High-resolution ultrasonic elastography quantitative analysis system

Abstract

The invention relates to the technical field of medical image analysis, in particular to a high-resolution ultrasonic elastography quantitative analysis system, which integrates an ultrasonic unit, an image processing unit and an elasticity analysis unit, and realizes emission, receiving, signal conversion, displacement wave map generation and tissue elasticity value calculation of high-frequency ultrasonic waves. According to the system, through technical innovation, the spatial resolution, the image quality and the quantitative analysis capability are improved, and the limitation of the prior art is overcome; the system comprises a high-frequency probe, a driving assembly, a scanning control unit, a high-speed analog / digital converter, a low-pass filter, an envelope detection processor, a displacement processor, an image processing algorithm module, an image drawing module and a calculation module, and provides a powerful tool for early diagnosis, accurate classification and treatment evaluation of soft tissue lesions. It is predicted that after the method is widely applied to clinical practice, the diagnosis accuracy is improved, invasive examination is reduced, reliable basis is provided for personalized medical treatment, and patients are benefited.

Description

Technical Field

[0001] The invention relates to the technical field of medical image analysis, in particular to a high-resolution ultrasonic elastic imaging quantitative analysis system. Background Art

[0002] Ultrasonic elastography, as a non-invasive method for assessing soft tissue stiffness, has received extensive attention in the field of clinical diagnosis in recent years. Traditional ultrasonic elastography systems usually use a probe frequency of 5-10 MHz to assess the stiffness of tissue by measuring the deformation of tissue under external force. However, this method still has many limitations in terms of spatial resolution, image quality, and quantitative analysis capabilities.

[0003] At present, the closest existing technologies usually use a single elastic parameter calculation method, such as the shear wave velocity method or the strain rate method. These methods can provide quantitative information on tissue hardness under specific conditions, but their accuracy and reliability are often unsatisfactory for complex lesion structures, especially early micro-lesions. In addition, existing technologies also face challenges in signal processing and image reconstruction. Due to the use of a single-batch transmission and reception strategy, the system has a low temporal resolution and it is difficult to capture the rapid deformation process of the tissue. At the same time, traditional signal processing algorithms have limited effects in denoising and contrast enhancement, resulting in poor quality of the final generated elastic image, affecting the accuracy of diagnosis.

[0004] Another significant problem is that existing systems lack comprehensive elastic parameter evaluation capabilities. Most systems only provide a single hardness index, such as elastic modulus value, while ignoring the spatial characteristics and dynamic changes of tissue elastic distribution. This simplified evaluation method is difficult to fully reflect the essential characteristics of complex lesions and is prone to misdiagnosis or missed diagnosis. Summary of the invention

[0005] In view of the above problems, there is an urgent need for an ultrasonic elastic imaging system that can provide high-resolution, high-quality elastic images and has comprehensive quantitative analysis capabilities. The present invention aims to solve the technical problems of low spatial resolution, poor image quality, and insufficient quantitative analysis capabilities in the prior art, and proposes an innovative high-resolution ultrasonic elastic imaging quantitative analysis system.

[0006] The present invention proposes a high-resolution ultrasonic elastic imaging quantitative analysis system, comprising:

[0007] Ultrasonic unit for:

[0008] Transmit high-frequency ultrasonic waves to the tissue being tested;

[0009] Receive reflected ultrasonic waves from the tissue being tested;

[0010] An image processing unit is electrically connected to the ultrasound unit and is used to:

[0011] receiving a reflected ultrasonic signal sent by the ultrasonic unit;

[0012] Based on the reflected ultrasonic signal, generating a displacement wave graph;

[0013] The elasticity analysis unit is electrically connected to the image processing unit and is used to:

[0014] Receiving the displacement wave graph sent by the image processing unit;

[0015] Calculating tissue elasticity value based on the displacement wave graph;

[0016] generating elastic images;

[0017] Wherein, the ultrasonic unit comprises:

[0018] High-frequency probe, used to transmit and receive ultrasonic waves;

[0019] A high-frequency driving component, electrically connected to the high-frequency probe, and used to drive the high-frequency probe;

[0020] A scanning control unit, electrically connected to the high-frequency driving component, and used to control the operation of the high-frequency driving component;

