CN109498207B - Inferior vena cava filtering system and intelligent recovery system thereof - Google Patents
Inferior vena cava filtering system and intelligent recovery system thereof Download PDFInfo
- Publication number
- CN109498207B CN109498207B CN201811515076.2A CN201811515076A CN109498207B CN 109498207 B CN109498207 B CN 109498207B CN 201811515076 A CN201811515076 A CN 201811515076A CN 109498207 B CN109498207 B CN 109498207B
- Authority
- CN
- China
- Prior art keywords
- filter
- module
- image
- vena cava
- drag hook
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/01—Filters implantable into blood vessels
-
- 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
- A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/01—Filters implantable into blood vessels
- A61F2/011—Instruments for their placement or removal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- 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
- A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
- A61B2034/2046—Tracking techniques
- A61B2034/2065—Tracking using image or pattern recognition
-
- 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
- A61B34/20—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
- A61B2034/2068—Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis using pointers, e.g. pointers having reference marks for determining coordinates of body points
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/01—Filters implantable into blood vessels
- A61F2002/016—Filters implantable into blood vessels made from wire-like elements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20004—Adaptive image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Physics & Mathematics (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- Heart & Thoracic Surgery (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Surgery (AREA)
- Vascular Medicine (AREA)
- Transplantation (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Molecular Biology (AREA)
- Cardiology (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Physiology (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Robotics (AREA)
- Artificial Intelligence (AREA)
- Medical Informatics (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Image Processing (AREA)
Abstract
The invention belongs to the technical field of medical devices, and discloses a novel inferior vena cava filtering system and an intelligent recovery system thereof. The device is provided with a drag hook positioned at one end, the drag hook is connected with a connecting strip, the connecting strip penetrates through a filter, one end of the connecting strip extends out of the filter, the middle part of the filter comprises a degradable section, and a compression spring is arranged between the filter and the connecting strip. The invention adopts the structure that the middle of the filter is provided with the degradable section and the two ends of the filter are provided with the non-degradable section, so that the degradable section can be completely degraded after the filter is placed in a blood vessel in a body. When the degradable section is complete, the filter is converted into two independent filters, the filters are contracted by the action of the springs, and the filters can be recovered by the drag hook. The invention has reasonable structure, safety, stability, reliable performance and convenient operation and use, and is an ideal recyclable vena cava filter.
Description
Technical Field
The invention belongs to the technical field of medical devices, and particularly relates to a novel inferior vena cava filtering system and an intelligent recovery system thereof.
Background
Temporary IVC filters are placed very similar to permanent filters, but are designed so that they can be retrieved by a separate intravascular procedure, typically from the femoral vein or internal jugular vein channel. Most currently available temporary filters include hooked features by which they can be captured and received within a catheter or sheath for removal by use of a gooseneck snare or a multiple loop snare. While retraction is a simple process in principle, a difficulty typically encountered is the use of snare loop(s) to capture the hooks of the filter. This difficulty is exacerbated when the filter is tilted or placed out of balance. Many filters are designed to avoid this orientation. However, this problem is still common because the device is not anchored in a stable manner within the IVC. In addition to blood clots, continued blood flow can confuse filters within the IVC, making reacquisition difficult. Accordingly, there is a need for a filter retrieval system that has improved ease of use and/or is less susceptible to filter orientation problems.
In summary, the problems of the prior art are:
(1) The existing inferior vena cava filtration system device is not provided with a positioning device, so that a guide wire is difficult to hang a drag hook when the inferior vena cava filtration system device is taken out, a filter is taken out, time is wasted, and working efficiency is reduced.
(2) In the prior art, in the process of positioning the hooks in the filter through the wireless positioning sensor, the wireless positioning sensor adopts a traditional algorithm, so that the positioning accuracy cannot be improved, the positioning speed is increased, and the positioning real-time performance is excellent.
(3) In the prior art, in the image processing process, the existing algorithm is adopted, so that the targeted enhancement of local details cannot be realized, and the visual effect of the image is improved.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a novel inferior vena cava filtering system and an intelligent recovery system thereof.
