A hand arthritis simulation teaching system based on ultrasonic images

Abstract

The application discloses a hand arthritis simulation teaching system based on ultrasonic images, relates to the technical field of medical simulation teaching, and aims to solve the technical problem that the existing hand arthritis ultrasonic teaching tool cannot dynamically display the complete evolution process from no urate deposition to point-shaped echo to continuous high echo double-line due to the lack of a calculation model that combines the pathophysiological process and the ultrasonic acoustic physical law, and comprises an image generation module used for generating an ultrasonic image simulating a pathological state of hand arthritis, wherein the image generation module comprises a urate deposition modeling unit used for constructing a composite acoustic model of urate scatterers and cartilage matrix. The application has the advantages that the acoustic model can be dynamically driven according to pathophysiological input variables to generate a double-track sign evolution image sequence with physical consistency, thereby realizing dynamic simulation of the whole process of characteristic ultrasonic signs of gouty arthritis.

Description

Technical Field

[0001] This invention relates to the field of medical simulation teaching technology, and more specifically, to a simulation teaching system for hand arthritis based on ultrasound imaging. Background Technology

[0002] Ultrasound imaging diagnosis of hand arthritis is a core clinical skill that rheumatologists and ultrasound specialists must master. Taking gouty arthritis as an example, its most diagnostically valuable ultrasound sign is the "double-track sign," which is a hyperechoic line formed by the deposition of urate crystals on the surface of the articular hyaline cartilage. This line is parallel to the hyperechoic line of the underlying bone cortex, forming a characteristic double-track appearance on ultrasound images. Accurate identification of this sign is of crucial clinical significance for the early diagnosis and differential diagnosis of gouty arthritis.

[0003] Currently, teaching and training in ultrasound imaging for hand arthritis mainly relies on two methods: physical ultrasound simulation models and static ultrasound image libraries. Physical simulation models are pre-fabricated using biomimetic materials to represent fixed pathological morphologies, which trainees then scan with real ultrasound probes to obtain simulated images. Static image libraries provide pre-acquired case images for trainees to browse and study. However, both of these existing teaching tools share a common fundamental flaw: they present a single, static state of the disease and fail to demonstrate the complete dynamic pathological evolution of urate deposition on the articular cartilage surface, from its initial absence to the eventual formation of continuous hyperechoic double lines. Trainees are presented with a pre-existing "result" but cannot observe how this result developed step by step from a normal physiological state.

[0004] From a deeper technical perspective, the root cause of this deficiency lies in the lack of a computational model in existing teaching tools that can organically integrate the pathophysiological process of arthritis with the physical laws of ultrasound imaging. Specifically, the deposition of urate on the cartilage surface is a dynamic process driven by multiple pathophysiological factors, including synovial fluid uric acid concentration and joint movement. Changes in the amount of deposition alter the acoustic properties of the cartilage surface, which are reflected in the evolution of echo morphology, brightness, and posterior acoustic shadowing in ultrasound images. Existing teaching tools fail to establish a complete mapping relationship from "pathophysiological drive" to "acoustic characteristic response," resulting in simulated ultrasound images that remain only static approximations at the morphological level, unable to generate physically consistent ultrasound image sequences in real time based on dynamic changes in pathophysiological parameters. This technical deficiency makes it difficult for learners to understand the disease progression mechanism and to grasp the intrinsic connection between the pathophysiological significance and acoustic physical laws behind ultrasound image signs. In view of this, we propose a hand arthritis simulation teaching system based on ultrasound imaging. Summary of the Invention

[0005] The purpose of this invention is to provide a simulation teaching system for hand arthritis based on ultrasound imaging, in order to solve the technical problem that existing ultrasound teaching tools for hand arthritis lack a computational model that integrates the pathophysiological process with the physical laws of ultrasound acoustics, resulting in the inability to dynamically display the complete evolution process of urate deposition from non-existence to presence, and from punctate echoes to continuous high-echo double lines.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a hand arthritis simulation teaching system based on ultrasound imaging, comprising:

[0007] An image generation module is used to generate ultrasound images simulating the pathological state of hand arthritis. The image generation module includes:

[0008] The urate deposition modeling unit is used to construct a composite acoustic model of urate scatterers and cartilage matrix. The composite acoustic model defines the impedance difference between the acoustic impedance characteristics of urate scatterers and the acoustic impedance characteristics of cartilage matrix, and establishes the physical correspondence between the volume fraction of scatterers and the morphology of deposition layers.

[0009] The deposition kinetics calculation unit, connected to the urate deposition modeling unit, is used to dynamically calculate the deposition flux of urate scatterers on the surface of articular cartilage based on the received pathophysiological input variables, and output the spatial distribution of scatterer volume fraction over time.

[0010] The ultrasound mapping unit, connected to the deposition dynamics calculation unit, is used to calculate and generate an ultrasound image frame reflecting the current deposition state based on a composite acoustic model according to the spatial distribution of the scatterer volume fraction and the preset ultrasound probe frequency. The ultrasound mapping unit dynamically determines whether the image presentation mode of the local area is discrete point echoes or continuous hyperecho lines based on the comparison between the local volume fraction and the preset fusion threshold. When continuous hyperecho lines are generated, the hyperecho lines remain parallel to the bone cortex echo lines in the image.

[0011] The sequence generation unit, connected to the ultrasound mapping unit, is used to combine a series of ultrasound image frames generated in chronological order into an image sequence that shows the dynamic evolution of urate deposition from nothing to something, from punctate echoes to the formation of continuous hyperechoic double lines parallel to the bone cortex.

[0012] A display module is used to present the image sequence.

[0013] Preferably, in the composite acoustic model constructed by the urate deposition modeling unit, the acoustic impedance value of the urate scatterer is configured to be higher than that of the cartilage matrix, so as to form the strong impedance mismatch condition required to produce high echo performance.

[0014] In the ultrasonic mapping unit, when the volume fraction of scatterers in a local area is lower than the fusion threshold, the image of that area appears as discrete point-like high echoes corresponding to the degree of local scatterer aggregation, and the thickness of the deposition layer is zero at this time.

