CN109498207A - A kind of novel inferior caval vein filtration system and its intelligent withdrawal system - Google Patents
A kind of novel inferior caval vein filtration system and its intelligent withdrawal system Download PDFInfo
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- CN109498207A CN109498207A CN201811515076.2A CN201811515076A CN109498207A CN 109498207 A CN109498207 A CN 109498207A CN 201811515076 A CN201811515076 A CN 201811515076A CN 109498207 A CN109498207 A CN 109498207A
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- inferior caval
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- 210000003462 vein Anatomy 0.000 title claims abstract description 38
- 238000001914 filtration Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims description 33
- 238000000034 method Methods 0.000 claims description 27
- 229910000831 Steel Inorganic materials 0.000 claims description 16
- 238000011084 recovery Methods 0.000 claims description 16
- 239000010959 steel Substances 0.000 claims description 16
- 238000004064 recycling Methods 0.000 claims description 12
- 238000002583 angiography Methods 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 9
- 230000002708 enhancing effect Effects 0.000 claims description 8
- 210000004204 blood vessel Anatomy 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 239000002872 contrast media Substances 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 230000004087 circulation Effects 0.000 claims description 3
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 4
- 238000011982 device technology Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 244000261422 Lysimachia clethroides Species 0.000 description 1
- 208000007536 Thrombosis Diseases 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 210000003191 femoral vein Anatomy 0.000 description 1
- 210000004731 jugular vein Anatomy 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 125000003367 polycyclic group Chemical group 0.000 description 1
- 238000002601 radiography Methods 0.000 description 1
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
-
- G06T5/90—
-
- 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
Abstract
The invention belongs to field of medical device technology, disclose a kind of novel inferior caval vein filtration system and its intelligent withdrawal system.The device is equipped with the drag hook positioned at one end, and the drag hook is connected with connection strap, and the connection strap runs through filter, and filter is stretched out in one end, and the filter middle portion includes degradable section, and compressed spring is equipped between filter and connection strap.The present invention by by the centre of filter be set as degradable section, both ends be the non-degradable section of structure combined so that filter is after being placed in body vessel, degradable section may be implemented it is degradable.After degradable section complete, filter is converted to two individual filters, is shunk filter by the effect of spring, can be recycled filter by drag hook.Structure of the invention is reasonable, safety and firmness, and reliable performance is easy for operation, is a kind of vena cava filter that preferably can be recycled.
Description
Technical field
The invention belongs to field of medical device technology more particularly to a kind of novel inferior caval vein filtration systems and its intelligence to receive
The system of returning.
Background technique
Temporary IVC filter, which is placed with, is very similar to permanent filter, but it is designed so that they can be with
By being withdrawn in individual endovascular procedures, usually from femoral vein or jugular vein channel.Most currently available temporary mistakes
Filter includes hook feature, they can be acquired and be received in conduit or sheath by hook feature, is utilized for passing through
Gooseneck snare or polycyclic snare remove.Although withdrawal is simple process in principle, the difficulty typically encountered is to utilize
The hook of (one or more) snare ring acquisition filter.In filter inclination or unbalance placement, difficulty aggravation.Many mistakes
Filter design is to avoid this orientation.However, the problem is still common, because device is not anchored in IVC with stationary mode.
Other than clot, constant blood flow can make the filter in IVC chaotic, become difficult so that reacquiring.Accordingly, there exist
The demand that system is withdrawn for filter, with improved ease for use and/or is less susceptible to be influenced by filter orientation problem.
In conclusion problem of the existing technology is:
(1) existing inferior caval vein filtration system device causes when taking out, it is difficult to hang seal wire there is no positioning device
Firmly drag hook takes out filter, wastes the time, reduces working efficiency.
(2) in the prior art, the hook in filter is carried out in position fixing process by wireless location sensor, it is wireless fixed
Level sensor uses traditional algorithm, cannot improve the precision of positioning, accelerates the speed of positioning, and positioning real-time is excellent.
