Three-dimensional face anti-aging shaping technology

Abstract

The invention relates to the technical field of face anti-aging shaping, and particularly discloses a three-dimensional face anti-aging shaping technology, which comprises the following steps: acquiring external geometric data of a face to obtain a face surface point cloud model; facial tissue mechanical parameters are obtained, and a tissue mechanical parameter calibration result is obtained; performing fusion processing on the face surface point cloud model and the tissue mechanical parameter calibration result to obtain a layered three-dimensional structure model; performing finite element numerical simulation to obtain an initial simulation result; performing path parameterization optimization processing to obtain an optimal path; inputting the optimal path into the simulation environment again to obtain a prediction effect; generating a clinical operation guidance scheme according to the prediction effect, wherein the clinical operation guidance scheme is used for realizing a face anti-aging shaping expected effect; according to the method, an initial operation strategy is imported into a simulation environment to generate a baseline response, continuous iteration is performed through a multi-objective optimization algorithm, a closed loop of'design-simulation-evaluation-optimization 'is realized, and the robustness and stability of the scheme are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of facial anti-aging and shaping, and specifically relates to a three-dimensional facial anti-aging and shaping technology. Background Art

[0002] With socioeconomic development and shifting aesthetics, demand for anti-aging facial contouring has exploded. Traditional facial anti-aging techniques (such as thread lifts, injectable fillers, and radiofrequency tightening) rely primarily on empirical procedures. Doctors assess tissue condition through palpation and design surgical procedures based on anatomical knowledge. However, this approach has significant limitations.

[0003] Traditional techniques make it difficult to quantitatively assess the differences in mechanical properties of facial tissue layers (such as fascia, muscle, and fat), resulting in a lack of biomechanical basis for the design of surgical paths. For example, when operating instruments such as fascia knives, doctors usually rely on subjective feel to adjust the incision depth and angle, making it difficult to achieve micron-level precision control. Studies have shown that the thickness of the facial fascia layer can vary by 200-800μm. Traditional empirical operations can easily lead to local stress concentration or insufficient energy deposition, affecting the durability of the effect.

[0004] The facial neurovascular network is complex, and while traditional techniques use anatomical maps to avoid risk areas, individual differences still result in a high risk of complications in actual operations. For example, the positional deviation of key structures such as the supraorbital nerve and facial nerve branches can reach 3-5mm, making it difficult for traditional methods to dynamically adapt to anatomical variations. Furthermore, the thermal effects and mechanical stress generated by fascial knife manipulation may cause nerve damage or fat necrosis, but existing technologies cannot predict risk areas before surgery.

[0005] Traditional techniques rely primarily on subjective visual assessments and lack quantitative metrics. For example, improving skin firmness often relies on the physician's judgment, but key parameters such as changes in elastic modulus and the degree of collagen fiber reorganization cannot be monitored in real time. This method of assessment results in significant variability between physicians, making it difficult to develop standardized treatment plans.

[0006] Furthermore, traditional technologies lack a comprehensive simulation platform for the entire process, from protocol design to clinical validation, resulting in treatment plan optimization relying on trial and error. For example, optimizing the fascial knife's operating path requires multiple intraoperative adjustments, but each adjustment potentially increases patient risk, and it's difficult to exhaust all parameter combinations. This "trial and error" R&D model is inefficient and makes it difficult to guarantee the optimality of the final solution.

[0007] In summary, existing facial anti-aging and reshaping technologies have core problems such as insufficient precision, uncontrollable risks, difficult to quantify effects, inefficient inheritance, data fragmentation and lack of simulation. It is urgent to integrate multimodal imaging, biomechanical modeling, numerical simulation and intelligent optimization algorithms to build a systematic and quantifiable three-dimensional anti-aging and reshaping technology system.

