CN113974829A - Vascular stent implantation simulation method - Google Patents
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- CN113974829A CN113974829A CN202111382404.8A CN202111382404A CN113974829A CN 113974829 A CN113974829 A CN 113974829A CN 202111382404 A CN202111382404 A CN 202111382404A CN 113974829 A CN113974829 A CN 113974829A
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- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000002792 vascular Effects 0.000 title claims abstract description 19
- 238000004088 simulation Methods 0.000 title claims abstract description 12
- 238000002513 implantation Methods 0.000 title claims abstract description 8
- 206010028980 Neoplasm Diseases 0.000 claims abstract description 66
- 230000017531 blood circulation Effects 0.000 claims abstract description 64
- 210000004204 blood vessel Anatomy 0.000 claims abstract description 48
- 239000000463 material Substances 0.000 claims abstract description 16
- 230000004087 circulation Effects 0.000 claims abstract description 5
- 230000011218 segmentation Effects 0.000 claims description 4
- 206010055031 vascular neoplasm Diseases 0.000 abstract description 6
- 201000011531 vascular cancer Diseases 0.000 description 4
- 238000010968 computed tomography angiography Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011960 computer-aided design Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 206010020772 Hypertension Diseases 0.000 description 1
- 208000031481 Pathologic Constriction Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000036770 blood supply Effects 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005489 elastic deformation Effects 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 206010020718 hyperplasia Diseases 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910001000 nickel titanium Inorganic materials 0.000 description 1
- 230000002980 postoperative effect Effects 0.000 description 1
- 238000002271 resection Methods 0.000 description 1
- 208000037803 restenosis Diseases 0.000 description 1
- 208000037804 stenosis Diseases 0.000 description 1
- 230000036262 stenosis Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 210000004881 tumor cell Anatomy 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
- A61B2034/108—Computer aided selection or customisation of medical implants or cutting guides
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
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Abstract
The invention discloses a vascular stent implantation simulation method, which comprises the following steps: extracting a first blood flow model from a medical image comprising blood vessels, blood flow and a tumor; extracting a tumor model and a blood vessel wall model according to the first blood flow model; converting the tumor model and the blood vessel wall model into finite element models; setting material properties of a tumor region and a vessel wall region in the finite element model; assembling the stent model and the finite element model, and adjusting the position and/or parameters of the stent model to enable the circulation radius expansion of the blood vessel wall in the finite element model to reach a preset value, wherein the parameters of the stent model comprise diameter and length. The technical scheme provided by the invention simulates the expansion of the vascular stent in the vascular tumor area, provides the vascular stent position and parameters meeting the flow radius of the vascular wall, and provides technical reference for doctors before operation.
Description
Technical Field
The invention relates to the technical field of virtual surgery, in particular to a vascular stent implantation simulation method.
Background
Vascular tumors are generally caused by invasion of blood vessels by malignant tumor cells, and most patients have lost the chance of radical surgical resection for tumors that have metastasized. The blood supply of normal organ tissues is blocked due to the compression of vascular tumors, and the development of vascular tumors can cause severe complications such as vascular hypertension, organ function damage and failure. In the case of vascular tumors, the expansion is generally performed clinically by means of implanting a vascular stent. However, for the operation of implanting the vascular stent, the suitability of the vascular stent is related to the post-operative health condition of the patient. If the supporting force of the blood vessel support is small, the problems of displacement, restenosis and the like can be caused after the blood vessel support is implanted, and the treatment effect is influenced. If the supporting force of the blood vessel stent is too large, the blood vessel wall can be damaged, intimal hyperplasia, wall thickness thinning and even rupture can be caused.
Therefore, a simulation method for implanting a blood vessel stent is needed to simulate the implantation position and parameters of the blood vessel stent and provide reference for physicians.
Disclosure of Invention
In view of this, the present invention provides a simulation method for implanting a stent into a blood vessel to alleviate the deficiencies of the prior art.
According to an embodiment, the present invention provides a vascular stent implantation simulation method, including: extracting a first blood flow model from a medical image comprising blood vessels, blood flow and a tumor; extracting a tumor model and a blood vessel wall model according to the first blood flow model; converting the tumor model and the blood vessel wall model into finite element models; setting material properties of a tumor region and a vessel wall region in the finite element model; assembling the stent model and the finite element model, and adjusting the position and/or parameters of the stent model to enable the circulation radius expansion of the blood vessel wall in the finite element model to reach a preset value, wherein the parameters of the stent model comprise diameter and length.