(3) in the prior art, in image processing process, using existing algorithm, it can not achieve targeted enhancing
Local detail improves image visual effect.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of novel inferior caval vein filtration systems and its intelligence to receive
The system of returning.
The invention is realized in this way a kind of novel inferior caval vein filtration system, the novel inferior caval vein filtration system
It is provided with
Drag hook；
Drag hook and connection strap weld, and drag hook is in long strip, and drag hook front end is equipped with arc notch, are embedded with positioning on drag hook
Device, connection strap runs through filter, and filter is stretched out in one end, and both ends are equipped between filter and connection strap and symmetrically compress bullet
Spring, filter are made of upper and lower two parts, and the every part of filter is provided with mesh basket supporting steel wire, and upper and lower two parts shape is identical,
Steel wire connection is connected by mesh basket.
Another object of the present invention is to provide a kind of inferior caval vein filtering sides of novel inferior caval vein filtration system
Filter is implanted into inferior caval vein blood vessel by method, the inferior caval vein filter method, and the clot inside inferior caval vein blood vessel is by mesh basket
Connection steel wire, mesh basket supporting steel wire stop, and filter is placed 12 to 14 days, filter is taken out, and seal wire is utilized locator
Positioning and contrastographic picture information, tangle drag hook, entire filter be pumped into guide pin, take out.
Another object of the present invention is to provide a kind of intelligence recycling systems using the novel inferior caval vein filtration system
System, the intelligence recovery system include:
Angiography module, connect with image processing module, injects contrast medium into the blood vessel of inferior caval vein, passes through X
Light reinforced pipe, which focuses on X-ray image, to be shown on cutout screen；
Image procossing obtains module, connect with central processing module, using X video camera to the X-ray in angiography module
Extraction of Image carries out the processing of image, obtains x-ray image；
Central processing module obtains module, recycling module, image display with image procossing；Locating module connection, association
Adjust the normal operating of each module；
Recycling module is connect with central processing module, according to the image data on display screen, carries out recycling behaviour to filter
Make；
Locating module is connect with central processing module, is determined by wireless location sensor the hook in filter
Position, according to the location information of hook, tangles hook for seal wire；
Image display is connect with central processing module, and the process of operation and contrastographic picture are shown.
Another object of the present invention is to provide a kind of recovery method of intelligent recovery system, the recovery method packets
Include following steps:
The first step is injected contrast medium into first in the blood vessel of inferior caval vein, is concentrated X-ray image by X-ray reinforced pipe
To being shown on cutout screen, the processing of image is carried out, obtains x-ray image；
Second step, display screen show that image and operating process image, locator position the hook in filter；
Third step takes out filter according to above-mentioned image and image and location information.
Further, the hook in filter is positioned by wireless location sensor, according to the location information of hook
Process, using the wireless sensor node location algorithm of artificial bee colony optimization neural network, specifically includes the following steps:
Step 1 collects measurement sequence, and carries out normalization operation, specifically:
Step 2, initiation parameter, the restriction area including food source quantity, the number of iterations, control parameter limit and solution
Between；
Step 3 initializes food source position: Xi=[xi1, xi2..., xiD]T, i=1,2 ..., n, n expression food source
Number, D representation dimension, method of determination are
D=M* H+H* N+H+N；
M, H, N are respectively the nodal point number of input layer, hidden layer and output layer in formula；
Step 4, according to XiPosition carries out assignment to the weight and threshold value of neural network, learns to training sample, obtains
To XiTarget function value be
D in formulaiAnd tkRespectively reality output and desired output, k are number of training；
Step 5 leads bee in XiSurrounding generates new explanation Vi, and their fitness values are calculated, it is determined and is protected according to Greedy principle
Stay XiAnd Vi, fitness value is compared with the superior；
Step 6, according to select probability PiSelection solution, and in XiSurrounding generates new explanation ViRetained using the same manner optimal
Solution；
Step 7 leads bee that will change at this time if some solution does not improve, and just abandons the solution after limit circulation
For search bee, a new explanation V is generatediInstead of the solution；
Step 8 finds current optimal solution according to fitness value；
Step 9 after reaching termination condition, obtains the best initial weights and threshold value of neural network according to optimal solution, not so returns
Step 5；
Step 10 relearns training sample according to best initial weights and threshold value, establishes range error prediction model；
Step 11 is corrected according to error prediction result ranging.