[0021] The image processing unit comprises:

[0022] A high-speed analog / digital converter, used for converting the reflected ultrasonic signal into a digital signal;

[0023] A low-pass filter, electrically connected to the high-speed analog / digital converter, for performing high-frequency filtering on the digital signal;

[0024] An envelope detection processor, electrically connected to the low-pass filter, and configured to perform envelope detection on the filtered signal;

[0025] A displacement processor, electrically connected to the envelope detection processor, for processing the envelope signal to generate a displacement wave graph;

[0026] The elasticity analysis unit comprises:

[0027] Multiple pairs of image processing algorithm modules are used to establish the corresponding relationship between ultrasonic motion characteristics and elasticity values;

[0028] An image rendering module, electrically connected to the plurality of pairs of image processing algorithm modules, for filtering and denoising the displacement image;

[0029] The calculation module is electrically connected to the image drawing module and is used to calculate the elasticity value and generate the elasticity image.

[0030] Preferably, the scanning control unit comprises:

[0031] A pulse emission control program, used to control the high-frequency probe to emit ultrasonic waves in batches;

[0032] A pulse receiving control program, used to control the high-frequency probe to receive reflected ultrasonic waves in batches;

[0033] Image recording program, used to record ultrasonic echo signals.

[0034] Preferably, the high-frequency probe is a high-frequency convex array probe, comprising:

[0035] A transmitting transducer array, used for transmitting ultrasonic waves;

[0036] A receiving transducer array, used for receiving reflected ultrasonic waves;

[0037] Wherein, the transmitting transducer array is arranged before the receiving transducer array, and the area of ​​the receiving transducer array is larger than the area of ​​the transmitting transducer array.

[0038] Preferably, the multiple pairs of image processing algorithm modules include:

[0039] The shear wave velocity correspondence module is used to establish the correspondence between the shear wave velocity and the elastic value;

[0040] The displacement peak correspondence module is used to establish the correspondence between the displacement peak and the elasticity value.

[0041] Preferably, the computing module is used for:

[0042] Calculating tissue elasticity value based on the shear wave velocity correspondence module or the displacement peak correspondence module;

[0043] A plurality of elastic images are generated, each of which corresponds to a pair of shear wave velocity correspondences and a pair of displacement peak correspondences.

[0044] Preferably, the displacement processor is used for:

[0045] Use a low-pass filter to perform high-frequency filtering on the echo signal to obtain an envelope signal;

[0046] Depending on the direction of ultrasonic movement, envelope peak subtraction or envelope valley subtraction is used to obtain displacement data.

[0047] As a preferred feature, it is characterized in that it also includes:

[0048] A signal preprocessing module, electrically connected to the image processing unit, and used for performing noise reduction processing on the filtered signal;

[0049] Wherein, the processing function of the signal preprocessing module is:

[0050] f(x)=Asin(ωt)exp(-λωt),

[0051] Where f(x) is the high-frequency filter output signal, λ is the attenuation constant, is the damping constant, and Asin(ωt) is the excitation signal.

[0052] Preferably, the elasticity analysis unit further comprises:

[0053] The elastic parameter evaluation module is electrically connected to the calculation module and is used to:

[0054] Establish a three-dimensional strain distribution database;

[0055] Evaluation of three-dimensional strain distribution;

[0056] Evaluate the abnormal areas of three-dimensional strain distribution, including shape, volume, tissue area and their ratios.

[0057] As a preferred embodiment, it also includes:

[0058] An imaging output module is electrically connected to the elasticity analysis unit and is used to:

[0059] Set the output image's slice position, pixel size, sampling depth, image size, frame rate and other system parameters;

[0060] Get echo and related parameters;

[0061] Set the initialization image parameter value and output the initialization image;

[0062] Reconstruct elastic parameter images.

[0063] As a preferred embodiment, it also includes:

[0064] The lesion parameter measurement module is electrically connected to the elasticity analysis unit and is used to:

[0065] Obtaining elastic parameter values ​​of the lesion area of ​​the target tissue;

[0066] Obtain the mean value of the elastic parameter of the lesion;

[0067] Obtaining the distribution of elastic parameter values ​​of the lesion;

[0068] Determine the characteristic value of the elastic parameter value of the lesion;

[0069] Output the elasticity difference between the lesion area and soft tissue;

[0070] Get the coordinates of the area of ​​interest;

[0071] Output the target tissue, lesion location, lesion area distribution shape and lesion area shape change.

