Automatic navigation method and system for ablation

Abstract

The invention provides an automatic navigation method and system for ablation, and relates to the technical field of computer-aided medicines.The method comprises the steps that hard constraint conditions and soft constraint conditions of an ablation path are set; based on a hard constraint condition, initializing a plurality of preliminary ablation paths meeting the hard constraint condition; calculating a risk index of each preliminary ablation path based on a soft constraint condition; sorting the initial ablation paths according to the risk indexes from low to high, and selecting the initial ablation path in the front as an initial solution of a sea gull optimization algorithm; optimizing the ablation path through a seagull optimization algorithm, and determining a plurality of candidate ablation paths; sorting the candidate ablation paths according to the fitness from low to high, and selecting the candidate ablation paths in the front for organ injury evaluation; determining an optimal ablation path from the candidate ablation paths according to an organ injury evaluation result; and performing ablation navigation according to the needle insertion position, angle and depth determined by the optimal ablation path.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-assisted medicine, and in particular to an automated ablation navigation method and system. Background Art

[0002] Through precise path planning and real-time feedback, ablation automated navigation improves the accuracy and safety of ablation treatment, reduces doctor operation errors, avoids damage to normal tissues, and improves treatment effects. It not only shortens the operation time and promotes the rapid recovery of patients, but also reduces the doctor's operating pressure and improves surgical efficiency through personalized treatment plans and decision support. It is an important technology in modern medicine to improve treatment quality and patient experience.

[0003] Current ablation navigation technology is mainly completed with the help of medical images to plan the ablation path. However, when facing multiple tumors or cases with deep and complex tumors, the current ablation navigation technology has limited ablation path planning capabilities, making it difficult to conduct a comprehensive risk assessment of the ablation path and unable to assess the potential damage of the path to surrounding tissues and organs, which may lead to improper puncture path selection and irreversible damage or complications. Summary of the invention

[0004] In order to solve the technical problems existing in the current ablation navigation technology, such as limited ablation path planning capability when facing cases with multiple tumors or deep and complex tumor locations, difficulty in comprehensive risk assessment of the ablation path, inability to assess the potential damage of the path to surrounding tissues and organs, which may lead to improper puncture path selection and irreversible damage or complications, the present invention provides an ablation automated navigation method and system.

[0005] The technical solution provided by the embodiment of the present invention is as follows:

[0006] First aspect:

[0007] An embodiment of the present invention provides an automated ablation navigation method, comprising:

[0008] S1: Setting hard and soft constraints of ablation path;

[0009] S2: based on the hard constraint condition, initializing a plurality of preliminary ablation paths satisfying the hard constraint condition;

[0010] S3: Calculating the risk index of each of the preliminary ablation paths based on the soft constraint conditions;

[0011] S4: sorting the preliminary ablation paths in order from low to high according to the risk index, and selecting a first preset number of preliminary ablation paths with the highest sorting as the initial solution of the Seagull optimization algorithm;

[0012] S5: Optimizing the ablation path by using the Seagull optimization algorithm to determine multiple candidate ablation paths;

[0013] S6: sorting the candidate ablation paths in order of fitness from low to high, and selecting a second preset number of candidate ablation paths with the highest rankings for organ damage assessment;

[0014] S7: determining an optimal ablation path from the candidate ablation paths according to the organ damage assessment result;

[0015] S8: Perform ablation navigation according to the needle insertion position, angle and depth determined by the optimal ablation path.

[0016] Second aspect:

[0017] An embodiment of the present invention provides an automated ablation navigation system, comprising:

[0018] processor;

[0019] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the automated ablation navigation method as described in the first aspect is implemented.

[0020] The third aspect:

[0021] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the automated ablation navigation method as described in the first aspect is implemented.

[0022] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0023] In an embodiment of the present invention, hard constraints, soft constraints, the Seagull optimization algorithm and organ damage assessment are used to determine the optimal ablation path through multi-stage path optimization. This can cope with cases with multiple tumors or tumors with deep and complex locations, and can conduct a comprehensive risk assessment of the ablation path to assess the potential damage of the path to surrounding tissues and organs. This can ensure that the ultimately selected path not only has a lower risk index, but also can avoid damage to normal organs to the greatest extent, thereby ensuring the safety and effectiveness of the ablation process and improving the accuracy of path selection and the efficiency of ablation navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of a flow chart of an automated ablation navigation method provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the structure of an automated ablation navigation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0028] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0029] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0030] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0031] Reference Manual Attached Figure 1 , showing a flow chart of an ablation automated navigation method provided by an embodiment of the present invention.

[0032] An embodiment of the present invention provides an ablation automation navigation method, which can be implemented by an ablation automation navigation device, which can be a terminal or a server. The processing flow of the ablation automation navigation method can include the following steps:

[0033] S1: Set the hard and soft constraints of the ablation path.