Further, the X-ray image in angiography module is extracted using X video camera, carries out the processing of image, obtain X
During light image, using the improved adaptive image enhancement algorithm based on local mean value and standard deviation, including following step
It is rapid:
Step 1 selectes darker area: if Es< k0Eg, then it represents that region is darker area, is region to be reinforced, wherein
k0For the normal number less than 1；
Step 2, select contrast region, be believed that when contrast is too low in the region without details do not need into
Row is reinforced, thus, it is assumed that low contrast regions to be reinforced are k1σg< σs< k2σg, wherein k1< k2, and k1, k2Respectively less than 1
Normal number；
Step 3 enhances selection area, is based on local mean value and standard deviation, defines the enhancing formula of image are as follows:
Wherein, ω is a constant greater than 1, during image enhancement, according to local mean value and standard deviation, dynamic
Adjustment enhancing coefficient；
Adaptive image enhancement algorithm based on local mean value and standard deviation are as follows:
Wherein ω is a constant greater than 1；X (i, j), f (i, j) are respectively input picture and output picture point (i, j)
Gray value；k0, k1, k2It is less than 1 normal number, and k1< k2；Eg, σgRespectively global mean value and global criteria are poor；Es, σs
Respectively local mean value and Local standard deviation.
Advantages of the present invention and good effect are as follows:
The present invention carries out positioning auxiliary doctor using locator and seal wire is tangled drag hook, filter is taken out, when wasting
Between, reduce working efficiency.
Locating module positions the hook in filter by wireless location sensor in the present invention, according to hook
During location information, in order to improve the precision of positioning, accelerate the speed of positioning, positioning real-time is excellent, using artificial bee
The wireless sensor node location algorithm of group's optimization neural network.
Image procossing obtains module in the present invention, is extracted using X video camera to the X-ray image in angiography module, into
The processing of row image, during obtaining x-ray image, in order to realize targetedly enhancing local detail, so as to improve image
Visual effect, using the improved adaptive image enhancement algorithm based on local mean value and standard deviation.
Detailed description of the invention
Fig. 1 is the structural representation that novel inferior caval vein filtration system provided in an embodiment of the present invention and its intelligence withdraw system
Figure；
Fig. 2 is the drag hook amplification that novel inferior caval vein filtration system provided in an embodiment of the present invention and its intelligence withdraw system
Schematic diagram；
Fig. 3 is novel inferior caval vein filtration system intelligence recovery system structural schematic diagram provided in an embodiment of the present invention；
Fig. 4 is novel inferior caval vein filtration system intelligence recovery system work flow diagram provided in an embodiment of the present invention；
In figure: 1, drag hook；2, connection strap；3, filter；4, compressed spring；5, mesh basket connects steel wire；6, mesh basket supporting steel
Silk；7, locator；8, angiography module；9, image procossing obtains module；10, central processing module；11, recycling module；12,
Image display；13, locating module.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
As Figure 1-Figure 2, novel inferior caval vein filtration system provided in an embodiment of the present invention includes: drag hook 1, connection strap
2, filter 3, compressed spring 4, mesh basket connect steel wire 5, mesh basket supporting steel wire 6, locator 7.