[0072] The present invention achieves efficient collaboration among functional modules through the optimized design of the overall system architecture. The high-frequency ultrasonic unit combined with the innovative signal processing algorithm significantly improves the spatial and temporal resolution of the system. The introduction of multi-modal elastic analysis methods solves the limitations of traditional single evaluation methods and enables the system to more comprehensively and accurately reflect the elastic characteristics of tissues. This multi-dimensional technological innovation not only brings about a comprehensive improvement in performance indicators, but more importantly, provides richer and more reliable information for clinical diagnosis.

[0073] From a macroscopic perspective, the high-resolution imaging capability of the system of the present invention opens up new possibilities for the detection of early micro-lesions. For example, in breast cancer screening, the system can identify sub-millimeter lesions that are difficult to detect with traditional methods, greatly improving the early diagnosis rate. At the same time, the real-time performance of the system enables doctors to dynamically observe the elastic changes of tissues, which is of great significance for evaluating the invasiveness of tumors and formulating personalized treatment plans.

[0074] From a microscopic perspective, the invention's innovations in signal processing and data analysis bring multiple benefits. The innovative signal preprocessing algorithm not only improves the signal-to-noise ratio and contrast of the image, but also effectively suppresses various artifacts, making the elastic image clearer and more reliable. The application of multimodal elastic analysis methods enables the system to evaluate the elastic characteristics of tissues from different angles, providing more comprehensive lesion information. This multi-dimensional analysis capability has unique advantages for the accurate diagnosis and classification of complex lesions.

[0075] In addition, the modular design and flexible parameter configuration of the system of the present invention make it widely adaptable. Whether it is the examination of superficial tissue or deep organs, the system can optimize performance by adjusting relevant parameters. This adaptability not only improves the efficiency of the system, but also lays the foundation for future functional expansion and upgrading.

[0076] In summary, the high-resolution ultrasonic elastic imaging quantitative analysis system of the present invention has achieved a comprehensive improvement in spatial resolution, image quality and quantitative analysis capabilities through various technical innovations. These improvements not only overcome many limitations of the prior art, but also provide a powerful tool for early diagnosis, accurate classification and treatment evaluation of soft tissue lesions. With the widespread application of the system in clinical practice, it is expected to significantly improve the diagnostic accuracy of related diseases, reduce unnecessary invasive examinations, and provide a more reliable basis for the formulation of personalized medical plans, ultimately benefiting a large number of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is the overall workflow diagram of the present invention;

[0078] Figure 2It is a sub-logic block diagram of the ultrasound unit 1 of the present invention;

[0079] Figure 3 It is a sub-logic block diagram of the elasticity analysis unit 3 of the present invention; DETAILED DESCRIPTION

[0080] Please refer to the attached Figure 1-3 The present invention provides a high-resolution ultrasonic elastic imaging quantitative analysis system, which includes an ultrasonic unit 1, an image processing unit 2 and an elasticity analysis unit 3. These three main units work together to achieve high-precision quantitative analysis of tissue elasticity.

[0081] The ultrasonic unit 1 is the basis of the system, which is used to transmit high-frequency ultrasonic waves to the tissue under test and receive reflected waves. The unit includes a high-frequency probe 11, a high-frequency drive component 12 and a scanning control unit 13. The high-frequency probe 11 is responsible for the transmission and reception of ultrasonic waves, and its operating frequency is usually in the range of 5-20MHz, which is higher than the 1-5MHz of traditional ultrasonic probes, thereby ensuring higher spatial resolution. The high-frequency drive component 12 provides a driving signal for the high-frequency probe 11 to control its working state. The scanning control unit 13 is responsible for overall coordination to ensure efficient operation of the system.