[0034] Among them, hard constraints refer to the basic requirements that must be met in ablation path planning, mainly including avoiding risk structures (such as important organs, blood vessels, etc.), puncture depth, puncture angle, needle length, etc. These hard constraints ensure the basic safety and feasibility of the ablation process and are the necessary prerequisite for path planning.

[0035] Optionally, the hard constraints specifically include: obstacle avoidance constraint, puncture depth constraint, puncture angle constraint and needle length constraint.

[0036] Obstacle avoidance constraints: The ablation path must avoid risky structures.

[0037] Among them, risk structures can be important organs, blood vessels, etc.

[0038] It should be noted that the obstacle avoidance constraint requires that the ablation path must avoid all risk structures, which minimizes the damage to surrounding healthy tissues and important organs during the ablation process, reduces the incidence of surgical complications, and ensures the safety of the ablation process. By avoiding collisions between the puncture path and risk structures, the risk of serious complications such as bleeding and infection can be reduced.

[0039] Puncture depth constraint: The puncture depth of the ablation path must be greater than the set depth threshold.

[0040] Among them, those skilled in the art can set the depth threshold according to actual conditions, and the present invention does not limit it.

[0041] It should be noted that the puncture depth constraint ensures that the puncture depth of the ablation needle is greater than the set depth threshold, which can ensure that the ablation needle can reach the depth of the tumor and effectively treat it. If the depth is insufficient, the ablation needle may not be able to accurately reach the tumor, affecting the treatment effect. Setting an appropriate depth threshold helps avoid excessive puncture or damage to the surface tissue and ensure the effectiveness and safety of the ablation area.

[0042] Puncture angle constraint: The puncture angle where the ablation path intersects the surface of the ablated organ must be greater than the set angle threshold.

[0043] Among them, those skilled in the art can set the size of the angle threshold according to actual conditions, and the present invention does not limit it.

[0044] It should be noted that the puncture angle constraint requires that the puncture angle where the ablation path intersects the surface of the ablation organ must be greater than the set angle threshold, which can ensure that the ablation needle enters the ablation organ at an appropriate angle when puncturing the ablation organ, thereby better avoiding the blood vessels and other key structures of the ablation organ and reducing the risk of bleeding and other complications. In addition, the appropriate angle helps to ensure the precise positioning of the ablation needle and improve the treatment effect.

[0045] Needle length constraint: The length of the ablation path is less than or equal to the maximum length of the needle.

[0046] It should be noted that the needle length constraint ensures that the length of the ablation path is less than or equal to the maximum length of the needle, which ensures the operability of the ablation path and avoids the situation where the needle is not suitable for actual treatment due to the path being too long. By limiting the path length, it can ensure that the ablation needle can be smoothly inserted into the target location, avoiding the accuracy and safety of the treatment being affected by insufficient or excessive length.

[0047] Among them, soft constraints are optimization requirements for ablation path planning, aiming to further improve the safety and efficiency of the path on the basis of meeting hard constraints. Soft constraints include avoiding the path passing through risky structures as much as possible, increasing the puncture angle, reducing the puncture depth, and reducing the number of CT layers traversed. These soft constraints do not have to be strictly met, but they can optimize path selection, reduce damage to normal tissues, and improve the overall effect of the ablation process and the patient's recovery speed.

[0048] Optionally, the soft constraint conditions specifically include: a first soft constraint, a second soft constraint, a third soft constraint and a fourth soft constraint.

[0049] The first soft constraint: the ablation path should be as far away from the risk structure as possible.

[0050] It should be noted that by avoiding risk structures near the ablated organ to the greatest extent possible, the risk of damage to surrounding normal tissues and organs along the puncture path can be effectively reduced. Reducing contact with these important structures can significantly reduce the incidence of postoperative complications such as bleeding, infection, and organ dysfunction, thereby improving the safety of treatment.

[0051] The second soft constraint: the puncture angle at which the ablation path intersects the surface of the ablated organ should be as large as possible.

[0052] It should be noted that by choosing a larger puncture angle, the stability of the puncture can be ensured, and the ablation needle can better avoid the blood vessels, bile duct and other key structures of the ablation organ, reducing the damage to these sensitive areas during the puncture. A larger puncture angle can usually help ensure that the insertion path of the ablation needle is more perpendicular to the surface of the ablation organ, avoiding excessive puncture of the outer tissue of the ablation organ, thereby ensuring the accuracy and effectiveness of the treatment.

[0053] The third soft constraint: the depth of the ablation path should be as shallow as possible.

[0054] It should be noted that by choosing a shallower ablation path, the puncture depth of the liver surface and other tissues can be reduced, reducing the risk of damage to the liver surface and other adjacent organs during the operation. A shallower path also helps to reduce the difficulty of operating the ablation needle, reduce trauma to the patient, and reduce the occurrence of postoperative complications such as bleeding and infection.

