Computer-implemented simulation method for interventional consumables and related products

Abstract

The present disclosure discloses a simulation method of interventional consumables implemented by a computer and related products. The simulation method includes: obtaining a reconstruction model of an interventional object; determining the interventional consumable to be modeled based on the reconstruction model; modeling the interventional consumable based on a discrete elastic rope simulation technology to obtain a discrete elastic rope model of the interventional consumable; and simulating the release effect of the discrete elastic rope model of the interventional consumable in the reconstruction model in response to the discrete elastic rope model of the interventional consumable being placed in the reconstruction model. Through the technical solution of the present disclosure, in the process of simulating the interventional consumable, the discrete elastic rope simulation technology is creatively used to achieve the modeling of the interventional consumable and the simulation of the release effect, so as to take advantage of the high accuracy and fast speed of the discrete elastic rope model to ensure that the simulation of the entire interventional consumable can take into account both accuracy and speed.

Description

Technical Field

[0001] The present disclosure generally relates to the field of medical imaging and computer technology. More specifically, the present disclosure relates to a simulation method of an interventional consumable implemented by a computer, and an electronic device and a computer-readable storage medium for executing the aforementioned simulation method. Background Art

[0002] With the advancement of medical technology, relevant data of intracranial lesions can be determined through quantitative analysis of medical images, and the cranial data of intracranial lesions can be used in the preparation of preoperative instruments and equipment and intraoperative process planning, so as to effectively improve the quality of surgery, shorten the operation time and reduce potential risks during surgery.

[0003] Among them, vascular interventional surgery has become the mainstream surgical method for the clinical treatment of vascular diseases such as aneurysms and vascular occlusions. Compared with traditional craniotomy, it has better patient prognosis and fewer complications. However, a variety of consumables (such as microcatheters, coils, dense mesh stents, Woven EndoBridge devices (WEB for short), etc.) are used in interventional surgery, and doctors with different experiences have different evaluations of consumable selection and post-operative release effects. If the type and size of the consumables are not appropriate, it will prolong the operation time and increase the risk of poor prognosis, causing patients to bear unnecessary additional financial burdens, and even lead to surgical failure. Therefore, how to simulate and select interventional consumables before surgery so that they are suitable for different neurointerventional surgeries has become an urgent problem to be solved. Summary of the invention

[0004] In order to at least solve one or more of the technical problems mentioned above, the present disclosure proposes, in multiple aspects, a simulation solution for interventional consumables implemented by a computer that can quickly and accurately simulate interventional consumables.

[0005] In a first aspect, the present disclosure provides a computer-implemented method for simulating interventional consumables, comprising: obtaining a reconstructed model of an interventional object; determining the interventional consumable to be modeled based on the reconstructed model; modeling the interventional consumable based on discrete elastic rope simulation technology to obtain a discrete elastic rope model of the interventional consumable; and in response to the discrete elastic rope model of the interventional consumable being placed in the reconstructed model, simulating the release effect of the discrete elastic rope model of the interventional consumable in the reconstructed model.

[0006] In some embodiments, acquiring the reconstructed model of the intervention object includes: acquiring a medical image of the intervention object; and restoring and reconstructing the intervention object based on the medical image to obtain the reconstructed model.

[0007] In some embodiments, determining the interventional consumables to be modeled based on the reconstructed model includes: determining measurement parameters of the interventional object according to the reconstructed model; and screening out the interventional consumables to be modeled according to the measurement parameters of the interventional object.

[0008] In some embodiments, modeling the interventional consumable based on discrete elastic rope simulation technology includes: obtaining simulation parameters of the interventional consumable; and modeling the interventional consumable according to the simulation parameters in combination with the discrete elastic rope simulation technology to output a discrete elastic rope model of the interventional consumable.

[0009] In some embodiments, wherein the interventional consumable includes a rope-shaped or braided consumable, modeling the interventional consumable includes: determining the geometric shape of each elastic rope or braided wire in the rope-shaped or braided consumable based on the simulation parameters; and modeling the geometric shape of each elastic rope or braided wire using the discrete elastic rope simulation technology to obtain a discrete elastic rope model of the rope-shaped or braided consumable.

[0010] In some embodiments, determining the geometric shape of each elastic rope or braided wire in the rope-shaped or braided consumable based on the simulation parameters includes: determining the nodes of each elastic rope or braided wire in the rope-shaped or braided consumable and the connection relationship between the nodes based on the simulation parameters.

[0011] In some embodiments, using the discrete elastic rope simulation technology to model the geometric shape of each elastic rope or braided wire includes: using the discrete elastic rope simulation technology to model the nodes of each elastic rope or braided wire and the connection relationship between the nodes to obtain a discrete elastic rope model of the rope-like or braided consumable in a free form and / or compressed form.

