An intervention guide wire without sensor distributed contact force estimation method

Abstract

The present application belongs to the field of interventional surgery robot and guide wire mechanical measurement technology, and discloses an interventional guide wire non-sensor distributed contact force estimation method, which comprises the following steps: obtaining the two-dimensional center line track of the interventional guide wire in the cavity plane, and discretizing it into a sample point sequence under the arc length coordinate; based on the sample point sequence, the contact interval and the friction interval of the interaction between the guide wire and the cavity are identified; under the arc length coordinate, the guide wire is modeled as a plane Kirchhoff slender rod model according to the Kirchhoff slender rod theory, and the plane Kirchhoff slender rod model establishes the mechanical balance relationship between the guide wire geometry, internal force and distributed external load based on the known bending stiffness; based on the plane Kirchhoff slender rod model, the distributed contact load between the guide wire and the cavity is parameterized as a parameter vector to be solved; the problem that it is difficult to realize stable and quantitative distributed contact force estimation only according to the guide wire shape information in the prior art is effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of interventional surgical robot and guidewire mechanical measurement technology, specifically relating to a sensorless distributed contact force estimation method for interventional guidewires. Background Technology

[0002] In interventional procedures such as percutaneous coronary intervention and neurointervention, the guidewire is advanced through complex cavities, interacting with the cavity walls through normal contact forces and tangential frictional forces. The guidewire contact force information not only reflects the environment in which the guidewire is located and the safety of the procedure, but also serves as a crucial basis for interventional surgical robots to achieve precise force control and tactile feedback.

[0003] Existing guidewire force measurement schemes mainly fall into two categories: one involves placing a force sensor at the proximal end of the guidewire or on the tool table, which can only obtain overall axial or resultant force information and is difficult to distinguish local loads in different contact areas; the other involves integrating a miniature force sensor at the distal end of the guidewire, which requires embedding the sensitive element in a confined space, severely altering the guidewire structure and mechanical properties, resulting in high costs and making it difficult to promote in disposable consumables. On the other hand, with the development of X-ray, endoscopy, and external imaging and shape reconstruction technologies, the shape information of the guidewire in the cavity is relatively easier to obtain. However, directly judging the contact load on the guidewire based solely on its shape has significant underdeterminacy: the same end configuration can be generated by different distributed loads, and simple geometric heuristics or empirical models are difficult to provide stable and quantitative estimates of distributed contact forces. Therefore, existing technologies suffer from the problem of being unable to achieve stable and quantitative estimates of distributed contact forces based solely on guidewire shape information. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a sensorless distributed contact force estimation method for interventional guidewires, which solves the problem in existing technologies that it is difficult to achieve stable and quantitative distributed contact force estimation based solely on guidewire shape information.

