CN108389193B - Method and system for analyzing three-dimensional voxel model behind carotid artery stent - Google Patents

Method and system for analyzing three-dimensional voxel model behind carotid artery stent Download PDF

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CN108389193B
CN108389193B CN201810146782.8A CN201810146782A CN108389193B CN 108389193 B CN108389193 B CN 108389193B CN 201810146782 A CN201810146782 A CN 201810146782A CN 108389193 B CN108389193 B CN 108389193B
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stent
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CN108389193A (en
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李颖
胡俊
陈伟
李可
熊宣淇
张小勤
刘丽
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Third Military Medical University TMMU
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/20—Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G06T5/30—Erosion or dilatation, e.g. thinning
    • 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
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • 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
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • 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 provides a carotid artery post-stent three-dimensional voxel model analysis method which can effectively separate a blood vessel wall, a blood vessel cavity and a stent according to the three-dimensional shape of a carotid artery, and the method comprises the following steps of S1 CT data acquisition; s2 blood vessel extraction step; s3 analyzing the blood vessel shape; and S4 a bracket analysis step. The invention has the beneficial effects that 1, the blood vessel is in a space hierarchy distribution state under the state of the bracket, and the invention distinguishes and codes different hierarchies, thereby being convenient for extracting a space interesting region to carry out quantitative analysis and characteristic extraction. The three-dimensional model established by the model data can more accurately reflect the spatial form of the blood vessel, the plaque characteristic spatial form and the dependency relationship of the two. 3, the quantitative parameters of the plaque are automatically calculated by a computer according to morphological characteristics, manual measurement is not needed, and only relevant parameters are adjusted.

Description

Method and system for analyzing three-dimensional voxel model behind carotid artery stent
Technical Field
The application relates to a three-dimensional voxel model analysis method and a system, in particular to a carotid three-dimensional voxel model analysis method and a system.
Background
In the prior art, the imaging technology of artery cta (computed tomography imaging) has the greatest advantage of being capable of rapidly extracting the lumen of a blood vessel with high density of images, but is limited in that the accurate distinction of the blood vessel wall is difficult. The boundary of the blood vessel wall in the CTA image is fuzzy, the blood vessel wall and the mural plaque are often interlaced together, and the features of the blood vessel wall are often misjudged as the features of the atherosclerotic plaque when people carry out quantitative analysis, which is a main reason for the error of the quantitative analysis. An operator needs to manually adjust the two-dimensional section image which is easy to observe to interactively select a blood vessel area to be analyzed to analyze the characteristics of the blood vessel and the cavity, the three-dimensional space morphological characteristics of an anatomical structure are not well utilized in the method, and the prior art cannot accurately and automatically track the boundary of the blood vessel wall and quantitatively analyze a carotid artery three-dimensional voxel model.
CTA image often has the support artifact behind the carotid artery support, and some vascular lumens are covered by high density shadow, have reduced image identifiability, influence the observation to plaque morphological feature, can not effectively separate vascular wall, vascular cavity and support according to the three-dimensional form of carotid among the prior art.
Disclosure of Invention
In order to solve the above problems, the present invention provides a carotid artery three-dimensional model analysis method capable of effectively separating a vessel wall, a vessel lumen and a stent according to the three-dimensional morphology of a carotid artery, comprising the steps of,
s1 CT data acquisition step;
s2 blood vessel extraction step;
s3 analyzing the blood vessel shape;
and S4 a bracket analysis step.
Further, the CT data parameters include,
tube voltage was 100KV, tube current was 370mAs, collimation was 64 x 0.6mm, rotation time was 0.28s, pitch was 1.2, reconstruction layer thickness was 1.0mm, spacing was 0.9mm, field of view was 230mm, image resolution was 512 x 512.
Further, the blood vessel extracting step includes,
s21 assigns a value to the set of voxel units that meet the threshold T using the following formula:
Figure GDA0001695918950000021
wherein T is a predetermined threshold value, fct(x, y, z) is a space voxel function of the CT image, x, y, z respectively represent the coordinates of the voxel in the space, and omega is the voxel space;
s22 assigns a set of voxel units in the CTA dataset that meet a threshold T using the following formula:
Figure GDA0001695918950000022
wherein f iscta(x, y, z) is the spatial voxel function of the CTA image;
s23 obtains a set of voxel-unit LA containing vessels using the following formula:
Figure GDA0001695918950000023
wherein
Figure GDA0001695918950000024
Is Lct(x, y, z) complement;
s24 obtaining ROI blood vessel section LA by adopting the following formulaROI：
Figure GDA0001695918950000025
Wherein A and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI blood vessel section.