[0012] In some embodiments, the method also includes: obtaining the insertion marking points of the interventional consumable in the reconstructed model; determining the target insertion position and posture of the interventional consumable in the reconstructed model based on the insertion marking points; and placing the discrete elastic rope model of the interventional consumable in the reconstructed model according to the target insertion position.

[0013] In some embodiments, wherein the implantation marking points include a distal end point and a proximal end point, determining the target implantation position of the interventional consumable in the reconstructed model based on the implantation marking points includes: obtaining an initial position of a discrete elastic rope model of the interventional consumable; determining an axial direction of the discrete elastic rope model of the interventional consumable based on the distal end point and the proximal end point; determining position information in different dimensions using the axial direction and a random unit vector; and determining the target implantation position using the axial direction, the position information in different dimensions, and the initial position.

[0014] In some embodiments, simulating the release effect of the discrete elastic rope model of the interventional consumable in the reconstructed model includes: calculating the movement process of the discrete elastic rope model of the interventional consumable in the reconstructed model, the interaction between the elastic ropes in the discrete elastic rope model of the interventional consumable, and the contact conditions with the contact surface of the reconstructed model, so as to achieve the simulation of the release effect.

[0015] In some embodiments, the method includes: in the process of calculating the interaction between elastic ropes, rasterizing the maximum bounding box of the space where the discrete elastic rope model of the interventional consumable is located into a target three-dimensional grid; saving the density field and velocity field of all nodes of the discrete elastic rope model of the interventional consumable in the target three-dimensional grid; and in each time step of the simulation, traversing the positions and velocities of all nodes in the discrete elastic rope model of the interventional consumable to update the saved density field and velocity field.

[0016] In some embodiments, where the calculation of the contact condition with the contact surface of the reconstructed model includes a collision detection calculation, the method further includes: calculating a directed distance field of a discrete elastic rope model of the interventional consumable; calculating the value of the distance field corresponding to each node in the discrete elastic rope model of the interventional consumable in each time step of the simulation; calculating the position of the collision in response to the existence of a distance field value less than a threshold; and correcting the position and velocity of the node using the calculation result of the collision position.

[0017] In some embodiments, the method further comprises: during the collision detection process, calculating the friction force and reaction force received by each node based on the density field and the velocity field.

[0018] In some embodiments, the friction force and reaction force on each node are calculated by the following formula:

[0019]

[0020]

[0021] in, represents the velocity of the x node, λ represents the static friction coefficient, γ represents the reaction force coefficient, V(x) represents the value of the velocity field at the x node, and D(x) represents the value of the density field at the x node. Represents the pressure gradient at the x-node location.

[0022] In a second aspect of the disclosed embodiments, an electronic device is provided, comprising: a processor; and a memory storing computer instructions for simulating an interventional consumable implemented by a computer, wherein when the computer instructions are executed by the processor, the electronic device executes the method described in the foregoing and following embodiments.

[0023] In a third aspect of the presently disclosed embodiment, a computer-readable storage medium is provided, comprising program instructions for simulating an interventional consumable implemented by a computer, wherein when the program instructions are executed by a processor, the method described in the foregoing and following embodiments is implemented.

[0024] Through the simulation scheme of interventional consumables implemented by a computer as provided above, the embodiment of the disclosure can use discrete elastic rope simulation technology to model the interventional consumables, and simulate the release effect of the constructed model in the reconstructed model of the interventional object. It can be seen that in the simulation process of the interventional consumables, the scheme of the disclosure introduces discrete elastic rope simulation technology to quickly model the interventional consumables, and uses the constructed model to accurately simulate the release effect, so that the entire simulation process can take into account both accuracy and speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0026] Figure 1 A schematic diagram showing a simulation method of an interventional consumable implemented by a computer according to an embodiment of the present disclosure is shown;

[0027] Figure 2 A schematic diagram showing a discrete elastic rope model according to the present disclosure is shown;

[0028] Figure 3 A schematic diagram showing a modeling process of a discrete elastic rope model according to the present disclosure;

[0029] Figure 4 A schematic diagram showing a simulation method of an interventional consumable implemented by a computer according to another embodiment of the present disclosure is shown;

[0030] Figure 5 A schematic diagram showing modeling results of different types of WEB consumables according to the present disclosure;

[0031] Figure 6 A schematic diagram showing a simulation method of an interventional consumable implemented by a computer according to yet another embodiment of the present disclosure is shown;

[0032] Figure 7 A schematic diagram of reconstructing a blood vessel model and an aneurysm model based on blood vessel enhanced images according to the present disclosure is shown;

[0033] Figure 8 A schematic diagram showing a simulation process of an interventional consumable according to the present disclosure is shown;

[0034] Fig. 9 A schematic diagram showing parallel transport involving discrete elastic cord technology according to the present disclosure;

[0035] Fig.10 A schematic diagram showing a parallel transport algorithm process according to the present disclosure is shown;