[0055] The fourth soft constraint is that the ablation path should pass through as few CT layers as possible.

[0056] It should be noted that by reducing the number of CT layers passed through, the radiation exposure time of the patient during the operation can be shortened, reducing the potential health risks caused by radiation. In addition, fewer CT layers means that the path planning is simpler and the path complexity is reduced, thereby reducing the difficulty of the operation and improving the efficiency and accuracy of the treatment.

[0057] S2: Based on the hard constraints, multiple preliminary ablation paths that satisfy the hard constraints are initialized.

[0058] Specifically, a path search algorithm (such as A* algorithm, Dijkstra algorithm, etc.) can be used to perform path search in the three-dimensional model to ensure that multiple preliminary ablation paths that meet the hard constraint conditions are found.

[0059] S3: Based on the soft constraints, the risk index of each preliminary ablation path is calculated.

[0060] In a possible implementation, S3 specifically includes sub-steps S301 to S303:

[0061] S301: Counting the distance between each preliminary ablation path and the risk structure, the puncture angle, the puncture depth, and the number of CT layers passed through.

[0062] S302: Normalizing the distance between each preliminary ablation path and the risk structure, the puncture angle, the puncture depth, and the number of CT layers passed through to determine the risk value of the risk structure, the puncture angle, the puncture depth, and the number of CT layers passed through of the preliminary ablation path.

[0063] It should be noted that in path planning, various soft constraints usually have different dimensions and numerical ranges. For example, the distance of a path may be in centimeters, while the angle may be in degrees, and the depth may be in milliseconds or centimeters. The differences between these values ​​will affect their comparison and integration in the calculation. Normalization can convert them to the same numerical range so that they can be weighted compared on the same basis.

[0064] Optionally, S302 specifically includes:

[0065] S3021: normalizing the distance between each preliminary ablation path and the risk structure to determine the risk value of the risk structure of the preliminary ablation path:

[0066]

[0067] Among them, f di represents the risk value of the risk structure of the i-th preliminary ablation path, d i represents the distance between the i-th preliminary ablation path and the risk structure, d max represents the maximum distance between each preliminary ablation path and the risk structure, d min Represents the minimum distance between each preliminary ablation path and the risk structure.

[0068] S3022: normalizing the puncture angles of each preliminary ablation path to determine the puncture angle risk value of the preliminary ablation path:

[0069]

[0070] Among them, f ai represents the puncture angle risk value of the i-th preliminary ablation path, a i represents the puncture angle of the i-th initial ablation path, d max represents the maximum puncture angle in each initial ablation path, d min Indicates the maximum puncture angle in each initial ablation path.

[0071] S3023: normalizing the puncture depth of each preliminary ablation path to determine the puncture depth risk value of the preliminary ablation path:

[0072]

[0073] Among them, f hi represents the puncture depth risk value of the i-th preliminary ablation path, h i represents the puncture depth of the i-th initial ablation path, h max represents the maximum puncture depth in each preliminary ablation path, h min Indicates the maximum puncture depth in each initial ablation path.

[0074] S3024: normalizing the number of CT layers passed by each preliminary ablation path to determine the risk value of the number of CT layers passed by the preliminary ablation path:

[0075]

[0076] Among them, f θi represents the risk value of the number of CT layers passed by the i-th preliminary ablation path, θ irepresents the number of CT layers traversed by the i-th preliminary ablation path, θ max represents the maximum number of CT layers traversed in each preliminary ablation path, θ min Indicates the minimum number of CT layers traversed in each preliminary ablation path.

[0077] S303: Calculate the risk index of the preliminary ablation path according to the risk value of the risk structure, the risk value of the puncture angle, the risk value of the puncture depth, and the risk value of the number of CT layers passed through of the preliminary ablation path.

[0078] Optionally, the risk index in S303 is calculated as follows:

[0079] ρ i =μ d f di +μ a f ai +μ h f hi +μ θ f θi

[0080] Among them, ρ i represents the risk index of the i-th preliminary ablation path, μ d represents the fusion coefficient of the risk structure term, μ a represents the fusion coefficient of the puncture angle term, μ h represents the fusion coefficient of the puncture depth term, μ θ Represents the fusion coefficient across the CT layers.

[0081] Among them, those skilled in the art can set the fusion coefficient μ of the risk structure term according to actual conditions. d , the fusion coefficient μ of the puncture angle term a , the fusion coefficient μ of the puncture depth term h And the fusion coefficient μ across the CT layer number term θ The present invention does not limit the size.

[0082] In the present invention, the calculation method of the risk index can comprehensively evaluate the safety of each preliminary ablation path by combining multiple soft constraints. The risk value of each factor represents the performance of the path in a specific aspect. The comprehensive calculation of these risk values ​​helps to obtain a more comprehensive evaluation and ensure that the path selection takes into account all key safety factors.

