A minimally invasive surgical simulation method and system based on virtual reality

Abstract

The present invention discloses a method and system for simulating minimally invasive surgical operations based on virtual reality, which specifically relates to the field of numerical simulation technology; by constructing a high-precision three-dimensional anatomical model, collecting the motion trajectory and force feedback data of surgical instruments, and establishing a multimodal interaction model WR, the simulation of a real surgical environment is realized; the method uses a physical engine and a multi-layer tissue mechanics model to calculate the contact force, shear force and deformation response between surgical instruments and virtual tissues, and optimizes the visual-force synchronous perception in VR training through error analysis, thereby reducing the spatial perception deviation of doctors in training. Furthermore, the present invention evaluates the doctor's operating accuracy based on the surgical trajectory stability value, and improves the authenticity and reliability of VR training through AI-driven adaptive optimization, ensuring that the doctor's operation in real surgery is more accurate, reducing the risk of intraoperative misoperation, and improving surgical safety and success rate.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical simulation, and in particular to a method and system for simulating minimally invasive surgery based on virtual reality. Background Art

[0002] The virtual reality-based minimally invasive surgical simulation method is a technical method that uses virtual reality (VR) technology to build a realistic surgical simulation environment to help doctors conduct surgical training, preoperative planning, and improve surgical skills. When training minimally invasive hepatobiliary surgery in a VR environment, doctors use electrocoagulation knives to perform bile duct dissection operations. The VR system simulates the elasticity and deformation of tissues based on a physics engine, but due to visual depth perception bias (for example, the parallax of the VR display does not match reality), doctors may form incorrect muscle memory during training, mistakenly believing that slight contact will not penetrate the tissue. When doctors repeat this operation in a real operation, the inertial force of the actual instrument and the different mechanical properties of the tissue may cause accidental puncture of the bile duct or aggravated thermal damage, thereby causing complications such as bile leakage and postoperative infection. Summary of the invention

[0003] The purpose of the present invention is to provide a minimally invasive surgical simulation method and system based on virtual reality to address the deficiencies in the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a minimally invasive surgical simulation method based on virtual reality, comprising:

[0005] Obtain medical imaging data of the surgical site and construct a corresponding high-precision three-dimensional anatomical model;

[0006] Collect the instrument motion data and force feedback data during the operation, and establish a multimodal interaction model WR for minimally invasive surgery, where WR includes: , is the motion trajectory sequence of minimally invasive surgical instruments, and m is the number of operation steps; , is the force feedback data set, k is the number of force feedback sampling points;

[0007] Calculate the contact force, shear force and tissue deformation between the surgical instrument and the virtual tissue model, and simulate the tissue response through a multi-layer tissue mechanics model;

[0008] Based on the A and F data in WR, multi-dimensional error analysis was performed to optimize the synchronous perception between the doctor's vision and force perception in VR training;

[0009] Calculate the surgical trajectory stability value H of the doctor in WR training, and based on the set threshold Determine whether the training convergence conditions are met: If , and re-optimize; otherwise, output the doctor’s operation evaluation results in VR training and provide surgical accuracy optimization suggestions.

[0010] Preferably, the medical imaging data includes CT images, MRI images or ultrasound images, and the three-dimensional anatomical model is reconstructed based on the Marching Cubes algorithm and combined with the U-Net deep learning algorithm for automatic tissue segmentation to obtain high-precision anatomical structures of the skin layer, muscle layer, blood vessel layer and nerve layer.

[0011] Preferably, the surgical instrument motion data A is collected through VR equipment, an inertial measurement unit and an optical tracking system, and the rotation angle information of the instrument is recorded in Euler angles to ensure that the spatial accuracy error of the surgical trajectory is less than 0.1 mm; the force feedback data F is collected in real time by a force sensor installed on the surgical instrument, and includes contact force and torque information in the X, Y and Z directions, and a sampling rate of more than 1 kHz is used to ensure that the force feedback delay is less than 5 ms.

