corresponding acceptable range, it can be determined that the second statistical value does not meet the preset conditions.

[0166] If the second statistical value does not meet the preset conditions, a prompt message can be output indicating that the first ultrasound image of the target tissue has been reacquired to assess the state of the target tissue based on texture analysis; alternatively, a prompt message indicating that the state assessment result of the target tissue cannot be obtained can also be output. The prompt message can be audio or text.

[0167] In the embodiments of this disclosure, a first ultrasound image of the region to be tested is segmented into multiple image blocks. Based on the grayscale information of each pixel in each image block, the parameter values ​​of the corresponding texture parameters for each image block are obtained. Then, the multiple image blocks are stitched together based on the parameter values ​​of the corresponding texture parameters to obtain a first stitched image. Based on the parameter values ​​of the texture parameters corresponding to the pixels in the first stitched image and mask data identifying the position of the target tissue in the region to be tested, the parameter values ​​of the texture parameters corresponding to the target tissue are obtained. This improves the accuracy and reliability of the texture analysis results of the target tissue.

[0168] Step S2300: Based on the tissue state prediction model, the first index value of the target evaluation index is obtained according to the first parameter value of at least one target ultrasound quantitative parameter.

[0169] Among them, the target assessment indicators should include at least those that characterize whether the target organization is in a normal or abnormal state.

[0170] In one embodiment of this disclosure, when the characterization of the target tissue state as normal or abnormal includes the risk level of sarcopenia, the target assessment indicators include at least one of grip strength, low skeletal muscle index, and low mean skeletal muscle density.

[0171] Through the embodiments of this disclosure, a first parameter value of at least one target ultrasound quantitative parameter is determined based on the first ultrasound echo signal of the target tissue. Based on the tissue state prediction model, a first index value of the target evaluation index is obtained based on the first parameter value of the at least one target ultrasound quantitative parameter. Based on the first index value of the target evaluation index, the state evaluation result of the target tissue is obtained. The quantitative, objective, and non-invasive technical advantages of ultrasound technology provide strong support for the evaluation of the tissue state of the target tissue.

[0172] In one embodiment of this disclosure, the method may further include: acquiring a plurality of predefined ultrasound quantitative parameters; obtaining a plurality of parameter combinations based on the plurality of ultrasound quantitative parameters, each parameter combination including at least one ultrasound quantitative parameter; determining the correlation between each parameter combination and a target evaluation index; selecting one or more parameter combinations based on the correlation, wherein the correlation of the selected parameter combination should be at least better than the correlation of an unselected set of parameter combinations, or the selected parameter combination is the one with the highest correlation among all parameter combinations; and determining a target ultrasound quantitative parameter based on the selected set of one or more parameter combinations.

[0173] When characterizing the target tissue state as normal or abnormal, including the risk level of sarcopenia, multiple ultrasound quantitative parameters are set, which may include the tissue thickness of the flexor digitorum superficialis under forearm muscle contraction, the tissue thickness of the extensor digitorum superficialis under forearm muscle contraction, the tissue thickness of the flexor digitorum superficialis under forearm muscle relaxation, the tissue thickness of the extensor digitorum superficialis under forearm muscle relaxation, the normalized nonlinear parameter of the flexor digitorum superficialis under forearm muscle contraction, the normalized nonlinear parameter of the extensor digitorum superficialis under forearm muscle contraction, the mean strength parameter of the flexor digitorum superficialis under forearm muscle contraction, the mean strength parameter of the extensor digitorum superficialis under forearm muscle relaxation, the normalized nonlinear parameter of the flexor digitorum superficialis under forearm muscle relaxation, the mean strength parameter of the flexor digitorum superficialis under forearm muscle relaxation, and the mean strength parameter of the extensor digitorum superficialis under forearm muscle relaxation.