[0082] Preferably, the scanning control unit 13 includes a pulse emission control program 131, a pulse receiving control program 132 and an image recording program 133. The pulse emission control program 131 controls the high-frequency probe 11 to emit ultrasound waves in batches. This batch emission method can effectively improve the time resolution of the system. For example, the probe array elements can be divided into 4-8 batches, and each batch is emitted at an interval of 20-50 μs. The pulse receiving control program 132 controls the high-frequency probe 11 to receive reflected ultrasound waves in batches accordingly. The image recording program 133 is responsible for recording ultrasound echo signals to provide raw data for subsequent processing.

[0083] In one embodiment of the present invention, the high-frequency probe 11 adopts a high-frequency convex array design, including a transmitting transducer array 111 and a receiving transducer array 112. The transmitting transducer array 111 is arranged before the receiving transducer array 112, and the area of ​​the receiving transducer array 112 is larger than the area of ​​the transmitting transducer array 111. This design can significantly improve the signal reception quality of the system. For example, the transmitting transducer array 111 can include 64-128 array elements, and the receiving transducer array 112 can include 128-256 array elements, and the receiving area is 20%-50% larger than the transmitting area.

[0084] The image processing unit 2 is electrically connected to the ultrasonic unit 1 and is responsible for processing the received reflected ultrasonic signal and generating a displacement wave graph. The unit includes a high-speed analog / digital converter 21, a low-pass filter 22, an envelope detection processor 23 and a displacement processor 24. The high-speed analog / digital converter 21 converts the analog ultrasonic echo signal into a digital signal, and its sampling rate is usually in the range of 50-100MHz to ensure the integrity of the signal. The low-pass filter 22 filters the high-frequency noise of the digital signal, and the typical cut-off frequency may be around 20-30MHz. The envelope detection processor 23 extracts the signal envelope, and the displacement processor 24 generates a displacement wave graph based on the envelope signal.

[0085] In another embodiment of the present invention, the displacement processor 24 adopts an innovative processing method. It first uses a low-pass filter to perform high-frequency filtering on the echo signal to obtain an envelope signal. Then, according to the direction of ultrasonic movement, the displacement data is obtained using envelope peak subtraction or envelope valley subtraction. This method can effectively improve the accuracy and stability of the displacement data.

[0086] The elasticity analysis unit 3 is the core of the system, responsible for calculating the tissue elasticity value based on the displacement wave map and generating an elastic image. The unit includes multiple pairs of image processing algorithm modules 31, image drawing modules 32 and calculation modules 33. Multiple pairs of image processing algorithm modules 31 establish the correspondence between ultrasonic motion characteristics and elasticity values, which is the key to achieving accurate quantitative analysis. The image drawing module 32 filters and reduces noise on the displacement image to improve image quality. The calculation module 33 is responsible for the final elasticity value calculation and elastic image generation.

[0087] The system of the present invention realizes high-resolution, real-time, quantitative analysis of tissue elasticity through the coordinated work of the above-mentioned units. Compared with the traditional ultrasonic elastic imaging system, the present invention has significantly improved spatial resolution, temporal resolution and quantitative analysis capabilities, providing a new technical solution for the accurate evaluation of soft tissue elasticity.

[0088] In the system of the present invention, multiple pairs of image processing algorithm modules 31 are the key to achieving accurate elastic analysis. The module includes a shear wave velocity correspondence module 311 and a displacement peak correspondence module 312. These two submodules respectively establish the correspondence between the shear wave velocity and the elastic value, and the displacement peak and the elastic value, providing a theoretical basis for the subsequent elastic value calculation.

[0089] Preferably, the shear wave velocity correspondence module 311 adopts an algorithm based on shear wave propagation theory. In the algorithm, there is the following relationship between the shear wave velocity c and the shear elastic modulus μ of the tissue:

[0090]

[0091] Where ρ is the tissue density. By measuring the shear wave velocity, the shear elastic modulus of the tissue can be directly calculated, thereby evaluating the hardness of the tissue.

[0092] The displacement peak correspondence module 312 utilizes the deformation characteristics of tissue under external force. When external pressure is applied, harder tissue deforms less, while softer tissue deforms more. By analyzing the displacement peak, the elasticity of the tissue can be indirectly evaluated. In one embodiment of the present invention, the following relationship is used:

[0093]

[0094] Where E is the elastic modulus, F is the applied force, ΔL is the displacement, and k is a constant related to tissue geometry.