[0008] In response to this, the inventors proposed a three-dimensional facial anti-aging shaping technology to solve the above problems. Summary of the Invention

[0009] The purpose of the present invention is to provide a three-dimensional facial anti-aging shaping technology to solve the problems raised in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A three-dimensional facial anti-aging shaping technology, including:

[0012] Acquire facial external geometric data to generate a facial surface point cloud model, thereby obtaining a facial surface point cloud model;

[0013] Obtain facial tissue mechanical parameters, which are used to calibrate the elastic modulus and shear modulus of each layer, and obtain tissue mechanical parameter calibration results;

[0014] The facial surface point cloud model is fused with the tissue mechanics parameter calibration result to construct a layered three-dimensional structure model, thereby obtaining a layered three-dimensional structure model;

[0015] Inputting the layered three-dimensional structural model into a computer simulation environment for performing finite element numerical simulation to obtain initial simulation results;

[0016] Performing path parameter optimization processing on the initial simulation results to adjust the action path of the fascia knife and obtain an optimal path;

[0017] The optimal path is input into the simulation environment again to verify the strain distribution and collagen activation to obtain the predicted effect;

[0018] A clinical operation guidance plan is generated based on the predicted effect to achieve the expected effect of facial anti-aging and shaping.

[0019] Preferably, the acquisition of facial external geometric data uses a structured light three-dimensional scanner to scan the face at multiple angles to obtain a facial surface point cloud model.

[0020] Preferably, the acquisition of facial tissue mechanical parameters includes obtaining the elastic moduli of four layers of fascia, muscle, fat and bone using ultrasonic elastography or magnetic resonance elastography, which are used to calibrate the interlayer mechanical properties and obtain tissue mechanical parameter calibration results.

[0021] Preferably, the fusing of the facial surface point cloud model with the tissue mechanics parameter calibration result comprises:

[0022] Register point cloud models with medical images;

[0023] Segmentation of tissue layers based on convolutional neural networks;

[0024] The segmentation results are mapped to the corresponding mechanical parameters to generate a layered three-dimensional mesh model with physical properties, thereby obtaining a layered three-dimensional structure model.

[0025] Preferably, when performing finite element numerical simulation, the material constitutive model adopts a hyperelastic or dual network model, and boundary conditions and contact parameters are set to obtain the stress-strain distribution of the tissue under the action of the initial fascia knife path.

[0026] Preferably, the parameterized expression of the fascia knife action path is:

[0027] P(t)=(x(t),y(t),z(t)),t∈[0,1]

[0028] With tool angle and speed parameters α(t), v(t);

[0029] P(t): trajectory coordinates of the fascia knife in three-dimensional space;

[0030] X(t), y(t) and z(t) are the parameters of the x, y and z axes respectively;

[0031] α(t): cutting angle, deflection angle relative to the normal;

[0032] v(t): Sliding velocity, affecting shear rate and thermal effect.

[0033] Preferably, the optimal path is input into the simulation environment again and the temperature field coupling is set to verify that the local strain is ≤0.2%, the temperature rise is ≤1°C and the collagen activation prediction area is ≥90% to obtain the final prediction effect.

[0034] Preferably, the prediction results are converted into augmented reality (AR) navigation instructions, which are used to guide the operator in real time during the operation to complete the fascia knife technique along the optimal path.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) The present invention uses multimodal imaging technology to obtain the geometric and mechanical properties of different tissue layers such as the patient's skin, fascia, muscle and bone, and constructs a layered three-dimensional model with physiological mechanical response. By accurately simulating the mechanical effects of the fascia knife between the tissue layers, the local stress-strain distribution can be quantitatively evaluated, so that the operation path and the cutting parameters can be personalized at the micron and angle levels, far exceeding the accuracy of traditional empirical techniques. The completion of the full-process numerical simulation before clinical implementation can pre-identify high-strain or high-heat accumulation areas to avoid blind trial and error during surgery. Parameterized path planning enables the coordinated optimization of the three elements of cutting depth, sliding angle and speed, theoretically minimizing the risk of damage to peripheral nerves and blood vessels.

[0037] (2) The present invention imports the initial operation strategy into the simulation environment to generate a baseline response, and then continuously iterates through a multi-objective optimization algorithm (such as genetic, particle swarm, etc.) to achieve a closed loop of "design-simulation-evaluation-optimization", thereby improving the robustness and stability of the solution. The optimization process does not require repeated human experiments and can be quickly completed on the computing platform, significantly shortening the cycle from solution design to clinical application. The optimal path and technique parameters finally generated can be converted into AR / VR navigation instructions to guide the operator to complete the operation according to the preset trajectory in real time. During the navigation process, the operation perspective and entry status can be fed back in real time to ensure that different operators can reproduce the same accurate solution in different environments, thereby improving the efficiency of technology inheritance and training.