[0174] In this embodiment, normally distributed continuous variables are described as mean ± standard deviation, and t-tests are used to assess differences between groups. For other continuous variables, the median (25th to 75th percentiles) is used, and in the case of non-normally distributed continuous variables, the Mann-Whitney U test is applied for comparisons between groups. Chi-square tests or Fisher probabilities are used to compare proportions. Regression coefficients are used to generate models to identify low grip strength, low SMI, and low SMD, and the area under the receiver operating curve (AUROC) is reported. Binary logistic regression analysis identifies independent factors of myelosuppression. A p-value <0.05 is used to indicate statistical significance in two-sided tests.

[0175] When assessing the correlation between ultrasound-quantified parameters and grip strength, a hydraulic hand dynamometer (HGS) can be used as the standard clinical method for evaluating hand grip strength. Measurements are taken with the non-dominant hand, and the maximum value of three measurements is recorded as the HGS. Table 1 shows the correlation between tissue thickness, normalized nonlinear parameter (MusQBOX.NLP), and normalized mean strength (MusQBOX.NMI) of the superficial digit flexors and extensors under muscle contraction and relaxation states. It can be seen that the tissue thickness of the superficial digit flexors has the highest correlation with grip strength (relaxed state: r = 0.836, contracted state: r = 0.825), while the tissue thickness of the extensors has a lower correlation (relaxed state: r = 0.689, contracted state: r = 0.724). The correlation between the normalized nonlinear parameter or normalized mean strength and grip strength is weak (see Table 1). Regardless of muscle contraction state, the tissue thickness of the superficial digit flexors and extensors is highly correlated with grip strength (r = 0.733–0.814). Compared to using ultrasound to measure forearm thickness, measuring the tissue thickness of the superficial flexor muscles alone showed a higher correlation with grip strength (r = 0.825–0.836). These studies highlight the importance of muscle thickness as a reliable indicator of muscle strength, particularly in lymphoma patients undergoing chemotherapy.

[0176] In Table 1, r is the correlation coefficient; P is the confidence interval for statistical significance, and P < 0.05 is considered statistically significant.

[0177] Table 1

[0178] When assessing the correlation between ultrasound quantitative parameters and low SMI and low SMD, CT was used as the standard clinical method for measuring SMI and SMD. As shown in Tables 2 and 3, binary logistic regression models were established for predicting low SMI and low SMD. In a relaxed forearm muscle state, the MT and MusQBOX.NLP of the superficial digitorum flexor muscles were associated with low SMI (odds ratio (OR) was 0.470 for tissue thickness, 95% CI (CI) was 0.328-0.672, P<0.001; OR for MusQBOX.NLP was 2.133, 95% CI 1.490-3.054, P<0.001), and the correlation between MusQBOX.NLP of the superficial digitorum flexor muscles and MusQBOX.NMI of the extensor muscles and low SMDOR was A13 2.024, 95% CI 1.474-2.780, P<0.001; OR was A58 1.045, 95% CI 1.005-1.086, P=0.026) (as shown in Table 2). Under forearm muscle contraction, the median tibial flexor digitorum (MT) and MusQBOX.NLP, and the extensor digitorum (MusQBOX.NLP) were associated with low SMI (OR: MT 0.512, 95% CI 0.367-0.716, P < 0.001; MT: 1.302, 95% CI 1.008-1.680, P = 0.043; MusQBOX.NLP in the superficial digitorum (1.302) was associated with low SMI (OR: MT 0.512, 95% CI 1.008-1.680, P = 0.043). The odds ratio (OR) for MusQBOX.NLP was 1.490 (95% CI 1.190–1.866, P = 0.001). MusQBOX.NLP in the superficial digit flexor muscles and MT in the extensor muscles were associated with low SMD (OR for MusQBOX.NLP was 1.466, 95% CI 0.907–1.042, P = 0.005; MT for OR was 0.675, 95% CI 0.488–0.935, P = 0.018).