[0036] Fig.11 A schematic diagram showing a WEB consumables modeling process according to the present disclosure is shown;

[0037] Fig.12 A schematic diagram showing an initialization frame after modeling of a WEB consumable according to the present disclosure is shown;

[0038] Fig.13 A schematic diagram showing a discrete elastic rope model of a WEB consumable in different states according to the present disclosure;

[0039] Fig.14 A schematic diagram showing a target placement position calculation process according to the present disclosure;

[0040] Fig.15 A schematic diagram showing the release of a discrete elastic rope model of a WEB consumable according to the present disclosure through a blood vessel collision detection within a tumor; and

[0041] Fig.16 A schematic structural diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0043] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0044] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0045] As used in this specification and claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0046] The inventors have found that the current simulation solutions for interventional consumables cannot meet actual needs. Among them, the current mainstream simulation methods for interventional consumables include the following two:

[0047] 1. Simulation based on simple geometric methods, which uses geometric shapes to adapt to the shape of blood vessels or aneurysms, such as using a hose to adapt to blood vessels, using a ball to adapt to aneurysms, etc. This method is fast, but has low accuracy and cannot reflect the real changes and forces of consumables in blood vessels or tumors, that is, this method has a poor simulation effect on interventional consumables.

[0048] 2. Finite Element Analysis (FEA) technology can be used to simulate the consumables release process. This method is more accurate, but the modeling and time-consuming are very long, ranging from a few hours to more than ten or even hundreds of hours, which is difficult to use in actual clinical use.

[0049] It can be seen that these two simulation methods have the problem of poor accuracy or serious time consumption. In this regard, the inventors found through further research that the discrete elastic rope model technology has the characteristics of fast speed and high accuracy, and can creatively integrate the discrete elastic rope model technology into the simulation process of interventional consumables, so that the entire simulation process has both accuracy and speed.

[0050] The specific implementation of the present disclosure is described in detail below with reference to the accompanying drawings.

[0051] Figure 1 A schematic diagram of a simulation method 100 of an interventional consumable implemented by a computer according to an embodiment of the present disclosure is schematically shown.

[0052] like Figure 1 As shown, at step S101, a reconstructed model of an interventional object may be obtained. The interventional object here may be understood as an object or carrier into which an interventional consumable is to be placed, such as a blood vessel, an aneurysm, etc. It should be noted that the present disclosure does not limit the specific type of interventional object, and adaptive adjustments are made according to the application scenario.

[0053] Then, at step S102, the interventional consumables to be modeled can be determined based on the aforementioned reconstruction model. In some embodiments, after obtaining the reconstruction model, the measurement parameters of the interventional object (such as the diameter, height, volume, and other information of the lesion vessel or aneurysm) can be detected according to the reconstruction model, and the interventional consumables to be modeled can be screened out using these measurement parameters. For example, interventional consumables that match the measurement parameters can be screened out from a database that records interventional consumable information (such as model, size, structure, and other information) as interventional consumables to be modeled. It should be noted that the detailed description of the determination process of the interventional consumables to be modeled here is only an example description, and the scheme disclosed herein is not limited to this. For example, the interventional consumables to be modeled can also be directly screened through the interventional consumables information uploaded by the user in real time, or a plurality of interventional consumables can be recommended to the user for the user to choose, and one or more interventional consumables selected by the user can be used as the interventional consumables to be modeled. In addition, the specific number of interventional consumables to be modeled is not limited in this embodiment, and it can be 1 or more.

[0054] Then, in step S103, the interventional consumable to be modeled may be modeled based on a discrete elastic rope simulation technology to obtain a discrete elastic rope model of the interventional consumable.

[0055] Among them, Discrete Elastic Rods (DER) is a hair simulation technology used by top special effects and animation studios in recent years. Discrete Elastic Rods can usually model different types of hair, including straight hair, big curly hair, small curly hair, hair bundles, braids, etc., and can achieve very realistic effects on all types of hair.

[0056] At the same time, due to its high calculation accuracy, DER has also begun to emerge in traditional engineering and scientific fields. For example, it is used to simulate scenes such as the movement of submarine cables and the circling of cucumber tendrils. Compared with the traditional finite element method, DER greatly simplifies the calculation amount while ensuring accuracy.

[0057] In addition, after a lot of analysis and research, it was found that DER technology can also be applied to the simulation of various rope-shaped or woven objects. Many interventional consumables in the medical field have rope-shaped or woven structures, so the DER technology can be creatively integrated into the simulation process of interventional consumables with rope-shaped or woven structures.

[0058] The following combination Figure 2 and Figure 3 The DER technology is described.