[0038] (3) The present invention obtains the stress-strain and collagen activation prediction areas of each layer of tissue through simulation before surgery, and compares them through three-dimensional scanning or elastic imaging after surgery to form a quantifiable effect evaluation index system. The complete quantitative evaluation process helps to verify the effectiveness of the plan and also provides a scientific basis for subsequent optimization and individualized adjustment. This method integrates imaging engineering, biomechanics, numerical simulation and intelligent optimization algorithms, and introduces systematic and quantifiable research and development and implementation ideas to the field of anti-aging and body shaping. The theoretical framework has good scalability and can be extended to soft tissue treatment and remodeling in other parts of the body, providing a new research and application paradigm for minimally invasive medical aesthetics and regenerative medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the three-dimensional facial anti-aging shaping technology of the present invention. DETAILED DESCRIPTION

[0040] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Example 1:

[0042] See also Figure 1 As shown, a three-dimensional facial anti-aging shaping technology includes:

[0043] Acquire facial external geometric data to generate a facial surface point cloud model, thereby obtaining a facial surface point cloud model;

[0044] The acquisition of facial external geometric data comprises scanning the face at multiple angles using a structured light 3D scanner to obtain a facial surface point cloud model;

[0045] Facial external geometry data: refers to the three-dimensional coordinate distribution of the skin surface, which is used to accurately depict the facial shape and texture features. Structured light or laser scanners can be used to capture the face synchronously or sequentially from different angles.

[0046] Point cloud model: A collection of a large number of three-dimensional discrete points representing the facial contour. Algorithms such as iterative closest point (ICP) can be used to align point clouds from different viewpoints into a unified coordinate system.

[0047] Filtering and resampling: Remove noise, fill holes, and downsample or refine the point cloud to balance accuracy and computational effort.

[0048] Hardware: Artec Eva, Creaform HandySCAN, RealSense RGB-D camera, etc.

[0049] Software: Geomagic, MeshLab, PCL (Point Cloud Library) and other open source / commercial tools;

[0050] Process example:

[0051] The subject sits on a rotating turntable, and the scanner collects images from multiple angles, including ±45°, ±20°, and directly in front;

[0052] Use pcl::IterativeClosestPoint in PCL to complete multi-frame alignment;

[0053] Apply the MLS (Moving Least Squares) algorithm to smooth the reconstructed surface;

[0054] Obtain facial tissue mechanical parameters, which are used to calibrate the elastic modulus and shear modulus of each layer, and obtain tissue mechanical parameter calibration results;

[0055] Among them, tissue mechanical parameters include the elastic modulus, shear modulus and other material constitutive properties of different tissue layers (skin, fascia, muscle, fat, bone), which are used to describe their mechanical response.

[0056] Elastography: obtaining local stress-strain response data through ultrasound elastography (SWE) or magnetic resonance elastography (MRE);

[0057] Inversion calibration: Finite element inversion or neural network regression is used to map the imaging signal to the corresponding modulus value;

[0058] Layer attribution: Assign corresponding mechanical parameters to each layer of tissue based on the anatomical depth and image segmentation results.

[0059] Acquisition equipment: Supersonic Aixplorer (ultrasound elastography), Siemens MR Elastography suite;

[0060] Inversion software: FEBio Studio, self-developed Python scripts combined with SciPy optimization library;

[0061] Calibration process: First verify the imaging-inversion accuracy on an in vitro calibration block (known modulus);

[0062] Several representative ROIs (regions of interest) were selected on the face, and stress-strain curves were extracted respectively, and parameters such as μ and κ were obtained by inversion;

[0063] The facial surface point cloud model is fused with the tissue mechanics parameter calibration result to construct a layered three-dimensional structure model, thereby obtaining a layered three-dimensional structure model;

[0064] Layered 3D structural model: Combines geometric point clouds with tissue mechanical properties to accurately present the anatomical and mechanical characteristics of different layers in the model.

[0065] Registration and synchronization: aligning the point cloud geometry model with the elastography image coordinate system;

[0066] Deep segmentation: Using convolutional neural networks such as U-Net and Mask R-CNN, the cortex, fascia, muscle, and bone are separated based on grayscale or color features.

[0067] Attribute mapping: On the segmented mesh or volume, the corresponding mechanical parameters are mapped according to the spatial position to generate a finite element mesh with physical attributes.