[0015] When the volume fraction of the scatterer in a local area reaches or exceeds the fusion threshold, the point-like hyperechoic areas in that area are automatically connected into a continuous hyperechoic line parallel to the underlying cortical bone echo line, and the thickness of the connected hyperechoic line is positively correlated with the local volume fraction.

[0016] The strong impedance mismatch condition is reflected through the difference in acoustic impedance. The acoustic impedance of the urate scatterer is set as... The acoustic impedance of the cartilage matrix is And satisfy This forms the impedance mismatch coefficient:

[0017] ;

[0018] The thickness of the high echo line The volume fraction of scattering in local regions The relationship between them can be represented as a piecewise function:

[0019] when hour, ;

[0020] when At that time, the image appears as discrete point-like high echoes, and the brightness of the point-like echoes... and satisfy:

[0021] ;

[0022] in, The fusion threshold, Based on thickness, For mapping coefficients, This is the reference brightness.

[0023] Preferably, the ultrasound mapping unit further includes a frequency adaptive processing subunit, which is used to adjust the display resolution of high echo structures and the representation of the acoustic shadow in the ultrasound image frame according to the preset ultrasound probe frequency, so that the image frames generated under different probe frequencies with the same scatterer volume fraction spatial distribution automatically present the corresponding acoustic feature differences.

[0024] Frequency adaptive processing is achieved through a frequency correction function, where the probe frequency is defined as... The adjustment of display resolution is reflected in the display thickness of the deposition layer. Effective visualization of thickness:

[0025] ;

[0026] in, The frequency resolution correction factor satisfies:

[0027] ;

[0028] in, For reference frequency, It is a frequency-dependent index;

[0029] The degree to which the sound and shadow are rendered from behind is determined by the sound and shadow attenuation coefficient. To take control:

[0030] ;

[0031] in, The attenuation constant is The frequency decay index;

[0032] Gray values ​​of the sound and shadow area: ;

[0033] in, The length of the sound propagation path. The background is grayscale.

[0034] Preferably, the pathophysiological input variables include synovial fluid uric acid concentration information and joint motion state information. The deposition kinetics calculation unit determines the local supersaturation based on the difference between synovial fluid uric acid concentration and urate solubility, and calculates the deposition rate of urate scattering body accordingly. At the same time, it calculates the shear stress of joint motion on the deposition layer based on the joint motion state information, and reduces the deposition flux based on the shear stress to simulate the mechanical removal effect of joint activity on urate deposition.

[0035] The deposition rate Due to local oversaturation Sure:

[0036] ;

[0037] in, This refers to the concentration of uric acid in synovial fluid. This is the saturation concentration of urate.

[0038] Deposition rate: ;

[0039] in, Let be the deposition rate constant. The supersaturation index;

[0040] The shear stress generated by the joint movement Motion velocity from joint motion state information and joint space Decide:

[0041] ;

[0042] in, The apparent viscosity of the synovial fluid;

[0043] The net deposition flux: ;

[0044] in, The modulation coefficient, This is the mechanical removal coefficient.

[0045] Preferably, the deposition dynamics calculation unit further includes a spatial bias subunit, which is used to apply a spatial bias when calculating the spatial distribution of the scatterer volume fraction according to the preset intra-articular gravity direction and the transport model based on the synovial fluid circulation path, so as to simulate the non-uniform distribution of urate deposition on different regions of the cartilage surface caused by the combined effects of gravity sedimentation and fluid flow path differences.

[0046] Spatial bias through net deposition flux Based on the superposition of transport flux term, volume fraction is achieved. Spatial evolution follows:

[0047] ;

[0048] in, The diffusion coefficient is... Let be the settling velocity of the crystal in the direction of gravity. Vertical coordinates This represents the velocity field of the slurry convection.

[0049] Preferably, it also includes an interactive input module and a quantitative measurement module. The interactive input module is used to receive user adjustment operations on pathophysiological input variables and transmit the adjusted variables to the deposition kinetics calculation unit in real time to trigger recalculation and image update.

[0050] The quantitative measurement module is used to provide a virtual measurement tool on the image frame presented by the display module to measure the apparent thickness of the deposition layer and the difference in echo intensity between two points.

[0051] Preferably, the display module further includes a linked display interface, which is used to simultaneously display an ultrasonic image frame, a schematic diagram of the distribution of microscopic scatterers corresponding to the current frame, and a physical quantity curve reflecting the evolution of the deposition state over time within the same screen area. The physical quantity curve includes the changing trends of the scatterer volume fraction and the deposition layer thickness over time.

[0052] Preferably, it also includes a reverse calibration module, which is used to receive clinical ultrasound images of the double-track sign of real gout patients as target reference images, and to solve the pathophysiological input variables of the deposition dynamics calculation unit in reverse through an iterative optimization algorithm, so that the simulated image generated by the image generation module converges with the target reference image in terms of the morphology, thickness and echo intensity of the double-track sign.

[0053] Iterative optimization algorithms minimize the objective function accomplish:

[0054] ;

[0055] in, The pixel index of the double-track feature region in the image. and Pixels in the simulated image The thickness of the sedimentary layer and the echo intensity at that location, and The measured thickness and intensity are extracted from the corresponding location in the target reference image. , These are weighting coefficients;

[0056] During the optimization process, pathophysiological input variables, including synovial fluid uric acid concentration, were adjusted. Movement velocity in joint motion state information ,make The system converges to below the preset threshold, completing the automatic calibration.

[0057] Preferably, the image generation module further includes a tophi simulation submodule, which is used to generate ultrasound features of simulated tophi in a specified soft tissue area. The simulated tophi appear as non-uniform hyperechoic masses in the image. The boundaries of the hyperechoic masses are either clear or irregular, and the hyperechoic masses contain hyperechoic punctate structures with acoustic shadowing behind them.