[0059] in, Figure 2 A schematic diagram of a discrete elastic rope model according to the present disclosure is shown. Compared to the traditional FEA modeling method (i.e., refining the simulated object through tetrahedral meshes), the DER model can simplify the rope or braided structure into a model of nodes and edges, and can simplify the method of calculating stress through deformation gradient in FEA to calculating edge torsion and bending forces.

[0060] like Figure 2 As shown in Figure 1, the DER model consists of nodes, edges, and coordinate frames. The nodes are the coordinates of each node after the elastic rope is discretized, the edges are the connection between nodes, and the coordinate frame defines the local coordinate system on each edge, which is usually composed of three orthogonal unit vectors (such as Figure 2 When an edge is twisted or an adjacent edge is bent, DER will generate torsion potential energy and bending potential energy. By calculating the potential energy, the bending force and torsion force on each node can be calculated, so that the acceleration can be calculated and the speed and position of the node can be updated.

[0061] Among them, the bending potential energy of the DER model can be calculated by the following formula:

[0062]

[0063] in: for j∈{i-1,i},l i =|e i-1 |+|e i |, l i is the side length (the one with a horizontal bar above is the initial side length), n is the number of side lengths, i refers to the i-th side length, and α represents the bending modulus.

[0064] The torsional potential energy of the DER model can be calculated by the following formula:

[0065]

[0066] Where θ represents the twist angle, β represents the torsional modulus, and mi = θ i-θ i-1 , which represents the torsion angle.

[0067] After the bending and torsion energy calculations are completed, the node acceleration and angular acceleration can be calculated by the node mass, and the node acceleration and position can be updated by explicit integration or implicit integration. In implicit integration, each simulation step can be regarded as a nonlinear optimization problem, and then the Newton method is used to solve the velocity and angular acceleration of all nodes, and then the node position is updated. The advantage of implicit integration is that a larger step size can be used for simulation, making the simulation more stable and more accurate. Of course, the node acceleration and position can also be updated by semi-implicit integration.

[0068] Figure 3 A schematic diagram of the modeling process of the discrete elastic rope model according to the present disclosure is shown. Figure 3 As shown, the DER model can be specifically modeled using information such as the position of each node in the free state, the initial velocity of the node, the normal vector of the first node, and boundary conditions.

[0069] After the discrete elastic rope model of the interventional consumable is completed, in step S104, in response to the discrete elastic rope model of the interventional consumable being placed in the aforementioned reconstruction model, the release effect of the discrete elastic rope model of the interventional consumable in the aforementioned reconstruction model can be simulated. The process of placing the discrete elastic rope model of the interventional consumable in the reconstruction model can be realized automatically, and of course can also be controlled manually.

[0070] Therefore, in the process of simulating interventional consumables, discrete elastic rope simulation technology is creatively used to achieve the modeling of interventional consumables and the simulation of release effects, so as to take advantage of the high accuracy and fast speed of the discrete elastic rope model to ensure that the simulation of the entire interventional consumables can take into account both accuracy and speed.

[0071] Figure 4 A schematic diagram of a computer-implemented method 400 for simulating an interventional consumable according to another embodiment of the present disclosure is schematically shown. It can be understood that the method 400 is a method for simulating an interventional consumable according to another embodiment of the present disclosure. Figure 1 Therefore, the above combined with the method 100 Figure 1 The relevant detailed description also applies to the following.

[0072] like Figure 4As shown, at step S401, a medical image of the intervention object can be obtained. As mentioned above, the intervention object may include blood vessels, aneurysms, etc. In some embodiments, medical images such as enhanced images of intervention objects such as blood vessels and aneurysms can be obtained, such as enhanced images of blood vessels. Among them, the medical image of the intervention object can be pre-stored locally or on the server side, or it can be collected in real time by an image acquisition device. It should be noted that the detailed description of the medical image here is only an exemplary description.

[0073] At step S402, the intervention object can be restored and reconstructed based on the aforementioned medical image to obtain a reconstructed model. Specifically, some general image processing techniques (such as thresholds, region growing methods, deep learning methods, etc.) can be used to restore and reconstruct the intervention object. In some embodiments, a mask of the region of the intervention object (such as blood vessels and aneurysms, etc.) can be obtained from a medical image (such as a vascular enhancement image) using a traditional threshold, region growing method, or deep learning method, and a reconstruction algorithm (such as a Marching Cube algorithm) can be used to reconstruct a reconstructed model of the intervention object (such as the surface of blood vessels and aneurysms) through the mask.

[0074] At step S403, the measurement parameters of the intervention object can be determined according to the aforementioned reconstruction model. After obtaining the reconstruction model, the measurement parameters of the intervention object (that is, the measurement parameters required for simulation) can be calculated by image processing algorithms and graphics algorithms. Among them, the measurement parameters can be used as a screening basis for the intervention consumables to be modeled, which may include but are not limited to the size information of some intervention objects. For example, the diameter of the diseased blood vessel, the diameter, width, height, volume of the aneurysm, etc. It should be noted that the detailed description of the measurement parameters of the intervention object here is only an exemplary description.