[0179] Table 2

[0180] Table 3

[0181] A model was established using both ultrasound imaging (EI) and muscle thickness, demonstrating the predictive power of combined tissue thickness and EI for sarcopenia. Results showed that muscle thickness, normalized nonlinear parameters, and normalized mean intensity were independent predictors of low muscle mass (SMI) and low muscle malignancy (SMD), exhibiting higher area under the curve (AUC) for predicting low SMI (AUC: contraction 0.874, relaxation 0.886, P = 0.604) and low SMD (AUC: contraction 0.768, relaxation 0.825, P = 0.030). Furthermore, while measuring quantitative ultrasound parameters after muscle contraction did not improve predictive ability, combining these parameters yielded higher AUC values ​​for diagnosing low SMI and low SMD. The selection and combined use of specific parameters highlights the potential of quantitative ultrasound parameters in accurately assessing muscle status in lymphoma patients.

[0182] As shown in Table 4, independent risk factors for chemotherapy toxicity were assessed using binary logistic regression analysis. These factors included tissue thickness, normalized nonlinear parameter, and normalized mean intensity of the flexor digitorum superficialis muscle, which were identified as important independent risk factors for hematologic toxicity. High tissue thickness was an inhibitor of chemotherapy toxicity, while the normalized nonlinear parameter and normalized mean intensity were significant promoters of chemotherapy toxicity (OR for tissue thickness: 0.655, 95% CI 0.491–0.894, P = 0.008; OR for normalized nonlinear parameter: 2.494, 95% CI 1.731–3.593, P < 0.001; OR for normalized mean intensity: 0.944, 95% CI 0.904–0.986, P = 0.010).

[0183] Table 4

[0184] This embodiment collected ultrasound echo signals of forearm muscles in relaxed and contracted states, and extracted quantitative ultrasound parameters, including tissue thickness, normalized nonlinear parameters, and normalized average intensity. Correlation analysis revealed that among these quantitative ultrasound parameters, only tissue thickness was closely related to muscle strength. Ultrasound scattering and attenuation parameters were used to predict low SMI and low SMD, achieving higher AUC. Furthermore, it was confirmed that quantitative ultrasound parameters are independent imaging factors of myelosuppression in lymphoma patients after chemotherapy.

[0185] In one embodiment of this disclosure, when the target evaluation index includes grip strength, the target ultrasound quantitative parameter includes the tissue thickness of the superficial flexor digitorum. When the target evaluation index includes a low skeletal muscle index, the target ultrasound quantitative parameter includes at least one of the following: the thickness of the superficial flexor digitorum under forearm muscle contraction, a normalized nonlinear parameter of the superficial flexor digitorum under forearm muscle contraction, a normalized nonlinear parameter of the extensor digitorum under forearm muscle contraction, the thickness of the superficial flexor digitorum under forearm muscle relaxation, and a normalized nonlinear parameter of the superficial flexor digitorum under forearm muscle relaxation. When the target evaluation index includes a low mean skeletal muscle density, the target ultrasound quantitative parameter includes at least one of the following: the thickness of the extensor digitorum under forearm muscle contraction, a normalized nonlinear parameter of the superficial flexor digitorum under forearm muscle contraction, a normalized nonlinear parameter of the superficial flexor digitorum under forearm muscle relaxation, and a mean strength parameter of the extensor digitorum under forearm muscle relaxation.

[0186] In one embodiment of this disclosure, when the target evaluation index includes low average skeletal muscle density, the target ultrasound quantitative parameters include at least one of the normalized nonlinear parameters of the superficial flexor muscles in the relaxed state of the forearm muscles and the average strength parameters of the extensor muscles in the relaxed state of the forearm muscles.

[0187] In one embodiment of this disclosure, when the target assessment indicator includes chemotherapy toxicity, the target ultrasound quantitative parameters include at least one of the following: tissue thickness of the superficial flexor digitorum, normalized nonlinear parameter, and mean intensity parameter.