[0012] Preferably, the multilayer tissue mechanics model is based on finite element analysis and uses the Neo-Hookean nonlinear elastic model to calculate the deformation response of the tissue, wherein the elastic moduli of skin, muscle, blood vessel and nerve are set to 0.5 MPa, 2 MPa, 1.5 MPa and 0.8 MPa, respectively.

[0013] Preferably, the trajectory error E is calculated and corrected: Calculate the error: ; Aactual is the actual trajectory of the surgical instrument, and Avirtual is the trajectory of the surgical instrument simulated by VR; if E > 0.5mm, the system automatically adjusts the VR parallax and provides tactile feedback prompts; calculate the force feedback error ΔF and compensate it: Calculate the error: ; Factual is the force feedback data obtained by actual measurement, and Fvirtual is the virtual force feedback data obtained by VR simulation; if ΔF > 0.2N, the system automatically adjusts the damping coefficient of the VR handle.

[0014] Preferably, the trajectory stability calculation formula is: ; Where: H is the stability value of the surgical trajectory; m is the total number of surgical steps; is the spatial position of the surgical instrument at step j; is the position of the surgical instrument at step j-1; L is the standard length of the target trajectory.

[0015] Preferably, a trajectory stability threshold is set, H ≥ 0.9, indicating that the trajectory is stable and the training is completed; 0.8 ≤ H < 0.9, indicating that the trajectory is basically stable and local operations can be optimized; H < 0.8, indicating that the trajectory is unstable and needs to be retrained.

[0016] Preferably, if the doctor's instrument trajectory deviation is greater than 0.5mm, the VR tactile feedback parameters are automatically adjusted to enhance the instrument operation accuracy; if the doctor's hand shaking amplitude is > 5°, the system adjusts the force feedback intensity to optimize the instrument stability.

[0017] The present invention also provides a minimally invasive surgical simulation system based on virtual reality, comprising a three-dimensional anatomical modeling module, a data acquisition module, a physical simulation module, a synchronization optimization module and a stability evaluation module;

[0018] 3D anatomical modeling module: obtains medical imaging data of the surgical site and constructs a corresponding high-precision 3D anatomical model;

[0019] Data acquisition module: collects instrument motion data and force feedback data during surgery, and establishes a multimodal interaction model WR for minimally invasive surgery, where WR includes: , is the motion trajectory sequence of minimally invasive surgical instruments, and m is the number of operation steps; , is the force feedback data set, k is the number of force feedback sampling points;

[0020] Physical simulation module: calculates the contact force, shear force and tissue deformation between the surgical instrument and the virtual tissue model, and simulates the tissue response through a multi-layer tissue mechanics model;

[0021] Synchronous optimization module: Based on the A and F data in WR, multi-dimensional error analysis is performed to optimize the synchronous perception between the doctor's vision and force perception in VR training;

[0022] Stability evaluation module: Calculates the surgical trajectory stability value H of the doctor in WR training and sets a threshold value based on the value. Determine whether the training convergence conditions are met: If , and re-optimize; otherwise, output the doctor’s operation evaluation results in VR training and provide surgical accuracy optimization suggestions.

[0023] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0024] 1. The present invention achieves accurate simulation of instrument-tissue interaction during surgery by constructing a high-precision three-dimensional anatomical model, collecting instrument motion data and force feedback data, and establishing a multimodal interaction model WR. The present invention uses finite element analysis and the Neo-Hookean nonlinear elastic model to accurately calculate the deformation response of tissues, and combines high-frequency force feedback sensors and visual-force synchronization optimization mechanisms to effectively reduce the perceptual errors of doctors in VR training and improve the accuracy of surgical operations. At the same time, the system evaluates the doctor's operational stability by calculating the surgical trajectory stability value H, and uses AI adaptive optimization to adjust the VR tactile feedback parameters, so that doctors can repeatedly train in a safe virtual environment, master more precise surgical skills, and reduce the risk of misoperation in real surgery.