[0075] At step S404, the interventional consumables to be modeled can be screened out based on the measured parameters of the interventional object. As described above, interventional consumables matching the measured parameters can be screened out from a database recording the information of the interventional consumables (e.g., the model, size, structure, etc. provided by the consumables manufacturer) as the interventional consumables to be modeled. It should be noted that the detailed description of the process of determining the interventional consumables to be modeled here is only an example description, and the disclosed solution is not limited thereto.

[0076] In step S405 , the interventional consumable may be modeled according to the simulation parameters of the interventional consumable in combination with the discrete elastic rope simulation technology to output a discrete elastic rope model of the interventional consumable.

[0077] In practical applications, the simulation parameters of the interventional consumable (such as the model, size, and structure information of the interventional consumable) can be obtained first. Then, the interventional consumable can be modeled based on the simulation parameters in combination with the discrete elastic rope simulation technology to output a discrete elastic rope model of the interventional consumable. Figure 5 The figure shows the modeling results of different types of WEB in free state. Figure 5 As shown, the modeling results of the SLS model and SL model woven WEB filaments in the free state are respectively shown.

[0078] Specifically, in some embodiments, the interventional consumables may include rope-shaped or braided consumables. The geometric shape of each elastic rope or braided wire in the rope-shaped or braided consumables can be determined based on the aforementioned simulation parameters. For example, the nodes of each elastic rope or braided wire in the rope-shaped or braided consumables and the connection relationship between the nodes can be determined based on the simulation parameters. Then, the geometric shape of each elastic rope or braided wire can be modeled using a discrete elastic rope simulation technology to obtain a discrete elastic rope model of the rope-shaped or braided consumables. For example, the discrete elastic rope simulation technology can be used to model the nodes of each elastic rope or braided wire and the connection relationship between the nodes to obtain a discrete elastic rope model of the rope-shaped or braided consumables in a free form and / or compressed form.

[0079] At step S406, the discrete elastic rope model of the interventional consumable can be placed in the reconstructed model according to the target placement position. In some embodiments, the target placement position can be obtained by automated calculation. Specifically, the placement mark point of the interventional consumable in the reconstructed model can be obtained. Then, the target placement position and posture of the interventional consumable in the reconstructed model can be determined based on the placement mark point. Finally, the discrete elastic rope model of the interventional consumable is placed in the reconstructed model according to the target placement position. It should be noted that the description of the model placement process here is only an exemplary description, and the scheme disclosed herein is not limited to this. For example, the discrete elastic rope model of the interventional consumable can also be placed in the reconstructed model by manual operation.

[0080] Furthermore, in some embodiments, the aforementioned insertion mark point may include a distal end point and a proximal end point. In this scenario, the initial position of the discrete elastic rope model of the interventional consumable can be obtained, and the axial direction of the discrete elastic rope model of the interventional consumable can be determined based on the distal end point and the proximal end point, and the position information in different dimensions can be determined using the axial direction and the random unit vector. Finally, the aforementioned target insertion position can be determined using the axial direction, the position information in different dimensions, and the initial position. It should be noted that the description of the target insertion position here is only an exemplary description, and the scheme disclosed herein is not limited to this. For example, the target insertion position can be set directly by humans.

[0081] In step S407, the movement process of the discrete elastic rope model of the interventional consumable in the reconstructed model, the interaction between the elastic ropes in the discrete elastic rope model of the interventional consumable, and the contact condition with the contact surface of the reconstructed model may be calculated to simulate the release effect.

[0082] In the process of calculating the interaction between the elastic ropes, the maximum bounding box of the space where the discrete elastic rope model of the interventional consumable is located can be rasterized into a target three-dimensional grid, and the density field and velocity field of all nodes of the discrete elastic rope model of the interventional consumable in the target three-dimensional grid are saved. Then, in each time step of the simulation, the positions and velocities of all nodes in the discrete elastic rope model of the interventional consumable are traversed to update the saved density field and velocity field.

[0083] In addition, the aforementioned calculation of the contact situation with the contact surface of the reconstructed model may include a collision detection calculation. During the collision detection calculation process, the directed distance field of the discrete elastic rope model of the interventional consumable may be calculated. In each time step of the simulation, the value of the distance field corresponding to each node in the discrete elastic rope model of the interventional consumable may be calculated. In response to the existence of a distance field value less than a threshold, the position of the collision may be further calculated, and the position and velocity of the node may be corrected using the calculation result of the position of the collision.

[0084] Furthermore, during the collision detection process, the friction force and reaction force on each node can be calculated based on the aforementioned density field and velocity field. For example, the friction force and reaction force can be calculated according to the following formula:

[0085]

[0086]

[0087] in, represents the velocity of the x node, λ represents the static friction coefficient, γ represents the reaction force coefficient, V(x) represents the value of the velocity field at the x node, and D(x) represents the value of the density field at the x node. Represents the pressure gradient at the x-node location.