[0058] Preferably, the image generation module further includes a bone erosion simulation submodule, an osteophyte simulation submodule, a spiculated destruction simulation submodule, and a pus accumulation simulation submodule;

[0059] The bone erosion simulation submodule is used to generate discontinuous concave defect areas on the surface of the bone cortex of a specified joint. The concave defect areas can be displayed on two mutually perpendicular cross-sections, with low-echo filling inside and irregular contours at the edges.

[0060] The osteophyte simulation submodule is used to generate a hyperechoic structure protruding outward from the bone cortex at the edge of the interphalangeal joint. The base of the hyperechoic structure is continuous with the bone cortex of the parent bone, and its morphology is selected from the morphology group composed of lip-like, spike-like and plateau-like.

[0061] The burr-like destruction simulation submodule is used to generate irregular hyperechoic protrusions at the edge of the phalanx. The morphology of the hyperechoic protrusions is selected from a group of morphologies consisting of needle-like and brush-like shapes, and the hyperechoic protrusions contrast with the smooth normal cortical bone area.

[0062] The pus accumulation simulation submodule is used to generate a non-uniform echo region within the joint cavity. The echo region is either a hypoechoic region or an anechoic region. The echo region contains fine punctate echoes and flocculent echoes that simulate the characteristics of pus, and the joint capsule appears to be in a swollen state.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] 1. This invention, through a technical architecture that integrates a urate deposition modeling unit and a deposition kinetics calculation unit, establishes for the first time a complete mapping link from pathophysiological drivers to acoustic image generation in the field of ultrasound simulation teaching for hand arthritis. Specifically, the urate deposition modeling unit defines the acoustic impedance difference between the urate scatterer and the cartilage matrix and establishes a physical correspondence between the scatterer volume fraction and the morphological appearance of the deposition layer; the deposition kinetics calculation unit receives pathophysiological input variables such as synovial fluid uric acid concentration and joint motion state, dynamically calculates the deposition flux of the scatterer on the cartilage surface, and outputs the spatiotemporal distribution of the volume fraction; the ultrasound mapping unit automatically switches between discrete point echoes and continuous parallel hyperechoic lines as image presentation modes based on a comparison of the local volume fraction with a preset fusion threshold. The collaborative operation of these units enables the system to fully demonstrate the complete dynamic evolution of urate deposition from its initial state to its gradual development from scattered point deposits into continuous hyperechoic double lines parallel to the bone cortex, solving the problem that existing solid models and static image libraries can only present a single, fixed pathological state and cannot demonstrate the disease progression pattern.

[0065] 2. This invention also incorporates a frequency adaptive processing subunit into the ultrasound mapping unit, making the ultrasound probe frequency a core variable in the image generation process within the acoustic mapping model. For the same scatterer volume fraction spatial distribution output by the deposition kinetics calculation unit, the frequency adaptive processing subunit can automatically adjust the display resolution of hyperechoic structures and the degree of posterior acoustic shadowing in the generated ultrasound image frames according to different preset probe frequencies. This allows for more refined structural resolution and more significant posterior acoustic shadowing when simulating scanning with a higher frequency probe, while automatically presenting corresponding acoustic feature differences when simulating scanning with a lower frequency probe. This frequency adaptive mechanism ensures that the generated image sequence always conforms to the physical laws of ultrasound acoustic attenuation and scattering, enabling the simulation teaching system to possess the frequency response characteristics of real ultrasound equipment for the first time. Students can intuitively understand the impact of frequency selection on image quality and sign display by switching different probe frequencies, thereby cultivating the ability to rationally select scanning parameters according to actual clinical needs.

[0066] 3. This invention also establishes a reverse calibration module, enabling automatic reverse calculation of parameters from real clinical case images to the internal parameters of the teaching system, thus creating a closed loop between clinical practice and simulated teaching. This module receives ultrasound images of the double-track sign from a real gout patient as a target reference. Through an iterative optimization algorithm, it automatically adjusts the pathophysiological input variables of the deposition kinetics calculation unit until the simulated image generated by the image generation module converges with the target reference image in terms of the morphology, thickness, and echo intensity of the double-track sign. This process imbues a static clinical image, which originally only reflected the terminal state of the disease, with a complete temporal dimension. Teachers only need to input an ultrasound image of a typical case, and the system can automatically deduce the pathophysiological parameter configuration matching that case, and generate a sequence of images showing the entire intermediate process from the normal state to the typical manifestation. This invention thus extends the teaching value of valuable clinical cases from a single-frame static display to a full-process dynamic demonstration, achieving a deep connection between teaching content and real clinical practice. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0068] Figure 2 This is a schematic diagram of the framework of the image generation module of the present invention;

[0069] Figure 3 This is a schematic diagram of the framework of the deposition dynamics calculation unit and the spatial offset subunit of the present invention;

[0070] Figure 4 This is a schematic diagram of the ultrasonic mapping unit and frequency adaptive processing subunit of the present invention. Detailed Implementation

[0071] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0072] Example 1, such as Figures 1-4 As shown, this invention provides a hand arthritis simulation teaching system based on ultrasound imaging, including an image generation module for generating ultrasound images simulating the pathological state of hand arthritis. The image generation module includes:

[0073] The urate deposition modeling unit is used to construct a composite acoustic model of urate scatterers and cartilage matrix. The composite acoustic model defines the impedance difference between the acoustic impedance characteristics of urate scatterers and the acoustic impedance characteristics of cartilage matrix, and establishes the physical correspondence between the volume fraction of scatterers and the morphology of deposition layers.

[0074] The deposition kinetics calculation unit, connected to the urate deposition modeling unit, is used to dynamically calculate the deposition flux of urate scatterers on the surface of articular cartilage based on the received pathophysiological input variables, and output the spatial distribution of scatterer volume fraction over time.

