CN112215941A - Method and system for evaluating aorta distortion based on differential geometry - Google Patents
Method and system for evaluating aorta distortion based on differential geometry Download PDFInfo
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Abstract
The invention discloses an aorta distortion assessment method and system based on differential geometry, wherein the method comprises the following steps: acquiring medical image data of a human body aorta to be evaluated; establishing a three-dimensional aorta anatomical model of a human aorta to be evaluated and extracting an aorta trunk central line; discretizing the central line of the main aorta and identifying the three-dimensional coordinates of each discrete point; compiling and calculating the 3D curvature and the bending rate of each discrete point; and adding the calculated 3D curvature and the calculated flexibility of each discrete point to the corresponding position of the central line of the trunk, and visually displaying the 3D curvature and the flexibility of each position of the central line of the trunk. The method can show the various characteristics of the aorta distortion, and makes up the defect that the same distortion value is measured on different aorta due to neglecting local detail and space distortion in the prior method. The method can be used for future morphological research, helps doctors to better understand the spatial structure of the aorta, further performs risk assessment, and determines an operation strategy or a scheme for postoperative review.
Description
Technical Field
The invention relates to the technical field of computer image processing combined with a differential geometry theory, in particular to an aorta distortion assessment method and system based on differential geometry.
Background
Aortic distortion is defined as the presence of abnormal aortic tortuosity and is identified as a common feature in many patients, including congenital aortic malformations and age-related aortic diseases such as Loeys Dietz syndrome, Marfa syndrome and bileaflet aortic valves. Performing a quantitative analysis of vessel distortion not only allows for risk assessment of disease, but also aids in the design of pre-operative intravascular devices and the placement of intra-operative catheters. Highly distorted anatomical features can easily lead to incomplete stent deployment, implant malposition, migration, endoleak, and the like.
In combination with existing 3D vessel imaging techniques and corresponding analysis software, it is theoretically possible to detect the true 3D properties and local details of any region of the aorta, and is not limited to performing 2D or global analysis. However, two widely used distortion ratio expressions today are based either on a measurement of the trigonometric distance in the 2D plane or on the entire centerline length of the aorta trunk, then both divided by the linear distance between the aorta entrance and the iliac bifurcation. Obviously, these ratio expressions fail to adequately provide a description of the spatial morphology and local details of the aorta, thereby affecting the accuracy of the aorta distortion assessment. Therefore, it is further necessary to propose a new quantitative indicator of the distortion to characterize the local "bending" and "torsion" of the aorta in 3D space, respectively.
Disclosure of Invention
The invention aims to provide an aorta distortion assessment method and system based on differential geometry, so that spatial and local distortion characteristics of an aorta are presented accurately, a doctor is helped to better master the distortion characteristics of the aorta, dependence on personal experience is reduced, and the decision efficiency of the doctor is improved.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the present invention provides a method for estimating aortic distortion based on differential geometry, including:
acquiring medical image data of a human body aorta to be evaluated;
establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated based on the medical image data and extracting an aorta trunk central line from the three-dimensional aorta anatomical model; wherein the aorta trunk is a portion of the aorta from the aortic inlet to the iliac branch on the aorta of the human body;
discretizing the main aortic trunk central line and identifying three-dimensional coordinates of each discrete point;
calculating the 3D curvature and the bending rate of each discrete point based on the three-dimensional coordinates of each discrete point;
and adding the calculated 3D curvature and the calculated flexibility of each discrete point to the corresponding position of the main aortic centerline, and visually displaying the 3D curvature and the flexibility of each position of the main aortic centerline.
Further, the acquiring medical image data of the human aorta to be evaluated comprises:
the 3D or 4D medical image data of the human aorta to be evaluated is acquired by any one or a combination of a plurality of medical imaging modes of computer tomography, magnetic resonance imaging, angiography and ultrasound imaging.
Further, based on the image data, establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated and extracting an aorta trunk centerline from the three-dimensional aorta anatomical model, including:
acquiring an aorta mask from the medical image data by using a threshold technique;
generating an aorta vessel path to be segmented based on the aorta mask;
performing primary segmentation on the aorta blood vessel according to the aorta blood vessel path to obtain an aorta blood vessel model of the primary segmentation;
removing small branches of the aortic vessel model to obtain a three-dimensional reconstruction geometry of an aortic trunk;
denoising the three-dimensional reconstruction geometric body to obtain a smooth main aorta model;
and extracting an aorta trunk central line from the smooth aorta trunk model.