Further, the step of analyzing the vessel morphology comprises,
positive swelling step, using the following formula:
Figure GDA0001695918950000026
where E denotes the unit voxel neighborhood, EzRepresenting voxels within a spherical region of radius 1, E being divided into three subsets, E1、E2、E3Respectively showing the sets of 1 unit voxel layer thickness, 2 unit voxel layer thicknesses and 3 unit voxel layer thicknesses of the vascular cavity expanding towards the three-dimensional boundary of the vascular wall;
and a negative etching step, adopting the following formula:
Figure GDA0001695918950000031
E-1、E-2、...E-nrespectively representing a voxel set which shrinks 1 unit voxel, 2 unit voxels and n unit voxels inwards from the outer boundary of the blood vessel cavity, wherein n is the maximum radius value of the blood vessel cavity;
a vessel wall voxel extraction step, which adopts the following formula:
Figure GDA0001695918950000032
the step of extracting the voxel containing the stent in the blood vessel cavity adopts the following formula:
Figure GDA0001695918950000033
further, the scaffold analysis step comprises:
a step of extracting the stent voxel by adopting the following formula:
Figure GDA0001695918950000034
a step of voxel extraction without a stent vessel cavity, which adopts the following formula:
Figure GDA0001695918950000035
a plaque analysis step, calculating a classification discriminant function C (x, y, z) of a voxel unit in the blood vessel cavity by adopting the following formula,
Figure GDA0001695918950000036
wherein 1, 2 and 3 are grading characteristics of soft plaque, 4, 5 and 6 are grading characteristics of calcified plaque,
mapping the plaque grading characteristic value to a voxel unit in the three-dimensional voxel model of the vessel cavity without the stent by adopting the following formula:
Figure GDA0001695918950000041
in order to ensure the implementation of the method, the invention also provides a carotid artery three-dimensional model analysis system, which comprises,
a CT data acquisition unit for CT data acquisition;
a blood vessel extraction unit for blood vessel extraction;
a vessel morphology analysis unit for analyzing vessel morphology;
and the bracket analysis unit is used for bracket analysis.
Further, the CT data parameters include,
tube voltage was 100KV, tube current was 370mAs, collimation was 64 x 0.6mm, rotation time was 0.28s, pitch was 1.2, reconstruction layer thickness was 1.0mm, spacing was 0.9mm, field of view was 230mm, image resolution was 512 x 512.
Further, the blood vessel extracting unit extracts the blood vessel by adopting the following steps,
s21 assigns a value to the set of voxel units that meet the threshold T using the following formula:
Figure GDA0001695918950000042
wherein T is a predetermined threshold value, fct(x, y, z) is a space voxel function of the CT image, x, y, z respectively represent the coordinates of the voxel in the space, and omega is the voxel space;
s22 assigns a set of voxel units in the CTA dataset that meet a threshold T using the following formula:
Figure GDA0001695918950000043
wherein f iscta(x, y, z) is the spatial voxel function of the CTA image;
s23 obtains a set of voxel-unit LA containing vessels using the following formula:
Figure GDA0001695918950000051
wherein
Figure GDA0001695918950000052
Is Lct(x, y, z) complement;
s24 obtaining ROI blood vessel section LA by adopting the following formulaROI：
Figure GDA0001695918950000053
Wherein A and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI blood vessel section.