[0095] The calculation module 33 calculates the tissue elasticity value and generates an elastic image based on the outputs of the two correspondence modules. The system of the present invention adopts an innovative dual-mode calculation method, and can choose to use the shear wave velocity correspondence or the displacement peak correspondence for calculation according to actual needs. This flexible design greatly improves the applicability of the system and can cope with different types of tissues and pathological conditions.

[0096] In a preferred embodiment of the present invention, the calculation module 33 can also generate multiple elastic images, each of which corresponds to a pair of shear wave velocity correspondences and a pair of displacement peak correspondences. This multi-image output method provides more comprehensive information for clinical diagnosis and helps to improve the accuracy of diagnosis. For example, for elastic imaging of breast tissue, an elastic image based on shear wave velocity and an elastic image based on displacement peak can be generated, and the combination of the two can more accurately identify and locate tumors.

[0097] The system of the present invention also includes a signal preprocessing module 4, which is electrically connected to the image processing unit 2. The module is used to perform noise reduction processing on the filtered signal to further improve the signal quality. The signal preprocessing module 4 adopts an innovative processing function:

[0098] f(x)=Asin(ωt)e -λωt ,

[0099] In this function, f(x) is the high-frequency filter output signal, Asin(ωt) is the excitation signal, and e -λωt is the attenuation term. λ is the attenuation constant, usually ranging from 0.1 to 0.5, ω is the angular frequency, and t is the time. is the damping constant, which is used to control the attenuation speed of the signal. This processing method can effectively remove high-frequency noise while retaining the useful information of the signal.

[0100] Preferably, the parameters of the signal preprocessing module 4 can be adjusted according to different application scenarios. For example, for imaging of superficial tissues, a larger λ value (such as 0.4-0.5) can be selected to quickly attenuate high-frequency components; while for imaging of deep tissues, a smaller λ value (such as 0.1-0.2) can be selected to retain more signal energy.

[0101] The system of the present invention realizes high-precision quantitative analysis of tissue elasticity through the collaborative work of the above modules. Compared with the traditional ultrasonic elastic imaging technology, this system has significant innovations in signal processing, elastic analysis algorithm and image generation, and provides a new technical solution for the accurate evaluation of soft tissue elasticity. This high-resolution, real-time and quantitative elastic analysis capability is of great significance for clinical applications such as early tumor diagnosis, surgical planning and treatment effect evaluation.

[0102] The system of the present invention also includes an innovative elastic parameter evaluation module 34 in the elasticity analysis unit 3. The module is electrically connected to the calculation module 33 and is used to perform an in-depth evaluation of the calculated elastic parameters, thereby providing more comprehensive and accurate tissue elasticity information.

[0103] The elastic parameter evaluation module 34 first establishes a three-dimensional strain distribution database. This database contains a large amount of strain distribution data of different types of tissues under various conditions, providing a reference standard for subsequent evaluation. Preferably, the database can be classified according to different tissue types (such as breast, liver, thyroid, etc.) and updated regularly to include the latest clinical data.

[0104] In one embodiment of the present invention, the process of evaluating the three-dimensional strain distribution by the elastic parameter evaluation module 34 includes the following steps: First, the obtained strain distribution is compared with the standard distribution in the database to calculate the similarity score. Then, the elastic state of the tissue is preliminarily judged based on the similarity score. For example, if the similarity score is higher than 0.9 (the full score is 1), it can be preliminarily judged as normal tissue; if the score is between 0.7-0.9, there may be a slight abnormality; if the score is lower than 0.7, there is a high probability of obvious lesions.

[0105] Furthermore, the elastic parameter evaluation module 34 can also evaluate the abnormal area of ​​the three-dimensional strain distribution. This process involves the analysis of multiple parameters, including shape, volume, tissue area and their ratio. For example, for the evaluation of breast tissue, if the abnormal area is found to be irregular in shape and the ratio of its volume to the surrounding normal tissue exceeds 0.2, it may indicate a malignant tumor. On the contrary, if the abnormal area is circular or elliptical and the volume ratio is less than 0.1, it is more likely to be a benign lesion.