[0083] S4: sorting the preliminary ablation paths in order of risk index from low to high, and selecting the first preset number of preliminary ablation paths with the highest sorting as the initial solution of the Seagull optimization algorithm.

[0084] Optionally, the first preset number is specifically 10.

[0085] S5: The ablation path is optimized by the Seagull optimization algorithm to determine multiple candidate ablation paths.

[0086] Among them, the seagull optimization algorithm is a natural heuristic algorithm that simulates the foraging behavior of seagull groups. It optimizes the problem-solving process by simulating the cooperation and competition between seagulls in the process of finding food. In this algorithm, each seagull represents a potential solution, and the seagulls update the quality of the solution through following and attacking behaviors to find the optimal solution. The seagull optimization algorithm has global search capabilities and strong exploratory nature. It can effectively solve complex optimization problems, such as ablation path planning, machine learning parameter tuning, etc. Its advantage is that it can find the global optimal or approximate optimal solution in the solution space, and has good convergence and robustness.

[0087] Optionally, the fitness function of the Seagull optimization algorithm is specifically:

[0088] F(l)=λ d d l +λ a a l -λ h h l -λ h θ l

[0089] Among them, F() represents the fitness function, l represents the ablation path, and d l represents the distance between the ablation path l and the risk structure, λ d represents the weight coefficient of the risk structure item, a l represents the puncture angle of the ablation path l, λ a represents the weight coefficient of the puncture angle term, h l represents the puncture depth of the ablation path l, λ h represents the weight coefficient of the penetration depth term, θ l represents the number of CT layers that the ablation path l passes through, λ θ Represents the weight coefficient of the number of CT layers.

[0090] Among them, those skilled in the art can set the weight coefficient λ of the risk structure item according to actual conditions. d , the weight coefficient of the puncture angle term λ a , the weight coefficient of the puncture depth term λ h And the weight coefficient λ of the number of CT layers θ The present invention does not limit the size.

[0091] Specifically, the ablation path l can be represented in a coded form to facilitate the search of the Seagull optimization algorithm.

[0092] In the present invention, through the design of such a fitness function, multiple factors of the ablation path can be comprehensively considered to comprehensively evaluate the safety and effectiveness of the path, while dynamically adjusting the optimization focus according to different clinical needs and treatment goals.

[0093] The present invention introduces a new Seagull optimization algorithm. The specific method of determining multiple candidate ablation paths by the Seagull optimization algorithm is as follows:

[0094] The preliminary ablation paths are sorted in order from low to high according to the risk index, and the first preset number of preliminary ablation paths with the highest ranking are selected as the initial solution of the seagull optimization algorithm to initialize the seagull individuals. Each seagull individual represents a feasible set of model parameters. Each seagull individual is composed of multiple dimensional components, and each component represents a model parameter.

[0095] In the global search phase, avoid collisions and move towards the optimal individual:

[0096]

[0097] in, represents the position of the i-th seagull individual after the global search phase at the t-th iteration, represents the position of the i-th seagull after anti-collision processing at the t-th iteration, A represents the control factor, represents the position of the i-th seagull at the t-th iteration, represents the displacement of the i-th seagull individual towards the optimal individual at the t-th iteration, B represents the search balance factor, represents the optimal individual position at the tth iteration.

[0098] In the present invention, anti-collision processing ensures that individuals in the search process will not produce unreasonable paths or overlaps, thereby avoiding unnecessary calculations and optimization deviations.

[0099] Furthermore, by controlling factors and search balance factors, the algorithm can balance the relationship between exploration and utilization, so that individuals can maintain a certain degree of randomness during the search process while gradually converging to the optimal solution. This mechanism enhances the global search capability of the algorithm, avoids falling into the local optimal solution, and improves the convergence speed and the accuracy of path optimization.

[0100]

[0101] Among them, t represents the current number of iterations, T represents the maximum number of iterations, and f c Indicates a linearly decreasing frequency.

[0102] In the present invention, the value of the control factor is gradually reduced as the number of iterations increases, so that the search process has a strong exploratory nature in the early stage and can search the solution space extensively. In the later stage, the local search capability is enhanced and gradually concentrated near the optimal solution. This dynamic adjustment can avoid premature convergence in the early stage, ensure the diversity of the search, and accelerate convergence in the later stage, improving the efficiency and stability of the algorithm.

[0103] B=2A 2 r 1

[0104] Among them, r 1 Represents a random number between 0 and 1.

[0105] In the present invention, the random number r 1 This makes the search steps in each iteration have a certain degree of uncertainty, thus preventing the algorithm from falling into the local optimal solution. As the control factor gradually decreases, the search balance factor is also adjusted accordingly, enhancing the flexibility of the search. In this way, the algorithm can explore more solution spaces in the early stage, and converge to the optimal solution more intensively in the later stage, balancing the ability of global search and local optimization, and improving the efficiency and robustness of the algorithm.