[0088] Figure 6 FIG. 6 is a schematic diagram of a method 600 for simulating an interventional consumable implemented by a computer according to another embodiment of the present disclosure. It should be noted that the method 600 can be understood as a specific technical implementation of the method 100 and the method 400. Figure 1 and Figure 4 The relevant detailed description of the above also applies to the following. In addition, this embodiment is described by taking the simulation of woven WEB consumables as an example, and the simulation process of the entire consumables involves steps S601 to S607. In addition, Figure 8 Corresponding to steps S601 to S607, Figure 8 As shown, each step is presented in the form of an effect diagram.

[0089] like Figure 6 As shown, in step S601, blood vessels and aneurysms are restored and reconstructed. This embodiment takes blood vessels and aneurysms as intervention objects for example. Figure 7 As shown, after obtaining the vascular enhanced image, the mask of the vascular and aneurysm area can be obtained from the vascular enhanced image through traditional thresholding, region growing methods or deep learning methods, and the vascular and aneurysm surfaces (that is, the reconstructed model of the vascular and aneurysm) can be reconstructed through the mask using reconstruction algorithms such as the Marching Cube algorithm.

[0090] At step S602, the blood vessels and aneurysms are measured. Specifically, after the blood vessel and aneurysm surfaces are obtained, the reconstructed blood vessel and aneurysm surfaces can be used to calculate the required size parameters (i.e., the measurement parameters described above) through image processing algorithms and graphics algorithms. The size parameters may include, but are not limited to, the diameter of the diseased blood vessel, the diameter, width, height, volume, and other information of the aneurysm.

[0091] In step S603, consumables are selected and modeled. Specifically, based on the above-mentioned size information of the blood vessel or aneurysm, the consumables model within the recommended range can be automatically given from the consumables information recommended by the manufacturer. There may be multiple consumables models, and there is no limit on the number of consumables.

[0092] Among them, the modeling of the selected consumables can be modeled into a DER model according to the geometric shape of each elastic rope in the consumables. Specifically, a discrete elastic rope can be modeled through node positions and edges. By initializing the first node coordinate frame, the generation of the entire elastic rope coordinate frame can be completed through the parallel transport algorithm, thereby completing the modeling of the consumables in a free state.

[0093] Combination Fig. 9 and Fig.10 The parallel transport algorithm is explained. Fig. 9 As shown, for example, to convert the vector u 0 Transfer to the next line segment, first calculate the tangent t of the two line segments 0 The cross product of t' and normalizes to get the binormal b. Then, t 0 Calculate the cross product of t' and b to get the normal n 0 and n'. Next, calculate u by dot product 0 In n 0 and the intercept above b. Assume that after this vector is transmitted, the intercepts at n' and b and at n 0The intercepts of and b are equal, and these two intercepts can be used to interpolate n' and b to obtain the vector u' after transmission. The specific calculation process of this parallel transport algorithm can be referred to Fig.10 .

[0094] The following combination Fig.11 Take WEB consumables as an example to further illustrate the consumables modeling process. Fig.11 As shown, the WEB consumable modeling first obtains the nodes in the WEB weaving structure and the connection relationship between the nodes through parametric calculation, and then the DER modeling method is used to complete the initialization modeling of the entire consumable, and then determine the initialization frame of the consumable. Specifically, the coordinates of the nodes on the contour line of the WEB cross section, the number of braided wires, and the rotation angle of the braided wires can be calculated through the parameters of the WEB model, and then the spatial position of each braided wire is calculated to finally obtain the DER model of the WEB. Among them, the initialization frame after the WEB consumable modeling (that is, the initialization frame in the free state) is as follows Fig.12 shown.

[0095] In practical applications, after completing the consumables modeling, since most interventional consumables need to be compressed and placed into the catheter before entering the lumen. Specifically, here, the consumables can be compressed into a small space by applying tension, compression, or other motion simulations to the consumables. For example, tension can be applied to both ends to stretch them, thereby achieving a compression effect. Fig.13 As shown in the figure, the DER model of the WEB in the free state and the compressed state is shown respectively. In the compression process, the coordinate frame of the consumables will change, thereby generating bending and torsion potential energy, which provides kinetic energy for the subsequent release process of the consumables.

[0096] At step S604, the control point is selected. Before the consumables are placed, the control point needs to be selected. Specifically, the control point (ie, the placement mark point) can be selected automatically or manually.

[0097] In step S605, the consumables are placed. In this embodiment, the placement marking points can be automatically calculated or manually set, and the target placement position and posture of the interventional consumables in the reconstructed model can be automatically calculated based on the placement marking points. Fig.14 FIG. 2 shows a schematic diagram of a target placement position calculation process according to the present disclosure. Fig.14 As shown, the target placement position can be calculated by placing marking points such as the distal and proximal marking points.