[0188] In one embodiment of this disclosure, the method further includes: acquiring a training sample set, wherein each training sample in the training sample set includes a second parameter value of at least one target ultrasound quantitative parameter and a second index value of a target evaluation index, wherein the second parameter value is determined based on the third ultrasound echo signal of the target tissue of the corresponding training sample, and the second index value is determined based on the actual tissue state of the target tissue of the corresponding training sample; and training a tissue state prediction model based on the training sample set.

[0189] In this embodiment, the method of determining the second parameter value of at least one target ultrasound quantitative parameter based on the second ultrasound echo signal can be specifically referred to the method of determining the first parameter value of at least one target ultrasound quantitative parameter based on the first ultrasound echo signal in the previous embodiment, and will not be repeated here.

[0190] When the target ultrasound quantitative parameters include texture parameters, the method for obtaining the parameter values ​​of the texture parameters corresponding to the training samples may include: acquiring a second ultrasound image of the region to be tested and mask data identifying the location of the target tissue in the region to be tested; dividing the second ultrasound image into multiple image blocks; obtaining the parameter values ​​of the texture parameters corresponding to each image block based on the grayscale information of the pixels in each image block; stitching the multiple image blocks together based on the parameter values ​​of the texture parameters corresponding to the multiple image blocks to obtain a second stitched image; extracting the target image region representing the target tissue in the second stitched image based on the mask data; determining the first statistical value of the parameter values ​​of the texture parameters corresponding to the pixels in the target image region; and obtaining the parameter values ​​of the texture parameters corresponding to the target tissue based on the first statistical value of the corresponding texture parameters. Specifically, refer to the aforementioned steps S3100 to S3500.

[0191] Furthermore, the method also includes steps S3600 to S3800 as shown below:

[0192] Step S3600: Determine the second statistical value of the texture parameter value corresponding to each pixel in the target image region.

[0193] In one embodiment of this disclosure, the second statistic includes at least one of variance, lower quartile, and upper quartile.

[0194] In this embodiment, the second statistical value corresponding to the texture parameter can be used to help determine the stability of the parameter value of the texture parameter corresponding to the target tissue.

[0195] Step S3700: Determine whether the texture parameters corresponding to the training sample meet the preset conditions based on the second statistical value of the corresponding texture parameters.

[0196] In this embodiment, a corresponding acceptable range can be set in advance for each texture parameter. The acceptable ranges for different texture parameters can be the same or different, and this is not limited here.

[0197] If the second statistical value of all corresponding texture parameters is within the corresponding acceptable range, it can be determined that the texture parameters of the training sample meet the preset conditions; if the second statistical value of any texture parameter exceeds the corresponding acceptable range, it can be determined that the texture parameters of the training sample do not meet the preset conditions.

[0198] Step S3800: Train the tissue state prediction model using training samples that meet preset conditions from the training sample set.

[0199] In this embodiment, the training samples used to train the tissue state prediction model are screened based on the second statistical value of the corresponding texture parameter, thereby ensuring the effectiveness and accuracy of the trained tissue state prediction model.

[0200] By optimizing the parameters of the organizational state prediction model, such as the learning rate and regularization coefficient, based on the training sample set, and iteratively training the model, the generalization ability and prediction accuracy of the organizational state prediction model can be improved. This process may involve steps such as cross-validation and hyperparameter tuning.

[0201] In this embodiment, by iteratively training the organization state prediction model, the cut-off value of each target evaluation index can also be obtained.

[0202] Step S2400: Obtain the status assessment result of the target organization based on the first indicator value of the target assessment indicator.

[0203] In this embodiment, the status assessment result of the target organization may include a first indicator value and a corresponding cutoff value of the target assessment indicator, a comparison result between the first indicator value and the corresponding cutoff value, and a detection result indicating whether the target organization is normal or abnormal. The detection result indicating whether the target organization is normal or abnormal can be determined based on the comparison result between the first indicator value and the corresponding cutoff value of the target assessment indicator.