[0005] The objective of this invention can be achieved through the following technical solutions: A sensorless distributed contact force estimation method for interventional guidewires includes the following steps: The two-dimensional centerline trajectory of the interventional guidewire in the cavity plane is obtained and discretized into a sequence of sampling points in arc length coordinates; Based on the sampling point sequence, the contact and friction zones where the guidewire interacts with the cavity were identified; In the arc length coordinate system, according to Kirchhoff's thin rod theory, the guide wire is modeled as a planar Kirchhoff thin rod model. The planar Kirchhoff thin rod model establishes the mechanical equilibrium relationship between the guide wire geometry, internal forces and distributed external loads based on the known bending stiffness. Based on the planar Kirchhoff thin rod model, the distributed contact load between the guidewire and the cavity is parameterized into a parameter vector to be determined. The parameter vector includes the resultant force and torque at the fixed end of the guidewire, the segment-level normal contact force and tangential proportional coefficient corresponding to each contact interval, and the friction load coefficient at each node in each friction interval. For each contact interval, a corresponding segment-level normal contact force parameter is introduced. The tangential friction force is used to characterize the average normal contact strength within the interval. The specific expression is as follows: In the formula, Represents the equivalent coefficient of friction in a given environment; Indicates the preset tangential scaling factor; For each friction interval, select several nodes along the arc length. s The point-level friction load is represented by a piecewise linear basis function, and the specific expression is as follows: In the formula, Indicates point-level frictional load; Represents piecewise linear basis functions; Represents a node s m The friction load coefficient to be determined; Given an initial parameter vector, the segmental tangential friction force Point-level friction load The combined load is the total external load along the arc length; Substituting the total external load into the mechanical equilibrium relationship, the corresponding governing equations are obtained; Under the geometric and mechanical boundary conditions at the starting point of a given arc length, the governing equations are numerically integrated along the arc length direction to obtain the simulated position coordinates at each arc length sampling point. Connect the simulated position coordinates at each arc length sampling point to obtain the simulated shape of the guide wire; The geometric deviation term is constructed by summing the squares of the deviations between the simulated shape and the two-dimensional centerline trajectory at each sampling point. Introduce a regularization term to constrain the parameter vector; The objective function is formed by combining the geometric deviation term and the regularization term. The specific expression is as follows: in, Represents the regularization term; , These represent the simulated shape of the guidewire and the two-dimensional centerline trajectory at each arc length sampling point, respectively; At the initial resolution, initial friction load nodes are arranged at initial node intervals within the friction range. An initial parameter vector containing the parameters corresponding to the initial friction load nodes is constructed, and the initial parameter vector is optimized to obtain the current optimized parameter vector. Based on the friction load distribution in the current optimized parameter vector, candidate arc length regions where the friction load amplitude exceeds a preset threshold are selected. Within the candidate arc length region, a higher resolution node interval, smaller than the current node interval, is used to introduce new friction load nodes, which together with the initial friction load nodes form an updated set of friction load nodes. Based on the current optimized parameter vector, construct a new parameter vector corresponding to the updated set of friction load nodes; Using the current optimized parameter vector as the initial value, optimize the new parameter vector to obtain the updated optimized parameter vector; The updated optimized parameter vector is used as the current optimized parameter vector. The above operation is repeated until the preset convergence condition is met. The final optimized parameter vector is then used as the optimal parameter vector. Based on the optimal parameter vector, the normal contact force and tangential friction force distributed along the guide wire arc length are reconstructed; The regularization terms include one or more of the following: smoothing regularization terms, grouping regularization terms, and trust regularization terms; The smoothing regularization term is used to constrain the magnitude relationship between point-level friction load parameters at adjacent arc length nodes, suppressing fluctuations along the arc length direction; Grouping regularization terms are used to maintain the consistency and coherence of load amplitude within the same contact interval; The trust regularization term is used when performing multi-resolution optimization to penalize the deviation between the parameter vector obtained by the current resolution layer and the parameter vector obtained by interpolating the friction load distribution corresponding to the optimization solution of the previous resolution layer to the node distribution of the current layer, so as to ensure the continuity of the multi-resolution solution process.