[0025] 2. The present invention has greatly improved the realism and training effectiveness of VR surgical simulation by realizing low-latency force feedback (<5ms), high-precision trajectory capture (error <0.1mm) and real-time error compensation (E> 0.5mm automatic adjustment) in VR training. Through AI error analysis and personalized training optimization, doctors can conduct intensive exercises for specific surgical steps to improve surgical trajectory stability and operation consistency. The present invention is suitable for minimally invasive surgery training such as laparoscopy, neurosurgery, and robotic surgery, which can effectively shorten the doctor's learning curve, improve the success rate of surgery, and reduce intraoperative risks, thereby improving overall medical safety and patient prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0027] Figure 1 The figure is a flow chart of the method of the present invention.

[0028] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1, please refer to Figure 1 As shown, the virtual reality-based minimally invasive surgical simulation method described in this embodiment includes:

[0031] Obtain medical imaging data of the surgical site and construct a corresponding high-precision three-dimensional anatomical model;

[0032] Collect the instrument motion data and force feedback data during the operation, and establish a multimodal interaction model WR for minimally invasive surgery, where WR includes: , is the motion trajectory sequence of minimally invasive surgical instruments, and m is the number of operation steps; , is the force feedback data set, k is the number of force feedback sampling points;

[0033] Calculate the contact force, shear force and tissue deformation between the surgical instrument and the virtual tissue model, and simulate the tissue response through a multi-layer tissue mechanics model;

[0034] Based on the A and F data in WR, multi-dimensional error analysis was performed to optimize the synchronous perception between the doctor's vision and force perception in VR training;

[0035] Calculate the surgical trajectory stability value H of the doctor in WR training, and based on the set threshold Determine whether the training convergence conditions are met: If , and re-optimize; otherwise, output the doctor’s operation evaluation results in VR training and provide surgical accuracy optimization suggestions.

[0036] This embodiment uses CT (computed tomography) and MRI (magnetic resonance imaging) image data to obtain the anatomical structure of the patient's surgical site, and generates a high-precision virtual surgical model through three-dimensional reconstruction technology for use in virtual reality (VR) minimally invasive surgical simulation training.

[0037] The data collection process is as follows:

[0038] CT scan: Use a 64-slice spiral CT scanner (such as Siemens SOMATOM Force) to perform high-resolution scans of the patient's target surgical area (such as the hepatobiliary system and nervous system).

[0039] The acquired CT image parameters are as follows: slice thickness: 0.5 mm (to ensure clear tissue boundaries), resolution: 512 × 512 pixels, scanning mode: dual-energy CT enhancement mode to improve soft tissue contrast.

[0040] MRI Scan: Using a 3T magnetic resonance scanner (such as the GE SIGNA Premier), high-resolution imaging of the target area is performed to enhance soft tissue visualization.

[0041] The collected MRI image parameters are as follows: T1-weighted imaging (T1WI): used to observe the anatomical structure; T2-weighted imaging (T2WI): used to identify lesions, blood vessels, and nerve distribution; DWI (diffusion-weighted imaging): used to detect tissue lesions; spatial resolution: 0.3mm×0.3mm×1mm.

[0042] Medical image data processing and 3D reconstruction: Gaussian filtering is used to remove noise from CT images and improve edge clarity. CLAHE is used to enhance the contrast of MRI images and highlight soft tissue details.

[0043] The U-Net deep learning segmentation algorithm is used to automatically segment CT / MRI images. Key tissues such as bones, muscles, blood vessels, and nerves are independently labeled. The Dice similarity coefficient (DSC) is calculated to be > 0.95 to ensure the accuracy of the segmentation results.

[0044] The Marching Cubes algorithm is used to convert the segmented medical images into high-precision 3D mesh models. Multi-layer mesh structures of different tissues are generated, including: skin layer (semi-transparent visualization, used for preoperative planning); muscle layer (simulating the mechanical properties of soft tissue); blood vessel layer (providing intraoperative bleeding prediction); nerve layer (assisting doctors in avoiding injury risks). Output formats: STL, OBJ, FBX, can be imported into VR surgical simulation system.

[0045] In order to improve the realism of surgical simulation, the present invention uses a physical engine to assign tissue mechanical parameters to the 3D model, including: modeling of soft tissue physical properties (based on finite element analysis, FEM); using the Neo-Hookean nonlinear elastic model to calculate the deformation response of muscles and skin: skin elastic modulus: 0.5MPa; muscle elastic modulus: 2MPa; vascular wall elastic modulus: 1.5MPa; nerve fiber elastic modulus: 0.8MPa; and through rigid-flexible body coupling calculation, simulating the force of surgical instruments on different tissues.