[0106] In the local search phase, a random number r is generated 2 , according to the random number r 2 , choose between the spiral search strategy and the bracketing strategy in parallel, and move in a spiral motion:

[0107]

[0108] x=rsinη

[0109] x=rcosη

[0110] z=rη

[0111] r=ue ηv

[0112] in, represents the position of the i-th seagull after spiral motion at the t-th iteration, x represents the spiral flight coefficient in the x-direction, y represents the spiral flight coefficient in the y-direction, z represents the spiral flight coefficient in the z-direction, r represents the radius of the spiral flight trajectory, η represents a random number between 0 and 2π, u and v represent spiral constants, and e represents a natural constant.

[0113] In the present invention, by generating random numbers and selecting between the spiral search strategy and the encirclement strategy in parallel, the flexibility and diversity of the search can be increased. The introduction of the spiral motion mode helps to make the seagull individuals expand and contract in the solution space in a spiral form, thereby avoiding falling into the local optimal solution. This strategy can perform a detailed local search when approaching the optimal solution, and provide more exploration opportunities when far away from the optimal solution, thereby balancing the needs of global search and local optimization. By introducing randomness and spiral paths, the algorithm can more effectively find the optimal solution in the solution space, improving the efficiency and accuracy of the optimization process.

[0114] Perform mutation operations on each seagull individual:

[0115]

[0116] Among them, V i t represents the position of the ith seagull individual after mutation at the tth iteration, P r represents a random individual, and γ represents an adaptive scaling factor.

[0117] In the present invention, the mutation operation can introduce a new solution based on the current solution, making the search

[0118] The process is more flexible, which not only enhances the exploration ability of the algorithm, but also improves the convergence speed, which helps to find a better solution.

[0119]

[0120] Among them, γ max represents the maximum scale factor, γ min represents the minimum scale factor, and sin represents the sine function.

[0121] In the present invention, the dynamic adjustment method can provide a larger variation range in the early stage of the search, thereby enhancing the global exploration ability and preventing the algorithm from falling into the local optimal solution. In the later stage of the iteration, as the scale factor gradually decreases, the algorithm gradually converges to the vicinity of the optimal solution and performs a more refined local search. This gradually reduced variation range ensures a good balance between global search and local optimization, improving the search efficiency and the quality of the final solution.

[0122] Determine whether the fitness value of the position after mutation is greater than the fitness value of the position before mutation. If so, use the position after mutation to replace the position before mutation. Otherwise, keep the position before mutation unchanged.

[0123] Update the fitness value of each seagull individual and the global optimal individual.

[0124] Determine whether the current number of iterations has reached the maximum number of iterations. If so, output the model parameter set representing the seagull individual with the highest current fitness. Otherwise, return to continue iterating.

[0125] In the present invention, the ablation path is optimized by using the Seagull optimization algorithm, which can effectively explore and optimize the path space and determine multiple candidate ablation paths.

[0126] S6: sorting the candidate ablation paths in order of fitness from low to high, and selecting a second preset number of candidate ablation paths with the highest ranking to perform organ damage assessment.

[0127] Optionally, the second preset number is specifically 5.

[0128] S7: Determine the optimal ablation path from the candidate ablation paths according to the organ damage assessment result.

[0129] In a possible implementation, S7 specifically includes sub-steps S701 to S703:

[0130] S701: Functionally zoning the ablated organ.

[0131] Specifically, the functional zoning of ablation organs can be carried out by referring to the current academic research results. For example, for the liver, the current academic research can use the blood vessels in the liver to divide the liver into 8 functionally independent units, namely the eight liver segments, which provides an anatomical basis for liver tumor surgery planning.

[0132] S702: Determine the number of functional partitions that each candidate ablation path passes through, and perform organ damage assessment based on the number of functional partitions passed through. The fewer the number of functional partitions passed through, the less organ damage there is, and the more the number of functional partitions passed through, the greater the organ damage there is.

[0133] It should be noted that by functionally zoning the ablation organ and evaluating organ damage based on the number of functional zones passed by each candidate ablation path, it is possible to ensure that the ablation path minimizes the impact on the function of organs such as the liver. The fewer the number of functional zones passed, the less damage the path causes to normal tissues, thereby reducing the risk of postoperative complications and protecting more healthy liver functional areas.

[0134] S703: Determine the candidate ablation path that passes through the smallest number of functional partitions as the optimal ablation path.

[0135] In the present invention, by selecting a path that passes through fewer functional areas as the optimal path, not only the treatment effect is optimized, but also the safety is improved, and the damage to important functional areas during the ablation process is reduced. Ultimately, it can ensure that the ablation treatment is more accurate, effective and safe, and improve the treatment effect and recovery speed of patients.