Further, the blood vessel shape analysis unit performs the blood vessel shape analysis by adopting the following steps,
positive swelling step, using the following formula:
Figure GDA0001695918950000054
where E denotes the unit voxel neighborhood, EzRepresenting voxels within a spherical region of radius 1, E being divided into three subsets, E1、E2、E3Respectively showing the sets of 1 unit voxel layer thickness, 2 unit voxel layer thicknesses and 3 unit voxel layer thicknesses of the vascular cavity expanding towards the three-dimensional boundary of the vascular wall;
and a negative etching step, adopting the following formula:
Figure GDA0001695918950000055
E-1、E-2、...E-nrespectively representing a voxel set which shrinks 1 unit voxel, 2 unit voxels and n unit voxels inwards from the outer boundary of the blood vessel cavity, wherein n is the maximum radius value of the blood vessel cavity;
a vessel wall voxel extraction step, which adopts the following formula:
Figure GDA0001695918950000056
the step of extracting the voxel containing the stent in the blood vessel cavity adopts the following formula:
Figure GDA0001695918950000057
further, the stent analysis unit performs stent analysis by using the following steps,
a step of extracting the stent voxel by adopting the following formula:
Figure GDA0001695918950000061
a step of voxel extraction without a stent vessel cavity, which adopts the following formula:
Figure GDA0001695918950000062
(x∈Ω，y∈Ω，z∈[A，B])；
a plaque analysis step, calculating a classification discriminant function C (x, y, z) of a voxel unit in the blood vessel cavity by adopting the following formula,
Figure GDA0001695918950000063
wherein 1, 2 and 3 are grading characteristics of soft plaque, 4, 5 and 6 are grading characteristics of calcified plaque,
mapping the plaque grading characteristic value to a voxel unit in the three-dimensional voxel model of the vessel cavity without the stent by adopting the following formula:
Figure GDA0001695918950000064
the invention has the advantages that
1 in the state of a bracket, the blood vessels are in a spatial hierarchy distribution state, the invention distinguishes and codes different hierarchies, and can conveniently extract a spatial region of interest for quantitative analysis and characteristic extraction.
The three-dimensional model established by the model data can more accurately reflect the spatial form of the blood vessel, the plaque characteristic spatial form and the dependency relationship of the two.
3, the quantitative parameters of the plaque are automatically calculated by a computer according to morphological characteristics, manual measurement is not needed, and only relevant parameters are adjusted.
And 4, mapping the plaque grading characteristic value to a voxel unit in the three-dimensional voxel model without the stent vessel cavity and displaying the voxel unit to a user through a display interface, so that the user can better observe the plaque morphological characteristics.
Drawings
FIG. 1 is a flow chart of a carotid artery three-dimensional model analysis method of the present invention.
FIG. 2 is a diagram of a carotid artery three-dimensional model analysis system according to the present invention.
Detailed Description
The three-dimensional model can provide spatial deformation and adjacent information, particularly for a blood vessel bifurcation part, the geometric characteristics are complex, and quantitative analysis of the three-dimensional model has more advantages than pure three-dimensional visualization. The two-dimensional continuous tomography images after classification can provide different types of image characteristic information such as plaque tissue components, lipid, calcification and the like. The two-dimensional image provided by the invention is not orthogonal deconstructed continuous tomography image in the conventional sense, but is the original image characteristic in the region of interest (ROI region of interest) covered by the three-dimensional blood vessel model. The method can focus quantitative analysis inside the ROI and exclude non-adjacent region voxel unit pair visual images.
As shown in fig. 1, the present invention provides a carotid artery three-dimensional model analysis method capable of effectively separating a vessel wall, a vessel lumen and a stent according to the three-dimensional morphology of a carotid artery, comprising the following steps,
s1 CT data acquisition step;
s2 blood vessel extraction step;
s3 analyzing the blood vessel shape;
and S4 a bracket analysis step.
Further, the CT data parameters include,
tube voltage was 100KV, tube current was 370mAs, collimation was 64 x 0.6mm, rotation time was 0.28s, pitch was 1.2, reconstruction layer thickness was 1.0mm, spacing was 0.9mm, field of view was 230mm, image resolution was 512 x 512.
Further, the blood vessel extracting step includes,
s21 assigns a value to the set of voxel units that meet the threshold T using the following formula:
Figure GDA0001695918950000081
wherein T is a predetermined threshold value, fct(x, y, z) is a space voxel function of the CT image, x, y, z respectively represent the coordinates of the voxel in the space, and omega is the voxel space;
threshold value T can be adjusted according to the image adaptability of difference, adjusts T according to the data source of difference and can realize better blood vessel body element model extraction effect.
The three-dimensional model of the vessel is made up of a set of voxel units.