The present invention is achieved by a novel inferior vena cava filtration system provided with:
a drag hook;
the drag hook is welded with the connecting strip, the drag hook is rectangular, the front end of the drag hook is provided with an arc notch, the drag hook is embedded with a locator, the connecting strip penetrates through the filter, one end of the connecting strip extends out of the filter, a compression spring with two symmetrical ends is arranged between the filter and the connecting strip, the filter consists of an upper part and a lower part, each part of the filter is provided with a basket supporting steel wire, the upper part and the lower part are identical in shape, and the upper part and the lower part are connected through the basket connecting steel wire.
Another object of the present invention is to provide a inferior vena cava filtering method of the novel inferior vena cava filtering system, which implants a filter into a inferior vena cava, blocks blood clots inside the inferior vena cava by basket connection wires and basket support wires, places the filter for 12 to 14 days, takes out the filter, hangs a drag hook by using positioning and contrast image information of a positioner, draws the whole filter into a guide pin, and takes out the filter.
Another object of the present invention is to provide an intelligent recovery system using the novel inferior vena cava filtration system, the intelligent recovery system comprising:
the angiography module is connected with the image processing module, injects contrast agent into blood vessels of inferior vena cava, and concentrates X-ray images to be displayed on an output screen through an X-ray enhancement tube;
the image processing and acquiring module is connected with the central processing module, and extracts an X-ray image in the angiography module by using an X-ray camera to process the image and acquire an X-ray image;
the central processing module is connected with the image processing acquisition module, the recycling module and the image display module; the positioning module is connected with each other to coordinate the normal operation of each module;
the recovery module is connected with the central processing module and is used for carrying out recovery operation on the filter according to the image data on the display screen;
the positioning module is connected with the central processing module, positions the hooks in the filter through the wireless positioning sensor, and hangs the guide wires on the hooks according to the position information of the hooks;
and the image display module is connected with the central processing module and displays the operation process and the contrast image.
Another object of the present invention is to provide a recycling method of the intelligent recycling system, the recycling method comprising the steps of:
firstly, injecting contrast agent into a blood vessel of a inferior vena cava, concentrating an X-ray image to be displayed on an output screen through an X-ray enhancement tube, and processing an image to obtain an X-ray image;
secondly, displaying images and operation process images on a display screen, and positioning hooks in a filter by a positioner;
and thirdly, taking out the filter according to the image, the image and the positioning information.
Further, the wireless positioning sensor is used for positioning the hooks in the filter, and according to the position information process of the hooks, a wireless sensor node positioning algorithm of the artificial bee colony optimization neural network is adopted, and the method specifically comprises the following steps:
step one, collecting a measurement sequence and carrying out normalization operation, wherein the normalization operation specifically comprises the following steps:
initializing parameters including the number of food sources, the iteration times, control parameters limit and a defined interval of solutions;
initializing the food source position: x is X i ＝[x i1 ，x i2 ，…，x iD ] T I=1, 2, …, n, n denotes the number of food sources, D denotes the dimension, which is determined in such a way that
D＝M*H+H*N+H+N；
M, H, N in the formula is the node number of the input layer, the hidden layer and the output layer respectively;
step four, according to X i The position carries out assignment on the weight and the threshold value of the neural network, and learns the training sample to obtain X i Is the objective function value of (2)
D in i And t k The actual output and the expected output are respectively, and k is the number of training samples;
step five, leading the bees in X i Ambient yieldNew solution V i And calculate their fitness values, determine the retention X according to a greedy principle i And V i The fitness value is better;
step six, according to the selection probability P i Select solution, and at X i Generating new solution V around i The optimal solution is reserved in the same way;
step seven, if a solution is not improved after limit cycles, discarding the solution, and converting the leading bee into a reconnaissance bee to generate a new solution V i Replacing the solution;
step eight, finding out the current optimal solution according to the fitness value;
step nine, after the termination condition is reached, obtaining the optimal weight and the threshold of the neural network according to the optimal solution, and otherwise returning to the step five;
a step ten of relearning training samples according to the optimal weight and the threshold value, and establishing a range error prediction model;
and step eleven, ranging and correcting according to the error prediction result.