[0006] The beneficial effects of this invention are: This application discretizes the two-dimensional centerline trajectory of the guidewire within the cavity plane to identify the contact and friction zones where the guidewire interacts with the cavity. By modeling the guidewire as a planar Kirchhoff rod model, physical constraints between shape and mechanical state are established, overcoming the underdeterminacy of purely geometric methods. Distributed contact loads are parameterized into a unified vector, and the contact and friction zones are described using "segment-level normal force" and "point-level friction force" respectively, balancing computational efficiency and local load resolution. A shape prediction model is established through forward numerical integration, mapping mechanical parameters to simulated shapes. An optimization problem is constructed with shape deviation as the objective and regularization terms as constraints, transforming contact force estimation into a well-posed numerical optimization process, thereby stably and quantitatively reconstructing the normal contact force and tangential friction force distributed along the guidewire arc length. This application relies solely on guidewire shape information, eliminating the need to integrate any force sensors on the guidewire, maintaining the structural integrity and clinical applicability of the guidewire, and providing crucial distributed tactile sensing capabilities for interventional surgical robots. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a schematic diagram of the sensorless distributed contact force estimation method for interventional guidewires of the present invention. Figure 2 This is a schematic diagram of the Kirchhoff thin rod model of the guide wire plane of the present invention; Figure 3 This is a schematic diagram of the division of the contact and friction zones and the parameterization of segment-level and point-level loads in this invention; Figure 4 This is a schematic diagram of the sensorless distributed contact force estimation system for interventional guidewires of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] like Figures 1 to 3 As shown, a sensorless distributed contact force estimation method for interventional guidewires includes the following steps: The two-dimensional centerline trajectory of the interventional guidewire in the cavity plane is obtained and discretized into a sequence of sampling points in arc-length coordinates, as follows: Let the total arc length of the guidewire be... , the arc length interval Evenly divided into Segment, corresponding sampling point ; Based on the sampling point sequence, the contact and friction zones where the guidewire interacts with the cavity are identified. It should be noted that the division of the contact and friction zones needs to be combined with the cavity geometry model, the relative positional relationship between the guidewire and the cavity, and clinical experience. For example, based on the minimum distance between the guidewire and the cavity wall, the curvature distribution, or the method of manual annotation in the image, several arc length intervals can be given as contact intervals, and friction zones with obvious friction effects can be selected from them. like Figure 2 As shown, in the arc-length coordinate system, according to Kirchhoff's thin rod theory, the guidewire is modeled as a planar Kirchhoff thin rod model. The planar Kirchhoff thin rod model establishes the mechanical equilibrium relationship between the guidewire geometry, internal forces, and distributed external loads based on the known bending stiffness. The specific expression is as follows: in, It is a tangential angle function. For bending moment, Given the bending stiffness; like Figure 3 As shown, based on the planar Kirchhoff thin rod model, the distributed contact load between the guidewire and the cavity is parameterized into a parameter vector to be determined; wherein, the load in the contact interval is described by segment-level normal contact force parameters, and the load in the friction interval is described by point-level friction load parameters based on piecewise linear basis functions. Given an initial parameter vector, substitute it into the mechanical equilibrium relationship to obtain the corresponding control equation. Perform a positive numerical integration of the control equation along the arc length to obtain the simulated shape of the guide wire. A regularization term is introduced to constrain the parameter vector. An objective function is constructed by combining the geometric deviation between the simulated shape and the two-dimensional centerline trajectory. The optimal parameter vector is solved by numerical optimization with the goal of minimizing the objective function. Based on the optimal parameter vector, the normal contact force and tangential friction force distributed along the guide wire arc length are reconstructed.

[0011] This application discretizes the two-dimensional centerline trajectory of the guidewire within the cavity plane to identify the contact and friction zones where the guidewire interacts with the cavity. By modeling the guidewire as a planar Kirchhoff rod model, physical constraints between shape and mechanical state are established, overcoming the underdeterminacy of purely geometric methods. Distributed contact loads are parameterized into a unified vector, and the contact and friction zones are described using "segment-level normal force" and "point-level friction force" respectively, balancing computational efficiency and local load resolution. A shape prediction model is established through forward numerical integration, mapping mechanical parameters to simulated shapes. An optimization problem is constructed with shape deviation as the objective and regularization terms as constraints, transforming contact force estimation into a well-posed numerical optimization process, thereby stably and quantitatively reconstructing the normal contact force and tangential friction force distributed along the guidewire arc length. This application relies solely on guidewire shape information, eliminating the need to integrate any force sensors on the guidewire, maintaining the structural integrity and clinical applicability of the guidewire, and providing crucial distributed tactile sensing capabilities for interventional surgical robots.

[0012] The parameter vector includes the resultant force and torque at the fixed end of the guidewire, the segment-level normal contact force and tangential proportional coefficient corresponding to each contact interval, and the friction load coefficient at each node within each friction interval.

[0013] For each contact interval, a corresponding segment-level normal contact force parameter is introduced. The tangential friction force is used to characterize the average normal contact strength within the interval. The specific expression is as follows: in, Represents the equivalent coefficient of friction in a given environment; Indicates the preset tangential scaling factor; For each friction interval, select several nodes along the arc length. s The point-level friction load is represented by a piecewise linear basis function, and the specific expression is as follows: in, Indicates point-level frictional load; Represents piecewise linear basis functions; Represents a node s m The friction load coefficient to be determined is given.

[0014] Given an initial parameter vector, substitute it into the mechanical equilibrium relationship to obtain the corresponding governing equations. Perform a positive numerical integration of the governing equations along the arc length to obtain the simulated shape of the guidewire. The specific steps include: Given an initial parameter vector, the segmental tangential friction force Point-level friction load The combined load is the total external load along the arc length; Substituting the total external load into the mechanical equilibrium relationship, the corresponding governing equations are obtained; Under the geometric and mechanical boundary conditions at the starting point of a given arc length, the governing equations are numerically integrated along the arc length direction to obtain the simulated position coordinates at each arc length sampling point. Connect the simulated position coordinates at each arc length sampling point to obtain the simulated shape of the guide wire.