Further, based on the aorta masking, generating an aorta vessel path to be segmented, comprising:
and combining the masks in a lofting mode to generate an aorta blood vessel path to be segmented.
Further, the preliminary segmentation of the aorta vessel according to the aorta vessel path comprises:
according to the aorta blood vessel path, carrying out primary segmentation on the aorta blood vessel by utilizing any one algorithm of a random walk algorithm, a region growing algorithm, an interval binary segmentation algorithm, a threshold segmentation algorithm, a voxel growing algorithm or a deep learning segmentation algorithm based on the image intensity and gradient along the tracking path.
Further, the removing of the small branch of the aortic vessel model comprises:
removing the three branches of the aortic arch, renal artery, celiac trunk and superior mesenteric artery in the aortic vascular model, and reserving ascending aorta, aortic arch and descending aorta to iliac branch;
the denoising processing of the three-dimensional reconstruction geometry comprises:
eliminating high-frequency noise in the three-dimensional reconstruction geometry by adopting a local polynomial filter of a Gaussian kernel;
the extracting the aorta trunk central line from the smooth aorta trunk model comprises the following steps:
and extracting the main aorta central line from the smooth main aorta model by adopting any one of a manual calibration method, a topology refinement method, a distance transformation method or a minimum cost path algorithm.
Further, discretizing the main aortic centerline and identifying the three-dimensional coordinates of each discrete point, comprising:
segmenting the aorta trunk centerline of the continuous curve into a series of discrete points at intervals of 1 mm;
identifying three-dimensional space coordinates of each discrete point;
and sequentially outputting the three-dimensional space coordinates of each discrete point according to the sequence from the entrance of the aorta to the iliac branch.
Further, calculating the 3D curvature and the deflection of each discrete point based on the three-dimensional coordinates of each discrete point includes:
the main aortic centerline is described in a parameterization mode through a curve variable s, and the expression is as follows:
γ(s)＝(x(s),y(s),z(s)),
wherein γ(s) represents a parameterized equation for the centerline, x(s), y(s), and z(s) represents three-dimensional coordinates;
the degree of curvature and the degree of spatial twist for the centerline are characterized by the 3D curvature and the rate of deflection, respectively, as follows:
where k(s) represents 3D curvature, τ(s) represents deflection, and "×" represents cross product;
for the 3D curvature and the deflection at each discrete point, finite difference quotient is used to approximate the first, second and third derivatives in the above equation as follows:
wherein, gamma'q(sj) Is first order forward differential quotient, gamma'h(sj) First order backward difference quotient, then:
wherein k is1，k2，k3The field radius relates to the number of first-order difference quotient values needed by the difference quotient target point; the maximum, total and average 3D curvature or deflection are also defined as follows:
where Mk represents the maximum 3D curvature, Tk represents the total 3D curvature, and Ak represents the average 3D curvature; k is a radical ofjRepresents the 3D curvature of the jth discrete point; m tau represents maximum flexibility, T tau represents total flexibility, and A tau represents average flexibility; tau isjRepresenting the deflection at the jth discrete point.
Further, the step of adding the calculated 3D curvature and flexibility of each discrete point to the corresponding position of the main aortic centerline to visually display the 3D curvature and flexibility of each position of the main aortic centerline includes:
corresponding the 3D curvature and the flexibility of each discrete point to the space position of the central line of the main aorta;
and straightening the central line of the main aorta, and transversely displaying the 3D curvature and the change of the flexibility.
In another aspect, the present invention also provides a system for estimating aortic distortion based on differential geometry, comprising:
the medical image data acquisition module is used for acquiring medical image data of a human body aorta to be evaluated;
the main aorta centerline extraction module is used for establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated and extracting a main aorta centerline from the three-dimensional aorta anatomical model based on the medical image data acquired by the medical image data acquisition module; wherein the aorta trunk is a portion from the aorta entrance to the iliac branch on the aorta of the human body to be evaluated;
the three-dimensional coordinate identification module is used for discretizing the main aorta centerline extracted by the main aorta centerline extraction module and identifying the three-dimensional coordinates of each discrete point;
the 3D curvature and flexibility calculation module is used for calculating the 3D curvature and flexibility of each discrete point based on the three-dimensional coordinates of each discrete point identified by the three-dimensional coordinate identification module;
and the visual display module is used for adding the 3D curvature and the curvature of each discrete point calculated by the 3D curvature and the curvature calculation module to the corresponding position of the main aortic centerline and visually displaying the 3D curvature and the curvature of each position of the main aortic centerline.