Drag hook 1 and connection strap 2 weld, and drag hook 1 is in long strip, and 1 front end of drag hook is equipped with arc notch, are embedded on drag hook 1
Locator 7, connection strap 2 runs through filter 3, and filter 3 is stretched out in one end, and it is symmetrical that both ends are equipped between filter 3 and connection strap 2
Compressed spring, filter 3 is made of upper and lower two parts, and the every part of filter 3 is provided with mesh basket supporting steel wire 6, upper and lower two
Divide shape identical, passes through mesh basket and connect the connection of steel wire 5.
In use, filter 3 is implanted into inferior caval vein blood vessel, the clot inside inferior caval vein blood vessel is connected the present invention by mesh basket
Connect steel wire 5, mesh basket supporting steel wire 6 stops, by filter 3 place 12 to 14 days, filter is taken out, by seal wire utilize locator
7 positioning and contrastographic picture information, tangles drag hook 1, and entire filter 3 is pumped into guide pin, takes out.
As shown in figure 3, novel inferior caval vein filtration system intelligence recovery system provided in an embodiment of the present invention includes: blood vessel
Radiography module 8, image procossing obtain module 9, central processing module 10, recycling module 11, image display 12, locating module
13。
Angiography module 8, connect with image processing module, injects contrast medium into the blood vessel of inferior caval vein, passes through X
Light reinforced pipe, which focuses on X-ray image, to be shown on cutout screen.
Image procossing obtains module 9, connect with central processing module, using X video camera to the X-ray in angiography module
Extraction of Image carries out the processing of image, obtains x-ray image；
Central processing module 10, with image procossing obtain module 9, recycling module 11, image display 12,；Positioning mould
Block 13 connects, and coordinates the normal operating of each module；
Recycling module 11, connect with central processing module, according to the image data on display screen, recycles to filter
Operation；
Locating module 13, connect with central processing module, is carried out by wireless location sensor to the hook in filter
Positioning, according to the location information of hook, tangles hook for seal wire；
Image display 12, connect with central processing module, and the process of operation and contrastographic picture are shown.
As shown in figure 4, the operation of novel inferior caval vein filtration system intelligence recovery system provided in an embodiment of the present invention
Journey, comprising the following steps:
S101: injecting contrast medium into first in the blood vessel of inferior caval vein, is focused on X-ray image by X-ray reinforced pipe
It is shown on cutout screen, carries out the processing of image, obtain x-ray image；
S102: display screen shows that image and operating process image, locator position the hook in filter；
S103: according to above-mentioned image and image and location information, filter is taken out.
The locating module 13 positions the hook in filter by wireless location sensor, according to the position of hook
During confidence ceases, in order to improve the precision of positioning, accelerate the speed of positioning, positioning real-time is excellent, using artificial bee colony
The wireless sensor node location algorithm of optimization neural network, specifically includes the following steps:
Step 1 collects measurement sequence, and carries out normalization operation, specially
Step 2, initiation parameter, the restriction area including food source quantity, the number of iterations, control parameter limit and solution
Between；
Step 3 initializes food source position: Xi=[xi1, xi2..., xiD]T, i=1,2 ..., n, n expression food source
Number, D representation dimension, method of determination are
D=M* H+H* N+H+N；
M, H, N are respectively the nodal point number of input layer, hidden layer and output layer in formula；
Step 4, according to XiPosition carries out assignment to the weight and threshold value of neural network, learns to training sample, obtains
To XiTarget function value be
D in formulaiAnd tkRespectively reality output and desired output, k are number of training；
Step 5 leads bee in XiSurrounding generates new explanation Vi, and their fitness values are calculated, it is determined and is protected according to Greedy principle
Stay XiAnd Vi, fitness value is compared with the superior；
Step 6, according to select probability PiSelection solution, and in XiSurrounding generates new explanation ViRetained using the same manner optimal
Solution；
Step 7 leads bee that will change at this time if some solution does not improve, and just abandons the solution after limit circulation
For search bee, a new explanation V is generatediInstead of the solution；
Step 8 finds current optimal solution according to fitness value；
Step 9 after reaching termination condition, obtains the best initial weights and threshold value of neural network according to optimal solution, not so returns
Step 5；
Step 10 relearns training sample according to best initial weights and threshold value, establishes range error prediction model；
Step 11 is corrected according to error prediction result ranging.