[0015] A regularization term is introduced to constrain the parameter vector. An objective function is constructed by combining the geometric deviation between the simulated shape and the two-dimensional centerline trajectory. The optimal parameter vector is solved numerically by minimizing this objective function. The specific steps include: The geometric deviation term is constructed by summing the squares of the deviations between the simulated shape and the two-dimensional centerline trajectory at each sampling point. Introduce a regularization term to constrain the parameter vector; The objective function is formed by combining the geometric deviation term and the regularization term. The specific expression is as follows: in, Represents the regularization term; , These represent the simulated shape of the guidewire and the two-dimensional centerline trajectory at each arc length sampling point, respectively; A numerical optimization algorithm is used to minimize the objective function by iteratively updating the parameter vector. The iteration stops when the preset convergence condition is met, and the current parameter vector is the optimal parameter vector.

[0016] The numerical optimization algorithm is executed using a multi-resolution optimization framework, specifically including the following steps: At the initial resolution, initial friction load nodes are arranged at initial node intervals within the friction range. An initial parameter vector containing the parameters corresponding to the initial friction load nodes is constructed, and the initial parameter vector is optimized to obtain the current optimized parameter vector. Based on the friction load distribution in the current optimized parameter vector, candidate arc length regions where the friction load amplitude exceeds a preset threshold are selected. Within the candidate arc length region, a higher resolution node interval, smaller than the current node interval, is used to introduce new friction load nodes, which together with the initial friction load nodes form an updated set of friction load nodes. Based on the current optimized parameter vector, construct a new parameter vector corresponding to the updated set of friction load nodes; Using the current optimized parameter vector as the initial value, optimize the new parameter vector to obtain the updated optimized parameter vector; The updated optimized parameter vector is used as the current optimized parameter vector. The above operation is repeated until the preset convergence condition is met. The final optimized parameter vector is then used as the optimal parameter vector.

[0017] Regularization terms include one or more of the following: smoothing regularization terms, grouping regularization terms, and trust regularization terms; The smoothing regularization term is used to constrain the magnitude relationship between point-level friction load parameters at adjacent arc length nodes, suppressing fluctuations along the arc length direction; Grouping regularization terms are used to maintain the consistency and coherence of load amplitude within the same contact interval; The trust regularization term is used when performing multi-resolution optimization to penalize the deviation between the parameter vector obtained by the current resolution layer and the parameter vector obtained by interpolating the friction load distribution corresponding to the optimization solution of the previous resolution layer to the node distribution of the current layer, so as to ensure the continuity of the multi-resolution solution process.

[0018] like Figure 4 As shown, this application also provides a specific embodiment of a sensorless distributed contact force estimation system for interventional guidewires, which is deployed in an interventional robot control platform and uses a sensorless distributed contact force estimation method for interventional guidewires to estimate the distributed contact force of the interventional guidewires. Specifically, it includes a guidewire shape acquisition and arc length discretization module, a mechanical modeling and forward solving module, a contact load inversion module, and a display and interaction module. Among them, the guidewire shape acquisition and arc length discretization module is used to acquire the two-dimensional centerline trajectory of the interventional guidewire in the cavity plane and discretize it into a sequence of sampling points in arc length coordinates; The mechanical modeling and forward solving module is used to model the guidewire as a planar Kirchhoff rod model, establish mechanical equilibrium relationships, and parameterize the distributed contact load between the guidewire and the cavity into a parameter vector to be solved, and solve to obtain the simulated shape of the guidewire. The contact load inversion module is used to introduce a regularization term and construct an objective function by combining the geometric deviation between the simulated shape and the two-dimensional centerline trajectory. The objective function is minimized, and the optimal parameter vector is solved by numerical optimization. At the same time, based on the optimal parameter vector, the normal contact force and tangential friction force distributed along the guide wire arc length are reconstructed. The display and interaction module is used to visualize the two-dimensional centerline trajectory, simulated shape, and contact load distributed along the arc length on cavity geometry or phantom images. It also provides interactive functions such as contact interval adjustment, parameter configuration, and result export, making it convenient for operators to debug and analyze.

[0019] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0020] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claims.