In yet another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
1. the invention can accurately describe the local bending and torsion of the aorta in space, and can quickly lock the areas corresponding to the maximum bending and the maximum torsion, thereby helping doctors to make decisions accurately and quickly.
2. The invention develops a corresponding evaluation system of the aorta distortion based on the differential geometry, which can be called quickly and anytime anywhere, thereby being convenient for doctors to use.
3. The method can express the distortion of the aorta in an anisotropic manner, and overcomes the defect that the same distortion value is measured by different aorta due to the omission of local details and spatial characteristics in the conventional method.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for estimating aortic distortion based on differential geometry according to an embodiment of the present invention;
FIG. 2 is a flow chart for building a three-dimensional aortic anatomical model and extracting the centerline of the main aorta according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a 3D structure (a) of an aorta, a centerline (b) of a main artery, a series of discrete points (c) on the centerline, and coordinates (D) of the discrete points, according to an embodiment of the present invention;
fig. 4 (a) is a schematic diagram of 3D curvature of the centerline of the main aorta in a three-dimensional space, (b) is a schematic diagram of flexibility of the centerline of the main aorta in a three-dimensional space, and (c) is a schematic diagram of changes of the 3D curvature and the flexibility of the centerline of the main aorta in a horizontal straightening direction.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
First embodiment
The present embodiment provides a method for estimating aortic distortion based on differential geometry, which characterizes the spatial "bending" and "torsion" characteristics of the aorta by estimating the 3D curvature and the bending rate of the main aortic centerline. The method may be implemented by an electronic device, which may be a terminal or a server. The flow of execution of the method for estimating aortic distortion based on differential geometry is shown in fig. 1, and comprises the following steps:
s101, acquiring medical image data of a human body aorta to be evaluated;
it should be noted that the medical image data of the human aorta may be 3D or 4D medical image data from one or more imaging modalities of the patient, and the present embodiment employs computed tomography CTA medical image data; of course any other type of medical imaging modality is possible; for example, medical imaging modalities such as computed tomography CTA, magnetic resonance imaging, angiography, and ultrasound imaging.
S102, establishing a three-dimensional aorta anatomical model corresponding to a human aorta to be evaluated based on the medical image data, and extracting an aorta trunk central line from the three-dimensional aorta anatomical model;
it should be noted that the main aorta is the portion of the aorta from the aortic inlet to the iliac branch on the aorta; the CTA image data of the aorta includes, in addition to the aorta, surrounding muscular tissue and blood vessels other than the aorta, and the present step aims to separate the aorta from other background regions in the image of the aorta of the human body, extract the aorta, i.e., obtain a region of interest, and perform three-dimensional reconstruction on the region of interest. The skeleton of the reconstructed anatomical model is then prepared for centerline extraction for subsequent distortion analysis. The steps of building a particular anatomical model of the aorta and extracting the trunk centerlines are described in detail below.
S103, discretizing the main aorta centerline and identifying the three-dimensional coordinates of discrete points;
it should be noted that, in this step, the centerline of the main aorta extracted in the above step is segmented into a series of discrete points and three-dimensional coordinates thereof are identified, so as to prepare for the subsequent distortion evaluation. The specific steps of discretizing the aorta centerline and identifying the three-dimensional coordinates of each discrete point will be described in detail below.
S104, calculating the 3D curvature and the bending rate of each discrete point based on the three-dimensional coordinates of each discrete point;
in this embodiment, the 3D curvature and the bending rate of each discrete point are calculated by a program of writing (MATLAB). The method adopts a numerical approximation method to solve the 3D curvature and the flexibility on the central line of the aorta so as to reflect the characteristics of the aorta in space. The 3D curvature reveals the degree of curvature of the curve in space, and the curvature reveals the degree of torsion of the curve away from a given plane. Curves in three-dimensional space can be obtained by bending and twisting a straight line. The specific steps of calculating the 3D curvature and the deflection for each discrete point will be described in detail below.
And S105, adding the calculated 3D curvature and the calculated curvature of each discrete point to the corresponding position of the main aortic centerline, and visually displaying the 3D curvature and the curvature of each position of the main aortic centerline.