[0204] In one embodiment, the organizational status assessment results can be output in a clear and intuitive manner, including necessary explanations and elaborations. The output format may include charts, reports, visualizations, etc. This embodiment facilitates user understanding and application of the assessment results by providing a user-friendly interface or report. This process may involve steps such as result visualization and result interpretation.

[0205] Through the embodiments of this disclosure, a first parameter value of at least one target ultrasound quantitative parameter is determined based on the first ultrasound echo signal of the target tissue, and a state assessment result of the target tissue is obtained based on the first parameter value of the at least one target ultrasound quantitative parameter. The quantitative, objective, and non-invasive technical advantages of ultrasound technology provide strong support for the state assessment of the target tissue.

[0206] Figure 4 is a flowchart of an example of an organizational status assessment method according to an embodiment of the present disclosure.

[0207] As shown in Figure 4, the method may include the following steps:

[0208] Step S4001: Acquire the second ultrasonic echo signal of at least one area to be tested.

[0209] Step S4002: Determine the target area in the region to be measured based on the second ultrasonic echo signal.

[0210] Step S4003: Transmit an ultrasonic signal to the target area and receive the ultrasonic echo signal reflected by the target area as the first ultrasonic echo signal.

[0211] Step S4004: Perform signal reconstruction processing on the first ultrasonic echo signal.

[0212] Step S4005: Perform signal analysis and processing on the reconstructed first ultrasound echo signal to obtain the first parameter value of the target ultrasound quantitative parameter.

[0213] The target ultrasound quantitative parameters are ultrasound quantitative parameters related to tissue state, and the signal analysis includes at least one of time domain analysis, frequency domain analysis, and time-frequency analysis.

[0214] Step S4006: Based on the tissue state prediction model, obtain the first index value of the target evaluation index according to the first parameter value of at least one target ultrasound quantitative parameter.

[0215] Step S4007: Obtain the status assessment result of the target organization based on the first indicator value of the target assessment indicator.

[0216] <Example of Ultrasonic Testing Equipment>

[0217] This embodiment provides an ultrasonic testing device. As shown in FIG5, the ultrasonic testing device 5000 may include a processor 5100 and a memory 5200. The memory 5200 is used to store a computer program, and the processor 5100 is used to control the ultrasonic testing device to execute the method of any embodiment of this disclosure under the control of the computer program.

[0218] <Example of a readable storage medium>

[0219] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the methods described in any of the method embodiments of this disclosure.

[0220] All steps of the methods in the embodiments of this disclosure can be implemented by a computer device.

[0221] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0222] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0223] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0224] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0225] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0226] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0227] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0228] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation in a combination of software and hardware are equivalent.

[0229] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of this disclosure is defined by the appended claims. Industrial applicability

[0230] Through the embodiments of this disclosure, a first parameter value of at least one target ultrasound quantitative parameter is determined based on the first ultrasound echo signal of the target tissue. Based on the first parameter value of the at least one target ultrasound quantitative parameter, a state assessment result of the target tissue is obtained. This provides strong support for the state assessment of the target tissue through the quantitative, objective, and non-invasive technical advantages of ultrasound technology. Therefore, this disclosure has strong industrial applicability.

Claims

1. A method for assessing organizational status, characterized in that, include: Acquire the first ultrasound echo signal of the target tissue; A first parameter value of at least one target ultrasound quantitative parameter is determined based on the first ultrasound echo signal, wherein the target ultrasound quantitative parameter is an ultrasound quantitative parameter related to tissue state. Based on the tissue state prediction model, a first index value of the target evaluation index is obtained according to the first parameter value of the at least one target ultrasound quantitative parameter; wherein, the target evaluation index includes at least an index characterizing whether the target tissue state is normal or abnormal. The status assessment result of the target organization is obtained based on the first indicator value of the target assessment indicator.