Claims

1. A sensorless distributed contact force estimation method for interventional guidewires, characterized in that, Includes the following steps: The two-dimensional centerline trajectory of the interventional guidewire in the cavity plane is obtained and discretized into a sequence of sampling points in arc length coordinates; Based on the sampling point sequence, the contact and friction zones where the guidewire interacts with the cavity were identified; In the arc length coordinate system, according to Kirchhoff's thin rod theory, the guide wire is modeled as a planar Kirchhoff thin rod model. The planar Kirchhoff thin rod model establishes the mechanical equilibrium relationship between the guide wire geometry, internal forces and distributed external loads based on the known bending stiffness. Based on the planar Kirchhoff thin rod model, the distributed contact load between the guidewire and the cavity is parameterized into a parameter vector to be determined. The parameter vector includes the resultant force and torque at the fixed end of the guidewire, the segment-level normal contact force and tangential proportional coefficient corresponding to each contact interval, and the friction load coefficient at each node in each friction interval. For each contact interval, a corresponding segment-level normal contact force parameter is introduced. The tangential friction force is used to characterize the average normal contact strength within the interval. The specific expression is as follows: In the formula, Represents the equivalent coefficient of friction in a given environment; Indicates the preset tangential scaling factor; For each friction interval, select several nodes along the arc length. s The point-level friction load is represented by a piecewise linear basis function, and the specific expression is as follows: In the formula, Indicates point-level frictional load; Represents piecewise linear basis functions; Represents a node s m The friction load coefficient to be determined; Given an initial parameter vector, the segmental tangential friction force Point-level friction load The combined load is the total external load along the arc length; Substituting the total external load into the mechanical equilibrium relationship, the corresponding governing equations are obtained; Under the geometric and mechanical boundary conditions at the starting point of a given arc length, the governing equations are numerically integrated along the arc length direction to obtain the simulated position coordinates at each arc length sampling point. Connect the simulated position coordinates at each arc length sampling point to obtain the simulated shape of the guide wire; The geometric deviation term is constructed by summing the squares of the deviations between the simulated shape and the two-dimensional centerline trajectory at each sampling point. Introduce a regularization term to constrain the parameter vector; The objective function is formed by combining the geometric deviation term and the regularization term. The specific expression is as follows: in, Represents the regularization term; , These represent the simulated shape of the guidewire and the two-dimensional centerline trajectory at each arc length sampling point, respectively; A numerical optimization algorithm is employed to minimize the objective function by iteratively updating the parameter vector, thereby obtaining the optimal parameter vector. The numerical optimization algorithm is executed using a multi-resolution optimization framework and specifically includes the following steps: S1. At the initial resolution, arrange the initial friction load nodes at the initial node interval within the friction interval, construct an initial parameter vector containing the parameters corresponding to the initial friction load nodes, and optimize the initial parameter vector to obtain the current optimized parameter vector. S2. Based on the friction load distribution in the current optimized parameter vector, select candidate arc length regions where the friction load amplitude exceeds the preset threshold. S3. Within the candidate arc length region, a higher resolution node interval, smaller than the current node interval, is used to introduce new friction load nodes, which together with the initial friction load nodes form an updated set of friction load nodes. S4. Based on the current optimized parameter vector, construct a new parameter vector corresponding to the updated set of friction load nodes; S5. Using the current optimized parameter vector as the initial value, optimize the new parameter vector to obtain the updated optimized parameter vector; S6. Use the updated optimized parameter vector as the current optimized parameter vector, and repeat S2 to S5 until the preset convergence condition is met. Then, use the final optimized parameter vector as the optimal parameter vector. Based on the optimal parameter vector, the normal contact force and tangential friction force distributed along the guide wire arc length are reconstructed; The regularization terms include one or more of the following: smoothing regularization terms, grouping regularization terms, and trust regularization terms; The smoothing regularization term is used to constrain the magnitude relationship between point-level friction load parameters at adjacent arc length nodes, suppressing fluctuations along the arc length direction; Grouping regularization terms are used to maintain the consistency and coherence of load amplitude within the same contact interval; The trust regularization term is used when performing multi-resolution optimization to penalize the deviation between the parameter vector obtained by the current resolution layer and the parameter vector obtained by interpolating the friction load distribution corresponding to the optimization solution of the previous resolution layer to the node distribution of the current layer, so as to ensure the continuity of the multi-resolution solution process.

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