Optionally, the step of extracting a first blood flow model from the medical image including blood vessels, blood flow and tumor comprises: and performing threshold segmentation according to the brightness of the blood flow in the medical image to obtain a first blood flow model.
Optionally, before extracting the first blood flow model from the medical image including blood vessels, blood flow and tumor, the method further includes: and interpolating pixels in the medical image.
Optionally, the step of extracting a tumor model and a blood vessel wall model from the first blood flow model comprises: repairing a narrow part in the first blood flow model to obtain a second blood flow model; subtracting the first blood flow model from the second blood flow model to obtain a tumor model; and expanding and shelling the second blood flow model to obtain a blood vessel wall model.
Optionally, the step of converting the tumor model and the vessel wall model into a finite element model comprises: and meshing the tumor model and the blood vessel wall model by adopting hexahedral meshes.
Optionally, setting material properties of the tumor and the vessel wall in the finite element model comprises: the elastic modulus and poisson's ratio of the tumor and the vessel wall in the finite element model are set.
Optionally, the step of assembling the stent model with the finite element model comprises: setting the stent model to a minimum diameter; setting the stent model in the tumor region in the finite element model; the stent model is arranged to expand to induce deformation of the tumor region.
The invention has the following beneficial effects:
the technical scheme provided by the invention can have the following beneficial effects: the vascular stent implantation simulation method simulates the expansion of a vascular stent in a vascular tumor area, provides the vascular stent position and parameters meeting the circulation radius of a vascular wall, provides reference for doctors before operation, and solves the problem that the vascular stent position and parameters are difficult to determine in the prior art.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are one embodiment of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a simulation method for implanting a stent into a blood vessel according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and the described embodiments are some, but not all embodiments of the present invention.
Fig. 1 is a schematic flow chart of a simulation method for implanting a stent into a blood vessel according to a first embodiment of the present invention, as shown in fig. 1, the method includes the following 6 steps.
Step S10: a first blood flow model is extracted. Specifically, a first blood flow model is extracted from a medical image including blood vessels, blood flow, and a tumor. It should be noted that the first blood vessel model includes blood flow in the medical image, and does not include blood vessels and tumors. Illustratively, the first vessel model is extracted from a CTA (Computed Tomography Angiography) image, which may be extracted according to time-domain or frequency-domain characteristics of blood flow in the image.
In one embodiment, the step of extracting a first blood flow model from a medical image comprising blood vessels, blood flow and a tumor comprises: and performing threshold segmentation according to the brightness of the blood flow in the medical image to obtain a first blood flow model. Illustratively, the highlight region in the CTA image is a blood flow, and the extraction is performed based on the luminance value of the blood flow.
In one embodiment, before extracting the first blood flow model from the medical image including the blood vessels, the blood flow and the tumor, the method further comprises: and interpolating pixels in the medical image. It should be noted that, by interpolating pixels in the medical image, the image can be made clear and continuous.
Step S11: and extracting a tumor model and a blood vessel wall model. Specifically, a tumor model and a blood vessel wall model are extracted from the first blood flow model. Illustratively, image data segmentation is performed according to regional features of blood vessels, blood flow, and tumors, thereby extracting a tumor model and a blood vessel wall model.
In one embodiment, the step of extracting a tumor model and a vessel wall model from the first blood flow model comprises: repairing a narrow part in the first blood flow model to obtain a second blood flow model; subtracting the first blood flow model from the second blood flow model to obtain a tumor model; and expanding and shelling the second blood flow model to obtain a blood vessel wall model.
It should be noted that the stenosis portion of the first blood flow model is a region of the tumor in the blood vessel, and the blood flow geometric flux in this region is low due to the existence of the tumor. And repairing image pixels according to the geometric form of the blood flow to obtain a second blood flow model. The second blood flow model is the blood flow state of the blood vessel without the tumor, and the tumor model is obtained by subtracting the first blood flow model from the second blood flow model. Expanding the pull-out shell may cause the second blood flow model to be hollow, thereby obtaining the vessel wall. And the expansion is carried out by geometric extension on the basis of the existing second blood flow model, and the size of the extension is determined according to the thickness of the blood vessel wall. The shell extraction can be realized by CAD (Computer Aided Design) software.