In this step, the calculated 3D curvature and the calculated curvature of each discrete point are indexes representing the "bending" and the "torsion" of the aorta in space. The specific steps of visualizing the 3D curvature and the curvature about the centerline will be described in detail below.
Specifically, the execution flow of S102 is shown in fig. 2, and includes the following steps:
s1021, acquiring an aorta mask from the medical image data by adopting a threshold technology;
among them, the threshold technique is also called a threshold segmentation method, and is an image segmentation technique based on a region. The aim is to divide the pixel geometry by gray level, in this embodiment by the gray level of the aorta to obtain its mask.
S1022, generating an aorta blood vessel path to be segmented based on the aorta mask;
in the present embodiment, the masks are combined in a lofting manner to generate the aortic blood vessel path to be segmented.
S1023, performing primary segmentation on the aorta blood vessel according to the aorta blood vessel path to obtain a primary segmented aorta blood vessel model;
the aorta may be initially segmented by using a random walk algorithm based on the image intensity and gradient along the tracking path, the segmented aorta is shown in (a) in fig. 3, and certainly, other segmentation methods may also be used to perform the aorta initial segmentation; for example, algorithms such as a region growing method, an interval binary segmentation method, a threshold segmentation algorithm, a voxel growing algorithm, and a deep learning segmentation algorithm.
S1024, removing small branches of the aortic vessel model to obtain a three-dimensional reconstruction geometric body of an aortic trunk;
wherein the removed small branches include the three branches of the aortic arch, renal artery, celiac trunk, superior mesenteric artery, etc. The remaining main trunk includes the ascending aorta, the aortic arch and the descending aorta to the iliac branches. The three-dimensional reconstruction geometry of the main aorta obtained after the removal of the small branches of the preliminarily segmented aortic vessels is shown in (b) of fig. 3.
S1025, denoising the three-dimensional reconstruction geometric solid to obtain a smooth main aorta model;
in the present embodiment, a local polynomial filter with a gaussian kernel is used to eliminate high-frequency noise in the three-dimensional reconstruction geometry, so as to obtain a smoother aorta trunk model.
And S1026, extracting an aorta trunk central line from the smooth aorta trunk model.
Where the centerline may represent the topology of the vascular network, its integrity and accuracy affect the effectiveness and robustness of the three-dimensional structure of the vessel. Any centerline extraction method can be used to extract the aortic main centerline in this embodiment; for example, a manual calibration method, a topology refinement method, a distance transformation method, a minimum cost path algorithm, etc. The extracted main aortic trunk centerline is shown in fig. 3 (b).
Furthermore, it should be noted that the above-mentioned anatomical modeling and centerline extraction method may be performed by a fully automatic method or a method involving interaction with a user. In the present embodiment, the aorta-specific anatomical model and the centerline are automatically generated from the medical image data, and the anatomical model and the centerline may be modified as appropriate to reflect morphological changes caused by the intervention.
Further, the execution flow of S103 includes the following steps:
s1031, dividing the main aortic trunk central line of the continuous curve into a series of discrete points at intervals of 1 mm; the segmentation result is shown in fig. 3 (c);
wherein, the selection principle of the central line segmentation distance can fully reflect the characteristics of the curve, and the limitation error is 10-4(linear connection between discrete points), this embodiment chooses 1 mm after balancing accuracy and computational complexity. I.e. for a centre line of 500 mm in length, there are 500 discrete points.
S1032, identifying three-dimensional space coordinates (x, y, z) of each discrete point;
s1033, sequentially outputting the spatial coordinates of the discrete points in order from the entrance of the aorta to the iliac branch.
Next, the execution flow of S104 will be specifically described with reference to fig. 3; firstly, it should be noted that in practical application, due to the influence of many factors such as manpower and instruments, the obtained data (a sequence of three-dimensional coordinate points) of the discrete curve often has a certain deviation or noise, so how to make the algorithm more accurate and robust becomes a difficulty in calculating the curvature and the flexibility of the three-dimensional space discrete curve.
The common calculation methods are roughly two, one is based on curve fitting, namely, an analytical equation of a space curve is found, and then the equation is calculated by using a differential geometry formula, but due to the complexity of the aorta structure, the analytical equation is difficult to find, so another method based on differential approximation is adopted, and the method is more general.