[0075] The ultrasound mapping unit, connected to the deposition dynamics calculation unit, is used to calculate and generate an ultrasound image frame reflecting the current deposition state based on the spatial distribution of the scatterer volume fraction and the preset ultrasound probe frequency, using a composite acoustic model. The ultrasound mapping unit dynamically determines whether the image presentation mode of the local area is discrete point echoes or continuous hyperecho lines based on the comparison between the local volume fraction and the preset fusion threshold. When generating continuous hyperecho lines, the hyperecho lines remain parallel to the bone cortex echo lines in the image.

[0076] To ensure the hyperechoic lines remain parallel to the cortical bone echo lines in the image, the ultrasound mapping unit employs a local gradient direction alignment algorithm when generating continuous hyperechoic lines based on local volume fractions. Specifically: First, the centerline of the cortical bone echo lines is extracted from the binary mask, and the normal direction of each pixel on this centerline is calculated. Second, the position of the cartilage surface is obtained along the normal direction. Third, the hyperechoic lines generated from the image are constrained to the position of the cartilage surface along this normal direction, thus ensuring a constant distance between the hyperechoic lines and the cortical bone lines, and that the hyperechoic lines themselves have the same curvature as the cortical bone lines, thereby achieving a parallel relationship.

[0077] The sequence generation unit, connected to the ultrasound mapping unit, is used to combine a series of ultrasound image frames generated in chronological order into an image sequence that shows the dynamic evolution of urate deposition from nothing to something, from punctate echoes to the formation of continuous hyperechoic double lines parallel to the bone cortex.

[0078] The display module is used to present image sequences.

[0079] In embodiments of the present invention, the above-mentioned modules and units work together;

[0080] The pre-constructed composite acoustic model of the urate deposition modeling unit provides a physical basis for the entire system and clarifies the acoustic performance that should be achieved under different deposition amounts;

[0081] The deposition dynamics calculation unit serves as the driving core, receiving externally inputted pathophysiological variables (such as uric acid concentration and joint range of motion), calculating and outputting the volume fraction of scatterers at each point in space in real time, thereby dynamically simulating the evolution of the disease.

[0082] The ultrasound mapping unit converts this physical distribution into ultrasound image frames. In particular, by using fusion threshold judgment, it intelligently determines whether the local area is presented as discrete point-like hyperechoes or continuous parallel hyperecho lines, accurately reproducing the imaging changes from early to typical.

[0083] The sequence generation unit connects time-series ultrasound image frames and outputs them through the display module, thus fully displaying the dynamic images of the entire process of the formation of the "dual-track sign," solving the problem that traditional static teaching materials cannot present the dynamic evolution of pathology.

[0084] Taking a specific teaching scenario as an example: Initially, the system has no pathological parameters input, there is no urate deposition on the cartilage surface, and the ultrasound mapping unit generates clean ultrasound image frames, showing only smooth cortical echo lines. As the user gradually increases the synovial fluid uric acid concentration through the interactive input module, the deposition kinetics calculation unit continuously calculates the deposition flux based on supersaturation and updates the spatial distribution of the scatterer volume fraction. When the scatterer volume fraction in a local soft tissue region begins to rise but remains below the fusion threshold, the ultrasound mapping unit generates discrete point-like hyperechoic areas at the corresponding pixel locations in that region. As time progresses and the concentration continues to increase, the local volume fraction exceeds the fusion threshold, and the ultrasound mapping unit automatically connects the discrete points in that region into a continuous hyperechoic line that closely follows but is independent of the cortical echo line. The thickness of this line gradually increases with the increase in volume fraction, forming a hyperechoic double line parallel to the cortical echo line, i.e., the "double-track sign." The sequence generation unit combines all intermediate frames into a smooth image sequence, allowing students to review the entire evolution process.

[0085] In one embodiment, for the composite acoustic model constructed for the urate deposition modeling unit, the acoustic impedance value of the urate scatterer is configured to be higher than that of the cartilage matrix to form the strong impedance mismatch condition required to produce high echo performance.

[0086] In the ultrasonic mapping unit, when the volume fraction of scatterers in a local area is lower than the fusion threshold, the image of that area appears as discrete point-like high echoes corresponding to the degree of local scatterer aggregation, and the thickness of the deposition layer is zero at this time.

[0087] When the volume fraction of scattering bodies in a local area reaches or exceeds the fusion threshold, the point-like hyperechoic areas in that area are automatically connected into a continuous hyperechoic line parallel to the underlying cortical bone echo line, and the thickness of the connected hyperechoic line is positively correlated with the local volume fraction. In this way, by setting the relationship between the acoustic impedance values, it is ensured that urate deposition always appears as hyperechoic, and by comparing the volume fraction with the fusion threshold, the timing of the "point-to-line" morphological transformation is precisely controlled, allowing the image to vividly reproduce the pathological process of urate from isolated crystal spots to the formation of a complete surface deposition.

[0088] Furthermore, this strong impedance mismatch condition is reflected through the acoustic impedance difference relationship, and the acoustic impedance of the urate scatterer is set as follows: The acoustic impedance of the cartilage matrix is And satisfy This forms the impedance mismatch coefficient:

[0089] ;

[0090] This coefficient The larger the surface area, the stronger the interface reflection and the brighter the echo.

[0091] Meanwhile, the thickness of the high echo line The volume fraction of scattering in local regions The relationship between them can be represented as a piecewise function:

[0092] when hour, ;

[0093] when At that time, the image appears as discrete point-like high echoes, and the brightness of the point-like echoes... and satisfy:

[0094] ;

[0095] in, The fusion threshold, Based on thickness, For mapping coefficients, The baseline brightness is used. In this way, the simulation system not only realizes morphological switching, but also accurately correlates deposition amount, echo intensity, and display thickness, ensuring quantitative consistency of the image.

[0096] In one embodiment, the ultrasound mapping unit further includes a frequency adaptive processing subunit. This subunit adjusts the display resolution of hyperechoic structures and the representation of posterior acoustic shadows in the ultrasound image frame according to a preset ultrasound probe frequency. This allows image frames generated at different probe frequencies with the same scatterer volume fraction spatial distribution to automatically exhibit corresponding acoustic feature differences. By introducing a frequency adaptive mechanism, the system can realistically reproduce the phenomenon of image resolution and acoustic shadow depth changing when probe frequencies are changed in clinical practice, enhancing the realism of the simulation and the understanding of equipment operation.