Step S12: the tumor model and the vessel wall model were transformed into finite element models. It should be noted that the finite element model is a model established by using a finite element analysis method, and is a group of element combinations which are connected only at nodes, transmit force only by the nodes, and are constrained only at the nodes, and the finite element model can be used for mechanical simulation. Illustratively, the tumor model and the vessel wall model are geometric models, and meshing the geometric models can establish a finite element model.
In one embodiment, the step of converting the tumor model and the vessel wall model into a finite element model comprises: and meshing the tumor model and the blood vessel wall model by adopting hexahedral meshes. In addition, dense grids are arranged at positions where the curvature changes of the tumor model and the vascular wall model are large, so that model deformation can be accurately represented.
Step S13: setting material properties of the finite element model. In particular, material properties of the tumor region and the vessel wall region in the finite element model are set. The material properties of the tumor region and the blood vessel wall region mean which material the tumor region and the blood vessel wall region belong to, or the values of the mechanical parameters of the setting material.
In one embodiment, setting material properties of a tumor and a vessel wall in a finite element model comprises: the elastic modulus and poisson's ratio of the tumor and the vessel wall in the finite element model are set. The elastic modulus refers to the stress and strain proportional relationship of the material in the elastic deformation stage. The poisson ratio is the ratio of the transverse deformation amount to the longitudinal deformation amount of a material in an elastic range when the material is subjected to longitudinal pressure or tensile force, and is also called a transverse deformation coefficient, and is an elastic constant reflecting the transverse deformation of the material.
Illustratively, the modulus of elasticity of the vessel wall in the finite element model was set at 175MPa and the Poisson's ratio was set at 0.499. The elastic modulus was set to 219MPa and the poisson ratio to 0.499 for the tumor model in the finite element model.
Step S14: and assembling the bracket model and the finite element model. It should be noted that the stent model and the finite element model are assembled to detect whether the tumor region in the finite element model can be mechanically expanded to a predetermined parameter after the stent model is expanded. Illustratively, the material properties of the stent model are nickel titanium alloy, which has superelasticity and recoverable strain of up to 10%.
In one embodiment, the step of assembling the stent model with the finite element model comprises: setting the stent model to a minimum diameter; setting the stent model in the tumor region in the finite element model; the stent model is arranged to expand to induce deformation of the tumor region. It should be noted that the minimum diameter D0 of the stent model is smaller than the diameter D of the tumor region in the finite element model1To facilitate placement of the stent model in the tumor region. The diameter D1 of the stent model in the expanded state is larger than the diameter D of the vessel wall region in the finite element model2E.g. D1 ═ 1.1D2. Illustratively, the stent model changes from a minimum diameter state to an expanded state as the temperature increases. After the stent model is implanted, the tumor area in the finite element model can deform due to the expansion stress of the stent model, so that the purpose of expansion is achieved.
Step S15: the position and/or parameters of the stent model are adjusted. Assembling the stent model and the finite element model, and adjusting the position and/or parameters of the stent model to enable the circulation radius expansion of the blood vessel wall in the finite element model to reach a preset value, wherein the parameters of the stent model comprise diameter and length.
It should be noted that, by the vessel wall flow radius in the finite element model, the area of the flow cross section of the vessel can be calculated, and whether the purpose of vessel expansion is achieved can be verified by the area. The position, the diameter and the length of the stent model are adjusted, so that the mechanical load of the stent model on the tumor area can be changed, and the expansion deformation of the tumor area is changed.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A vascular stent implantation simulation method is characterized by comprising the following steps:
extracting a first blood flow model from a medical image comprising blood vessels, blood flow and a tumor;
extracting a tumor model and a blood vessel wall model according to the first blood flow model;
converting the tumor model and the vessel wall model into finite element models;
setting material properties of a tumor region and a vessel wall region in the finite element model;
assembling a stent model with the finite element model, and adjusting the position and/or parameters of the stent model to enable the circulation radius expansion of the blood vessel wall in the finite element model to reach a preset value, wherein the parameters of the stent model comprise diameter and length.