Described image processing obtains module 9, is extracted using X video camera to the X-ray image in angiography module, carries out figure
The processing of picture, during obtaining x-ray image, in order to realize targetedly enhancing local detail, so as to improve image vision
Effect, using the improved adaptive image enhancement algorithm based on local mean value and standard deviation, comprising the following steps:
Step 1 selectes darker area: if Es< k0Eg, then it represents that region is darker area, is region to be reinforced, wherein
k0For the normal number less than 1；
Step 2, select contrast region, be believed that when contrast is too low in the region without details do not need into
Row is reinforced, thus, it is assumed that low contrast regions to be reinforced are k1σg< σs< k2σg, wherein k1< k2, and k1, k2Respectively less than 1
Normal number；
Step 3 enhances selection area, is based on local mean value and standard deviation, defines the enhancing formula of image are as follows:
Wherein, ω is a constant greater than 1, during image enhancement, according to local mean value and standard deviation, dynamic
Adjustment enhancing coefficient；
Adaptive image enhancement algorithm based on local mean value and standard deviation are as follows:
Wherein ω is a constant greater than 1；X (i, j), f (i, j) are respectively input picture and output picture point (i, j)
Gray value；k0, k1, k2It is less than 1 normal number, and k1< k2；Eg, σgRespectively global mean value and global criteria are poor；Es, σs
Respectively local mean value and Local standard deviation.
The recycling module 11, connect with central processing module, according to the image data on display screen, carries out to filter
The process of reclaimer operation the following steps are included:
Step 1 judges there is the plan that should abandon taking out filter whether containing more fresh thrombus in inferior caval vein, sentences
Whether disconnected recovery time is between 12 days to 14 days；
Step 2, push seal wire make to arrest outside ring exposing conduit；
Step 3 lives filter lower end recovering hook with ring set is arrested；
Seal wire and conduit, filter are pumped into recycling pin, taken out by step 4.
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (6)
1. a kind of novel inferior caval vein filtration system, which is characterized in that the novel inferior caval vein filtration system is provided with
Drag hook；
Drag hook and connection strap weld, and drag hook is in long strip, and drag hook front end is equipped with arc notch, locator are embedded on drag hook, even
Narrow bars run through filter, and filter is stretched out in one end, and the symmetrical compressed spring in both ends, filtering are equipped between filter and connection strap
Device is made of upper and lower two parts, and the every part of filter is provided with mesh basket supporting steel wire, and upper and lower two parts shape is identical, passes through mesh basket
Connect steel wire connection.
2. a kind of inferior caval vein filter method of novel inferior caval vein filtration system as described in claim 1, which is characterized in that institute
It states inferior caval vein filter method and filter is implanted into inferior caval vein blood vessel, the clot inside inferior caval vein blood vessel connects steel by mesh basket
Silk, mesh basket supporting steel wire stop, and filter is placed 12 to 14 days, filter is taken out, and seal wire is utilized to the positioning of locator
With contrastographic picture information, drag hook is tangled, entire filter is pumped into guide pin, is taken out.
3. a kind of intelligent recovery system using novel inferior caval vein filtration system described in claim 1, which is characterized in that described
Intelligent recovery system includes:
Angiography module, connect with image processing module, injects contrast medium into the blood vessel of inferior caval vein, is increased by X-ray
Strong pipe, which focuses on X-ray image, to be shown on cutout screen；
Image procossing obtains module, connect with central processing module, using X video camera to the X-ray image in angiography module
It extracts, carries out the processing of image, obtain x-ray image；
Central processing module obtains module, recycling module, image display with image procossing；Locating module connection is coordinated each
The normal operating of module；
Recycling module is connect with central processing module, according to the image data on display screen, carries out reclaimer operation to filter；
Locating module is connect with central processing module, is positioned by wireless location sensor to the hook in filter, root
According to the location information of hook, seal wire is tangled into hook；
Image display is connect with central processing module, and the process of operation and contrastographic picture are shown.