[0097] Furthermore, frequency adaptive processing is achieved through a frequency correction function, whereby the probe frequency is defined as... The adjustment of display resolution is reflected in the display thickness of the deposition layer. Effective visualization of thickness:

[0098] ;

[0099] in, The frequency resolution correction factor satisfies:

[0100] ;

[0101] in, For reference frequency, It is a frequency-dependent index;

[0102] The degree to which the sound and shadow are rendered from behind is determined by the sound and shadow attenuation coefficient. To take control:

[0103] ;

[0104] in, The attenuation constant is The frequency decay index;

[0105] Gray values ​​of the sound and shadow area: ;

[0106] in, The length of the sound propagation path. The background is grayscale.

[0107] As a result, the simulation system automatically presents thinner high-echo lines, clearer resolution, and more obvious rear acoustic shadows under high-frequency probes, while presenting wider lines and weaker acoustic shadows under low-frequency probes, which conforms to the physical laws of ultrasound.

[0108] In one embodiment, pathophysiological input variables include synovial fluid uric acid concentration information and joint motion status information. The deposition kinetics calculation unit determines the local supersaturation based on the difference between the synovial fluid uric acid concentration and the urate solubility, and calculates the deposition rate of the urate scatterer accordingly. Simultaneously, it calculates the shear stress exerted on the deposition layer by joint motion based on the joint motion status information, and reduces the deposition flux based on this shear stress to simulate the mechanical removal effect of joint activity on urate deposition. This allows the simulation system to not only provide static pathological images but also dynamically simulate the scouring and stripping effects of joint movements (such as finger flexion and extension) on deposition. Trainees can observe the thickening or thinning process of the "double-track sign" by changing motion parameters, gaining a deeper understanding of the impact of physical activity on disease progression.

[0109] Specifically, deposition rate Due to local oversaturation Sure:

[0110] ;

[0111] in, This refers to the concentration of uric acid in synovial fluid. This is the saturation concentration of urate.

[0112] Deposition rate: ;

[0113] in, Let be the deposition rate constant. The supersaturation index;

[0114] Shear stress generated by joint movement Motion velocity from joint motion state information and joint space Decide:

[0115] ;

[0116] in, The apparent viscosity of the synovial fluid;

[0117] Net deposition flux: ,in The modulation coefficient ensures that when the shear stress... Not exceeding At that time, the deposition rate is always positive, thus realistically simulating the temporal relationship between deposition and removal. This is the mechanical removal coefficient.

[0118] This model enables the simulation of the dynamic balance between deposition and removal.

[0119] In one embodiment, the deposition kinetics calculation unit further includes a spatial bias subunit. This subunit applies a spatial bias when calculating the spatial distribution of the scatterer volume fraction based on a preset intra-articular gravity direction and a transport model based on the synovial fluid circulation path. This bias simulates the non-uniform distribution of urate deposition on different regions of the cartilage surface caused by the combined effects of gravity settling and differences in fluid flow paths. With this spatial bias, the simulated image no longer shows uniform deposition, but rather exhibits thicker deposition and earlier appearance of continuous hyperechoic lines in certain regions (such as areas with lower gravity direction or slower fluid flow). This closely matches the uneven distribution of the "double-track sign" observed in real clinical settings, enhancing the diversity and realism of the cases.

[0120] Spatial bias through net deposition flux Based on the superposition of transport flux term, volume fraction is achieved. Spatial evolution follows:

[0121] ;

[0122] in, The diffusion coefficient is... Let be the settling velocity of the crystal in the direction of gravity. Vertical coordinates The velocity field of sluice fluid convection; the velocity field of sluice fluid convection. The settling velocity is determined by the preset joint motion pattern and cavity geometric boundary conditions. It is related to crystal size, density difference, and gravitational acceleration.

[0123] As a result, the system can produce gravity-dependent bottom deposition enhancement and depositional differential along the liquid flow direction.

[0124] In one embodiment, the system further includes an interactive input module and a quantitative measurement module. The interactive input module is used to receive user adjustments to pathophysiological input variables and transmit the adjusted variables to the deposition kinetics calculation unit in real time to trigger recalculation and image updates.

[0125] The quantitative measurement module provides a virtual measurement tool on the image frame presented by the display module to measure the apparent thickness of the deposited layer and the difference in echo intensity between two points.

[0126] This design transforms passive viewing into active exploration. Students can instantly see changes in the image by adjusting sliders for uric acid concentration, movement speed, etc., and use virtual calipers or strength measurement tools to quantitatively verify the changes, greatly enhancing interactivity and teaching depth.

[0127] In one embodiment, the display module further includes a linked display interface, which is used to simultaneously present an ultrasonic image frame, a schematic diagram of the distribution of microscopic scatterers corresponding to the current frame, and a physical quantity curve reflecting the evolution of the deposition state over time within the same screen area. The physical quantity curve includes the changing trends of the scatterer volume fraction and the deposition layer thickness over time.

[0128] Through the three-zone linkage display, trainees are able to match image morphology, microscopic essence, and macroscopic curves, establishing a three-dimensional understanding of "microscopic deposition - ultrasonic manifestation - evolution trend".

[0129] In one embodiment, the system further includes a reverse calibration module, which receives clinical ultrasound images of the double-track sign of a real gout patient as a target reference image. The module then uses an iterative optimization algorithm to solve the pathophysiological input variables of the deposition kinetics calculation unit in reverse, so that the simulated image generated by the image generation module converges with the target reference image in terms of the morphology, thickness, and echo intensity of the double-track sign, thereby completing the automatic calibration of the input parameters of the teaching system.

[0130] The reverse calibration module enables the teaching system to match real cases and generate personalized simulated images.