In detail, in this embodiment, a differential form is adopted to replace the first, second and third derivatives in the formula of the 3D curvature and the flexibility, and meanwhile, in order to make the calculation accuracy higher, the difference quotient in the two-sided domain is adopted to solve the 3D curvature and the flexibility of the target point, specifically, the step S104 includes the following steps:
the geometric centerline of each aortic trunk can be described parametrically by a curvilinear variable s, as follows:
γ(s)＝(x(s),y(s),z(s)),
wherein γ(s) represents a parameterized equation for the centerline, x(s), y(s), and z(s) represents three-dimensional coordinates;
the degree of curvature and the degree of spatial twist for the centerline are characterized by the 3D curvature and the rate of deflection, respectively, as follows:
where k(s) represents 3D curvature, τ(s) represents deflection, and "×" represents cross product;
for the 3D curvature and the deflection at each discrete point, finite difference quotient is used to approximate the first, second and third derivatives in the above equation as follows:
wherein, gamma'q(sj) Is first order forward differential quotient, gamma'h(sj) First order backward difference quotient, then:
in a similar manner to that described above,
wherein k is1，k2，k3The field radius relates to the number of first-order difference quotient values needed by the difference quotient target point; the maximum, total and average 3D curvature or deflection are also defined as follows:
where Mk represents the maximum 3D curvature, Tk represents the total 3D curvature, and Ak represents the average 3D curvature; k is a radical ofjRepresents the 3D curvature of the jth discrete point; m tau represents maximum flexibility, T tau represents total flexibility, and A tau represents average flexibility; tau isjRepresenting the deflection at the jth discrete point.
Further, the execution flow of S105 includes the following steps:
s1051, corresponding the 3D curvature and the flexibility of each discrete point to the space position of the central line of the trunk; as shown in fig. 4 (a), (b), it is clear that the corresponding 3D region of greater curvature and flexibility can be locked quickly.
S1052, straightening according to the central line of the trunk, and transversely displaying the 3D curvature and the change of the bending rate. As shown in fig. 4 (c), while the maximum, total and average 3D curvature and deflection along the centerline can be quickly known.
In summary, the present embodiment provides a method for evaluating aortic distortion based on differential geometry, which can accurately describe the local "bending" and "torsion" of the aorta in space, and can rapidly lock the region corresponding to the maximum bending and maximum torsion, thereby helping the physician make a decision accurately and rapidly. The local and spatial characteristics of the aorta can be described in an individualized manner, and the defect that different aorta measure the same distortion value due to the fact that the local details and the spatial characteristics are omitted in the existing method is overcome. Has certain guiding significance for disease prediction, risk stratification, surgical schemes and postoperative risk assessment.
Second embodiment
The present embodiment provides a system for the assessment of aortic distortion based on differential geometry; in particular, the system for the assessment of aortic distortion based on differential geometry comprises the following modules:
the medical image data acquisition module is used for acquiring medical image data of a human body aorta to be evaluated;
the main aorta centerline extraction module is used for establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated and extracting a main aorta centerline from the three-dimensional aorta anatomical model based on the medical image data acquired by the medical image data acquisition module; wherein the aorta trunk is a portion from the aorta entrance to the iliac branch on the aorta of the human body to be evaluated;
the three-dimensional coordinate identification module is used for discretizing the main aorta centerline extracted by the main aorta centerline extraction module and identifying the three-dimensional coordinates of each discrete point;
the 3D curvature and flexibility calculation module is used for calculating the 3D curvature and flexibility of each discrete point based on the three-dimensional coordinates of each discrete point identified by the three-dimensional coordinate identification module;
and the visual display module is used for adding the 3D curvature and the curvature of each discrete point calculated by the 3D curvature and the curvature calculation module to the corresponding position of the main aortic centerline and visually displaying the 3D curvature and the curvature of each position of the main aortic centerline.
The system for evaluating aortic distortion based on differential geometry of the present embodiment corresponds to the method for evaluating aortic distortion based on differential geometry of the first embodiment described above; the functions realized by the functional modules in the system for evaluating aortic distortion based on differential geometry according to the present embodiment correspond to the flow steps in the method for evaluating aortic distortion based on differential geometry according to the first embodiment one by one; therefore, it is not described herein.