Further, the X-ray image in the angiography module is extracted by an X-ray camera, the image is processed, and an improved self-adaptive image enhancement algorithm based on local mean and standard deviation is adopted in the process of acquiring the X-ray image, and the method comprises the following steps:
step one, selecting darker areas: if E s ＜k 0 E g The representation area is a darker area, which is the area to be enhanced, where k 0 A positive constant less than 1;
selecting a region of contrast, wherein the region is considered to be free of detail and not required to be reinforced when the contrast is too low, and assuming that the region of low contrast to be reinforced is k 1 σ g ＜σ s ＜k 2 σ g Wherein k is 1 ＜k 2 And k is 1 ，k 2 Positive constants all less than 1;
step three, enhancing the selected area, and defining an enhancement formula of the image based on the local mean and the standard deviation as follows:
wherein ω is a constant larger than 1, and during image enhancement, the enhancement coefficient is dynamically adjusted according to the local mean and standard deviation;
the self-adaptive image enhancement algorithm based on the local mean and the standard deviation is as follows:
wherein ω is a constant greater than 1; x (i, j), f (i, j) are the gray values of the input image and the output image point (i, j), respectively; k (k) 0 ，k 1 ，k 2 Is a normal number less than 1, and k 1 ＜k 2 ；E g ，σ g The global mean value and the global standard deviation are respectively; e (E) s ，σ s The local mean and the local standard deviation, respectively.
The invention has the advantages and positive effects that:
according to the invention, the positioner is used for positioning to assist a doctor in hanging the guide wire on the drag hook, and the filter is taken out, so that the time is wasted, and the working efficiency is reduced.
According to the invention, the positioning module positions the hooks in the filter through the wireless positioning sensor, and in the process of positioning according to the position information of the hooks, in order to improve the positioning accuracy, speed up the positioning, and have excellent positioning real-time performance, a wireless sensor node positioning algorithm of an artificial bee colony optimized neural network is adopted.
The image processing and acquiring module extracts the X-ray image in the angiography module by using the X-ray camera, processes the image, and adopts an improved self-adaptive image enhancement algorithm based on local mean and standard deviation in order to realize targeted enhancement of local details in the process of acquiring the X-ray image, thereby improving the visual effect of the image.
Drawings
Fig. 1 is a schematic structural diagram of a novel inferior vena cava filtration system and an intelligent retraction system thereof provided by an embodiment of the invention;
FIG. 2 is an enlarged schematic drawing of a drag hook of the novel inferior vena cava filtration system and the intelligent retraction system provided by the embodiment of the invention;
FIG. 3 is a schematic diagram of a novel inferior vena cava filtration system intelligent recovery system according to an embodiment of the invention;
FIG. 4 is a flow chart of the novel inferior vena cava filtration system intelligent recovery system provided by an embodiment of the invention;
in the figure: 1. a drag hook; 2. a connecting strip; 3. a filter; 4. a compression spring; 5. the basket is connected with the steel wire; 6. basket supporting steel wires; 7. a positioner; 8. an angiography module; 9. an image processing acquisition module; 10. a central processing module; 11. a recovery module; 12. an image display module; 13. and a positioning module.
Detailed Description
For a further understanding of the invention, its features and advantages, reference is now made to the following examples, which are illustrated in the accompanying drawings.
The principle of application of the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1-2, the novel inferior vena cava filtering system provided by the embodiment of the invention comprises: the device comprises a draw hook 1, a connecting strip 2, a filter 3, a compression spring 4, a basket connecting steel wire 5, a basket supporting steel wire 6 and a positioner 7.
The drag hook 1 is welded with the connecting strip 2, the drag hook 1 is long strip-shaped, the front end of the drag hook 1 is provided with an arc notch, the drag hook 1 is embedded with a locator 7, the connecting strip 2 penetrates through the filter 3, one end of the connecting strip extends out of the filter 3, a compression spring with two symmetrical ends is arranged between the filter 3 and the connecting strip 2, the filter 3 consists of an upper part and a lower part, each part of the filter 3 is provided with a basket supporting steel wire 6, the upper part and the lower part are identical in shape, and the two parts are connected through the basket connecting steel wire 5.
When the invention is used, the filter 3 is implanted into the inferior vena cava, blood clots in the inferior vena cava are blocked by the basket connecting steel wire 5 and the basket supporting steel wire 6, the filter 3 is placed for 12 to 14 days, the filter is taken out, the guide wire is hung on the drag hook 1 by utilizing the positioning and radiography image information of the positioner 7, and the whole filter 3 is drawn into the guide pin and taken out.