2. The method according to claim 1, characterized in that, The method further includes: Acquire multiple predefined quantitative ultrasound parameters; Based on the multiple ultrasound quantitative parameters, multiple parameter combinations are obtained, and each parameter combination includes at least one ultrasound quantitative parameter; Determine the correlation between each parameter combination and the target evaluation index; One or more parameter combinations are selected based on the relevance, and the relevance of the selected parameter combination should be at least better than the relevance of the unselected parameter combination, or the selected parameter combination is the one with the highest relevance among all parameter combinations. The target ultrasound quantitative parameters are determined based on one or more selected combinations of parameters.

3. The method according to claim 2, characterized in that, The at least one target ultrasound quantitative parameter includes at least one of tissue thickness, ultrasound scattering parameter, nonlinear parameter, attenuation parameter, texture parameter, motion parameter, and elastic parameter; wherein, the ultrasound scattering parameter includes at least one of average intensity parameter, scattering peak value, scatterer density, and scatterer distribution characteristics; the nonlinear characteristic parameter includes at least one of normalized nonlinear parameter, ultrasound nonlinear coefficient, second harmonic amplitude, phase matching, nonzero energy flow, and higher-order elastic constant; the motion parameter includes at least one of displacement and strain; the elastic parameter includes parameters used to characterize the elastic coefficient and / or viscosity coefficient.

4. The method according to any one of claims 1 to 3, characterized in that, The acquisition of the first ultrasound echo signal of the target tissue includes: The first ultrasound echo signal of the target tissue under different conditions was obtained.

5. The method according to claim 4, characterized in that, The different conditions include when the target tissue is subjected to different pressures, or when it actively contracts or relaxes, or at different times.

6. The method according to any one of claims 1 to 5, characterized in that, The ways in which the target tissue is normal or abnormal include the risk level of at least one of sarcopenia, the degree of tissue adipose tissue, liver inflammation, portal hypertension, renal failure, pancreatic cancer, fatty pancreas, articular cartilage damage, local muscle damage, liver fibrosis, and cirrhosis. The target tissue is a tissue associated with at least one of the following: sarcopenia, degree of tissue adipose tissue, liver inflammation, portal hypertension, renal failure, pancreatic cancer, fatty pancreas, articular cartilage damage, local muscle damage, liver fibrosis, and cirrhosis.

7. The method according to any one of claims 1 to 6, characterized in that, The acquisition of the first ultrasound echo signal of the target tissue includes: Acquire the second ultrasound echo signal of at least one test area of ​​the target tissue; The target region in the area to be tested is determined based on the second ultrasonic echo signal; An ultrasonic signal is emitted toward the target area, and the ultrasonic echo signal reflected by the target area is received as the first ultrasonic echo signal.

8. The method according to claim 3, characterized in that, When the at least one target ultrasound quantitative parameter includes a nonlinear parameter, determining the parameter value of the at least one target ultrasound quantitative parameter based on the ultrasound echo signal includes: Wherein, β represents a nonlinear parameter, ρ0 represents the density of the target tissue, p2(x) represents the amplitude of the second harmonic in the first ultrasonic echo signal, p0 represents the amplitude of the first ultrasonic echo signal at the target tissue, c0 represents the propagation speed of the first ultrasonic echo signal in the target tissue, f represents the frequency of the first ultrasonic echo signal, α1 represents the attenuation coefficient of the fundamental wave in the first ultrasonic echo signal, α2 represents the attenuation coefficient of the second harmonic in the first ultrasonic echo signal, and x represents the propagation distance of the first ultrasonic echo signal.

9. The method according to any one of claims 1 to 8, characterized in that, The organization status prediction model was obtained using the following method: Obtain a training sample set, wherein each training sample in the training sample set includes a second parameter value of the at least one target ultrasound quantitative parameter and a second index value of the target evaluation index, wherein the second parameter value is determined based on the third ultrasound echo signal of the corresponding training sample; The organization state prediction model is trained based on the training sample set.

10. An ultrasonic testing device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, and the processor being used, under the control of the computer program, to execute the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, the computer program implementing the method as described in any one of claims 1 to 9 when executed by a processor.

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