[0131] Iterative optimization algorithms minimize the objective function accomplish:

[0132] ;

[0133] in, The pixel index of the double-track feature region in the image. and Pixels in the simulated image The thickness of the sedimentary layer and the echo intensity at that location, and The measured thickness and intensity are extracted from the corresponding location in the target reference image. , These are weighting coefficients;

[0134] During the optimization process, pathophysiological input variables, including synovial fluid uric acid concentration, were adjusted. Movement velocity in joint motion state information ,make The simulation converges to below a preset threshold, completing the automatic calibration. This method of inferring input parameters from the image itself ensures a high degree of fidelity between the simulation and clinical practice.

[0135] In one embodiment, the image generation module further includes a tophi simulation submodule. This submodule generates simulated tophi ultrasound features in a specified soft tissue region. The simulated tophi appear as heterogeneous hyperechoic masses in the image, with boundaries that are either clear or irregular. The hyperechoic masses contain hyperechoic punctate structures with posterior acoustic shadowing. This submodule also superimposes irregular bright spots and posterior dark areas onto the soft tissue around joints, simulating multiple microcrystals within the tophi through random punctate hyperechoic structures, thus enriching the pathological types in the simulated images.

[0136] Based on the Gaussian speckle model and sound-shadow simulation algorithm, a circular or elliptical speckle with an adjustable radius (e.g., 8-15 pixels) is generated at specified coordinates in the image. Its grayscale value is determined by a two-dimensional Gaussian function. Generate, where The size of the speckle is controlled. Several high-brightness (grayscale value 180-220) 1-2 pixel points are randomly superimposed internally to simulate the internal core. The sound and shadow generation employs a rear pixel grayscale attenuation algorithm, which exponentially attenuates the grayscale along the depth direction in the area vertically below the speckle. Calculate the grayscale of the sound and shadow region, where Original grayscale The attenuation coefficient is the pixel distance from the origin, which causes the grayscale of the sound and shadow area to gradually change to the grayscale of the background.

[0137] In one embodiment, the image generation module further includes a bone erosion simulation submodule, an osteophyte simulation submodule, a spiculated destruction simulation submodule, and a pus accumulation simulation submodule;

[0138] The bone erosion simulation submodule is used to generate discontinuous concave defect areas on the cortical bone surface of a specified joint. The concave defect areas can be displayed on two mutually perpendicular cross-sections, with low-echo filling inside and irregular contours at the edges.

[0139] Based on image masking operations and morphological thinning algorithms, a binary mask (with a pixel value of 1 representing the erosion area) is first generated according to the preset location and size of the bone erosion region. The Canny edge detection algorithm is then used to extract the contours of this mask, and contour deformation noise (with an amplitude of 1-3 pixels) is randomly added. Finally, the grayscale value of the original bone cortex region (original value 255) under this mask is replaced with the grayscale value of the Gaussian noise background (mean 50, variance 10) to simulate low-echo filling.

[0140] To meet the requirement of "two mutually perpendicular cuts", the mask generation logic operates simultaneously on different scanning cuts (sagittal and coronal planes) to ensure the consistency of erosion in two-dimensional space.

[0141] The osteophyte simulation submodule is used to generate hyperechoic structures protruding outward from the bone cortex at the edge of the interphalangeal joint. The base of the hyperechoic structure is continuous with the bone cortex of the parent bone, and its morphology is selected from the morphology group consisting of lip-like, spiky, and plateau-like structures.

[0142] The spiky destruction simulation submodule is used to generate irregular hyperechoic protrusions at the edge of the phalanx. The morphology of the hyperechoic protrusions is selected from a group of morphological groups consisting of needle-like and brush-like features, and the hyperechoic protrusions contrast with the smooth normal cortical bone area.

[0143] The pus accumulation simulation submodule is used to generate a non-uniform echo region within the joint cavity. The echo region is either a hypoechoic region or an anechoic region. Within the echo region, there are fine punctate echoes and flocculent echoes simulating the characteristics of pus, and the joint capsule appears to be in a swollen state.

[0144] These sub-modules can be overlaid on the same ultrasound image frame generated by the image generation module to simulate complex cases of multiple types of arthritis. For example, instructors can set up simultaneous bone erosion, osteophytes, and double-track signs to generate comprehensive images representing advanced and complex lesions, greatly expanding the diversity of teaching cases.

[0145] It should be noted that, in some optional implementations, the above-mentioned sub-modules and units can be implemented based on pixel-level image synthesis algorithms.

[0146] For example, the bone erosion simulation submodule reduces the pixel values ​​of the bone cortex region using a predefined erosion mask and fills it with a low grayscale noise texture.

[0147] Osteophytes and spiky protrusions stretch the cortical bone pixels outward while preserving high grayscale through shape templates (such as parametric curves or radial basis function deformations);

[0148] The abscess area is generated by reducing the background grayscale of the joint cavity area and overlaying random dot-like and flocculent hyperechoic textures;

[0149] The tophi simulation submodule overlays bright patches with Gaussian blur onto a soft tissue background and uses grayscale gradients to simulate sound and shadow in the background. All ultrasound image frames are then seamlessly fused with features such as dual-track features through the main synthesis pipeline of the ultrasound mapping unit, outputting a composite teaching image for students.

[0150] In some alternative implementations, the ultrasonic probe frequency Users can select commonly used clinical frequencies, such as 3.5MHz, 7.5MHz, 10MHz, and 15MHz. Reference frequency. Set to 7.5MHz, frequency dependence index The attenuation constant can be between -0.7 and -0.3. and frequency decay index It can be adjusted according to different tissue characteristics; typical value A value of 1.1 to 1.3 is acceptable.

[0151] The adjustment of pathophysiological input variables can be achieved through sliders and numerical input boxes in the graphical user interface. The interactive input module binds these UI controls to the parameters of the deposition kinetics calculation unit to achieve real-time interaction. The virtual measurement tool in the quantitative measurement module draws measurement lines through mouse or touch operations and calculates and displays the thickness and grayscale difference based on the pixel index.