[0098] In step S606, consumables simulation is performed. Specifically, by calculating the movement process of discrete elastic ropes, the interaction between the elastic ropes, and the contact with the inner wall of the blood vessel, the release effect of the consumables is simulated and displayed.

[0099] The interaction force between the elastic ropes can be calculated by constructing a Spatial Grid. Specifically, the maximum bounding box of the space where the interventional consumables are located can be rasterized into a three-dimensional grid with a specified resolution (such as 0.1 mm), and the density field and velocity field of the consumable node position can be saved separately. In each time step of the simulation, the density field and velocity field can be updated by traversing the positions and velocities of all nodes in the consumables.

[0100] In the collision detection stage, the friction and reaction force of each node can be calculated by density field and velocity field, so as to avoid penetration inside the elastic rope and between the elastic ropes. The friction and reaction force here can be calculated by the calculation formula of friction and reaction force mentioned in the method 400 above.

[0101] In addition, the collision detection with the inner wall of the blood vessel follows the method of coarse detection first and then fine detection. Specifically, the coarse detection can be achieved by calculating the signed distance field (SDF) of the blood vessel model in advance. Among them, the value of each point in the distance field represents the distance from the surface of the blood vessel. In each time step, the value of the distance length corresponding to each node is calculated, and fine detection is entered only when it is less than the specified threshold. Fine detection uses continuous collision detection (Continuous Collision Detection, CCD for short) to calculate the exact position of the collision, and then corrects the position and speed of the node to avoid the situation where consumables penetrate the blood vessel. Specifically, if Fig.15 Figure 2 shows the release of the WEB's DER model through vascular collision detection within an aneurysm.

[0102] At step S607, the optimal consumables are recommended. The optimal consumables can be selected according to the specific release conditions of the interventional consumables in the interventional object. Therefore, the discrete elastic rope simulation technology is applied to the simulation process of the interventional consumables, making full use of the advantages of the discrete elastic rope simulation technology in terms of efficiency and accuracy, so that the simulation process of the interventional consumables can take into account both accuracy and speed.

[0103] It should be noted that the solution disclosed herein is not limited to the simulation of WEB consumables, but can also simulate any rope-shaped (such as microcatheters, spring coils, guide wires, etc.) or woven (such as dense mesh stents, etc.) consumables.

[0104] Furthermore, the disclosed solution also proposes a simulation system for interventional consumables. The simulation system may include five parts: a blood vessel and aneurysm segmentation measurement module, a consumable selection module, consumable modeling, consumable placement, and consumable simulation. Among them, the input of the blood vessel and aneurysm segmentation measurement module is the effect of blood vessel enhancement, and the output is the segmentation model and measurement parameters of blood vessels and aneurysms; the input of the consumable selection module is the measurement parameters of blood vessels and aneurysms, and the output is the recommended consumable model; the consumable modeling module can automatically model the consumables according to the consumable model parameters, and output the free form and compressed form models of the consumables. The consumable placement module can automatically calculate the position and posture of the consumable placement by automatically calculating or manually setting the marking points. The consumable simulation module simulates the release effect of the consumables and displays it by calculating the movement process of discrete elastic ropes, the interaction between the elastic ropes, and the contact with the inner wall of the blood vessel. It can be understood that the various modules in the above-mentioned model system can be distributed on different devices, and of course can also be integrated on one device.

[0105] Fig.16 Schematically shows a schematic block diagram of an electronic device 1600 according to an embodiment of the present invention. Fig.16 As shown, the electronic device 1600 may include a processor 1601 and a memory 1602. The memory 1602 stores computer instructions for simulating an interventional consumable. When the computer instructions are executed by the processor 1601, the electronic device 1600 executes the simulation according to the above description. Figure 1 , Figure 3 , Figure 4 , Figure 6 , Fig.10 , Fig.11 and Fig.14 For example, in some embodiments, the electronic device 1600 can creatively use the discrete elastic rope simulation technology to achieve modeling of the interventional consumable and simulation of the release effect during the simulation of the interventional consumable, so as to take advantage of the high accuracy and high speed of the discrete elastic rope model to ensure that the simulation of the entire interventional consumable can take into account both accuracy and speed.

[0106] It should be noted that although several devices or sub-devices of the device are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more devices described above can be embodied in one device. Conversely, the features and functions of one device described above can be further divided into multiple devices to be embodied.

[0107] Although the spirit and principles of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A method for simulating an interventional consumable implemented by a computer, characterized in that: include: obtaining a reconstructed model of the intervention object; Determining the interventional consumables to be modeled based on the reconstructed model; The interventional consumable is modeled based on a discrete elastic rope simulation technology to obtain a discrete elastic rope model of the interventional consumable, wherein the modeling of the interventional consumable based on the discrete elastic rope simulation technology includes: obtaining simulation parameters of the interventional consumable; and modeling the interventional consumable according to the simulation parameters in combination with the discrete elastic rope simulation technology to output a discrete elastic rope model of the interventional consumable, wherein the interventional consumable includes a rope-shaped or braided consumable, and the discrete elastic rope model of the interventional consumable includes simplifying the rope-shaped or braided consumable into a model of nodes and edges, wherein, Modeling the interventional consumables includes: Determine the geometric shape of each elastic rope or braided wire in the rope-shaped or braided consumable based on the simulation parameters, and specifically determine the nodes of each elastic rope or braided wire in the rope-shaped or braided consumable and the connection relationship between the nodes based on the simulation parameters; The discrete elastic rope simulation technology is used to model the geometric shape of each elastic rope or braided wire to obtain a discrete elastic rope model of the rope-shaped or braided consumable. Specifically, the discrete elastic rope simulation technology is used to model the nodes of each elastic rope or braided wire and the connection relationship between the nodes to obtain a discrete elastic rope model of the rope-shaped or braided consumable in a free form and / or a compressed form; and In response to the discrete elastic rope model of the interventional consumable being placed in the reconstructed model, simulating the release effect of the discrete elastic rope model of the interventional consumable in the reconstructed model, wherein simulating the release effect of the discrete elastic rope model of the interventional consumable in the reconstructed model includes: The movement process of the discrete elastic rope model of the interventional consumable in the reconstructed model, the interaction between the elastic ropes in the discrete elastic rope model of the interventional consumable and the contact condition with the contact surface of the reconstructed model are calculated to simulate the release effect.

2. The simulation method according to claim 1, characterized in that Obtaining a reconstructed model of the interventional object includes: Acquiring medical images of the interventional object; and The intervention object is restored and reconstructed based on the medical image to obtain the reconstructed model.

3. The simulation method according to claim 1, characterized in that Determining the interventional consumables to be modeled based on the reconstructed model includes: determining measurement parameters of the intervention object according to the reconstructed model; and The interventional consumables to be modeled are screened out according to the measurement parameters of the interventional object.

4. The simulation method according to claim 1, characterized in that: The method further comprises: Acquiring placement marking points of the interventional consumable in the reconstructed model; Determining a target placement position and posture of the interventional consumable in the reconstructed model based on the placement marking points; and The discrete elastic rope model of the interventional consumable is placed in the reconstructed model according to the target placement position.

5. The simulation method according to claim 4, characterized in that: The placement marking points include a distal end point and a proximal end point, and determining the target placement position of the interventional consumable in the reconstructed model based on the placement marking points includes: Obtaining an initial position of a discrete elastic rope model of the interventional consumable; Determining an axial direction of a discrete elastic rope model of the interventional consumable based on the distal end point and the proximal end point; Determining position information in different dimensions using the axial direction and the random unit vector; and The target placement position is determined using the axial direction, the position information in different dimensions, and the initial position.

6. The simulation method according to claim 1, characterized in that: The method comprises: In the process of calculating the interaction between the elastic ropes, the maximum bounding box of the space where the discrete elastic rope model of the interventional consumable is located is rasterized into a target three-dimensional grid; Preserving the density field and velocity field of all nodes of the discrete elastic rope model of the interventional consumable in the target three-dimensional grid; and In each time step of the simulation, the positions and velocities of all nodes in the discrete elastic rope model of the interventional consumable are traversed to update the stored density field and velocity field.

7. The simulation method according to claim 6, characterized in that: The calculation of the contact situation with the contact surface of the reconstructed model includes collision detection calculation, and the method further includes: Calculating a directed distance field of a discrete elastic rope model of the interventional consumable; In each time step of the simulation, calculating the value of the distance field corresponding to each node in the discrete elastic rope model of the interventional consumable; In response to there being a value of the distance field less than a threshold, calculating a location of the collision; and The position and velocity of the node are corrected using the calculation results of the collision position.

8. The simulation method according to claim 7, characterized in that: The method further comprises: During the collision detection process, the friction force and reaction force on each node are calculated based on the density field and the velocity field.

9. The simulation method according to claim 8, characterized in that: The friction and reaction forces at each node are calculated using the following formula: in, represents the velocity of the x-node, λ represents the static friction coefficient, γ represents the reaction force coefficient, V(x) represents the value of the velocity field at the x-node position, D(x) represents the value of the density field at the x-node position, and ▽P(x) represents the pressure gradient at the x-node position.

10. An electronic device, characterized in that: include: processor; as well as A memory storing computer instructions for simulating an interventional consumable implemented by a computer, wherein when the computer instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that: The invention comprises program instructions for simulating an interventional consumable implemented by a computer, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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