[0136] S8: Perform ablation navigation according to the needle insertion position, angle, and depth determined by the optimal ablation path.

[0137] In a possible implementation, S8 specifically includes:

[0138] S801: Paste multiple markers on the patient's abdominal skin surface.

[0139] S802: Based on multiple landmarks, a preliminary registration is performed between the three-dimensional model constructed based on the preoperative image and the patient point cloud:

[0140]

[0141] Among them, L intra represents the coordinates of the landmarks in the patient’s intraoperative point cloud image, T initial represents the initial registration matrix, which is used to roughly align the 3D model constructed based on preoperative images with the patient point cloud. Represents the marker transformation matrix, which is tracked in real time by the optical tracking system. represents the ultrasonic calibration matrix, which represents the relationship between the ultrasonic device coordinate system and the tracking coordinate system. pre Represents the coordinates of the landmarks in the preoperative image.

[0142] In the present invention, the three-dimensional model constructed based on the preoperative image is connected with the patient's point cloud, which can achieve high-precision matching between the preoperative image and the actual point cloud during the operation, thereby ensuring the accuracy of the ablation path, improving the controllability and safety of the treatment effect, and reducing intraoperative errors.

[0143] S803: Based on the preliminary registration, an improved non-rigid ICP algorithm is used to perform forward point matching by minimizing the Euclidean distance:

[0144]

[0145] in, indicates forward point matching, represents the i-th data point in the deformed preoperative 3D model, argmin represents the variable value that minimizes the function value, represents the jth data point in the patient point cloud, P represents the patient point cloud, || || 2 represents the calculation of the second norm, N Q represents the total number of data points in the 3D model constructed based on preoperative images, N P Represents the total number of data points in the patient point cloud.

[0146] It should be noted that by minimizing the Euclidean distance, each data point in the preoperative 3D model is matched with the closest point in the patient's point cloud, thus ensuring the refinement of the initial registration. This method can minimize the errors caused by changes in patient position or anatomical deformation, ensuring that the preoperative model is highly consistent with the actual situation during surgery.

[0147] Backward point matching via global search:

[0148]

[0149] in, Indicates backward point matching.

[0150] It should be noted that, through global search, backward point matching can confirm whether the points in each patient's point cloud have been matched with the points in the preoperative 3D model, thereby further improving the matching accuracy. Backward matching ensures the correctness and consistency of the registration, helps to further refine the matching results and avoid incorrect matching.

[0151] In the present invention, through the combination of forward and backward point matching, the preoperative image and the patient's actual intraoperative anatomy can be accurately aligned, the registration accuracy can be improved, and a reliable basis can be provided for the precise planning of the ablation path, thereby ensuring the safety and effectiveness of the treatment.

[0152] S804: Based on the forward point matching result and the backward point matching result, set the registration cost function:

[0153]

[0154] Where J represents the registration cost function, Q′ represents the data points in the deformed preoperative 3D model, and ω i represents the registration result parameter of the i-th data point in the deformed preoperative 3D model. When the i-th data point in the deformed preoperative 3D model has a corresponding data point, ω i is 1, otherwise it is 0, M i represents the affine transformation matrix for transforming the i-th data point in the preoperative 3D model, ω j Represents the registration result parameter of the jth data point in the patient point cloud. When the jth data point in the patient point cloud has a corresponding data point, ω j is 1, otherwise it is 0, M j represents the affine transformation matrix for transforming the jth data point in the patient point cloud, and α represents the regularization coefficient.

[0155] In the present invention, the registration quality between the preoperative model and the patient point cloud can be accurately evaluated by calculating the registration cost function based on forward point matching and backward point matching. The registration cost function combines the Euclidean distance between matching points and the difference in transformation matrices to optimize the alignment accuracy of the model.

[0156] Furthermore, by introducing the registration result parameters, the validity of the matching points is flexibly considered to avoid the interference of mismatching points on the registration results. At the same time, the regularization term makes the registration process smoother, reduces unnecessary deformation, and ensures more accurate and stable registration results, thereby improving the accuracy of ablation path planning and navigation, and providing reliable guidance for surgery.

[0157] S805: Iterative optimization is performed with the goal of reducing the registration cost function, and the three-dimensional model constructed based on the preoperative image is accurately registered with the patient point cloud.

[0158] S806: Mapping the optimal ablation path to the patient point cloud.

[0159] S807: Perform ablation navigation according to the needle insertion position, angle, and depth determined after mapping.

[0160] In the present invention, after accurate registration is achieved through iterative optimization, the optimal ablation path is mapped to the patient's point cloud to provide accurate guidance for ablation navigation. It can provide accurate path planning in real time during surgery, reduce errors and complications, and ensure that the ablation needle reaches the target area accurately, thereby improving the safety and effectiveness of treatment.