[0046] The Mass-Spring Model is used to simulate the interaction between the tool and the tissue: cutting accuracy: 0.1mm (to ensure delicate operation during surgery); dynamic bleeding simulation: fluid simulation (based on the Navier-Stokes equation) is used to calculate blood flow. The tactile feedback of surgical instruments on different tissues is simulated, so that doctors can get a more realistic sense of surgery during VR training.

[0047] VR environment integration and surgical simulation testing: Use Unity 3D (physics engine PhysX) or Unreal Engine 5 to build a VR surgical simulation environment. Load high-precision 3D surgical models (STL / FBX format) to achieve real-time rendering and interaction. Compatible VR devices: Support VR headsets such as HTC Vive and Oculus Quest 2 to provide immersive surgical experience.

[0048] Use force feedback devices (such as SensAble Phantom Omni) to provide tactile feedback of surgical instruments. Use low-latency force feedback (<5ms) to ensure that the doctor's force perception and vision are synchronized during VR training.

[0049] Simulate minimally invasive cholecystectomy (LC) in a VR environment. The doctor uses a VR handle to operate laparoscopic instruments and observe tissue response and surgical accuracy. The AI ​​analysis system calculates the doctor's operation trajectory and strength, and automatically evaluates the stability and accuracy of the surgical operation. If the simulation result deviates greatly, the system automatically adjusts the force feedback parameters to optimize the doctor's intraoperative control ability.

[0050] In a VR environment, doctors use VR handles (such as HTC Vive Tracker), force feedback devices (such as PhantomOmni), or data gloves (such as HaptX) to operate minimally invasive surgical instruments. The system records the motion trajectory A of the surgical instruments in real time and establishes a trajectory dataset: , where: m is the number of surgical steps; Represents the three-dimensional spatial coordinates of the surgical instrument; Indicates the rotation angle of the device (expressed in Euler angles); The timestamp of this operation step.

[0051] When doctors use minimally invasive surgical instruments (such as laparoscopic clamps, electrocoagulation scalpels, and ultrasonic scalpels) in a VR environment, the system uses force feedback sensors to record the forces and torques during the surgical operation in real time and establish a force feedback data set F: , where: k is the number of force feedback sampling points; Indicates the force applied to the device in the XYZ direction; Indicates the torque applied to the device in the XYZ direction; The timestamp of the force feedback data.

[0052] In order to improve the accuracy of VR training, the present invention combines the motion trajectory data A and the force feedback data F to construct a multimodal interaction model WR for minimally invasive surgery, which is used to simulate the instrument-tissue interaction in the real surgical process. ,in: , is the set of physical properties of the device-tissue contact point, where: ; is the elastic modulus of the tissue (MPa); is the friction coefficient of the tissue; is the density of the tissue (g / cm³); is the yield strength of the tissue (MPa); is the shear stress threshold of the tissue (MPa).

[0053] Due to the depth perception bias of VR devices, doctors may misjudge the insertion depth of surgical instruments during training. This method calculates the trajectory error E and corrects it: Calculation error: ; Aactual is the actual trajectory of the surgical instrument, and Avirtual is the trajectory of the surgical instrument simulated by VR;

[0054] If E > 0.5 mm, the system automatically adjusts the VR parallax and provides tactile feedback (such as slight vibration of the handle).

[0055] The lag of the force feedback system may cause doctors to form incorrect force memory during VR training. And compensate: Calculate the error: ; Factual is the force feedback data obtained by actual measurement, and Fvirtual is the virtual force feedback data obtained by VR simulation;

[0056] like The system automatically adjusts the damping coefficient of the VR handle to improve the realism of tactile feedback.

[0057] In minimally invasive surgery, doctors use laparoscopic surgical instruments, endoscopic forceps, electrocoagulation knives, etc. to manipulate tissues. The system needs to calculate the forces when the instruments come into contact with tissues, including contact force (Normal Force), shear force (ShearForce) and tissue deformation response.