S22 assigns a set of voxel units in the CTA dataset that meet a threshold T using the following formula:
Figure GDA0001695918950000082
wherein f iscta(x, y, z) is the spatial voxel function of the CTA image;
s23 obtains a set of voxel-unit LA containing vessels using the following formula:
Figure GDA0001695918950000083
wherein
Figure GDA0001695918950000084
Is Lct(x, y, z) complement;
s24 obtaining ROI blood vessel section LA by adopting the following formulaROI：
Figure GDA0001695918950000085
Wherein A and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI blood vessel section.
The ROI vessel segment is set by an operator, and the operator determines the specific range of the ROI vessel segment by determining the lowest plane and the highest plane of the ROI vessel segment.
In this embodiment, the three-dimensional voxel model of the blood vessel lumen is operated by using the spatial position of the established three-dimensional voxel unit model and a mathematical morphology method, so as to achieve the purpose of analyzing and identifying the target of the space of interest. The morphological operation of the vascular cavity related to the present invention comprises two parts of positive bulging operation and negative corrosion operation.
Further, the step of analyzing the vessel morphology comprises,
positive swelling step, using the following formula:
Figure GDA0001695918950000091
where E denotes the unit voxel neighborhood, EzRepresenting voxels within a spherical region of radius 1, E being divided into three subsets, E1、E2、E3Respectively shows that the vascular cavity expands 1 unit voxel layer thickness and 2 unit bodies to the three-dimensional boundary of the vascular wallA set of voxel thicknesses and 3 unit voxel thicknesses;
and a negative etching step, adopting the following formula:
Figure GDA0001695918950000092
E-1、E-2、...E-nrespectively representing a voxel set which shrinks 1 unit voxel, 2 unit voxels and n unit voxels inwards from the outer boundary of the blood vessel cavity, wherein n is the maximum radius value of the blood vessel cavity;
the positive bulging operation and the negative erosion operation effectively utilize the three-dimensional deformation of the blood vessel, so that the boundary between the blood vessel wall and the surrounding environment is more definite, and preparation is made for subsequent analysis operation.
A vessel wall voxel extraction step, which adopts the following formula:
Figure GDA0001695918950000093
the step of extracting the voxel containing the stent in the blood vessel cavity adopts the following formula:
Figure GDA0001695918950000094
further, the scaffold analysis step comprises:
a step of extracting the stent voxel by adopting the following formula:
Figure GDA0001695918950000101
T2preset by the operator, ct value is greater than T2Indicating that the voxel is a metal stent.
A step of voxel extraction without a stent vessel cavity, which adopts the following formula:
Figure GDA0001695918950000102
a plaque analysis step, calculating a classification discriminant function C (x, y, z) of a voxel unit in the blood vessel cavity by adopting the following formula,
Figure GDA0001695918950000103
wherein, 1, 2 and 3 are grading characteristics of soft plaque, and 4, 5 and 6 are grading characteristics of calcified plaque.
The classification discriminant function can be expressed as:
Figure GDA0001695918950000104
better observation of intravascular plaque is achieved by classifying intravascular voxel units that do not contain a stent according to ct values.
In the invention, the three-dimensional voxel model is a set of voxels and is expressed by a mathematical formula, and the assignment of a voxel value of 0 indicates that the three-dimensional voxel model does not contain the voxel. After the three-dimensional voxel model function without the stent vessel cavity is multiplied by the classification discrimination function, the plaque grading characteristic value is mapped to a voxel unit in the three-dimensional voxel model without the stent vessel cavity and is displayed to a user through a display interface, so that the user can better observe the plaque morphological characteristics.
In one embodiment of the invention, the three-dimensional voxel model is displayed to a user on a display interface, different colors are used for displaying the plaque according to the characteristic value of the plaque, the soft plaque adopts a cool tone, the calcified plaque adopts a warm tone, and the user can observe the morphological characteristics of the plaque more intuitively.
Finally, the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, which should be covered by the claims of the present invention.