[0161] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0162] In an embodiment of the present invention, hard constraints, soft constraints, the Seagull optimization algorithm and organ damage assessment are used to determine the optimal ablation path through multi-stage path optimization. This can cope with cases with multiple tumors or tumors with deep and complex locations, and can conduct a comprehensive risk assessment of the ablation path to assess the potential damage of the path to surrounding tissues and organs. This can ensure that the ultimately selected path not only has a lower risk index, but also can avoid damage to normal organs to the greatest extent, thereby ensuring the safety and effectiveness of the ablation process and improving the accuracy of path selection and the efficiency of ablation navigation.

[0163] Reference Manual Attached Figure 2 , showing a schematic structural diagram of an ablation automated navigation system provided by the present invention.

[0164] The present invention further provides an ablation automated navigation system 20, which is applied to the above-mentioned ablation automated navigation method, comprising:

[0165] Processor 201.

[0166] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201 , the automated ablation navigation method of the method embodiment is implemented.

[0167] The ablation automated navigation system 20 provided by the present invention can execute the above-mentioned ablation automated navigation method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on them.

[0168] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0169] In an embodiment of the present invention, hard constraints, soft constraints, the Seagull optimization algorithm and organ damage assessment are used to determine the optimal ablation path through multi-stage path optimization. This can cope with cases with multiple tumors or tumors with deep and complex locations, and can conduct a comprehensive risk assessment of the ablation path to assess the potential damage of the path to surrounding tissues and organs. This can ensure that the ultimately selected path not only has a lower risk index, but also can avoid damage to normal organs to the greatest extent, thereby ensuring the safety and effectiveness of the ablation process and improving the accuracy of path selection and the efficiency of ablation navigation.

[0170] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0171] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0172] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0173] 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.

[0174] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0175] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0176] Those skilled 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 the present invention.

[0177] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0178] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0179] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0180] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0181] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., various media that can store program codes.

[0182] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the automated ablation navigation method as described in the method embodiment is implemented.

[0183] A computer-readable storage medium provided by the present invention can implement the steps and effects of the ablation automated navigation method of the above method embodiment, and to avoid repetition, the present invention will not elaborate on them.

[0184] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0185] In an embodiment of the present invention, hard constraints, soft constraints, the Seagull optimization algorithm and organ damage assessment are used to determine the optimal ablation path through multi-stage path optimization. This can cope with cases with multiple tumors or tumors with deep and complex locations, and can conduct a comprehensive risk assessment of the ablation path to assess the potential damage of the path to surrounding tissues and organs. This can ensure that the ultimately selected path not only has a lower risk index, but also can avoid damage to normal organs to the greatest extent, thereby ensuring the safety and effectiveness of the ablation process and improving the accuracy of path selection and the efficiency of ablation navigation.

[0186] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0187] There are a few points to note:

[0188] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0189] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0190] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0191] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An automated ablation navigation method, characterized in that: include: S1: Setting hard and soft constraints of ablation path; S2: based on the hard constraint condition, initializing a plurality of preliminary ablation paths satisfying the hard constraint condition; S3: Calculating the risk index of each of the preliminary ablation paths based on the soft constraint condition; S4: sorting the preliminary ablation paths in order from low to high according to the risk index, and selecting a first preset number of preliminary ablation paths with the highest sorting as the initial solution of the Seagull optimization algorithm; S5: Optimizing the ablation path by using the Seagull optimization algorithm to determine multiple candidate ablation paths; S6: sorting the candidate ablation paths in order of fitness from low to high, and selecting a second preset number of candidate ablation paths with the highest rankings for organ damage assessment; S7: determining an optimal ablation path from the candidate ablation paths according to the organ damage assessment result; S8: Perform ablation navigation according to the needle insertion position, angle and depth determined by the optimal ablation path.

2. The ablation automated navigation method according to claim 1, characterized in that: The hard constraints specifically include: Avoid obstacle constraints: the ablation path must avoid risky structures; Puncture depth constraint: The puncture depth of the ablation path must be greater than the set depth threshold; Puncture angle constraint: The puncture angle at which the ablation path intersects the surface of the ablated organ must be greater than the set angle threshold; Needle length constraint: The length of the ablation path is less than or equal to the maximum length of the needle.

3. The ablation automated navigation method according to claim 1, characterized in that: The soft constraints specifically include: The ablation path should be as far away from the risk structure as possible; The puncture angle where the ablation path intersects the surface of the ablated organ should be as large as possible; The depth of the ablation path should be as shallow as possible; The ablation path should pass through as few CT layers as possible.

4. The ablation automated navigation method according to claim 1, characterized in that: The first preset number is specifically 10, and the second preset number is specifically 5.