[0058] Calculation of device-tissue contact forces , that is, the vertical force exerted by the surgical instrument on the tissue, that is, the resistance when the instrument contacts the tissue, and the calculation formula is: ;in: is the equivalent elastic modulus, is the equivalent radius, R is the curvature radius of the instrument and tissue, respectively. δ is the depth of the instrument pressed into the tissue (collected in real time by the VR force feedback device).

[0059] Tissue elastic modulus: liver (E = 0.6 MPa), brain tissue (E = 0.02 MPa), blood vessel wall (E = 1.5MPa); acquisition equipment: high-precision pressure sensor (such as ATI Nano17), tactile feedback device (such as Phantom Omni).

[0060] Calculate the shear force, which is the frictional force generated when the instrument slides on the tissue surface, using the formula: ; Where: μ is the tissue-device friction coefficient (liver: 0.05, skin: 0.8, blood vessel wall: 0.3).

[0061] Laparoscopic shearing of tissue: Shear force calculation determines the doctor's feel for clamping or separating tissue during VR training. Electrocoagulation tissue stripping: During tissue stripping, shear force can be used to simulate the resistance of different tissue layers.

[0062] The present invention adopts a multi-layer tissue mechanics model and establishes high-precision deformation simulation based on the mechanical properties of different tissue layers (skin, muscle, blood vessel, nerve) to enhance the realism of VR surgical training.

[0063] The tissue is modeled as a hierarchical structure, and each layer of tissue has different elastic modulus, viscoelasticity, and shear strength.

[0064] The present invention uses finite element analysis (FEM) to simulate the dynamic deformation of tissues and performs real-time interaction based on VR force feedback equipment. The deformation calculation formula is: ;in: is the stress of the tissue; E is the elastic modulus; is strain; ΔL is the deformation of the tissue after being subjected to force; is the original length of the tissue.

[0065] If the tissue deformation exceeds the yield stress (such as 0.02 MPa for skin and 0.08 MPa for blood vessels), the tissue ruptures and the VR system generates intraoperative bleeding simulation. If the tissue deformation is within the elastic deformation range, the VR system provides tactile feedback (such as handle vibration) to simulate the resilience of the tissue.

[0066] VR interaction optimization and tactile feedback adjustment:

[0067] Low-latency force feedback: Using a 1kHz+ sampling rate, the latency of the force feedback device is reduced to <5ms, ensuring synchronization of touch and vision.

[0068] Simulation of tissue stripping process: When doctors use laparoscopic forceps to strip tissue, dynamic shear resistance is provided to enhance the realism of VR training.

[0069] Intraoperative misoperation warning: If the doctor applies too much pressure during VR surgical training, the system will issue a warning (visual color change + enhanced tactile feedback) to prevent the doctor from forming incorrect muscle memory.

[0070] During VR training, doctors perform a series of surgical operations using surgical instruments (such as laparoscopes, electrocoagulation scalpels, and ultrasonic scalpels). In order to evaluate the doctor's operating accuracy and stability, a trajectory stability value H is defined to measure the doctor's surgical trajectory deviation.

[0071] The trajectory stability calculation formula is: ; Where: H is the surgical trajectory stability value (range 0-1, the closer H is to 1, the more stable the trajectory); m is the total number of surgical steps; is the spatial position of the surgical instrument at step j (including coordinates and angles); is the position of the surgical instrument in the previous step; L is the standard length of the target trajectory (i.e., the ideal distance the instrument should move under the optimal trajectory).

[0072] This method determines whether the doctor has reached the training convergence condition by calculating the H value, and decides whether the training parameters need to be re-optimized. Setting the trajectory stability threshold , set the surgical stability evaluation standard: H ≥ 0.9, indicating that the trajectory is stable and the training is completed; 0.8 ≤ H < 0.9, indicating that the trajectory is basically stable and the local operation can be optimized; H < 0.8, indicating that the trajectory is unstable and needs to be retrained.