Claims (8)

1. A method for analyzing a three-dimensional voxel model after carotid artery stent is characterized by comprising the following steps,
s1 CT data acquisition step;
s2 blood vessel extraction step;
s3 analyzing the blood vessel shape;
s4 a step of analyzing the stent;
the scaffold analysis step comprises:
a step of extracting the stent voxels, which is to obtain a set f of the stent voxels by adopting the following formulastent(x,y,z)：
Figure FDA0002920291500000011
LumenROI(x, y, z) is the set of voxels in the lumen of the vessel containing the stent; x, y and z respectively represent the coordinates of the voxel in the space, and omega is the voxel space; a and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI vascular segment; t is2Is a preset threshold value;
a step of extracting voxels in the lumen of the vessel without the stent, wherein the set of voxels in the vessel without the stent is obtained by adopting the following formula
Figure FDA0002920291500000015
Figure FDA0002920291500000012
A plaque analysis step, calculating a classification discriminant function C (x, y, z) of a voxel unit in the blood vessel cavity by adopting the following formula,
Figure FDA0002920291500000013
wherein 1, 2 and 3 are grading characteristics of soft plaque, 4, 5 and 6 are grading characteristics of calcified plaque,
mapping the plaque grading characteristic value to a voxel unit in the three-dimensional voxel model of the vessel cavity without the stent by adopting the following formula:
Figure FDA0002920291500000014
i is a set of integers.
2. The method of claim 1, wherein the CT data parameters include,
tube voltage was 100KV, tube current was 370mAs, collimation was 64 x 0.6mm, rotation time was 0.28s, pitch was 1.2, reconstruction layer thickness was 1.0mm, spacing was 0.9mm, field of view was 230mm, image resolution was 512 x 512.
3. The method of analyzing a three-dimensional voxel model after carotid artery stent according to claim 1, characterized in that the blood vessel extracting step includes,
s21 uses the following formula for the set L of voxel units that meet the threshold Tct(x, y, z) assignment:
Figure FDA0002920291500000021
wherein T is a predetermined threshold value, fct(x, y, z) is a space voxel function of the CT image, x, y, z respectively represent the coordinates of the voxel in the space, and omega is the voxel space;
s22 uses the following formula for a set L of voxel units in the CTA dataset that meet a threshold Tcta(x, y, z) assignment:
Figure FDA0002920291500000022
wherein f iscta(x, y, z) is the spatial voxel function of the CTA image;
s23 obtains a set of voxel-unit LA containing vessels using the following formula:
Figure FDA0002920291500000023
wherein
Figure FDA0002920291500000024
Is Lct(x, y, z) complement;
s24 obtaining ROI blood vessel section voxel set LA by adopting the following formulaROI：
Figure FDA0002920291500000025
Wherein A and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI blood vessel section.
4. The method of analyzing a three-dimensional voxel model after carotid artery stent according to claim 1, wherein the step of analyzing blood vessel morphology includes,
positive swelling step, using the following formula:
Figure FDA0002920291500000026
E＝[E1 E2 E3]，
where E denotes the unit voxel neighborhood, EsRepresenting voxels within a spherical region of radius 1, E being divided into three subsets, E1、E2、E3Respectively showing the sets of 1 unit voxel layer thickness, 2 unit voxel layer thicknesses and 3 unit voxel layer thicknesses of the vascular cavity expanding towards the three-dimensional boundary of the vascular wall; LAROIIs a set of ROI vessel segment voxels;
and a negative etching step, adopting the following formula:
Figure FDA0002920291500000027
E＝[E-1 E-2 … E-n]，
E-1、E-2、…E-nrespectively representing a voxel set which shrinks 1 unit voxel, 2 unit voxels and n unit voxels inwards from the outer boundary of the blood vessel cavity, wherein n is the maximum radius value of the blood vessel cavity;
a step of extracting vessel Wall voxels, which is to obtain a set Wall of the vessel Wall voxels by adopting the following formulaROI(x,y,z)：
Figure FDA0002920291500000031
fcta(x, y, z) is the spatial voxel function of the CTA image; x, y and z respectively represent the coordinates of the voxel in the space, and omega is the voxel space; a and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI vascular segment;
extracting the voxels containing the stent in the vascular cavity, and acquiring a voxel set Lumen containing the stent in the vascular cavity by adopting the following formulaROI(x,y,z)：
Figure FDA0002920291500000032
5. A carotid artery post-stenting three-dimensional voxel model analysis system is characterized by comprising,
a CT data acquisition unit for CT data acquisition;
a blood vessel extraction unit for blood vessel extraction;
a vessel morphology analysis unit for analyzing vessel morphology;
a stent analysis unit for stent analysis;
the stent analysis unit performs the stent analysis using the following steps,
a step of extracting the stent voxels, which is to obtain a set f of the stent voxels by adopting the following formulastent(x,y,z)：
Figure FDA0002920291500000033
LumenROI(x, y, z) is the set of voxels in the lumen of the vessel containing the stent; x, y and z respectively represent the coordinates of the voxel in the space, and omega is the voxel space; a and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI vascular segment; t is2Is a preset threshold value;
a step of extracting voxels in the lumen of the vessel without the stent, wherein the set of voxels in the vessel without the stent is obtained by adopting the following formula
Figure FDA0002920291500000034
Figure FDA0002920291500000035
A plaque analysis step, calculating a classification discriminant function C (x, y, z) of a voxel unit in the blood vessel cavity by adopting the following formula,
Figure FDA0002920291500000036
wherein 1, 2 and 3 are grading characteristics of soft plaque, 4, 5 and 6 are grading characteristics of calcified plaque,
mapping the plaque grading characteristic value to a voxel unit in the three-dimensional voxel model of the vessel cavity without the stent by adopting the following formula:
Figure FDA0002920291500000041
i is a set of integers.