[0106] The system of the present invention also includes an imaging output module 5, which is electrically connected to the elasticity analysis unit 3. This module is responsible for converting the analysis results into intuitive images, which are convenient for doctors to make diagnoses and patients to understand. The workflow of the imaging output module 5 includes the following key steps:

[0107] First, set the parameters of the output image, including the section position, pixel size, sampling depth, image size, and frame rate. The selection of these parameters directly affects the quality and information content of the final image. Preferably, for most soft tissue imaging, the pixel size can be set to 0.1-0.2mm, the sampling depth to 4-6cm, and the frame rate to 10-20fps. These settings can achieve real-time imaging while ensuring image quality.

[0108] Secondly, the echo and related parameters are acquired. This step involves data interaction with the ultrasound unit 1 and the image processing unit 2 to ensure that all necessary information is transmitted and processed correctly.

[0109] Again, set the initialization image parameter values ​​and output the initialization image. This step provides the basis for the subsequent elastic image reconstruction. The initialization image usually uses a B-ultrasound image to show the anatomical structure of the tissue.

[0110] Finally, the elastic parameter image is reconstructed. This is the most critical step of the imaging output module 5, which converts the elastic parameters calculated previously into a visual image. In one embodiment of the present invention, a color coding method is used to represent different elasticity values. For example, blue can be used to represent harder tissues, red can represent softer tissues, and intermediate hardness can be represented by transitional colors such as green and yellow. This intuitive color coding method can help doctors quickly identify suspicious areas.

[0111] In order to further improve the diagnostic capability of the system, the present invention also includes a lesion parameter measurement module 6. This module is electrically connected to the elasticity analysis unit 3 and is specifically used to perform a detailed analysis of suspicious lesions. The functions of the lesion parameter measurement module 6 include:

[0112] 1. Obtain the elastic parameter values ​​of the target tissue lesion area. This step involves extracting and analyzing the elastic data of a specific area.

[0113] 2. Calculate the mean value of the elastic parameters of the lesion. This mean value can be used as an important indicator to determine the nature of the lesion.

[0114] 3. Analyze the distribution of elastic parameter values ​​of the lesion. By studying the distribution of parameters, more information about the internal structure of the lesion can be obtained.

[0115] 4. Determine the characteristics of the lesion elasticity parameter values. This step involves a complex pattern recognition algorithm that compares the measured parameters with known pathological types.

[0116] 5. Output the elastic difference between the lesion area and the surrounding soft tissue. This difference value is one of the key indicators for judging whether the lesion is benign or malignant.

[0117] 6. Get the coordinates of the area of ​​interest. This provides the basis for subsequent precise positioning and tracking.

[0118] 7. Output the target tissue and lesion location information, including the distribution shape and shape change of the lesion area. This information is important for evaluating the invasiveness of the lesion and formulating treatment plans.

[0119] Preferably, the lesion parameter measurement module 6 can also give preliminary diagnostic suggestions based on the measurement results. For example, if the elasticity value of the lesion is more than 3 times higher than that of the surrounding normal tissue, and the shape is irregular and the boundaries are blurred, the system will prompt "highly suspicious, biopsy is recommended". This intelligent diagnostic suggestion can provide valuable reference for clinicians and improve the accuracy and efficiency of diagnosis.

[0120] In general, the high-resolution ultrasonic elastic imaging quantitative analysis system provided by the present invention realizes high-precision, real-time, quantitative analysis of soft tissue elasticity through the collaborative work of multiple innovative modules. The system can not only generate high-quality elastic images, but also provide detailed parameter analysis and intelligent diagnostic suggestions, providing strong support for clinical diagnosis, surgical planning, and treatment effect evaluation. This comprehensive and accurate elastic analysis capability has important clinical value in improving the diagnosis rate of early tumors, reducing unnecessary invasive examinations, and optimizing treatment plans.

[0121] In order to verify the superiority of the high-resolution ultrasonic elastic imaging quantitative analysis system of the present invention, a set of simulation experiments was designed to simulate the presence of a tumor in breast tissue. The experiment used three different schemes for comparison: the embodiment of the present invention, the traditional ultrasonic elastic imaging system (Comparative Example 1) and the pure B-ultrasound imaging system (Comparative Example 2).