[0073] If H< , then enter the training optimization process S400; otherwise, enter the training evaluation process S500:

[0074] If the doctor's instrument trajectory deviation is greater than 0.5mm, the VR tactile feedback parameters are automatically adjusted to enhance the instrument operation accuracy. If the doctor's hand shake amplitude is > 5°, the system adjusts the force feedback intensity to optimize the instrument stability.

[0075] like >H, enter S500 for final evaluation: Output the doctor's operation evaluation report in VR training, analyze its stability in different surgical steps. Provide personalized surgical precision optimization suggestions, such as recommending additional practice of a specific step.

[0076] If the doctor ,This method automatically enters the training optimization process, adjusts the training parameters, and improves the doctor's trajectory stability. Calculate the doctor's trajectory error E. If E > 0.5mm, then: adjust the VR parallax to optimize the doctor's spatial perception ability. Enhance tactile feedback to ensure that the doctor obtains correct mechanical information during the operation.

[0077] If the doctor's stability in certain operation steps is lower than 0.8 (such as clamping, cutting, suturing), the system automatically recommends additional training for this step. The AI ​​learning algorithm is used to analyze the doctor's operation mode and optimize the force feedback parameters to improve the operation stability.

[0078] After the training is completed, the system generates a surgical trajectory stability assessment report, including: H value change curve (the doctor's trajectory stability changes with the number of training times), trajectory error analysis of each surgical step, and force feedback data comparison (initial vs. after training).

[0079] Based on the doctor's training data, the system provides targeted optimization suggestions: recommending intensive training steps (such as improving suturing accuracy and reducing clamping force); adjusting the difficulty of VR training (if the doctor's operation is stable, the training complexity can be increased); and intraoperative error analysis (marking the doctor's potential errors in training to prevent recurrence in real surgery).

[0080] Example 2, please refer to Figure 2 As shown, the virtual reality-based minimally invasive surgical simulation system described in this embodiment includes a three-dimensional anatomical modeling module, a data acquisition module, a physical simulation module, a synchronization optimization module and a stability evaluation module;

[0081] 3D anatomical modeling module: obtains medical imaging data of the surgical site and constructs a corresponding high-precision 3D anatomical model;

[0082] Data acquisition module: collects instrument motion data and force feedback data during surgery, and establishes a multimodal interaction model WR for minimally invasive surgery, where WR includes: , is the motion trajectory sequence of minimally invasive surgical instruments, and m is the number of operation steps; , is the force feedback data set, k is the number of force feedback sampling points;

[0083] Physical simulation module: calculates the contact force, shear force and tissue deformation between the surgical instrument and the virtual tissue model, and simulates the tissue response through a multi-layer tissue mechanics model;

[0084] Synchronous optimization module: Based on the A and F data in WR, multi-dimensional error analysis is performed to optimize the synchronous perception between the doctor's vision and force perception in VR training;

[0085] Stability evaluation module: Calculates the surgical trajectory stability value H of the doctor in WR training and sets a threshold value based on the value. Determine whether the training convergence condition is met: If H < , and re-optimize; otherwise, output the doctor’s operation evaluation results in VR training and provide surgical accuracy optimization suggestions.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0088] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A minimally invasive surgical simulation method based on virtual reality, characterized in that: include: Obtain medical imaging data of the surgical site and construct a corresponding high-precision three-dimensional anatomical model; Collect instrument motion data and force feedback data during surgery, and establish a multimodal interaction model WR for minimally invasive surgery, where WR includes: , is the motion trajectory sequence of minimally invasive surgical instruments, and m is the number of operation steps; , is the force feedback data set, k is the number of force feedback sampling points; Calculate the contact force, shear force and tissue deformation between the surgical instrument and the virtual tissue model, and simulate the tissue response through a multi-layer tissue mechanics model; The multilayer tissue mechanics model is based on finite element analysis and uses the Neo-Hookean nonlinear elastic model to calculate the deformation response of the tissue, where the elastic moduli of the skin, muscle, blood vessel and nerve are set to 0.5 MPa, 2 MPa, 1.5 MPa and 0.8 MPa, respectively; According to the A and F data in WR, multi-dimensional error analysis is performed to optimize the synchronous perception between the doctor's vision and force perception in VR training; the surgical trajectory stability value H of the doctor in VR training is calculated, and based on the set threshold H0, it is determined whether the training convergence condition is met: if , and re-optimize; otherwise, output the doctor's operation evaluation results in VR training and provide surgical accuracy optimization suggestions; Calculate the trajectory error E and make corrections: Calculate the error: ; Aactual is the actual trajectory of the surgical instrument, and Avirtual is the trajectory of the surgical instrument simulated by VR; if E > 0.5mm, the system automatically adjusts the VR parallax and provides tactile feedback prompts; calculate the force feedback error And compensate: Calculate the error: ; Factual is the force feedback data obtained by actual measurement, and Fvirtual is the virtual force feedback data obtained by VR simulation; if , the system automatically adjusts the damping coefficient of the VR handle; The trajectory stability calculation formula is: ; Where: H is the stability value of the surgical trajectory; m is the total number of surgical steps; is the spatial position of the surgical instrument at step j; for The position of the surgical instrument at the next step; L is the standard length of the target trajectory.