5. The ablation automated navigation method according to claim 3, characterized in that: The S3 specifically includes: S301: Counting the distance between each of the preliminary ablation paths and the risk structure, the puncture angle, the puncture depth, and the number of CT layers passed through; S302: normalizing the distance between each of the preliminary ablation paths and the risk structure, the puncture angle, the puncture depth, and the number of CT layers passed through, to determine the risk value of the risk structure, the puncture angle, the puncture depth, and the number of CT layers passed through of the preliminary ablation path; S303: Calculate the risk index of the preliminary ablation path according to the risk value of the risk structure, the risk value of the puncture angle, the risk value of the puncture depth, and the risk value of the number of CT layers passed through of the preliminary ablation path.

6. The ablation automated navigation method according to claim 5, characterized in that: The S302 specifically includes: S3021: normalizing the distance between each of the preliminary ablation paths and the risk structure to determine the risk value of the risk structure of the preliminary ablation path: Among them, f di represents the risk value of the risk structure of the i-th preliminary ablation path, d i represents the distance between the i-th preliminary ablation path and the risk structure, d max represents the maximum distance between each preliminary ablation path and the risk structure, d min represents the minimum distance between each preliminary ablation path and the risk structure; S3022: normalizing the puncture angles of the preliminary ablation paths to determine the puncture angle risk value of the preliminary ablation paths: Among them, f ai represents the puncture angle risk value of the i-th preliminary ablation path, a i represents the puncture angle of the i-th initial ablation path, d max represents the maximum puncture angle in each initial ablation path, d min represents the maximum puncture angle in each initial ablation path; S3023: normalizing the puncture depth of each of the preliminary ablation paths to determine the puncture depth risk value of the preliminary ablation path: Among them, f hi represents the puncture depth risk value of the i-th preliminary ablation path, h i represents the puncture depth of the i-th initial ablation path, h max represents the maximum puncture depth in each preliminary ablation path, h min represents the maximum puncture depth in each preliminary ablation path; S3024: normalizing the number of CT layers passed by each of the preliminary ablation paths to determine a risk value of the number of CT layers passed by the preliminary ablation path: Among them, f θi represents the risk value of the number of CT layers passed by the i-th preliminary ablation path, θ i represents the number of CT layers traversed by the i-th preliminary ablation path, θ max represents the maximum number of CT layers traversed in each preliminary ablation path, θ min Indicates the minimum number of CT layers traversed in each preliminary ablation path; The calculation method of the risk index in S303 is specifically as follows: r i =μ d f di +m a f ai +m h f hi +m θ f θi ; Among them, ρ i represents the risk index of the i-th preliminary ablation path, μ d represents the fusion coefficient of the risk structure term, μ a represents the fusion coefficient of the puncture angle term, μ h represents the fusion coefficient of the puncture depth term, μ θ Represents the fusion coefficient across the CT layers.

7. The ablation automated navigation method according to claim 1, characterized in that: The fitness function of the Seagull optimization algorithm is specifically: F(l)=λ d d l +λ a a l -l h h l -l h i l ; Among them, F() represents the fitness function, l represents the ablation path, and d l represents the distance between the ablation path l and the risk structure, λ d represents the weight coefficient of the risk structure item, a l represents the puncture angle of the ablation path l, λ a represents the weight coefficient of the puncture angle term, h l represents the puncture depth of the ablation path l, λ h represents the weight coefficient of the puncture depth term, θ l represents the number of CT layers that the ablation path l passes through, λ θ Represents the weight coefficient of the number of CT layers.

8. The ablation automated navigation method according to claim 1, characterized in that: The S7 specifically includes: S701: functional zoning of the ablated organ; S702: Determine the number of functional partitions that each candidate ablation path passes through, and perform organ damage assessment based on the number of functional partitions passed through. The fewer the number of functional partitions passed through, the less organ damage there is, and the more the number of functional partitions passed through, the greater the organ damage there is. S703: Determine the candidate ablation path that passes through the smallest number of functional partitions as the optimal ablation path.

9. The ablation automated navigation method according to claim 1, characterized in that: The S8 specifically includes: S801: Paste multiple markers on the patient's abdominal skin surface; S802: Preliminary registration of the three-dimensional model constructed based on the preoperative image with the patient point cloud based on multiple landmarks; S803: Based on the preliminary registration, an improved non-rigid ICP algorithm is used to perform forward point matching by minimizing the Euclidean distance, and backward point matching is performed by global search; S804: Setting a registration cost function based on the forward point matching result and the backward point matching result; S805: performing iterative optimization with the goal of reducing the registration cost function, and accurately registering the three-dimensional model constructed based on the preoperative image with the patient point cloud; S806: Mapping the optimal ablation path to the patient point cloud; S807: Perform ablation navigation according to the needle insertion position, angle, and depth determined after mapping.

10. An automated ablation navigation system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the automated ablation navigation method according to any one of claims 1 to 9 is implemented.

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