[0122] The experimental conditions are as follows:

[0123] Simulation software: FIELD II ultrasound simulation platform;

[0124] Simulated tissue: 20mm×20mm×20mm uniform background, simulating normal breast tissue;

[0125] Simulated tumor: a spherical area with a diameter of 5 mm, located in the center;

[0126] Tissue parameters: background elastic modulus was 20 kPa, tumor elastic modulus was 60 kPa;

[0127] External pressure: 0.5% axial compression;

[0128] The embodiment of the present invention adopts a 20MHz high-frequency probe, a multi-batch transmission and reception strategy, and an innovative elastic parameter evaluation algorithm. Comparative Example 1 uses a traditional 7.5MHz probe and a single-batch transmission and reception method. Comparative Example 2 only performs B-ultrasound imaging and does not include elasticity analysis function.

[0129] The following indicators are focused on to evaluate system performance: spatial resolution, contrast, signal-to-noise ratio (SNR), and tumor detection rate. The test methods and standards for these indicators are as follows:

[0130] 1. Spatial resolution: Using the point spread function (PSF) method, measuring the -6dB width.

[0131] 2. Contrast: Calculate the ratio of the difference between the average grayscale values ​​of the tumor area and the background area to the sum.

[0132] 3. Signal-to-noise ratio: Calculate the ratio of the average signal intensity of the region of interest to the standard deviation of the background noise.

[0133] 4. Tumor detection rate: The percentage of times a tumor is correctly identified in 100 independent simulations.

[0134] The test results are shown in the following table:

[0135]

[0136]

[0137] It can be seen from the test results that the embodiments of the present invention are significantly better than the comparative examples in all indicators. In particular, in terms of spatial resolution, the present invention achieves a high resolution of 0.1 mm, thanks to the high-frequency probe and innovative signal processing algorithm. High resolution enables the system to identify smaller lesions, which is of great significance for the diagnosis of early tumors.

[0138] The present invention also shows obvious advantages in terms of contrast and signal-to-noise ratio. This is mainly due to the multi-batch transmission and reception strategy and the advanced signal preprocessing module. High contrast and signal-to-noise ratio mean that the system can more clearly distinguish tissues of different hardness and improve the accuracy of diagnosis.

[0139] The most noteworthy thing is the significant improvement in tumor detection rate. The embodiment of the present invention achieved a detection rate of 98%, which is much higher than the traditional method. This result fully demonstrates the innovation and effectiveness of the present invention in elastic parameter evaluation and lesion analysis. The high detection rate means that the missed diagnosis rate can be greatly reduced, which is of great significance to improving the level of clinical diagnosis.

[0140] In addition, although not directly reflected in the table, the real-time performance of the present invention is also better than that of the traditional method. Thanks to the efficient data processing algorithm and optimized system architecture, the present invention can achieve a real-time imaging speed of more than 20 frames per second, while the comparative example 1 can only reach about 10 frames per second.

[0141] In summary, the high-resolution ultrasonic elastic imaging quantitative analysis system of the present invention has shown significant advantages in spatial resolution, image quality and diagnostic accuracy. These advantages are due to the breakthroughs in the system's high-frequency probe design, multi-batch signal processing, and innovative elastic analysis algorithms. This comprehensive performance improvement can not only help doctors detect and diagnose lesions earlier and more accurately, but also provide a more reliable basis for formulating personalized treatment plans, thereby significantly improving the level of clinical diagnosis and treatment.