[0152] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the functions of each module in the above-mentioned simulation teaching system and generates an ultrasound image sequence.

[0153] The following provides an objective explanation of some key terms used in this invention:

[0154] Composite acoustic model: refers to a mathematical model that simultaneously considers the acoustic properties (such as acoustic impedance and sound velocity) of urate scatterers and cartilage matrix, and calculates the overall acoustic response (such as acoustic reflection coefficient) of the mixed medium based on the differences and volume ratios between the two.

[0155] Scatter volume fraction: The ratio of the volume occupied by urate scatterers to the total volume of a local region is a core physical quantity that determines the local characteristics of an ultrasound image.

[0156] Fusion threshold: A preset critical value for the volume fraction of scattering bodies, used as a criterion to distinguish between discrete point echoes and continuous high echo lines in ultrasound images.

[0157] Deposition flux: The net mass or net volume of urate scatterers deposited per unit area of ​​articular cartilage surface per unit time.

[0158] Sound shadow: The dark area that appears behind a strongly attenuated structure after the sound wave penetrates it, due to the significant reduction in energy.

[0159] Double-track sign: The double-line structure formed in ultrasound images by the high-echoic line and the bone cortical echo line below it, parallel to it, is one of the hallmark features of gouty arthritis.

[0160] Supersaturation: The degree to which the actual uric acid concentration in synovial fluid exceeds the saturation concentration of urate.

[0161] In embodiments of the present invention, examples of specific parameter values ​​are as follows:

[0162] Acoustic impedance of urate scatterer Pick Cartilage matrix acoustic impedance Pick Therefore, the impedance mismatch coefficient is calculated. It is approximately 0.09.

[0163] Fusion threshold Take 0.12, base thickness Take 0.5mm, mapping coefficient Take 0.8 as the reference brightness. Take 255.

[0164] Deposition rate constant Take 0.01 mm / s, supersaturation index Take 1.5.

[0165] apparent viscosity of synovial fluid at 37°C Take 0.04 Pa·s as the mechanical removal coefficient. Take 0.005.

[0166] diffusion coefficient Pick .

[0167] Settling velocity of crystals in the direction of gravity By Stokes' formula Calculation, where Take the density of urate crystals , Take the synovial fluid density , Take the acceleration due to gravity Crystal radius Taking 5μm, substituting it into the calculation yields... Approximately .

[0168] Lubricating fluid convection velocity field The Navier-Stokes equations are determined by solving the Navier-Stokes equations using the finite element method, based on preset joint motion patterns and cavity geometric boundary conditions. The boundary conditions are set according to the flexion and extension angles of the joint, for example, at a frequency of 1 Hz and a range of motion of 30 degrees. It should be noted that the above parameter values ​​are merely illustrative examples to enable those skilled in the art to implement the present invention and do not constitute a limitation on the scope of protection of the present invention. In practical applications, adjustments can be made according to different teaching needs.

[0169] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A hand arthritis simulation teaching system based on ultrasound imaging, characterized in that, include: An image generation module is used to generate ultrasound images simulating the pathological state of hand arthritis. The image generation module includes: The urate deposition modeling unit is used to construct a composite acoustic model of urate scatterers and cartilage matrix. The composite acoustic model defines the impedance difference between the acoustic impedance characteristics of urate scatterers and the acoustic impedance characteristics of cartilage matrix, and establishes the physical correspondence between the volume fraction of scatterers and the morphology of deposition layers. The deposition kinetics calculation unit, connected to the urate deposition modeling unit, is used to dynamically calculate the deposition flux of urate scatterers on the surface of articular cartilage based on the received pathophysiological input variables, and output the spatial distribution of scatterer volume fraction over time. The ultrasound mapping unit, connected to the deposition dynamics calculation unit, is used to calculate and generate an ultrasound image frame reflecting the current deposition state based on a composite acoustic model according to the spatial distribution of the scatterer volume fraction and the preset ultrasound probe frequency. The ultrasound mapping unit dynamically determines whether the image presentation mode of the local area is discrete point echoes or continuous hyperecho lines based on the comparison between the local volume fraction and the preset fusion threshold. When continuous hyperecho lines are generated, the hyperecho lines remain parallel to the bone cortex echo lines in the image. The sequence generation unit, connected to the ultrasound mapping unit, is used to combine a series of ultrasound image frames generated in chronological order into an image sequence that shows the dynamic evolution of urate deposition from nothing to something, from punctate echoes to the formation of continuous hyperechoic double lines parallel to the bone cortex. A display module is used to present the image sequence.

2. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 1, characterized in that, In the composite acoustic model constructed by the urate deposition modeling unit, the acoustic impedance value of the urate scatterer is configured to be higher than that of the cartilage matrix to form the strong impedance mismatch condition required to produce high echo performance. In the ultrasonic mapping unit, when the volume fraction of scatterers in a local area is lower than the fusion threshold, the image of that area appears as discrete point-like high echoes corresponding to the degree of local scatterer aggregation, and the thickness of the deposition layer is zero at this time. When the volume fraction of the scatterer in a local area reaches or exceeds the fusion threshold, the point-like hyperechoic areas in that area are automatically connected into a continuous hyperechoic line parallel to the underlying cortical bone echo line, and the thickness of the connected hyperechoic line is positively correlated with the local volume fraction. The strong impedance mismatch condition is reflected through the difference in acoustic impedance. The acoustic impedance of the urate scatterer is set as... The acoustic impedance of the cartilage matrix is And satisfy This forms the impedance mismatch coefficient: ； The thickness of the high echo line The volume fraction of scattering in local regions The relationship between them can be represented as a piecewise function: when hour, ; when At that time, the image appears as discrete point-like high echoes, and the brightness of the point-like echoes... and satisfy: ； in, The fusion threshold, Based on thickness, For mapping coefficients, This is the reference brightness.

3. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 2, characterized in that, The ultrasound mapping unit also includes a frequency adaptive processing subunit, which is used to adjust the display resolution of high echo structures and the degree of representation of the acoustic shadow in the ultrasound image frame according to the preset ultrasound probe frequency, so that the image frames generated under different probe frequencies with the same scatterer volume fraction spatial distribution automatically present the corresponding acoustic feature differences. Frequency adaptive processing is achieved through a frequency correction function, where the probe frequency is defined as... The adjustment of display resolution is reflected in the display thickness of the deposition layer. Effective visualization of thickness: ； in, The frequency resolution correction factor satisfies: ； in, For reference frequency, It is a frequency-dependent index; The degree to which the sound and shadow are rendered from behind is determined by the sound and shadow attenuation coefficient. To take control: ； in, The attenuation constant is The frequency decay index; Gray values ​​of the sound and shadow area: ; in, The length of the sound propagation path. The background is grayscale.

4. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 3, characterized in that, The pathophysiological input variables include synovial fluid uric acid concentration information and joint motion state information. The deposition kinetics calculation unit determines the local supersaturation based on the difference between synovial fluid uric acid concentration and urate solubility, and calculates the deposition rate of urate scatterers accordingly. At the same time, it calculates the shear stress of joint motion on the deposition layer based on the joint motion state information, and reduces the deposition flux based on the shear stress to simulate the mechanical removal effect of joint activity on urate deposition. The deposition rate Due to local oversaturation Sure: ； in, This refers to the concentration of uric acid in synovial fluid. This is the saturation concentration of urate. Deposition rate: ; in, Let be the deposition rate constant. The supersaturation index; The shear stress generated by the joint movement Motion velocity from joint motion state information and joint space Decide: ； in, The apparent viscosity of the synovial fluid; The net deposition flux: ; in, The modulation coefficient, This is the mechanical removal coefficient.

5. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 4, characterized in that: The deposition dynamics calculation unit also includes a spatial bias subunit, which is used to apply a spatial bias when calculating the spatial distribution of the scatterer volume fraction according to the preset intra-articular gravity direction and the transport model based on the synovial fluid circulation path, so as to simulate the non-uniform distribution of urate deposition on different regions of the cartilage surface caused by the combined effects of gravity sedimentation and fluid flow path differences. Spatial bias through net deposition flux Based on the superposition of transport flux term, volume fraction is achieved. Spatial evolution follows: ； in, The diffusion coefficient is... Let be the settling velocity of the crystal in the direction of gravity. Vertical coordinates This represents the velocity field of the slurry convection.

6. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 1, characterized in that: It also includes an interactive input module and a quantitative measurement module. The interactive input module is used to receive user adjustment operations on pathophysiological input variables and transmit the adjusted variables to the deposition kinetics calculation unit in real time to trigger recalculation and image update. The quantitative measurement module is used to provide a virtual measurement tool on the image frame presented by the display module to measure the apparent thickness of the deposition layer and the difference in echo intensity between two points.

7. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 1, characterized in that, The display module also includes a linked display interface, which is used to simultaneously display an ultrasonic image frame, a schematic diagram of the distribution of microscopic scatterers corresponding to the current frame, and a physical quantity curve reflecting the evolution of the deposition state over time within the same screen area. The physical quantity curve includes the changing trends of the scatterer volume fraction and the deposition layer thickness over time.

8. The hand arthritis simulation teaching system based on ultrasound imaging according to claim 1, characterized in that, It also includes a reverse calibration module, which is used to receive clinical ultrasound images of the double-track sign of real gout patients as target reference images. The module uses an iterative optimization algorithm to solve the pathophysiological input variables of the deposition dynamics calculation unit in reverse, so that the simulated image generated by the image generation module converges with the target reference image in terms of the morphology, thickness and echo intensity of the double-track sign. Iterative optimization algorithms minimize the objective function accomplish: ； in, The pixel index of the double-track feature region in the image. and Pixels in the simulated image The thickness of the sedimentary layer and the echo intensity at that location, and The measured thickness and intensity are extracted from the corresponding location in the target reference image. , These are weighting coefficients; During the optimization process, pathophysiological input variables, including synovial fluid uric acid concentration, were adjusted. Movement velocity in joint motion state information ,make The system converges to below the preset threshold, completing the automatic calibration.

9. A hand arthritis simulation teaching system based on ultrasound imaging according to claim 1, characterized in that, The image generation module also includes a tophi simulation submodule, which is used to generate ultrasound features of simulated tophi in a specified soft tissue area. The simulated tophi appear as non-uniform hyperechoic masses in the image. The boundaries of the hyperechoic masses are either clear or irregular, and the hyperechoic masses contain hyperechoic punctate structures with acoustic shadowing behind them.

10. A hand arthritis simulation teaching system based on ultrasound imaging according to claim 1, characterized in that, The image generation module also includes a bone erosion simulation submodule, an osteophyte simulation submodule, a spiculated destruction simulation submodule, and a pus accumulation simulation submodule; The bone erosion simulation submodule is used to generate discontinuous concave defect areas on the surface of the bone cortex of a specified joint. The concave defect areas can be displayed on two mutually perpendicular cross-sections, with low-echo filling inside and irregular contours at the edges. The osteophyte simulation submodule is used to generate a hyperechoic structure protruding outward from the bone cortex at the edge of the interphalangeal joint. The base of the hyperechoic structure is continuous with the bone cortex of the parent bone, and its morphology is selected from the morphology group composed of lip-like, spike-like and plateau-like. The burr-like destruction simulation submodule is used to generate irregular hyperechoic protrusions at the edge of the phalanx. The morphology of the hyperechoic protrusions is selected from a group of morphologies consisting of needle-like and brush-like shapes, and the hyperechoic protrusions contrast with the smooth normal cortical bone area. The pus accumulation simulation submodule is used to generate a non-uniform echo region within the joint cavity. The echo region is either a hypoechoic region or an anechoic region. The echo region contains fine punctate echoes and flocculent echoes that simulate the characteristics of pus, and the joint capsule appears to be in a swollen state.

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