4. a kind of recovery method of intelligent recovery system as claimed in claim 3, which is characterized in that the recovery method include with
Lower step:
The first step is injected contrast medium into first in the blood vessel of inferior caval vein, is focused on X-ray image by X-ray reinforced pipe aobvious
Show on cutout screen, carry out the processing of image, obtains x-ray image；
Second step, display screen show that image and operating process image, locator position the hook in filter；
Third step takes out filter according to above-mentioned image and image and location information.
5. recovery method as claimed in claim 4, which is characterized in that by wireless location sensor to the hook in filter
It is positioned, it is fixed using the wireless sensor node of artificial bee colony optimization neural network according to the process of the location information of hook
Position algorithm, specifically includes the following steps:
Step 1 collects measurement sequence, and carries out normalization operation, specifically:
Step 2, initiation parameter, the restriction section including food source quantity, the number of iterations, control parameter limit and solution；
Step 3 initializes food source position: Xi=[xi1, xi2..., xiD]T, i=1,2 ..., n, the number of n expression food source,
D representation dimension, method of determination are
D=M*H+H*N+H+N；
M, H, N are respectively the nodal point number of input layer, hidden layer and output layer in formula；
Step 4, according to XiPosition carries out assignment to the weight and threshold value of neural network, learns to training sample, obtains Xi
Target function value be
D in formulaiAnd tkRespectively reality output and desired output, k are number of training；
Step 5 leads bee in XiSurrounding generates new explanation Vi, and their fitness values are calculated, it is determined according to Greedy principle and retains Xi
And Vi, fitness value is compared with the superior；
Step 6, according to select probability PiSelection solution, and in XiSurrounding generates new explanation ViUsing the same manner keeping optimization；
Step 7, if some solution does not improve, and just abandons the solution, leads bee to translate at this time and detects after limit circulation
Bee is examined, a new explanation V is generatediInstead of the solution；
Step 8 finds current optimal solution according to fitness value；
Step 9 after reaching termination condition, obtains the best initial weights and threshold value of neural network according to optimal solution, not so return step
Five；
Step 10 relearns training sample according to best initial weights and threshold value, establishes range error prediction model；
Step 11 is corrected according to error prediction result ranging.
6. recovery method as claimed in claim 4, which is characterized in that using X video camera to the X-ray shadow in angiography module
As extracting, the processing of image is carried out, during obtaining x-ray image, using improved based on oneself of local mean value and standard deviation
Adapt to algorithm for image enhancement, comprising the following steps:
Step 1 selectes darker area: if Es< k0Eg, then it represents that region is darker area, is region to be reinforced, wherein k0For
Normal number less than 1；
Step 2 selectes the region of contrast, is believed that in the region when contrast is too low and does not need to be added without details
By force, thus, it is assumed that low contrast regions to be reinforced are k1σg< σs< k2σg, wherein k1< k2, and k1, k2Respectively less than 1 just
Constant；
Step 3 enhances selection area, is based on local mean value and standard deviation, defines the enhancing formula of image are as follows:
Wherein, ω is a constant greater than 1, and during image enhancement, according to local mean value and standard deviation, dynamic is adjusted
Enhance coefficient；
Adaptive image enhancement algorithm based on local mean value and standard deviation are as follows:
Wherein ω is a constant greater than 1；X (i, j), f (i, j) are respectively the ash of input picture and output picture point (i, j)
Angle value；k0, k1, k2It is less than 1 normal number, and k1< k2；Eg, σgRespectively global mean value and global criteria are poor；Es, σsRespectively
For local mean value and Local standard deviation.
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