[0142] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. High-resolution ultrasonic elastic imaging quantitative analysis system, characterized by ,include: Ultrasonic unit for: Transmit high-frequency ultrasonic waves to the tissue being tested; Receive reflected ultrasonic waves from the tissue being tested; An image processing unit is electrically connected to the ultrasound unit and is used to: receiving a reflected ultrasonic signal sent by the ultrasonic unit; Based on the reflected ultrasonic signal, generating a displacement wave graph; The elasticity analysis unit is electrically connected to the image processing unit and is used to: Receiving the displacement wave graph sent by the image processing unit; Calculating tissue elasticity value based on the displacement wave graph; generating elastic images; Wherein, the ultrasonic unit comprises: High-frequency probe, used to transmit and receive ultrasonic waves; A high-frequency driving component, electrically connected to the high-frequency probe, and used to drive the high-frequency probe; A scanning control unit, electrically connected to the high-frequency driving component, and used to control the operation of the high-frequency driving component; The image processing unit comprises: A high-speed analog / digital converter, used for converting the reflected ultrasonic signal into a digital signal; A low-pass filter, electrically connected to the high-speed analog / digital converter, for performing high-frequency filtering on the digital signal; An envelope detection processor, electrically connected to the low-pass filter, and configured to perform envelope detection on the filtered signal; A displacement processor, electrically connected to the envelope detection processor, for processing the envelope signal to generate a displacement wave graph; The elasticity analysis unit comprises: Multiple pairs of image processing algorithm modules are used to establish the corresponding relationship between ultrasonic motion characteristics and elasticity values; An image rendering module, electrically connected to the plurality of pairs of image processing algorithm modules, for filtering and denoising the displacement image; The calculation module is electrically connected to the image drawing module and is used to calculate the elasticity value and generate the elasticity image.

2. The system according to claim 1, characterized in that , the scanning control unit comprises: A pulse emission control program, used to control the high-frequency probe to emit ultrasonic waves in batches; A pulse receiving control program, used to control the high-frequency probe to receive reflected ultrasonic waves in batches; Image recording program, used to record ultrasonic echo signals.

3. The system according to claim 1, characterized in that , the high-frequency probe is a high-frequency convex array probe, comprising: A transmitting transducer array, used for transmitting ultrasonic waves; A receiving transducer array, used for receiving reflected ultrasonic waves; Wherein, the transmitting transducer array is arranged before the receiving transducer array, and the area of ​​the receiving transducer array is larger than the area of ​​the transmitting transducer array.

4. The system according to claim 1, characterized in that , the multiple pairs of image processing algorithm modules include: The shear wave velocity correspondence module is used to establish the correspondence between the shear wave velocity and the elastic value; The displacement peak correspondence module is used to establish the correspondence between the displacement peak and the elasticity value.

5. The system according to claim 4, characterized in that , the calculation module is used for: Calculating tissue elasticity value based on the shear wave velocity correspondence module or the displacement peak correspondence module; A plurality of elastic images are generated, each of which corresponds to a pair of shear wave velocity correspondences and a pair of displacement peak correspondences.

6. The system according to claim 1, characterized in that , the displacement processor is used to: Use a low-pass filter to perform high-frequency filtering on the echo signal to obtain an envelope signal; Depending on the direction of ultrasonic movement, envelope peak subtraction or envelope valley subtraction is used to obtain displacement data.

7. The system according to claim 1, characterized in that , also includes: A signal preprocessing module, electrically connected to the image processing unit, and used for performing noise reduction processing on the filtered signal; Wherein, the processing function of the signal preprocessing module is: f(x)=Asin(ωt)exp(-λωt), Where f(x) is the high-frequency filter output signal, λ is the attenuation constant, is the damping constant, and Asin(ωt) is the excitation signal.

8. The system according to claim 1, characterized in that , the elasticity analysis unit also includes: The elastic parameter evaluation module is electrically connected to the calculation module and is used to: Establish a three-dimensional strain distribution database; Evaluation of three-dimensional strain distribution; Evaluate the abnormal areas of three-dimensional strain distribution, including shape, volume, tissue area and their ratios.

9. The system according to claim 1, characterized in that , also includes: An imaging output module is electrically connected to the elasticity analysis unit and is used to: Set the output image's slice position, pixel size, sampling depth, image size, frame rate and other system parameters; Get echo and related parameters; Set the initialization image parameter value and output the initialization image; Reconstruct elastic parameter images.

10. The system according to claim 1, characterized in that , also includes: The lesion parameter measurement module is electrically connected to the elasticity analysis unit and is used to: Obtaining elastic parameter values ​​of the lesion area of ​​the target tissue; Obtain the mean value of the elastic parameter of the lesion; Obtaining the distribution of elastic parameter values ​​of the lesion; Determine the characteristic value of the elastic parameter value of the lesion; Output the elasticity difference between the lesion area and soft tissue; Get the coordinates of the area of ​​interest; Output the target tissue, lesion location, lesion area distribution shape and lesion area shape change.

Images