[0068] Image processing: ITK, SimpleITK, 3D Slicer scripting operations;

[0069] Deep learning: PyTorch+MONAI realizes automatic segmentation of anatomical structures;

[0070] Mesh generation: Gmsh and HyperMesh convert layered volume data into high-quality tetrahedral / hexahedral elements;

[0071] Inputting the layered three-dimensional structural model into a computer simulation environment for performing finite element numerical simulation to obtain initial simulation results;

[0072] Finite element numerical simulation: solving the stress-strain distribution of a structure under given loads or boundary conditions in the form of discrete elements on a computer.

[0073] Initial simulation results: Tissue mechanical response data obtained under the preset initial manipulation path and parameters.

[0074] Material model setup: configure hyperelastic constitutive models (Neo-Hookean, Ogden, etc.) for layered meshes;

[0075] Boundary and load: fix the bone layer or mandibular node and apply tool contact force / displacement boundary;

[0076] Solution and post-processing: Run static / dynamic analysis, extract maximum principal strain, stress contour, and temperature field (if coupled with thermal analysis).

[0077] Simulation software: Abaqus, ANSYS, FEBio, COMSOL Multiphysics;

[0078] Script automation: Python + Abaqus ODB API or Matlab interface to batch generate analysis tasks;

[0079] Post-processing: ParaView, EnSight, or built-in visualization modules to export cloud maps and numerical reports;

[0080] Performing path parameter optimization processing on the initial simulation results to adjust the action path of the fascia knife and obtain an optimal path;

[0081] Parametric path: A continuous trajectory described by a set of adjustable variables (entry point coordinates, angle, speed, curve shape, etc.).

[0082] Optimal path: Under multi-objective optimization, the fascia knife trajectory and parameter set that minimizes minimally invasive damage and maximizes collagen activation is determined.

[0083] Parameter encoding: Encode path and technique parameters into chromosome vectors or particle swarm individuals;

[0084] Objective function evaluation: A composite cost function is constructed based on indicators such as local strain, energy consumption, and activation area extracted from the initial simulation results;

[0085] Algorithm iteration: Genetic algorithms, particle swarm algorithms, differential evolution, etc. are used to search the parameter space and iteratively update;

[0086] Optimization libraries: Platypus (multi-objective optimization), PyGAD (genetic algorithm), PySwarms (particle swarm);

[0087] Parallel computing: Evaluate multiple candidate paths in parallel using HPC clusters or GPU acceleration;

[0088] Convergence criterion: terminate when the objective function improvement falls below a threshold or reaches the maximum number of iterations;

[0089] The optimal path is input into the simulation environment again to verify the strain distribution and collagen activation to obtain the predicted effect;

[0090] Prediction results: Quantitative safety and activation indicators obtained in simulation based on the optimal path, such as maximum strain, activation area ratio, temperature rise, etc.

[0091] Treatment

[0092] Secondary simulation: Under the same environment settings, replace the initial path with the optimal path E and rerun the analysis;

[0093] Indicator extraction: automated script calculation of key results (strain cloud map extraction, thermal field analysis, activation threshold statistics);

[0094] Qualification judgment: Compare with the preset safety range (such as ε<0.2, ΔT<1℃, activation area>90%) to confirm that the solution meets the requirements.

[0095] Implementation methods

[0096] Automation script: Combined with simulation software API, generate and submit secondary simulation tasks;

[0097] Result analysis: Use Python pandas, NumPy or Matlab to generate statistics and visualize comparison charts;

[0098] Generating a clinical operation guidance plan based on the predicted effect for achieving the expected effect of anti-aging and facial shaping;

[0099] Clinical operation guidance plan: Convert numerical simulation and optimization results into operator-friendly technique instructions or real-time navigation commands.

[0100] Data format conversion: export 3D trajectory and angle / velocity parameters to JSON, XML, or proprietary instruction sets;

[0101] Navigation integration: Loading trajectories and camera perspectives in AR / VR platforms (Unity3D, Unreal) to generate a dynamic navigation interface;

[0102] Documentation and training: Output detailed operation manual, including path diagram, force-angle recommended curve and precautions.

[0103] AR navigation: Microsoft HoloLens2 or Magic Leap2, and use MRTK (Mixed Reality Toolkit) for secondary development;

[0104] Operation manual: 3D animation explanation based on GPU rendering, PDF embedded interactive model;

[0105] On-site training: Combining a simulated human model with real-time navigation, supplemented by tactile feedback equipment, strengthens the operator's memory and grasp of the optimal path.