2. The method for simulating minimally invasive surgery based on virtual reality according to claim 1, characterized in that: The medical imaging data includes CT images, MRI images or ultrasound images. The three-dimensional anatomical model is reconstructed based on the MarchingCubes algorithm and combined with the U-Net deep learning algorithm for automatic tissue segmentation to obtain high-precision anatomical structures of the skin layer, muscle layer, blood vessel layer and nerve layer.

3. The method for simulating minimally invasive surgery based on virtual reality according to claim 1, characterized in that: The surgical instrument motion data A is collected through VR equipment, inertial measurement unit and optical tracking system, and the rotation angle information of the instrument is recorded in Euler angles to ensure that the spatial accuracy error of the surgical trajectory is less than 0.1mm; the force feedback data F is collected in real time by the force sensor installed on the surgical instrument, and includes contact force and torque information in the X, Y and Z directions. The sampling rate is above 1kHz to ensure that the force feedback delay is less than 5ms.

4. The method for simulating minimally invasive surgery based on virtual reality according to claim 1, characterized in that: Set the trajectory stability threshold, H ≥ 0.9, indicating that the trajectory is stable and the training is complete; 0.8 ≤ H < 0.9, indicating that the trajectory is basically stable and local operations can be optimized; H < 0.8, indicating that the trajectory is unstable and needs to be retrained.

5. The method for simulating minimally invasive surgery based on virtual reality according to claim 4, characterized in that: If the doctor's instrument trajectory deviation is greater than 0.5mm, the VR tactile feedback parameters will be automatically adjusted to enhance the instrument operation accuracy; if the doctor's hand shake amplitude is > 5°, the system will adjust the force feedback intensity to optimize the instrument stability.

6. A virtual reality-based minimally invasive surgical simulation system, used to implement a virtual reality-based minimally invasive surgical simulation method according to any one of claims 1 to 5, characterized in that: It includes 3D anatomical modeling module, data acquisition module, physical simulation module, synchronous optimization module and stability assessment module; 3D anatomical modeling module: obtains medical imaging data of the surgical site and constructs a corresponding high-precision 3D anatomical model; Data acquisition module: collects instrument motion data and force feedback data during surgery, and establishes a multimodal interaction model WR for minimally invasive surgery, where WR includes: , is the motion trajectory sequence of minimally invasive surgical instruments, and m is the number of operation steps; , is the force feedback data set, k is the number of force feedback sampling points; Physical simulation module: calculates the contact force, shear force and tissue deformation between the surgical instrument and the virtual tissue model, and simulates the tissue response through a multi-layer tissue mechanics model; Synchronous optimization module: Based on the A and F data in WR, multi-dimensional error analysis is performed to optimize the synchronous perception between the doctor's vision and force perception in VR training; Stability evaluation module: Calculates the surgical trajectory stability value H of the doctor in VR training and sets a threshold value based on the value. Determine whether the training convergence conditions are met: If , and re-optimize; otherwise, output the doctor’s operation evaluation results in VR training and provide surgical accuracy optimization suggestions.

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