As shown in fig. 3, the novel intelligent recovery system of the inferior vena cava filtering system provided by the embodiment of the invention comprises: an angiography module 8, an image processing and acquiring module 9, a central processing module 10, a recycling module 11, an image display module 12 and a positioning module 13.
The angiography module 8 is connected with the image processing module, injects contrast agent into the blood vessel of the inferior vena cava, and concentrates the X-ray image on the output screen through the X-ray enhancement tube.
The image processing and acquiring module 9 is connected with the central processing module, and extracts an X-ray image in the angiography module by using an X-ray camera to process the image and acquire an X-ray image;
a central processing module 10, an image processing acquisition module 9, a recycling module 11, an image display module 12; the positioning module 13 is connected and coordinates the normal operation of each module;
the recovery module 11 is connected with the central processing module and is used for carrying out recovery operation on the filter according to the image data on the display screen;
the positioning module 13 is connected with the central processing module, positions the hooks in the filter through the wireless positioning sensor, and hangs the guide wires on the hooks according to the position information of the hooks;
the image display module 12 is connected with the central processing module and displays the operation process and the contrast image.
As shown in fig. 4, the operation process of the novel intelligent recovery system of the inferior vena cava filtering system provided by the embodiment of the invention comprises the following steps:
s101: firstly, injecting contrast agent into a blood vessel of a inferior vena cava, centralizing an X-ray image on an output screen through an X-ray enhancement tube, and processing an image to obtain an X-ray image;
s102: the display screen displays images and operation process images, and the positioner positions hooks in the filter;
s103: and taking out the filter according to the image, the image and the positioning information.
The positioning module 13 positions the hooks in the filter through the wireless positioning sensor, and in order to improve the positioning accuracy and speed and realize excellent positioning real-time performance in the process of positioning according to the position information of the hooks, the wireless sensor node positioning algorithm of the artificial bee colony optimized neural network is adopted, and specifically comprises the following steps:
step one, collecting measurement sequences and performing normalization operation, specifically
Initializing parameters including the number of food sources, the iteration times, control parameters limit and a defined interval of solutions;
initializing the food source position: x is X i ＝[x i1 ，x i2 ，…，x iD ] T I=1, 2, …, n, n denotes the number of food sources, D denotes the dimension, which is determined in such a way that
D＝M*H+H*N+H+N；
M, H, N in the formula is the node number of the input layer, the hidden layer and the output layer respectively;
step four, according to X i The position carries out assignment on the weight and the threshold value of the neural network, and learns the training sample to obtain X i Is the objective function value of (2)
D in i And t k The actual output and the expected output are respectively, and k is the number of training samples;
step five, leading the bees in X i Generating new solution V around i And calculate their fitness values, determine the retention X according to a greedy principle i And V i The fitness value is better;
step six, according to the selection probability P i Select solution, and at X i Generating new solution V around i The optimal solution is reserved in the same way;
step seven, if a solution is not improved after limit cycles, discarding the solution, and converting the leading bee into a reconnaissance bee to generate a new solution V i Replacing the solution;
step eight, finding out the current optimal solution according to the fitness value;
step nine, after the termination condition is reached, obtaining the optimal weight and the threshold of the neural network according to the optimal solution, and otherwise returning to the step five;
a step ten of relearning training samples according to the optimal weight and the threshold value, and establishing a range error prediction model;
and step eleven, ranging and correcting according to the error prediction result.
The image processing and acquiring module 9 extracts an X-ray image in the angiography module by using an X-ray camera, processes the image, and in order to realize targeted enhancement of local details in the process of acquiring a v-ray image, thereby improving the visual effect of the image, adopts an improved self-adaptive image enhancement algorithm based on local mean and standard deviation, and comprises the following steps:
step one, selecting darker areas: if E s ＜k 0 E g The representation area is a darker area, which is the area to be enhanced, where k 0 A positive constant less than 1;
selecting a region of contrast, wherein the region is considered to be free of detail and not required to be reinforced when the contrast is too low, and assuming that the region of low contrast to be reinforced is k 1 σ g ＜σ s ＜k 2 σ g Wherein k is 1 ＜k 2 And k is 1 ，k 2 Positive constants all less than 1;
step three, enhancing the selected area, and defining an enhancement formula of the image based on the local mean and the standard deviation as follows:
wherein ω is a constant larger than 1, and during image enhancement, the enhancement coefficient is dynamically adjusted according to the local mean and standard deviation;
the self-adaptive image enhancement algorithm based on the local mean and the standard deviation is as follows:
wherein ω is a constant greater than 1; x (i, j), f (i, j) are the gray values of the input image and the output image point (i, j), respectively; k (k) 0 ，k 1 ，k 2 Is a normal number less than 1, and k 1 ＜k 2 ；E g ，σ g The global mean value and the global standard deviation are respectively; e (E) s ，σ s The local mean and the local standard deviation, respectively.