Third embodiment
The present embodiment provides an electronic device, which includes a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may generate a large difference due to different configurations or performances, and may include one or more processors (CPUs) and one or more memories, where at least one instruction is stored in the memory, and the instruction is loaded by the processor and performs the following steps:
s101, acquiring medical image data of a human body aorta to be evaluated;
s102, establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated based on the medical image data and extracting an aorta trunk central line from the three-dimensional aorta anatomical model; wherein the aorta trunk is a portion of the aorta from the aortic inlet to the iliac branch on the aorta of the human body;
s103, discretizing the main aorta centerline and identifying the three-dimensional coordinates of discrete points;
s104, calculating the 3D curvature and the bending rate of each discrete point based on the three-dimensional coordinates of each discrete point;
and S105, adding the calculated 3D curvature and the calculated curvature of each discrete point to the corresponding position of the main aortic centerline, and visually displaying the 3D curvature and the curvature of each position of the main aortic centerline.
Fourth embodiment
The present embodiments provide a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above-mentioned method. The computer readable storage medium may be, among others, ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. The instructions stored therein may be loaded by a processor in the terminal and perform the steps of:
s101, acquiring medical image data of a human body aorta to be evaluated;
s102, establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated based on the medical image data and extracting an aorta trunk central line from the three-dimensional aorta anatomical model; wherein the aorta trunk is a portion of the aorta from the aortic inlet to the iliac branch on the aorta of the human body;
s103, discretizing the main aorta centerline and identifying the three-dimensional coordinates of discrete points;
s104, calculating the 3D curvature and the bending rate of each discrete point based on the three-dimensional coordinates of each discrete point;
and S105, adding the calculated 3D curvature and the calculated curvature of each discrete point to the corresponding position of the main aortic centerline, and visually displaying the 3D curvature and the curvature of each position of the main aortic centerline.
Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (10)
1. A method for differential geometry based assessment of aortic distortion, comprising:
acquiring medical image data of a human body aorta to be evaluated;
establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated based on the medical image data and extracting an aorta trunk central line from the three-dimensional aorta anatomical model; wherein the aorta trunk is a portion of the aorta from the aortic inlet to the iliac branch on the aorta of the human body;
discretizing the main aortic trunk central line and identifying three-dimensional coordinates of each discrete point;
calculating the 3D curvature and the bending rate of each discrete point based on the three-dimensional coordinates of each discrete point;
and adding the calculated 3D curvature and the calculated flexibility of each discrete point to the corresponding position of the main aortic centerline, and visually displaying the 3D curvature and the flexibility of each position of the main aortic centerline.
2. The method for estimating aortic distortion based on differential geometry as claimed in claim 1, wherein the acquiring of medical image data of the human aorta to be estimated comprises:
the 3D or 4D medical image data of the human aorta to be evaluated is acquired by any one or a combination of a plurality of medical imaging modes of computer tomography, magnetic resonance imaging, angiography and ultrasound imaging.
3. The method for estimating aortic distortion based on differential geometry according to claim 1, wherein the step of building a three-dimensional aortic anatomical model corresponding to the human aorta to be estimated and extracting an aorta trunk centerline from the three-dimensional aortic anatomical model based on the image data comprises:
acquiring an aorta mask from the medical image data by using a threshold technique;
generating an aorta vessel path to be segmented based on the aorta mask;
performing primary segmentation on the aorta blood vessel according to the aorta blood vessel path to obtain an aorta blood vessel model of the primary segmentation;
removing small branches of the aortic vessel model to obtain a three-dimensional reconstruction geometry of an aortic trunk;
denoising the three-dimensional reconstruction geometric body to obtain a smooth main aorta model;
and extracting an aorta trunk central line from the smooth aorta trunk model.
4. The method for differential geometry based aortic distortion assessment as claimed in claim 3, wherein the generating of the aortic vessel path to be segmented based on the aortic mask comprises:
and combining the masks in a lofting mode to generate an aorta blood vessel path to be segmented.
5. The method for differential geometry based aortic distortion assessment as claimed in claim 3, wherein the preliminary segmentation of aortic vessels from the aortic vessel path comprises:
according to the aorta blood vessel path, carrying out primary segmentation on the aorta blood vessel by utilizing any one algorithm of a random walk algorithm, a region growing algorithm, an interval binary segmentation algorithm, a threshold segmentation algorithm, a voxel growing algorithm or a deep learning segmentation algorithm based on the image intensity and gradient along the tracking path.