2. The method of claim 1, wherein the step of extracting the first blood flow model from the medical image comprising blood vessels, blood flow and tumor comprises:
and performing threshold segmentation according to the brightness of the blood flow in the medical image to obtain the first blood flow model.
3. The method of claim 1, wherein prior to extracting the first blood flow model from the medical image comprising blood vessels, blood flow and tumor, the method further comprises:
and interpolating pixels in the medical image.
4. The method of claim 1, wherein the step of extracting a tumor model and a vessel wall model from the first blood flow model comprises:
repairing a narrow part in the first blood flow model to obtain a second blood flow model;
subtracting the first blood flow model from the second blood flow model to obtain the tumor model;
and expanding and shelling the second blood flow model to obtain the blood vessel wall model.
5. The method of claim 1, wherein the step of converting the tumor model and the vessel wall model into finite element models comprises:
and adopting hexahedron grids to perform grid division on the tumor model and the blood vessel wall model.
6. The method of claim 1, wherein the setting material properties of the tumor and the vessel wall in the finite element model comprises:
and setting the elastic modulus and Poisson's ratio of the tumor and the blood vessel wall in the finite element model.
7. The method of claim 1, wherein the step of assembling the stent model with the finite element model comprises:
setting the stent model to a minimum diameter;
setting the stent model in a tumor region in a finite element model;
the stent model is arranged to expand to cause deformation of the tumor region.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103198202A (en) * | 2012-12-19 | 2013-07-10 | 首都医科大学 | Image simulation method for intracranial aneurysm interventional therapy stent implantation |
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| CN104720894A (en) * | 2015-02-11 | 2015-06-24 | 中山大学附属第一医院 | Rationality analyzing method for blood vessel operation mode |
| CN104837407A (en) * | 2012-12-07 | 2015-08-12 | 株式会社东芝 | Blood vessel analysis device, medical diagnostic imaging apparatus, and blood vessel analysis method |
| CN109961850A (en) * | 2019-03-19 | 2019-07-02 | 肖仁德 | A kind of method, apparatus, computer equipment for assessing rupture of intracranial aneurysm risk |
| CN110782988A (en) * | 2019-11-04 | 2020-02-11 | 北京理工大学 | Intracranial aneurysm virtual support diagnosis and treatment system and diagnosis and treatment method thereof |
| CN110852010A (en) * | 2019-11-07 | 2020-02-28 | 大连理工大学 | Method for predicting mechanical property of polymer vascular stent by considering scale effect |
| US11026749B1 (en) * | 2019-12-05 | 2021-06-08 | Board Of Regents Of The University Of Nebraska | Computational simulation platform for planning of interventional procedures |
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- 2021-11-22 CN CN202111382404.8A patent/CN113974829A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103402433A (en) * | 2012-03-02 | 2013-11-20 | 株式会社东芝 | Medical image processing device and medical image processing method |
| CN104837407A (en) * | 2012-12-07 | 2015-08-12 | 株式会社东芝 | Blood vessel analysis device, medical diagnostic imaging apparatus, and blood vessel analysis method |
| CN103198202A (en) * | 2012-12-19 | 2013-07-10 | 首都医科大学 | Image simulation method for intracranial aneurysm interventional therapy stent implantation |
| CN104720894A (en) * | 2015-02-11 | 2015-06-24 | 中山大学附属第一医院 | Rationality analyzing method for blood vessel operation mode |
| CN109961850A (en) * | 2019-03-19 | 2019-07-02 | 肖仁德 | A kind of method, apparatus, computer equipment for assessing rupture of intracranial aneurysm risk |
| CN110782988A (en) * | 2019-11-04 | 2020-02-11 | 北京理工大学 | Intracranial aneurysm virtual support diagnosis and treatment system and diagnosis and treatment method thereof |
| CN110852010A (en) * | 2019-11-07 | 2020-02-28 | 大连理工大学 | Method for predicting mechanical property of polymer vascular stent by considering scale effect |
| US11026749B1 (en) * | 2019-12-05 | 2021-06-08 | Board Of Regents Of The University Of Nebraska | Computational simulation platform for planning of interventional procedures |
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