The recycling module 11 is connected with the central processing module, and the process of recycling the filter according to the image data on the display screen comprises the following steps:
step one, judging whether the inferior vena cava contains more fresh thrombus, and judging whether the recovery time is between 12 days and 14 days according to a plan of abandoning the filter taking-out;
pushing the guide wire to enable the capturing ring to be exposed out of the catheter;
step three, sleeving a recovery hook at the lower end of the filter by using a capture ring;
and step four, the guide wire, the guide tube and the filter are drawn into the recovery pin and taken out.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the invention in any way, but any simple modification, equivalent variation and modification of the above embodiments according to the technical principles of the present invention are within the scope of the technical solutions of the present invention.
Claims (1)
1. A inferior vena cava filtration system, wherein the inferior vena cava filtration system is provided with:
a drag hook;
the drag hook is welded with the connecting strip, the drag hook is in a strip shape, an arc notch is formed in the front end of the drag hook, a wireless positioning sensor is embedded in the drag hook, the connecting strip penetrates through the filter, one end of the connecting strip extends out of the filter, a compression spring with two symmetrical ends is arranged between the filter and the connecting strip, the filter consists of an upper part and a lower part, each part of the filter is provided with a basket supporting steel wire, and the upper part and the lower part are identical in shape and are connected through the basket connecting steel wire;
the system further comprises:
the angiography module is connected with the image processing module, injects contrast agent into blood vessels of inferior vena cava, and concentrates X-ray images to be displayed on an output screen through an X-ray enhancement tube;
the image processing and acquiring module is used for extracting the X-ray image in the angiography module by using an X-ray camera, and processing the image to acquire an X-ray image;
the central processing module is connected with the image processing acquisition module, the recovery module, the image display module and the positioning module and used for coordinating the normal operation of the modules;
the recovery module is used for carrying out recovery operation on the filter according to the image data on the display screen;
the positioning module is used for positioning the hooks in the filter through the wireless positioning sensor and hanging the guide wires on the hooks according to the position information of the hooks;
and the image display module displays the operation process and the contrast image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811515076.2A CN109498207B (en) | 2018-12-12 | 2018-12-12 | Inferior vena cava filtering system and intelligent recovery system thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811515076.2A CN109498207B (en) | 2018-12-12 | 2018-12-12 | Inferior vena cava filtering system and intelligent recovery system thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109498207A CN109498207A (en) | 2019-03-22 |
| CN109498207B true CN109498207B (en) | 2023-09-19 |
Family
ID=65752344
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811515076.2A Active CN109498207B (en) | 2018-12-12 | 2018-12-12 | Inferior vena cava filtering system and intelligent recovery system thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109498207B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110082717A (en) * | 2019-04-30 | 2019-08-02 | 上海海事大学 | A kind of underwater wireless sensor node positioning method |
| CN111001086B (en) * | 2019-12-20 | 2021-11-05 | 创领心律管理医疗器械（上海）有限公司 | Leadless pacemaker and its tail and head parts |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106510897A (en) * | 2016-12-30 | 2017-03-22 | 科塞尔医疗科技（苏州）有限公司 | Thrombus filter capable of stably releasing and preparation method thereof |
| CN106901869A (en) * | 2017-03-31 | 2017-06-30 | 浙江归创医疗器械有限公司 | Venous filter |
| CN107374777A (en) * | 2017-08-25 | 2017-11-24 | 重庆医科大学附属第二医院 | It is a kind of be easily recycled and safety interim Vena cava filter |
| WO2018107466A1 (en) * | 2016-12-16 | 2018-06-21 | 北京阿迈特医疗器械有限公司 | Biodegradable thrombus filter, and manufacturing method, application, and delivery device thereof |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3849397B2 (en) * | 2000-03-23 | 2006-11-22 | ニプロ株式会社 | Venous filter |