[0106] Specifically, obtaining the facial tissue mechanical parameters includes using ultrasound elastography or magnetic resonance elastography to obtain the elastic moduli of the four layers of fascia, muscle, fat and bone, which are used to calibrate the interlayer mechanical properties and obtain the tissue mechanical parameter calibration results.

[0107] Specifically, the fusing of the facial surface point cloud model with the tissue mechanics parameter calibration result includes:

[0108] Register point cloud models with medical images;

[0109] Segmentation of tissue layers based on convolutional neural networks;

[0110] The segmentation results are mapped to the corresponding mechanical parameters to generate a layered three-dimensional mesh model with physical properties, thereby obtaining a layered three-dimensional structure model.

[0111] Specifically, when performing the finite element numerical simulation, the material constitutive model adopts a hyperelastic or dual network model, and boundary conditions and contact parameters are set to obtain the stress-strain distribution of the tissue under the action of the initial fascia knife path.

[0112] Specifically, the parameterized expression of the fascia knife action path is:

[0113] P(t)=(x(t),y(t),z(t)),t∈[0,1]

[0114] With tool angle and speed parameters α(t), v(t);

[0115] P(t): trajectory coordinates of the fascia knife in three-dimensional space;

[0116] X(t), y(t) and z(t) are the parameters of the x, y and z axes respectively;

[0117] α(t): cutting angle, deflection angle relative to the normal;

[0118] v(t): Sliding velocity, affecting shear rate and thermal effect.

[0119] Specifically, the optimal path was input into the simulation environment again and the temperature field coupling was set to verify that the local strain was ≤0.2%, the temperature rise was ≤1°C, and the collagen activation prediction area was ≥90% to obtain the final prediction effect.

[0120] Specifically, the prediction results are converted into augmented reality (AR) navigation instructions, which are used to guide the operator in real time during surgery to complete the fascia knife technique along the optimal path.

[0121] From the above, we can see that multimodal imaging technology is used to obtain the geometric and mechanical properties of different tissue layers such as the patient's skin, fascia, muscle and bone, and to construct a layered three-dimensional model with physiological and mechanical responses.

[0122] By accurately simulating the mechanical effects of the fascia knife between tissue layers, the local stress-strain distribution can be quantitatively evaluated, enabling personalized matching of the operation path and cutting parameters at the micron and angle levels, far exceeding the accuracy of traditional empirical techniques.

[0123] Completing the full-process numerical simulation before clinical implementation can pre-identify areas of high strain or high heat accumulation and avoid blind trial and error during surgery.

[0124] Example 2:

[0125] Cheek lift and collagen activation for 30-year-old women

[0126] 1. Patient Information

[0127] Gender / Age: Female / 30 years old

[0128] Chief complaint: Mild sagging of the cheeks on both sides, decreased skin elasticity, and slightly blurred facial contours

[0129] 2. Data Acquisition

[0130] Optical 3D scanning

[0131] Equipment: Structured light 3D scanner (resolution ≤ 0.1mm)

[0132] Output: Facial surface point cloud A1 (about 2 million points)

[0133] Ultrasound elastography

[0134] Equipment: High-frequency linear array probe (10MHz), Swept-frequency mode

[0135] Get the elastic modulus of four layers of tissue:

[0136] Epidermis: μ1=15kPa

[0137] Fascia layer: μ2=8kPa

[0138] Muscle layer: μ3 = 12 kPa

[0139] Bone layer: μ4 = 2500kPa

[0140] Binding bulk modulus κi = 10 × μi

[0141] 3. Model Construction

[0142] The point cloud is registered with the elastic imaging data and segmented based on U-Net to generate a hierarchical grid C1 with a total of approximately 120k nodes and 450k cells.

[0143] Neo-Hookean constitutive parameters are applied: C1,i=μi / 2; D1,i=κi / 2.

[0144] 4. Initial simulation

[0145] Finite element platform: FEBio

[0146] Boundary conditions: mandible fixed, facial soft tissue free deformation

[0147] Initial path P0: along the tension line of the cheek fascia, sliding from the outer edge of the zygomatic bone to the mandibular edge

[0148] Cutting angle α0 = 15°, speed v0 = 5 mm / s

[0149] The maximum principal strain εmax0 = 0.12, the average strain εˉ0 = 0.06, and the activation volume fraction C0 = 68% are obtained.

[0150] 5. Optimization Iteration (E1)

[0151] Optimization algorithm: genetic algorithm

[0152] Objective function weights: w1:w2:w3=1.0:0.8:1.2

[0153] Parameterization: The entry point is fine-tuned along the lateral side of the zygomatic bone by ±5 mm, α∈[5°,30°], v∈[3,8] mm / s. The optimal result after 100 iterations is:

[0154] Path length L1 = 18 mm

[0155] Maximum strain εmax1=0.085

[0156] Average strain εˉ1=0.055

[0157] Collagen activation C1=91%

[0158] Total damage energy Edmg1 is 27% lower than Edmg0

[0159] 6. Verification and technical effects

[0160] Simulation verification again: local temperature rise ΔT < 0.8℃

[0161] Comparison of postoperative 3D scans: The cheeks were lifted by about 2.3mm, and the contours became tighter and more natural.

[0162] The amount of collagen regeneration (via skin biopsy) was approximately 35% higher than with the unoptimized regimen.

[0163] From the above, we can see that parameterized path planning enables the coordinated optimization of the three elements of cutting depth, sliding angle and speed, theoretically minimizing the risk of damage to peripheral nerves and blood vessels.

[0164] The initial operation strategy is imported into the simulation environment to generate a baseline response, and then continuously iterated through multi-objective optimization algorithms (such as genetic, particle swarm, etc.) to achieve a closed loop of "design-simulation-evaluation-optimization", thereby improving the robustness and stability of the solution.

[0165] Example 3:

[0166] Improvement of nasolabial folds and regional collagen activation in a 55-year-old male

[0167] 1. Patient Information

[0168] Gender / Age: Male / 55 years old

[0169] Chief complaint: deep nasolabial folds, drooping corners of mouth, and loose skin;

[0170] 2. Data Acquisition

[0171] Structured light 3D scanning

[0172] Resolution 0.15mm, point cloud A2≈1.5 million points

[0173] Magnetic resonance elastography (MRE)

[0174] Carrier frequency 50Hz, whole skull imaging

[0175] Elastic modulus:

[0176] Epidermis μ1=12kPa

[0177] Fascia layer μ2=7kPa

[0178] Muscle layer μ3=10kPa

[0179] Bone layer μ4=2400kPa

[0180] Bulk modulus κi=15×μi

[0181] 3. Model Construction

[0182] The point cloud is registered with the MRE data. The mesh C2 after four-layer segmentation has 100k nodes and 400k elements. The Neo-Hookean parameter settings are the same as those in Example 2.

[0183] 4. Initial simulation (D2)

[0184] Platform: Abaqus

[0185] Boundary conditions: the temporal region is fixed and the lower part of the face is free;

[0186] The initial path P0 slides downward along the nasolabial groove;

[0187] a0=20°,v0=4mm / s

[0188] Results: εmax0 = 0.14, εˉ0 = 0.065%

[0189] 5. Optimization Iteration (E2)

[0190] Algorithm: Particle Swarm Optimization

[0191] Weight: w1:w2:w3=1.2:1.0:1.0

[0192] Parameter space: entry point ±4 mm, α∈[10°,25°], v∈[2,6] mm / s

[0193] The best result after 200 iterations:

[0194] L2=12mm

[0195] Emax2=0.09

[0196] εˉ2=0.058

[0197] C2=88%

[0198] Edmg2 decreased by 22% compared with the initial level

[0199] 6. Verification and technical effects

[0200] Coupled thermal-mechanical simulation: ΔT<1°C, no significant thermal damage risk;

[0201] Postoperative 3D scan: nasolabial folds decreased in depth by 1.8mm, and mouth corners lifted by 1.2mm;

[0202] Ultrasound measurement at 3 months postoperative follow-up showed that collagen thickness increased by 28% compared with preoperative level;

[0203] The above two cases verified the excellent effects of this scheme in cheek lifting and nasolabial fold improvement through different imaging methods (optical scanning + ultrasound elastography and optical scanning + magnetic resonance elastography), optimization algorithms (genetic algorithm and particle swarm algorithm), and parameter settings:

[0204] High coverage (activation rate > 88%),

[0205] Low damage (local maximum strain <0.09),

[0206] Predictable (temperature rise <1°C),

[0207] Quantifiable clinical gains (cortical lift, collagen thickness increase 30%+).

[0208] As can be seen from the above, the optimization process can be quickly completed on the computing platform without repeated human trials, significantly shortening the cycle from program design to clinical application. The optimal path and technique parameters finally generated can be converted into AR / VR navigation instructions, guiding the operator to complete the operation according to the preset trajectory in real time. During the navigation process, the operation perspective and entry status can be fed back in real time, ensuring that different operators can reproduce the same precise plan in different environments, improving the efficiency of technology inheritance and training. Preoperatively, the stress-strain and collagen activation prediction areas of each layer of tissue are obtained through simulation, and after surgery, they are compared through three-dimensional scanning or elastic imaging to form a quantifiable effect evaluation index system.

[0209] A comprehensive quantitative evaluation process helps validate the effectiveness of the proposed approach and provides a scientific basis for subsequent optimization and individualized adjustments. This method integrates imaging engineering, biomechanics, numerical simulation, and intelligent optimization algorithms, introducing a systematic, quantifiable approach to research, development, and implementation in the field of anti-aging and body shaping. The theoretical framework is highly scalable and can be extended to soft tissue treatment and remodeling in other areas of the body, providing a new research and application paradigm for minimally invasive aesthetic and regenerative medicine.

[0210] In the description of this specification, the reference terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0211] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0212] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional facial anti-aging shaping technology, characterized in that: include: Acquire facial external geometric data to generate a facial surface point cloud model, thereby obtaining a facial surface point cloud model; Obtain facial tissue mechanical parameters, which are used to calibrate the elastic modulus and shear modulus of each layer, and obtain tissue mechanical parameter calibration results; Fusing the facial surface point cloud model with the tissue mechanics parameter calibration result to construct a layered three-dimensional structure model, thereby obtaining a layered three-dimensional structure model; Inputting the layered three-dimensional structural model into a computer simulation environment for performing finite element numerical simulation to obtain initial simulation results; Performing path parameter optimization processing on the initial simulation results to adjust the action path of the fascia knife and obtain an optimal path; The optimal path is input into the simulation environment again to verify the strain distribution and collagen activation to obtain the predicted effect; A clinical operation guidance plan is generated based on the predicted effect to achieve the expected effect of facial anti-aging and shaping.

2. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: The acquisition of facial external geometric data uses a structured light three-dimensional scanner to scan the face at multiple angles to obtain a facial surface point cloud model.

3. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: The acquisition of facial tissue mechanical parameters includes obtaining the elastic moduli of four layers, fascia, muscle, fat and bone, using ultrasonic elastography or magnetic resonance elastography to calibrate the mechanical properties between layers and obtain tissue mechanical parameter calibration results.

4. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: The fusing of the facial surface point cloud model and the tissue mechanics parameter calibration result comprises: Register point cloud models with medical images; Segmentation of tissue layers based on convolutional neural networks; The segmentation results are mapped to the corresponding mechanical parameters to generate a layered three-dimensional mesh model with physical properties, thereby obtaining a layered three-dimensional structure model.

5. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: When performing the finite element numerical simulation, the material constitutive model adopts a hyperelastic or dual-network model, and boundary conditions and contact parameters are set to obtain the stress-strain distribution of the tissue under the action of the initial fascia knife path.

6. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: The parameterized expression of the fascia knife action path is: P(t)=(x(t),y(t),z(t)),t∈[0,1] With tool angle and speed parameters α(t), v(t); P(t): trajectory coordinates of the fascia knife in three-dimensional space; X(t), y(t) and z(t) are the parameters of the x, y and z axes respectively; α(t): cutting angle, deflection angle relative to the normal; v(t): Sliding velocity, affecting shear rate and thermal effect.

7. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: The optimal path was re-entered into the simulation environment and the temperature field coupling was set to verify that the local strain was ≤0.2%, the temperature rise was ≤1°C, and the collagen activation prediction area was ≥90% to obtain the final prediction effect.

8. The three-dimensional facial anti-aging shaping technology according to claim 1 is characterized in that: The prediction results are converted into augmented reality (AR) navigation instructions, which are used to guide the operator in real time during surgery to complete the fascia knife technique along the optimal path.

Images