6. The system of claim 5, wherein the CT data parameters include,
tube voltage was 100KV, tube current was 370mAs, collimation was 64 x 0.6mm, rotation time was 0.28s, pitch was 1.2, reconstruction layer thickness was 1.0mm, spacing was 0.9mm, field of view was 230mm, image resolution was 512 x 512.
7. The system of claim 5, wherein the vessel extraction unit extracts the vessel by using the following steps,
s21 uses the following formula for the set L of voxel units that meet the threshold Tct(x, y, z) assignment:
Figure FDA0002920291500000042
wherein T is a predetermined threshold value, fct(x, y, z) is a space voxel function of the CT image, x, y, z respectively represent the coordinates of the voxel in the space, and omega is the voxel space;
s22 uses the following formula for a set L of voxel units in the CTA dataset that meet a threshold Tcta(x, y, z) assignment:
Figure FDA0002920291500000043
wherein f iscta(x, y, z) is the spatial voxel function of the CTA image;
s23 obtains a set of voxel-unit LA containing vessels using the following formula:
Figure FDA0002920291500000044
wherein
Figure FDA0002920291500000045
Is Lct(x, y, z) complement;
s24 obtaining ROI blood vessel section LA by adopting the following formulaROISet of voxels:
Figure FDA0002920291500000046
wherein A and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI blood vessel section.
8. The system of claim 5, wherein the vessel morphology analysis unit performs vessel morphology analysis by using the following steps,
positive swelling step, using the following formula:
Figure FDA0002920291500000047
E＝[E1 E2 E3]，
where E denotes the unit voxel neighborhood, EzRepresenting voxels within a spherical region of radius 1, E being divided into three subsets, E1、E2、E3Respectively showing the sets of 1 unit voxel layer thickness, 2 unit voxel layer thicknesses and 3 unit voxel layer thicknesses of the vascular cavity expanding towards the three-dimensional boundary of the vascular wall; LAROIIs a set of ROI vessel segment voxels;
and a negative etching step, adopting the following formula:
Figure FDA0002920291500000051
E＝[E-1 E-2 … E-n]，
E-1、E-2、…E-nrespectively representing a voxel set which shrinks 1 unit voxel, 2 unit voxels and n unit voxels inwards from the outer boundary of the blood vessel cavity, wherein n is the maximum radius value of the blood vessel cavity;
a step of extracting vessel Wall voxels, which is to obtain a set Wall of the vessel Wall voxels by adopting the following formulaROI(x,y,z)：
Figure FDA0002920291500000052
fcta(x, y, z) is the spatial voxel function of the CTA image; x, y and z respectively represent the coordinates of the voxel in the space, and omega is the voxel space; a and B are respectively the lowest plane coordinate value and the highest plane coordinate value of the Z axis of the ROI vascular segment;
extracting the voxels containing the stent in the vascular cavity, and acquiring a voxel set Lumen containing the stent in the vascular cavity by adopting the following formulaROI(x,y,z)：
Figure FDA0002920291500000053
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