6. The differential geometry-based aortic distortion assessment method of claim 3, wherein the removing of small branches from the aortic vessel model comprises:
removing the three branches of the aortic arch, renal artery, celiac trunk and superior mesenteric artery in the aortic vascular model, and reserving ascending aorta, aortic arch and descending aorta to iliac branch;
the denoising processing of the three-dimensional reconstruction geometry comprises:
eliminating high-frequency noise in the three-dimensional reconstruction geometry by adopting a local polynomial filter of a Gaussian kernel;
the extracting the aorta trunk central line from the smooth aorta trunk model comprises the following steps:
and extracting the main aorta central line from the smooth main aorta model by adopting any one of a manual calibration method, a topology refinement method, a distance transformation method or a minimum cost path algorithm.
7. The differential geometry-based method for assessing aortic distortion as claimed in claim 1, wherein discretizing the aortic trunk centerline and identifying the three-dimensional coordinates of each discrete point comprises:
segmenting the aorta trunk centerline of the continuous curve into a series of discrete points at intervals of 1 mm;
identifying three-dimensional space coordinates of each discrete point;
and sequentially outputting the three-dimensional space coordinates of each discrete point according to the sequence from the entrance of the aorta to the iliac branch.
8. The differential geometry-based method for assessing aortic distortion as claimed in claim 1, wherein the calculating the 3D curvature and the deflection of each discrete point based on the three-dimensional coordinates of each discrete point comprises:
the main aortic centerline is described in a parameterization mode through a curve variable s, and the expression is as follows:
γ(s)＝(x(s),y(s),z(s))
wherein γ(s) represents a parameterized equation for the centerline, x(s), y(s), and z(s) represents three-dimensional coordinates;
the degree of curvature and the degree of spatial twist for the centerline are characterized by the 3D curvature and the rate of deflection, respectively, as follows:
where k(s) represents 3D curvature, τ(s) represents deflection, and "×" represents cross product;
for the 3D curvature and the deflection at each discrete point, finite difference quotient is used to approximate the first, second and third derivatives in the above equation as follows:
wherein, gamma'q(sj) Is first order forward differential quotient, gamma'h(sj) First order backward difference quotient, then:
wherein k is1，k2，k3The field radius relates to the number of first-order difference quotient values needed by the difference quotient target point; the maximum, total and average 3D curvature or deflection are also defined as follows:
Mκ＝Max{κj},Tκ＝∫κjds,
Mτ＝Max{τj},Tτ＝∫τjds,
where Mk represents the maximum 3D curvature, Tk represents the total 3D curvature, and Ak represents the average 3D curvature; k is a radical ofjRepresents the 3D curvature of the jth discrete point; m tau represents maximum flexibility, T tau represents total flexibility, and A tau represents average flexibility; tau isjRepresenting the deflection at the jth discrete point.
9. The method for differential geometry-based aortic distortion assessment according to claim 1 wherein the step of adding the calculated 3D curvature and flexibility of each discrete point to the corresponding position of the aortic main centerline to visually display the 3D curvature and flexibility of each point of the aortic main centerline comprises:
corresponding the 3D curvature and the flexibility of each discrete point to the space position of the central line of the main aorta;
and straightening the central line of the main aorta, and transversely displaying the 3D curvature and the change of the flexibility.
10. A differential geometry-based aortic distortion assessment system, comprising:
the medical image data acquisition module is used for acquiring medical image data of a human body aorta to be evaluated;
the main aorta centerline extraction module is used for establishing a three-dimensional aorta anatomical model corresponding to the human aorta to be evaluated and extracting a main aorta centerline from the three-dimensional aorta anatomical model based on the medical image data acquired by the medical image data acquisition module; wherein the aorta trunk is a portion from the aorta entrance to the iliac branch on the aorta of the human body to be evaluated;
the three-dimensional coordinate identification module is used for discretizing the main aorta centerline extracted by the main aorta centerline extraction module and identifying the three-dimensional coordinates of each discrete point;
the 3D curvature and flexibility calculation module is used for calculating the 3D curvature and flexibility of each discrete point based on the three-dimensional coordinates of each discrete point identified by the three-dimensional coordinate identification module;
and the visual display module is used for adding the 3D curvature and the curvature of each discrete point calculated by the 3D curvature and the curvature calculation module to the corresponding position of the main aortic centerline and visually displaying the 3D curvature and the curvature of each position of the main aortic centerline.
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