| DE602005026207D1 (en) * | 2004-04-15 | 2011-03-17 | Cordis Corp | LONG-TERM RETRIEVABLE MEDICAL FILTER |
| US8025668B2 (en) * | 2005-04-28 | 2011-09-27 | C. R. Bard, Inc. | Medical device removal system |
| EP2381890A1 (en) * | 2008-12-17 | 2011-11-02 | Abbott Laboratories Vascular Enterprises Limited | Implantable lumen filter with enhanced durability |
| US11224382B2 (en) * | 2016-02-16 | 2022-01-18 | Bruce Reiner | Method and apparatus for embedded sensors in diagnostic and therapeutic medical devices |
| US10529088B2 (en) * | 2016-12-02 | 2020-01-07 | Gabriel Fine | Automatically determining orientation and position of medically invasive devices via image processing |
-
2018
- 2018-12-12 CN CN201811515076.2A patent/CN109498207B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018107466A1 (en) * | 2016-12-16 | 2018-06-21 | 北京阿迈特医疗器械有限公司 | Biodegradable thrombus filter, and manufacturing method, application, and delivery device thereof |
| CN106510897A (en) * | 2016-12-30 | 2017-03-22 | 科塞尔医疗科技（苏州）有限公司 | Thrombus filter capable of stably releasing and preparation method thereof |
| CN106901869A (en) * | 2017-03-31 | 2017-06-30 | 浙江归创医疗器械有限公司 | Venous filter |
| CN107374777A (en) * | 2017-08-25 | 2017-11-24 | 重庆医科大学附属第二医院 | It is a kind of be easily recycled and safety interim Vena cava filter |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109498207A (en) | 2019-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110232383B (en) | Focus image recognition method and focus image recognition system based on deep learning model | |
| CN107203692B (en) | Electrocardio data digital signal processing method based on deep convolutional neural network | |
| CN108171698B (en) | Method for automatically detecting human heart coronary calcified plaque | |
| CN104156935B (en) | Image segmenting device, image partition method and medical image equipment | |
| CN109498207B (en) | Inferior vena cava filtering system and intelligent recovery system thereof | |
| US7376253B2 (en) | Analysis of successive data sets | |
| CN104036484B (en) | Image segmenting device, image partition method and medical image equipment | |
| CN104978730B (en) | A kind of dividing method and device of myocardium of left ventricle | |
| CN109166124A (en) | A kind of retinal vascular morphologies quantization method based on connected region | |
| CN102722882B (en) | Elastic registration method of CAG image sequence | |
| WO2021129234A1 (en) | Cardiac medicine image segmentation method and system for atrial septal occlusion patient | |
| CN110648338B (en) | Image segmentation method, readable storage medium, and image processing apparatus | |
| CN111899250B (en) | Remote disease intelligent diagnosis system based on block chain and medical image | |
| CN111047607A (en) | Method for automatically segmenting coronary artery | |
| CN109871808A (en) | Atrial fibrillation model training and detecting method and device | |
| CN108615045A (en) | Screen the method, apparatus and equipment of the image of capsule endoscope shooting | |
| CN111863232A (en) | Remote disease intelligent diagnosis system based on block chain and medical image | |
| CN115100230B (en) | Artery and vein separation method and device based on blood vessel image, storage medium and terminal | |
| CN116704600A (en) | Gait analysis method and system based on multi-source information fusion | |
| CN109712707A (en) | A kind of lingual diagnosis method, apparatus calculates equipment and computer storage medium | |
| CN116530981A (en) | Facial recognition-based qi and blood state analysis system and method | |
| CN118592919B (en) | Medical auxiliary monitoring method and device based on monitor screen waveform camera | |
| CN111093506B (en) | Motion compensated heart valve reconstruction | |
| CN109285164A (en) | Medical image target-region locating method and system | |
| Li et al. | Actin filament segmentation using spatiotemporal active-surface and active-contour models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200703 Address after: Chongqing city Shapingba street 400030 gaotanyan No. 30 Applicant after: THE FIRST HOSPITAL AFFILIATED TO ARMY MEDICAL University Address before: 400038 No. 100, Second Village, Xinqiao, Shapingba District, Chongqing City, with No. 1, No. 4-2 Applicant before: Zhang Hui |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |