CN113288167B - Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy - Google Patents

Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy Download PDF

Info

Publication number
CN113288167B
CN113288167B CN202110358329.5A CN202110358329A CN113288167B CN 113288167 B CN113288167 B CN 113288167B CN 202110358329 A CN202110358329 A CN 202110358329A CN 113288167 B CN113288167 B CN 113288167B
Authority
CN
China
Prior art keywords
surface mesh
mesh model
target
ventricular
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110358329.5A
Other languages
Chinese (zh)
Other versions
CN113288167A (en
Inventor
庄盛盛
田庄
张抒扬
任淼
赵心悦
郑坤玉
滕雅群
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Original Assignee
Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Peking Union Medical College Hospital Chinese Academy of Medical Sciences filed Critical Peking Union Medical College Hospital Chinese Academy of Medical Sciences
Priority to CN202110358329.5A priority Critical patent/CN113288167B/en
Publication of CN113288167A publication Critical patent/CN113288167A/en
Application granted granted Critical
Publication of CN113288167B publication Critical patent/CN113288167B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Abstract

The invention discloses an auxiliary diagnosis device, a device and a computer readable storage medium for cardiomyopathy, wherein the device comprises: a memory and a processor; a memory for storing program instructions; a processor for invoking program instructions, which when executed, are for performing the following: receiving first electrocardiogram related physical data, first electrocardiogram related biological constants and a first actual target voltage value of a target patient; transforming the standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of the cardiomyopathy of the target patient; obtaining a first body surface potential; obtaining a first analog target voltage value according to the first body surface potential; and acquiring a cardiomyopathy diagnosis result of the target patient by using the first simulated target voltage value and the first actual target voltage value. The invention can well eliminate the influence of the body form factor of the target patient on the judgment of the cardiomyopathy.

Description

Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy
Technical Field
The invention relates to the technical field of electrocardiogram simulation, in particular to auxiliary diagnostic equipment, a device and a computer readable storage medium for cardiomyopathy.
Background
Cardiomyopathy is a group of heterogeneous myocardial diseases, in which abnormalities in the mechanical and electrical activity of the heart are caused by different causes, manifested by inappropriate thickening or dilatation of the ventricles, such as myocardial fibrosis, myocardial amyloidosis, left ventricular thickening and myocardial fat infiltration. Electrocardiography (ECG) is a technique that uses an electrocardiograph to record from the body surface the pattern of electrical activity changes produced by each cardiac cycle of the heart. The voltage value of the electrocardiogram is the most basic information provided by clinical doctors, and is often used for assisting in diagnosing cardiomyopathy clinically.
However, the magnitude of the voltage value of the electrocardiogram is greatly influenced by the physical factors of the patient (such as fat, the position of the heart in the thorax, and the geometric data of the heart), but the physical factors are not the pathological factors of the cardiomyopathy of the patient, so the physical factors interfere with the interpretation of the voltage value of the electrocardiogram by the clinical doctor, thereby affecting the accuracy of the voltage value, and in severe cases, the voltage value can cause a clinically wrong diagnosis.
Therefore, how to eliminate the influence of physical factors on the electrocardiogram voltage value and realize accurate diagnosis of cardiomyopathy becomes a technical problem to be solved urgently.
Disclosure of Invention
In view of the above problems, the present invention provides an auxiliary diagnostic apparatus, a device and a computer readable storage medium for cardiomyopathy.
In a first aspect, an embodiment of the present invention provides an auxiliary diagnostic apparatus for cardiomyopathy, where the apparatus includes: a memory and a processor;
the memory to store program instructions;
the processor, configured to invoke the program instructions, and when executed, configured to perform the following:
receiving first electrocardiogram related body data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient;
transforming a preset standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of the cardiomyopathy of the target patient;
acquiring a first myocardial depolarization sequence corresponding to a target patient, and acquiring a first body surface potential of a target surface grid model according to the first myocardial depolarization sequence, the first electrocardiogram related biological constant and the target surface grid model;
obtaining a first analog target voltage value according to the first integral potential; and
and acquiring a cardiomyopathy diagnosis result of the target patient by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy.
Further, the standard surface mesh model comprises a second thoracic surface mesh model, a second ventricular surface mesh model, and a second lung surface mesh model; the first electrocardiogram related body data comprise first thoracic body data, first ventricle body data and first heart thoracic body position relation data which correspond to the target patient;
the processor transforms a preset standard surface mesh model by using the first electrocardiogram related body data, and is specifically configured to, when obtaining a target surface mesh model corresponding to a target patient:
acquiring second thoracic body data, acquiring a first proportional relation between the first thoracic body data and the second thoracic body data, and transforming the second thoracic surface mesh model by using the first proportional relation to obtain a first thoracic surface mesh model;
acquiring second ventricle shape data, acquiring a second proportional relation between the first ventricle shape data and the second ventricle shape data, and transforming the second ventricle surface mesh model by using the second proportional relation to obtain a first ventricle surface mesh model;
according to the first heart thorax position relation data, carrying out translation transformation on the first heart chamber surface mesh model; and
and performing translation and/or scaling transformation on the second lung surface mesh model to obtain a first lung surface mesh model, wherein the first lung surface mesh model has no overlapping part with the first ventricle surface mesh model, and the target surface mesh model comprises the first thoracic surface mesh model, the first ventricle surface mesh model and the first lung surface mesh model.
Further, the first ventricular volume data comprises a first left ventricular end-diastolic inner diameter, a first ventricular septum thickness, and a first left ventricular wall thickness; the second ventricular surface mesh model comprises a second ventricular surface mesh basic model and a plurality of second ventricular surface mesh submodels, wherein the second ventricular surface mesh submodels are obtained by transforming the ventricular interval thickness or the left ventricular wall thickness of the second ventricular surface mesh basic model, only the ventricular interval thickness and/or the left ventricular wall thickness are different between different second ventricular surface mesh submodels, and other parts are the same; the second ventricular volume data corresponds to a second ventricular surface mesh basis model, including a second left ventricular end-diastolic inner diameter, a second ventricular septum thickness, and a second left ventricular wall thickness;
the processor obtains second ventricle shape data, obtains a second proportional relationship between the first ventricle shape data and the second ventricle shape data, and transforms the second ventricle surface mesh model by using the second proportional relationship, so as to obtain the first ventricle surface mesh model, specifically configured to:
obtaining and summing the first left ventricular end-diastolic inner diameter, the first ventricular septum thickness and the first left ventricular wall thickness to obtain a first summed value;
obtaining and summing a second left ventricular end-diastolic inner diameter, a second ventricular septum thickness and a second left ventricular wall thickness to obtain a second summed value;
obtaining a transform coefficient using the first sum value and the second sum value, wherein the transform coefficient is equal to a ratio of the second sum value and the first sum value;
obtaining a weight for each second ventricular surface mesh model using the transform coefficients, the first ventricular interval thickness, the first left ventricular wall thickness, the second ventricular interval thickness, the second left ventricular wall thickness, the ventricular interval thicknesses of a plurality of second ventricular surface mesh models, and left ventricular wall thicknesses;
linearly combining the second ventricular surface mesh sub-models by using the weights to obtain a ventricular surface mesh transition model; and
and transforming the ventricular surface mesh transition model according to the transformation coefficient to obtain the first ventricular surface mesh model.
Furthermore, the number of the second ventricle surface grid submodels is four, and the four second ventricle surface grid submodels are respectively a first submodel with a left ventricle wall thickness larger than the second left ventricle wall thickness, a second submodel with a left ventricle wall thickness smaller than the second left ventricle wall thickness, a third submodel with a ventricular interval thickness larger than the second ventricular interval thickness, and a fourth submodel with a ventricular interval thickness smaller than the second ventricular interval thickness;
wherein, the weights W corresponding to the first submodel, the second submodel, the third submodel and the fourth submodel respectively 1 、W 2 、W 3 And W 4 Is obtained according to formula (3) to formula (6); the ventricular surface mesh transition model and the first ventricular surface mesh model are obtained according to equation (1);
W 1 ×C 1 +W 2 ×C 2 +W 3 ×C 3 +W 4 ×C 4 ＝C target /R size (1)
W 1 +W 2 ＝0.5 (3)
W 1 +W 2 ＝0.5 (4)
W 1 ×T 1,left +W 2 ×T 2,left +0.5×T 0,left ＝T target,left /R size (5)
W 3 ×T 3,inter +W 4 ×T 4,inter +0.5×T 0,inter ＝T target,inter /R size (6)
wherein, the first and the second end of the pipe are connected with each other,
C 1 、C 2 、C 3 and C 4 Respectively representing a first submodel, a second submodel, a third submodel and a fourth submodel,
W 1 、W 2 、W 3 and W 4 Respectively representing the weights of the first submodel, the second submodel, the third submodel and the fourth submodel,
R size representing a transformation systemThe number of the first and second groups is,
C target /R size a model of the ventricular surface mesh transition is represented,
C target a first ventricular surface mesh model is represented,
T target,left representing a first left wall thickness, T target,inter The thickness of the first chamber spacing is indicated,
T 0,left denotes the second left wall thickness, T 0,inter The thickness of the second chamber spacing is indicated,
T 1,left representing the left wall thickness of the first sub-model,
T 2,left representing the left wall thickness of the second submodel,
T 3,inter the cell gap thickness of the third submodel is indicated,
T 4,inter the cell gap thickness of the fourth submodel is indicated.
Further, the second ventricular septum thickness and the second left ventricular wall thickness are both 10mm; the number of the second ventricle surface grid model is four, and the combination of the left ventricle wall thickness and the ventricular interval thickness of the four second ventricle surface grid models is respectively 26mm, 10mm, 6mm, 10mm, 26mm and 10mm, 6 mm.
Further, when the processor acquires a first myocardial depolarization sequence corresponding to the target patient, the processor is specifically configured to:
receiving an initial activation point and a velocity constant of a target patient, wherein the velocity constant is a velocity ratio of conduction of a myocardial depolarization wave on a myocardial surface and in a myocardial interior; and
and obtaining the depolarization sequence of the first myocardium according to Dijkstra algorithm by using the initial excitation point, the velocity constant and the first ventricular surface mesh model.
Further, the first thorax volume data includes a first thorax width and a first thorax thickness, and the second thorax volume data includes a second thorax width and a second thorax thickness.
Further, when the processor obtains a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, the processor is specifically configured to:
comparing and calculating the first actual target voltage value and the first simulated target voltage value to obtain a comparison and calculation result;
comparing the comparison calculation result with a preset threshold value to obtain a comparison result; and
and judging whether the target patient has cardiomyopathy or not according to the comparison result.
In a second aspect, an embodiment of the present invention provides an auxiliary diagnostic apparatus for cardiomyopathy, including:
the data receiving unit is used for receiving first electrocardiogram related physical data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient;
a target surface mesh model obtaining unit, configured to transform a preset standard surface mesh model by using the first electrocardiographic related physical data to obtain a target surface mesh model corresponding to a target patient, where the target surface mesh model represents a physical factor of the target patient but does not represent a pathological factor of cardiomyopathy of the target patient;
the body surface potential acquisition unit is used for acquiring a first myocardial depolarization sequence corresponding to a target patient and acquiring a first body surface potential of the target surface grid model according to the first myocardial depolarization sequence, the first electrocardio-related biological constant and the target surface grid model;
the simulation target voltage value acquisition unit is used for acquiring a first simulation target voltage value according to the first body table potential; and
and the diagnosis result acquisition unit is used for acquiring a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy or not.
In a second aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
receiving first electrocardiogram related body data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient;
transforming a preset standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of the cardiomyopathy of the target patient;
acquiring a first myocardial depolarization sequence corresponding to a target patient, and acquiring a first body surface potential of a target surface grid model according to the first myocardial depolarization sequence, the first electrocardiogram related biological constant and the target surface grid model;
obtaining a first analog target voltage value according to the first body table potential; and
acquiring a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy.
In the embodiment of the invention, a target surface mesh model obtained by transforming a standard surface mesh model by using first electrocardiogram related body data is firstly utilized, wherein the target surface mesh model is a surface mesh model individualized in the aspect of the electrocardiogram related body data, and the target surface mesh model reflects the body factors of a target patient but does not reflect the pathological factors of cardiomyopathy of the target patient; a first sheet potential and a first simulated target voltage value derived from the target surface mesh model are then obtained. The first actual target voltage value represents the pathological factors and physical factors of the cardiomyopathy of the target patient at the same time. In the embodiment of the invention, the shape factor in the first actual target voltage value is eliminated by utilizing the first simulated target voltage value, so that the pathological factor of the cardiomyopathy reflected by the first actual target voltage value is more truly and prominently displayed, and the accurate judgment of the cardiomyopathy is further realized.
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 shows a flowchart of an auxiliary diagnostic method of cardiomyopathy performed by an auxiliary diagnostic apparatus of cardiomyopathy according to an embodiment of the present invention.
Fig. 2 shows a flowchart of a method for obtaining a target surface mesh model corresponding to a target patient by transforming a preset standard surface mesh model with the first electrocardiogram related physical data by the auxiliary diagnostic equipment for cardiomyopathy according to an embodiment of the present invention.
Fig. 3 shows an annotation on the image of the method for determining the specific position of the heart in the thorax according to an exemplary embodiment of the invention.
Fig. 4 is a flowchart of a method for acquiring second ventricular volume data, acquiring a second proportional relationship between the first ventricular volume data and the second ventricular volume data, and transforming the second ventricular surface mesh model by using the second proportional relationship to obtain a first ventricular surface mesh model by using the auxiliary myocardial disease diagnosis apparatus according to an embodiment of the present invention.
FIG. 5 shows a flow diagram of a method of obtaining a standard surface mesh model according to one embodiment of the invention.
Fig. 6 is a block diagram showing a configuration of an auxiliary diagnosing apparatus for cardiomyopathy according to an embodiment of the present invention.
Fig. 7 shows an internal structural diagram of a computer apparatus according to another embodiment of the present invention.
Fig. 8 shows a simulated QRS wave electrocardiogram in different cases according to experimental example 1 of the present invention.
Fig. 9 shows a comparison of an auxiliary diagnosis apparatus for cardiomyopathy using an embodiment of the present invention and a conventional ROC curve of Sokolow index in experimental example 2 according to the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present invention provides an electronic device, which may be a computer device, where the computer device may be a terminal, and an internal structure diagram of the electronic device may be as shown in fig. 7. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a phenotype-based gene prioritization method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
In one embodiment, there is provided an auxiliary diagnostic apparatus for cardiomyopathy comprising a memory for storing program instructions and a processor for invoking said program instructions and when said program instructions are executed for performing the steps of the auxiliary diagnostic method for cardiomyopathy.
Fig. 1 is a schematic flow diagram of a method for auxiliary diagnosis of cardiomyopathy according to an embodiment of the present invention, where the method is applied to a terminal for example, it may be understood that the method may also be applied to a server, may also be applied to a system including the terminal and the server, and is implemented through interaction between the terminal and the server. In the present embodiment the method is performed by an auxiliary diagnostic device for cardiomyopathy. The auxiliary diagnosis method of cardiomyopathy in the embodiment comprises the following steps.
Step 100, receiving a first electrocardiogram related physical data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient.
In the embodiment of the invention, the auxiliary diagnostic equipment for the cardiomyopathy receives the first electrocardiogram related physical data, the first electrocardiogram related biological constant and the first actual target voltage value corresponding to the target patient, and further can store the received data.
The electrocardio-related body data refer to body data influencing electrocardio-voltage measured values. In an embodiment of the present invention, the first electrocardiographically related physical data refers to electrocardiographically related physical data of the target patient.
In one embodiment, the electrocardiography related physical data comprises thoracic physical data, ventricular physical data and cardiac thoracic position relation data. The data can substantially represent body figure factors that can have an effect on the cardiac voltage measurements. In one embodiment, the thoracoscopical shape data includes the thickness of the thorax and the width of the thorax; ventricular volume data includes left ventricular end-diastolic inner diameter, ventricular septum thickness, and left ventricular wall thickness; the heart thorax position relation data comprises the transverse position of the heart center in the thorax and the longitudinal position of the heart center in the thorax; the specifically defined electrocardio-related body data can be obtained according to an imaging method, and the obtaining method is simple and rapid, so that the electrocardio-related body data is convenient for clinical application.
In the embodiment of the present invention, the electrocardiograph-related biological constant specifically refers to some biological data related to the voltage of the electrocardiogram, but because the difference of the biological data among different individuals is small, such biological data is processed according to the constant in the embodiment of the present invention, thereby reducing the collection of unnecessary biological data and reducing the examination cost of the target patient. In one embodiment, the cardiac-electrical related biological constants specifically include body tissue conductivity, left and right lung tissue conductivity, and myocardial depolarization intensity. In one embodiment, the cardiorelated biological constants are literature-recommended values, such as a body tissue conductivity of 2.16mS/cm, a lung tissue conductivity of 0.389mS/cm, and a myocardial depolarization intensity of 40mV.
In the embodiment of the present invention, the target voltage value refers to a voltage value that is of interest to a clinician. For example, the RV5+ SV1 voltage value may be one of the main diagnostic conditions for electrocardiographic examination of left ventricular hypertrophy, RV5 is the electric potential of small R wave appearing on V5 lead, SV1 is the electric potential of S wave on V1 lead, and RV5+ SV1 is obtained by adding the two electric potentials.
In the embodiment of the present invention, the actual target voltage value refers to a target voltage value obtained according to an actual electrocardiographic detection device. In particular, the auxiliary diagnostic device for cardiomyopathy can be to derive the actual voltage value of interest from the measured electrocardiogram data. The first actual target voltage value refers to the actual target voltage value of the target patient.
And 200, transforming a preset standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of cardiomyopathy of the target patient.
In the embodiment of the present invention, the surface mesh model refers to a surface model represented by surface mesh, the surface mesh model is composed of mesh points and edges connecting the mesh points, the edges connecting the mesh points form a plurality of polygons by connecting different mesh points, and the entire surface mesh model is composed of a plurality of polygons. Specifically, the surface mesh model may be a triangular surface mesh model, that is, a triangle is used as a basic unit of the surface mesh model, and the vertex of the triangle is a mesh point; triangles have stronger geometric adaptability relative to quadrilaterals, can accurately fit the set boundary of an object, and can also more conveniently process topological changes caused by cutting and sewing operations.
In the embodiment of the invention, the auxiliary diagnostic equipment for the cardiomyopathy transforms a preset standard surface mesh model by utilizing the first electrocardio-related body data to obtain a target surface mesh model; three-dimensional modeling based on medical images is not needed, so that the modeling efficiency of the target surface mesh model can be greatly improved.
In one embodiment, the target surface mesh model transforms a preset standard surface mesh model by using the first electrocardiographic related physical data to obtain a target surface mesh model corresponding to the target patient, specifically: and transforming the coordinates of grid points of a preset standard surface grid model by using the first electrocardiogram related body data so as to obtain a target surface grid model corresponding to the target patient. The process belongs to modeling according to parameters (electrocardio-related body data), and the modeling efficiency is high.
In an embodiment, wherein the standard surface mesh model comprises a second thoracic surface mesh model, a second ventricular surface mesh model and a second lung surface mesh model, wherein the target representation mesh model comprises a first thoracic surface mesh model, a first ventricular surface mesh model and a first lung surface mesh model. In a human body, the thorax, the lung and the ventricle are key factors influencing an electrocardio voltage value related to the cardiomyopathy, so the surface mesh model in the embodiment of the invention mainly models the three.
Step 300, obtaining a first myocardial depolarization sequence corresponding to a target patient, and obtaining a first body surface potential of the target surface mesh model according to the first myocardial depolarization sequence, the first electrocardiogram correlation biological constant and the target surface mesh model.
In the embodiment of the present invention, the myocardial depolarization sequence refers to an activation sequence of each point on the surface of the human heart, and in the embodiment, the myocardial depolarization sequence corresponds to an activation sequence of each grid cell on the surface grid model. The order of myocardial depolarization may be considered the same for individuals with normal cardiac conduction function, and thus in one embodiment, the order of myocardial depolarization may assume a default value. Some people may have abnormalities in the conduction function of the heart, for example, some people may have bundle branch block, and the depolarization sequence of the cardiac muscle may be different, and in some embodiments, the depolarization sequence of the cardiac muscle may be confirmed according to the actual condition of the target patient. In an embodiment of the present invention, the first myocardial depolarization sequence refers to a myocardial depolarization sequence of the target patient.
In one embodiment, the auxiliary diagnostic apparatus for cardiomyopathy obtains a first myocardial depolarization sequence corresponding to a target patient, specifically: receiving an initial excitation point and a velocity constant of a target patient, wherein the velocity constant is a velocity ratio of conduction of a myocardial depolarization wave on the surface of a myocardium and in the interior of the myocardium; and obtaining the depolarization sequence of the first myocardium according to Dijkstra algorithm by using the initial excitation point, the velocity constant and the first ventricular surface mesh model. The initial activation point in the embodiment of the present invention refers to the point on the ventricle where activation is started in one cardiac cycle, and is manually selected on the target surface mesh model. In one embodiment, the initial actuation points for the default state are the actuation points dominated by the left anterior, left posterior, and right bundle branches.
In some special cases, for example, when there is an abnormality such as conduction block in the target patient, the medical staff may select an initial activation point corresponding to the actual situation of the target patient on the target surface mesh model according to the actual blocking situation of the target patient, and the auxiliary diagnosis device for cardiomyopathy derives the first myocardial depolarization sequence of the target patient on the target surface mesh model according to the manually set initial activation point, the velocity constant and the Dijkstra algorithm. In general, the ratio of the velocities (i.e., velocity constants) at which the myocardial depolarization wave propagates between the myocardial surface and the myocardial interior is 4 or 4.7.
In an embodiment of the invention, the target surface mesh model comprises a first thoracic surface mesh model, a first ventricular surface mesh model and a first lung surface mesh model, wherein the first sheet potential is equivalent to a potential on the first thoracic surface mesh model in the target surface mesh model.
And the auxiliary diagnostic equipment for the cardiomyopathy obtains the first body surface potential of the target surface grid model according to the first myocardial depolarization sequence, the first electrocardio-related biological constant and the target surface grid model, and specifically obtains the first body surface potential of the target surface grid model through calculation by using the first myocardial depolarization sequence, the first electrocardio-related biological constant and the target surface grid model through an EDL (edge-directed learning) model and a BEM (belief-based modeling) algorithm. The EDL (Equivalent Double Layer source) model equivalently converts a myocardial depolarization wave surface in a UDL (Uniform Double Layer source) model into a depolarization wave surface of a heart model (in the embodiment of the invention, a ventricular surface grid model) surface, and BEM (Boundary Element) is a Boundary Element Method commonly used in the field of electrocardio.
In the embodiment of the invention, the body surface potential can be a matrix formed by numerical values, the row number of the matrix is equal to the number of grid units in the grid model of the thoracic surface, the time period of one-time electrocardio activity is divided into a plurality of frames, and the column number of the matrix is equal to the number of the divided frames; one value in the matrix thus represents the potential value of a point on the thoracic surface represented by a certain grid cell in the thoracic surface grid model at a point in time (time period) represented by a certain frame.
Step 400, obtaining a first analog target voltage value according to the first body surface potential.
In the embodiment of the invention, the auxiliary diagnostic equipment for cardiomyopathy obtains the first analog target voltage value according to the first body surface potential, specifically, the auxiliary diagnostic equipment can extract potential values corresponding to different electrocardio leads from the first body surface potential so as to calculate the first analog target voltage value.
In one embodiment, the auxiliary diagnostic apparatus for cardiomyopathy obtaining a first analog voltage value of interest from a first body surface potential may further comprise: firstly, mapping the position of an electrocardiogram lead in the measurement process of the electrocardiogram corresponding to a first actual target voltage value to the target surface grid model to obtain the simulated electrocardiogram lead position on the target surface grid model; and then extracting electric potential corresponding to the position of the simulated electrocardio lead from the electric potential of the first body table, and calculating to obtain a first simulated target voltage value. The position of the electrocardiogram lead in the measurement process of the electrocardiogram refers to the specific position of the electrode plate of the electrocardiogram lead on the body of the target patient when the electrocardiogram of the target patient is detected.
In one embodiment, specifically, according to the grid unit corresponding to the position of the electrocardiogram lead corresponding to the first target voltage value on the target surface grid model, the electric potential value corresponding to the grid unit is searched from the matrix formed by the electric potential values, and finally, the first simulated target voltage value is obtained through calculation.
In one embodiment, to make the result more intuitive, the auxiliary diagnostic apparatus for cardiomyopathy obtaining a first analog voltage value of interest from a first body surface potential may further comprise: firstly, mapping the position of an electrocardiogram lead in the measurement process of the electrocardiogram corresponding to a first actual target voltage value to a target surface grid model to obtain the position of the electrocardiogram lead on the target surface grid model; then, according to the position of the electrocardio lead on the target surface grid model and the first body surface potential, obtaining a first simulated electrocardiogram; and finally, obtaining a first simulated target voltage value according to the first simulated electrocardiogram.
Step 500, obtaining a cardiomyopathy diagnosis result of the target patient by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy.
In an embodiment of the present invention, specifically, when the auxiliary diagnostic device for cardiomyopathy obtains a cardiomyopathy diagnostic result of the target patient by using the first simulated target voltage value and the first actual target voltage value, the method includes: comparing and calculating the first actual target voltage value and the first simulated target voltage value to obtain a comparison and calculation result; then comparing the comparison calculation result with a preset threshold value to obtain a comparison result; and finally, judging whether the target patient has the cardiomyopathy or not according to the comparison result. The comparison calculation can be directly dividing the first actual target voltage value and the first simulated target voltage value to obtain a ratio; other comparison calculations may also be selected as desired, such as subtracting the first actual target voltage value from the first simulated target voltage value, and so forth. And is not particularly limited herein.
In one embodiment, the auxiliary diagnosis device for cardiomyopathy obtains a cardiomyopathy diagnosis result of the target patient by using the first analog target voltage value and the first actual target voltage value, specifically: performing division calculation on the first actual target voltage value and the first simulated target voltage value to obtain a ratio of the first actual target voltage value to the first simulated target voltage value; then comparing the ratio with a preset threshold value to obtain a comparison result; and finally, judging whether the target patient has the cardiomyopathy or not according to the comparison result. And if the ratio is greater than or equal to the preset threshold value, the judgment that the patient has the cardiomyopathy is made.
The auxiliary diagnostic equipment for the cardiomyopathy in the embodiment of the invention executes the method, firstly, a target surface grid model obtained by transforming a standard surface grid model by utilizing first electrocardio-related body data is utilized, wherein the target surface grid model is a surface grid model individualized in the aspect of the electrocardio-related body data, and the target surface grid model embodies the body factors of a target patient but does not embody the pathological factors of the cardiomyopathy of the target patient; a first sheet potential and a first simulated target voltage value derived from the target surface mesh model are then obtained. The first actual target voltage value represents the pathological factors and physical factors of the cardiomyopathy of the target patient at the same time. In the embodiment of the invention, the shape factor in the first actual target voltage value is eliminated by utilizing the first simulated target voltage value, so that the pathological factor of the cardiomyopathy reflected by the first actual target voltage value is more truly and prominently displayed, and the accurate judgment of the cardiomyopathy is further realized.
In one embodiment, the standard surface mesh model comprises a second thoracic surface mesh model, a second ventricular surface mesh model, and a second lung surface mesh model; the first electrocardiogram related body data comprise first thoracic body data, first ventricle body data and first heart thoracic body position relation data corresponding to the target patient; as shown in fig. 2, the auxiliary diagnostic device for cardiomyopathy specifically includes the following steps when transforming a preset standard surface mesh model by using the first electrocardiographic related body data to obtain a target surface mesh model corresponding to a target patient.
Step 210, obtaining second thoracic body data, obtaining a first proportional relationship between the first thoracic body data and the second thoracic body data, and transforming the second thoracic surface mesh model by using the first proportional relationship to obtain a first thoracic surface mesh model.
In the embodiment of the present invention, second thoracic body data are obtained, a first proportional relationship between the first thoracic body data and the second thoracic body data is obtained, the second thoracic surface mesh model is transformed by using the first proportional relationship, and the first thoracic surface mesh model is obtained, specifically, coordinate transformation is performed on mesh points on the second thoracic surface mesh model according to the proportional relationship between the second thoracic body data and the first thoracic body data; stretching or compressing of the second thoracic surface mesh model may be accomplished by coordinate transformation of the mesh points to transform the second thoracic surface mesh model into a first thoracic surface mesh model corresponding to the first thoracic shape data.
In one embodiment, the thoracic physical data includes thoracic width and thickness, which can be measured by imaging, to facilitate and substantially reflect the influence of thoracic physical factors on the electrocardiographic voltage measurements. The first thorax width and the first thorax thickness represent thorax volume data of the target patient, and the second thorax width and the second thorax thickness represent thorax volume data corresponding to the standard surface mesh model.
Step 220, obtaining second ventricle shape data, obtaining a second proportional relation between the first ventricle shape data and the second ventricle shape data, and transforming the second ventricle surface mesh model by using the second proportional relation to obtain the first ventricle surface mesh model.
Step 230, performing translation transformation on the first ventricular surface mesh model according to the first heart thoracic position relation data.
In the embodiment of the invention, the heart thorax position relation data describes the specific position of the heart in the thorax. In one embodiment, the cardiac thorax positional relationship data includes a lateral position of a center of the heart in the thorax and a longitudinal position of the center of the heart in the thorax. Wherein the first heart thorax position relationship data corresponds to a specific position of the heart of the target patient in the thorax thereof.
In an embodiment, the first cardiac thoracic positional relationship data is used to translate the first cardiac surface mesh model, in particular to translate coordinates of mesh points of the first cardiac surface mesh model.
In one embodiment, the positional relationship data of the thorax of the heart is determined by the following method: firstly, when a first rectangular frame containing ventricular muscle in a medical image is taken as a maximum section, a second rectangular frame contains the outer edge of a thorax, and the specific position of the heart in the thorax is represented by the relative position of the first rectangular frame and the second rectangular frame. Specifically, as shown in fig. 3, the transverse position of the heart center of the patient in the thorax = (50.1 +0.5 + 109.9)/320.5 =0.328, and the longitudinal position of the heart center in the thorax = (106.1 +0.5 + 94.7)/224.2 =0.684, corresponding to the medical image. Therefore, the measuring method can utilize an imaging image (such as CT) to obtain the transverse position of the heart center in the thorax and the longitudinal position of the heart center in the thorax through simple calculation, skillfully correlates the positions of the heart and the thorax, and is simple and convenient to implement.
Step 240, performing translation and/or scaling transformation on the second lung surface mesh model to obtain a first lung surface mesh model, where the first lung surface mesh model and the first ventricle surface mesh model do not have an overlapping portion, and the target surface mesh model includes the first thoracic surface mesh model, the first ventricle surface mesh model and the first lung surface mesh model.
In an embodiment of the present invention, the second lung surface mesh model is subjected to translation and/or scaling transformation to obtain the first lung surface mesh model, and specifically, according to relative positions of mesh points of the second lung surface mesh model in the second thoracic surface mesh model and the second ventricular surface mesh model, the mesh points of the second lung surface mesh model are placed between the first thoracic surface mesh model and the first ventricular surface mesh model, so that the obtained first lung surface mesh model and the first ventricular surface mesh model have no overlapping part. The construction of the lung surface mesh model of the target patient in this embodiment is an operation of inserting mesh points between the first thoracic surface mesh model and the first ventricular surface mesh model. The essence of the above is to map the second lung surface mesh model in the standard surface mesh model directly into the target surface mesh model. Therefore, the first lung surface mesh model, the first ventricle surface mesh model and the first thorax surface mesh model of the target patient in the finally obtained target surface mesh model can be ensured to be reasonable in position relation, and the occurrence of mold crossing is well avoided.
In one embodiment, the first ventricular volume data comprises a first left ventricular end-diastolic inner diameter, a first ventricular septum thickness, and a first left ventricular wall thickness; the second ventricular surface mesh model comprises a second ventricular surface mesh basic model and a plurality of second ventricular surface mesh submodels, wherein the second ventricular surface mesh submodels are obtained by transforming the ventricular interval thickness or the left ventricular wall thickness of the second ventricular surface mesh basic model, only the ventricular interval thickness and/or the left ventricular wall thickness are different between different second ventricular surface mesh submodels, and other parts are the same; the second ventricular volume data corresponds to a second ventricular surface mesh basis model including a second left ventricular end-diastolic radius, a second ventricular septum thickness, and a second left ventricular wall thickness.
In one embodiment, the auxiliary cardiomyopathy diagnosing apparatus transforms the ventricular interval thickness or the left ventricular wall thickness of the second ventricular surface mesh base model to obtain a plurality of second ventricular surface mesh models, specifically: and moving the grid points corresponding to the left ventricle surface on the second ventricle surface grid basic model so as to change the interval thickness and/or the left ventricle wall thickness of the second ventricle surface grid basic model and obtain a plurality of second ventricle surface grid submodels. Wherein the left ventricular face refers to the face of the ventricular cavity that encloses the left ventricle.
As shown in fig. 4, the method specifically includes the following steps when the auxiliary diagnostic device for cardiomyopathy obtains second ventricle shape data, obtains a second proportional relationship between the first ventricle shape data and the second ventricle shape data, and transforms the second ventricle surface mesh model by using the second proportional relationship to obtain the first ventricle surface mesh model;
step 221, obtaining and summing the first left ventricular end-diastolic inner diameter, the first ventricular septum thickness and the first left ventricular wall thickness to obtain a first summed value.
Step 222, obtaining and summing a second left ventricular end-diastolic inner diameter, a second ventricular septum thickness, and a second left ventricular wall thickness to obtain a second summed value.
Step 223, using the first summation value and the second summation value, obtaining a transform coefficient, where the transform coefficient is equal to a ratio of the second summation value and the first summation value.
Step 224, obtaining the weight of each second ventricular surface mesh model by using the transformation coefficients, the first ventricular interval thickness, the first left ventricular wall thickness, the second ventricular interval thickness, the second left ventricular wall thickness, the ventricular interval thicknesses of the plurality of second ventricular surface mesh models, and the left ventricular wall thickness.
And 225, linearly combining the second ventricular surface mesh sub-models by using the weights to obtain a ventricular surface mesh transition model. In an embodiment of the present invention, the second ventricular surface mesh model comprises a plurality of second ventricular surface mesh models, and the ventricular surface mesh transition model having the first ventricular interval thickness and the first left ventricular wall thickness is obtained by linearly combining the plurality of second ventricular surface mesh models in step 225, specifically, where linearly combining the plurality of second ventricular surface mesh models refers to linearly combining coordinates of mesh points of the plurality of second ventricular surface mesh models.
Step 226, transforming the ventricular surface mesh transition model according to the transformation coefficient to obtain the first ventricular surface mesh model. In the embodiment of the present invention, the auxiliary diagnostic device for cardiomyopathy transforms the ventricular surface mesh transition model by using the transformation coefficient to obtain the first ventricular surface mesh model, specifically, performs scaling transformation on coordinates of mesh points of the ventricular surface mesh transition model.
In the embodiment of the invention, the plurality of second ventricular surface mesh submodels are linearly combined to obtain the ventricular surface transition model, and then the ventricular surface transition model is transformed to obtain the first ventricular surface mesh model. The method avoids directly converting the second ventricle surface grid basic model, and because the first ventricle surface grid model is obtained by directly converting the second ventricle surface grid basic model, very complex conversion needs to be carried out on grid points on the second ventricle surface grid basic model, and the conversion directions and the moving distances of different grid points are different, each grid point needs to be converted one by one, and the calculation process has higher requirements on equipment and consumes a large amount of time, the method in the embodiment of the invention converts the complex conversion into linear combination calculation and simple conversion, so that the method has lower requirements on the equipment, reduces the time loss of the calculation, and common hospital equipment can meet the requirements.
In one embodiment, the second ventricular surface mesh submodel is four, namely a first submodel with a left ventricular wall thickness larger than the second left ventricular wall thickness, a second submodel with a left ventricular wall thickness smaller than the second left ventricular wall thickness, a third submodel with a ventricular interval thickness larger than the second ventricular interval thickness, and a fourth submodel with a ventricular interval thickness smaller than the second ventricular interval thickness; wherein, the first submodel, the second submodel, the third submodel and the fourth submodel respectively correspond to weights W 1 、W 2 、 W 3 And W 4 Is obtained according to formulae (3) to (6); the ventricular surface mesh transition model and the first ventricular surface mesh model are obtained according to equation (1); equation (2) is used to calculate the transform coefficients;
W 1 ×C 1 +W 2 ×C 2 +W 3 ×C 3 +W 4 ×C 4 ＝C target /R size (1)
R size ＝(T target,LVEDD +T target,left +T target,inter )/(T 0,LVEDD +T 0,left +T 0,inter )(2)
W 1 +W 2 ＝0.5 (3)
W 1 +W 2 ＝0.5 (4)
W 1 ×T 1,left +W 2 ×T 2,left +0.5×T 0,left ＝T target,left /R size (5)
W 3 ×T 3,inter +W 4 ×T 4,inter +0.5×T 0,inter ＝T target,inter /R size (6)
wherein, the first and the second end of the pipe are connected with each other,
C 1 、C 2 、C 3 and C 4 Respectively representing a first submodel, a second submodel, a third submodel and a fourth submodel,
W 1 、W 2 、W 3 and W 4 Respectively representing the weights of the first submodel, the second submodel, the third submodel and the fourth submodel,
R size the representation of the transform coefficients is represented by,
C target /R size a model of the ventricular surface mesh transition is represented,
C target a first ventricular surface mesh model is represented,
T target,left representing a first left wall thickness, T target,inter The thickness of the first compartment spacing is indicated,
T target,LVEDD representing the first left ventricular end-diastolic inner diameter,
T 0,left representing a second left wall thickness, T 0,inter The second cell gap thickness is shown,
T 0,LVEDD representing the second left ventricular end-diastolic inner diameter,
T 1,left representing the left wall thickness of the first sub-model,
T 2,left representing the left wall thickness of the second submodel,
T 3,inter the cell gap thickness of the third submodel is indicated,
T 4,inter the cell gap thickness of the fourth submodel is indicated.
In one embodiment, the second ventricular septum thickness and the second left ventricular wall thickness are both 10mm, i.e. the ventricular septum thickness and the left ventricular wall thickness of the second ventricular surface mesh base model are both 10mm. The combination of the left chamber wall thickness and the chamber interval thickness of the first sub-model, the second sub-model, the third sub-model and the fourth sub-model is respectively 26mm, 10mm, 6mm, 10mm, 26mm and 10mm, 6 mm.
In one embodiment, the auxiliary diagnostic apparatus for cardiomyopathy obtains a standard surface mesh model using the method shown in fig. 5, specifically comprising:
step 501, a chest medical tomographic image of a standard human body is received.
In the embodiment of the invention, the standard human body is a real healthy human body, and particularly refers to a human body of which the cardiac ultrasound is not abnormal. The function of adopting a real healthy human body as a standard human body is to enable the obtained standard surface grid model to conform to the real anatomical structure of the human body, and the main function of the standard human body is to provide the size proportion and the position relation of each visceral organ (heart, lung and thorax). The medical tomographic image of the breast can be a nuclear magnetic image or an enhanced CT image.
Step 502, according to the chest medical tomography image, three-dimensional reconstruction of a thorax, a lung and a heart of a standard human body is performed to obtain a standard surface mesh model.
In an embodiment of the present invention, a three-dimensional reconstruction of a thorax, a lung and a heart of a standard human body is performed according to the medical thoracic tomography image to obtain a standard surface mesh model, which specifically includes: stacking a certain target organ (thorax, heart or lung) of each layer on the chest medical tomographic image, and using the vertex of a triangle to represent a voxel on the chest medical tomographic image to obtain an initial surface mesh model of the target organ with the most triangles; then, adjusting the grid density of the initial surface grid model; and finally, fine-adjusting part of unreasonable vertexes of the initial surface mesh model after the mesh density is adjusted to obtain a final standard surface mesh model of the target organ. The obtained initial surface mesh model has the most triangles, the challenge on the memory is large when the initial surface mesh model is directly transformed, and the number of meshes is reduced through mesh density adjustment, so that the calculated amount is within an acceptable range, and finally the calculated amount of the method in the embodiment of the invention is within the acceptable range of a desktop CPU (central processing unit); and the unreasonable fine adjustment of the vertexes enables the sizes and the distribution of the triangles in the surface mesh model to be more uniform, and finally the surface of the standard surface mesh model is smoother.
In one embodiment, 3D silicon software is used to form an initial surface mesh model, then Meshlab software is used to adjust mesh density, and finally Blender software is used to fine tune the surface mesh model.
In one embodiment, the plurality of second ventricular surface mesh models are obtained by adjusting the ventricular septum thickness and the left ventricular wall thickness of a standard human ventricular surface mesh model obtained by three-dimensional reconstruction. Preferably, the ventricular septum thickness and the left ventricular wall thickness of the second plurality of ventricular surface mesh models are each combinable over a plurality of different thicknesses. The standard human ventricular surface mesh model can be used as a second ventricular surface mesh basic model, and the left ventricular wall thickness and the ventricular interval thickness of the standard human ventricular surface mesh model can be adjusted on the basis of the standard human ventricular surface mesh model. In one specific embodiment, the thickness of the septum and the thickness of the left chamber wall can have three different thicknesses, namely "thick", "medium" and "thin", wherein "thick" means 26mm thick, "medium" means 10mm thick, and "thin" means 4mm thick; the number of the second ventricle surface grid submodels is four, and the combination of the left ventricle wall thickness and the ventricular interval thickness is 'thick', medium ',' thin ', medium', 'medium, thick', 'medium, thin'; the combination of the left wall thickness and the ventricular septum thickness of the second ventricular surface mesh base model is "medium, medium". The combination of the thickness of the second ventricular surface mesh model, namely the thickness of the left ventricular wall and the ventricular septum of most people can be covered by linear combination, namely, the target ventricular surface mesh model with any ventricular septum thickness and left ventricular wall thickness can be obtained by linearly combining a plurality of standard ventricular surface mesh models. Specifically, in one embodiment, blender software is used to modify grid points on a ventricular surface of a standard human ventricular surface grid model obtained by three-dimensional reconstruction to obtain a plurality of second ventricular surface grid sub-models.
In some embodiments, existing surface mesh models of the thorax, the lung, and the ventricle may also be used as standard surface mesh models in the embodiments of the present invention, which are not limited in particular.
In one embodiment, as shown in fig. 6, there is provided an auxiliary diagnostic apparatus for cardiomyopathy, comprising: a data receiving unit 601, a target surface mesh model acquiring unit 602, a body surface potential acquiring unit 603, a target voltage value acquiring unit 604, and a detection result acquiring unit 605; wherein:
a data receiving unit 601, configured to receive first electrocardiographic-related physical data, a first electrocardiographic-related biological constant, and a first actual target voltage value corresponding to a target patient;
a target surface mesh model obtaining unit 602, configured to transform a preset standard surface mesh model by using the first electrocardiographic related physical data to obtain a target surface mesh model corresponding to a target patient, where the target surface mesh model represents a physical factor of the target patient but does not represent a pathological factor of cardiomyopathy of the target patient;
a body surface potential obtaining unit 603, configured to obtain a first myocardial depolarization sequence corresponding to a target patient, and obtain a first body surface potential of the target surface mesh model according to the first myocardial depolarization sequence, the first electrocardiographic-related biological constant, and the target surface mesh model;
an analog target voltage value obtaining unit 604 for obtaining a first analog target voltage value according to the first body potential; and
the detection result obtaining unit 605 obtains a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy.
For specific definition of the apparatus for auxiliary diagnosis of cardiomyopathy, reference may be made to the above definition of the apparatus and method for auxiliary diagnosis of cardiomyopathy, which are not described in detail herein. The units in the auxiliary diagnostic device for cardiomyopathy can be wholly or partially realized by software, hardware and a combination thereof. The units may be embedded in hardware or independent from a processor in the computer device, or may be stored in a memory in the computer device in software, so that the processor can call and execute operations corresponding to the units.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of:
receiving first electrocardiogram related physical data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient;
transforming a preset standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of cardiomyopathy of the target patient;
acquiring a first myocardial depolarization sequence corresponding to a target patient, and acquiring a first body surface potential of a target surface grid model according to the first myocardial depolarization sequence, the first electrocardiogram related biological constant and the target surface grid model;
obtaining a first analog target voltage value according to the first body table potential; and
acquiring a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy.
Experimental example 1 QRS wave ECG simulation calculation for different transmission blocking conditions
In this experimental example, a healthy human body (heart ultrasound is not abnormal) is selected, and the specific body data includes: the width of the thorax is 309.6mm, the thickness of the thorax is 207.8mm, the transverse orientation of the heart (the measuring method is shown in figure 3 and the description) is 0.407, the longitudinal orientation of the heart (the measuring method is shown in figure 3 and the description) is 0.646, the ventricular septal thickness is 7mm, the posterior wall thickness of the left ventricle is 7mm, and the internal diameter of the left ventricular end diastole is 42mm.
The method and the device in the embodiment of the invention are utilized to respectively obtain the target surface mesh models, then the body surface potentials are respectively calculated under the condition that the heart is normal in conduction and different conduction blocks exist, and then the simulated QRS wave electrocardiograms under different conditions are drawn according to the calculation result of the body surface potentials, which is specifically shown in figure 8.
In fig. 8, a column suffixed to normal corresponds to cardiac conduction normality, a column suffixed to CLBBB corresponds to a case where there is a complete left bundle branch block, a column suffixed to LABB corresponds to a case where there is a left anterior bundle block, a column suffixed to LPBB corresponds to a case where there is a left posterior branch block, and a column suffixed to RBBB corresponds to a case where there is a complete right bundle branch block.
As can be seen from FIG. 8, the individual surface mesh model obtained by the method in the embodiment of the present invention has a high degree of simulation for QRS wave electrocardiograms of different heart block transmission conditions.
Experimental example 2 verification of diagnostic Effect
In this experimental example, for 60 amyloid cardiomyopathy patients and 60 healthy patients, the cardiomyopathy diagnosis and verification of all patients and all healthy patients are performed by using the auxiliary cardiomyopathy diagnosis device and the conventional Sokolow index in the embodiment of the present invention, respectively. An ROC curve of the auxiliary diagnostic equipment for cardiomyopathy and the Sokolow index in the embodiment of the present invention is shown in fig. 9. Therefore, the auxiliary diagnostic equipment for the cardiomyopathy in the embodiment of the invention has a high diagnostic effect and is obviously due to the traditional Sokolow index.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by hardware that is instructed to implement by a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
While the invention has been described in detail with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (9)

1. An auxiliary diagnostic apparatus for cardiomyopathy, the apparatus comprising: a memory and a processor;
the memory to store program instructions;
the processor, configured to invoke the program instructions, and when the program instructions are executed, configured to:
receiving first electrocardiogram related body data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient; the first electrocardiogram related body data comprise first thoracic body data, first ventricle body data and first heart thoracic position relation data corresponding to a target patient, and the first electrocardiogram related biological constants specifically comprise body tissue conductivity, left and right lung tissue conductivity and myocardial depolarization intensity;
transforming a preset standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of cardiomyopathy of the target patient; wherein the standard surface mesh model comprises a second thoracic surface mesh model, a second ventricular surface mesh model, and a second lung surface mesh model; the target surface mesh model comprises a first thoracic surface mesh model, a first ventricular surface mesh model, and a first lung surface mesh model;
acquiring a first myocardial depolarization sequence corresponding to a target patient, and acquiring a first body surface potential of a target surface grid model according to the first myocardial depolarization sequence, the first electrocardiogram correlation biological constant and the target surface grid model;
obtaining a first analog target voltage value according to the first integral potential; and
acquiring a cardiomyopathy diagnosis result of the target patient by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy;
when the preset standard surface mesh model is transformed by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to a target patient, the method is specifically used for:
acquiring second thoracic body data, acquiring a first proportional relation between the first thoracic body data and the second thoracic body data, and transforming the second thoracic surface mesh model by using the first proportional relation to acquire a first thoracic surface mesh model;
acquiring second ventricle shape data, acquiring a second proportional relation between the first ventricle shape data and the second ventricle shape data, and transforming the second ventricle surface mesh model by using the second proportional relation to obtain a first ventricle surface mesh model;
according to the first heart thorax position relation data, carrying out translation transformation on the first heart chamber surface mesh model; and
and performing translation and/or scaling transformation on the second lung surface mesh model to obtain a first lung surface mesh model, wherein the first lung surface mesh model has no overlapping part with the first ventricle surface mesh model, and the target surface mesh model comprises the first thoracic surface mesh model, the first ventricle surface mesh model and the first lung surface mesh model.
2. The aided diagnosis apparatus of cardiomyopathy of claim 1 wherein said first ventricular volume data comprises a first left ventricular end-diastolic inner diameter, a first ventricular septum thickness, and a first left ventricular wall thickness; the second ventricular surface mesh model comprises a second ventricular surface mesh basic model and a plurality of second ventricular surface mesh submodels, wherein the second ventricular surface mesh submodels are obtained by transforming the ventricular interval thickness or the left ventricular wall thickness of the second ventricular surface mesh basic model, only the ventricular interval thickness and/or the left ventricular wall thickness are different between different second ventricular surface mesh submodels, and other parts are the same; the second ventricular volume data corresponds to a second ventricular surface mesh basis model, including a second left ventricular end-diastolic inner diameter, a second ventricular septum thickness, and a second left ventricular wall thickness;
the processor obtains second ventricle shape data, obtains a second proportional relationship between the first ventricle shape data and the second ventricle shape data, and transforms the second ventricle surface mesh model by using the second proportional relationship, so as to obtain the first ventricle surface mesh model, specifically configured to:
obtaining and summing the first left ventricular end-diastolic inner diameter, the first ventricular septum thickness and the first left ventricular wall thickness to obtain a first summed value;
obtaining and summing a second left ventricular end-diastolic inner diameter, a second ventricular septum thickness and a second left ventricular wall thickness to obtain a second summed value;
obtaining a transform coefficient using the first sum value and the second sum value, wherein the transform coefficient is equal to a ratio of the second sum value and the first sum value;
obtaining a weight for each second ventricular surface mesh model using the transform coefficients, the first ventricular interval thickness, the first left ventricular wall thickness, the second ventricular interval thickness, the second left ventricular wall thickness, the ventricular interval thicknesses of the plurality of second ventricular surface mesh models, and the left ventricular wall thickness;
linearly combining the plurality of second ventricular surface mesh submodels by using the weights to obtain a ventricular surface mesh transition model; and
and transforming the ventricular surface mesh transition model according to the transformation coefficient to obtain the first ventricular surface mesh model.
3. The cardiomyopathy aided diagnosis apparatus of claim 2, wherein the second ventricular surface mesh submodel comprises four, namely a first submodel having a left ventricular wall thickness greater than the second left ventricular wall thickness, a second submodel having a left ventricular wall thickness less than the second left ventricular wall thickness, a third submodel having a ventricular septum thickness greater than the second ventricular septum thickness, and a fourth submodel having a ventricular septum thickness less than the second ventricular septum thickness;
wherein, the weights W corresponding to the first submodel, the second submodel, the third submodel and the fourth submodel respectively 1 、W 2 、W 3 And W 4 Is obtained according to formula (3) to formula (6); the ventricular surface mesh transition model and the first ventricular surface mesh model are obtained according to equation (1);
W 1 ×C 1 + W 2 ×C 2 + W 3 ×C 3 + W 4 ×C 4 =C target /R size (1)
W 1 +W 2 =0.5 (3)
W 1 +W 2 =0.5 (4)
W 1 ×T 1,left +W 2 ×T 2,left +0.5×T 0,left =T target,left /R size (5)
W 3 ×T 3,inter +W 4 ×T 4,inter +0.5×T 0,inter =T target,inter /R size (6)
wherein the content of the first and second substances,
C 1 、C 2 、C 3 and C 4 Respectively representing a first submodel, a second submodel, a third submodel and a fourth submodel,
W 1 、W 2 、W 3 and W 4 Respectively representing the weights of the first submodel, the second submodel, the third submodel and the fourth submodel,
R size the representation of the transform coefficients is represented by,
C target /R size a model of the ventricular surface mesh transition is represented,
C target a first ventricular surface mesh model is represented,
T target,left representing a first left wall thickness, T target,inter The thickness of the first compartment spacing is indicated,
T 0,left representing a second left wall thickness, T 0,inter The second cell gap thickness is shown,
T 1,left representing the left wall thickness of the first sub-model,
T 2,left representing the left wall thickness of the second submodel,
T 3,inter the cell gap thickness of the third submodel is indicated,
T 4,inter the cell gap thickness of the fourth submodel is indicated.
4. A diagnostic aid for cardiomyopathy according to claim 2 wherein said second ventricular septum thickness and said second left ventricular wall thickness are both 10mm; the number of the second ventricular surface mesh models is four, and the combination of the left ventricular wall thickness and the ventricular interval thickness of the four second ventricular surface mesh models is respectively 26mm, 10mm, 6mm, 10mm, 26mm and 10mm, 6 mm.
5. The cardiomyopathy auxiliary diagnostic apparatus of claim 1, wherein the processor is specifically configured to, when obtaining a first myocardial depolarization sequence corresponding to a target patient:
receiving an initial excitation point and a velocity constant of a target patient, wherein the velocity constant is a velocity ratio of conduction of a myocardial depolarization wave on the surface of a myocardium and in the interior of the myocardium; and
and obtaining the depolarization sequence of the first myocardium according to a Dijkstra algorithm by using the initial excitation point, the velocity constant and the first ventricular surface grid model.
6. The aided diagnosis apparatus of cardiomyopathy of claim 1 wherein said first thoracoscopic data includes a first thoracic width and a first thoracic thickness and said second thoracoscopic data includes a second thoracic width and a second thoracic thickness.
7. The cardiomyopathy auxiliary diagnosing apparatus of any one of claims 1 to 6, wherein when the processor obtains a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, the processor is specifically configured to:
comparing and calculating the first actual target voltage value and the first simulated target voltage value to obtain a comparison and calculation result;
comparing the comparison calculation result with a preset threshold value to obtain a comparison result; and
and judging whether the target patient has cardiomyopathy or not according to the comparison result.
8. An auxiliary diagnostic apparatus for cardiomyopathy, comprising:
the data receiving unit is used for receiving first electrocardiogram related body data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient; the first electrocardiogram related body data comprise first thoracic body data, first ventricle body data and first heart thoracic position relation data corresponding to a target patient, and the first electrocardiogram related biological constants specifically comprise body tissue conductivity, left and right lung tissue conductivity and myocardial depolarization intensity;
a target surface mesh model obtaining unit, configured to transform a preset standard surface mesh model by using the first electrocardiographic related physical data to obtain a target surface mesh model corresponding to a target patient, where the target surface mesh model represents a physical factor of the target patient but does not represent a pathological factor of cardiomyopathy of the target patient; wherein the standard surface mesh model comprises a second thoracic surface mesh model, a second ventricular surface mesh model and a second lung surface mesh model, and the target surface mesh model comprises a first thoracic surface mesh model, a first ventricular surface mesh model and a first lung surface mesh model; when the preset standard surface mesh model is transformed by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to a target patient, the method is specifically used for:
acquiring second thoracic body data, acquiring a first proportional relation between the first thoracic body data and the second thoracic body data, and transforming the second thoracic surface mesh model by using the first proportional relation to acquire a first thoracic surface mesh model;
acquiring second ventricle shape data, acquiring a second proportional relation between the first ventricle shape data and the second ventricle shape data, and transforming the second ventricle surface mesh model by using the second proportional relation to obtain a first ventricle surface mesh model;
according to the first heart thorax position relation data, carrying out translation transformation on the first heart chamber surface mesh model; and
performing translation and/or scaling transformation on the second lung surface mesh model to obtain a first lung surface mesh model, wherein the first lung surface mesh model has no overlapping part with the first ventricle surface mesh model, and the target surface mesh model comprises the first thoracic surface mesh model, the first ventricle surface mesh model and the first lung surface mesh model;
the body surface potential acquisition unit is used for acquiring a first myocardial depolarization sequence corresponding to a target patient and acquiring a first body surface potential of the target surface grid model according to the first myocardial depolarization sequence, the first electrocardio-related biological constant and the target surface grid model;
the simulation target voltage value acquisition unit is used for acquiring a first simulation target voltage value according to the first body table potential; and
and the diagnosis result obtaining unit is used for obtaining a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of:
receiving first electrocardiogram related body data, a first electrocardiogram related biological constant and a first actual target voltage value corresponding to a target patient; the first electrocardiogram related body data comprise first thoracic body data, first ventricle body data and first heart thoracic position relation data corresponding to a target patient, and the first electrocardiogram related biological constants specifically comprise body tissue conductivity, left and right lung tissue conductivity and myocardial depolarization intensity;
transforming a preset standard surface mesh model by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to the target patient, wherein the target surface mesh model reflects the body factors of the target patient but does not reflect the pathological factors of the cardiomyopathy of the target patient; wherein the standard surface mesh model comprises a second thoracic surface mesh model, a second ventricular surface mesh model and a second lung surface mesh model, and the target surface mesh model comprises a first thoracic surface mesh model, a first ventricular surface mesh model and a first lung surface mesh model;
acquiring a first myocardial depolarization sequence corresponding to a target patient, and acquiring a first body surface potential of a target surface grid model according to the first myocardial depolarization sequence, the first electrocardiogram related biological constant and the target surface grid model;
obtaining a first analog target voltage value according to the first body table potential; and
acquiring a cardiomyopathy diagnosis result by using the first simulated target voltage value and the first actual target voltage value, wherein the diagnosis result reflects whether the target patient has cardiomyopathy;
when the preset standard surface mesh model is transformed by using the first electrocardiogram related body data to obtain a target surface mesh model corresponding to a target patient, the method is specifically used for:
acquiring second thoracic body data, acquiring a first proportional relation between the first thoracic body data and the second thoracic body data, and transforming the second thoracic surface mesh model by using the first proportional relation to obtain a first thoracic surface mesh model;
acquiring second ventricle shape data, acquiring a second proportional relation between the first ventricle shape data and the second ventricle shape data, and transforming the second ventricle surface mesh model by using the second proportional relation to obtain a first ventricle surface mesh model;
according to the first heart thorax position relation data, carrying out translation transformation on the first heart chamber surface mesh model; and
and performing translation and/or scaling transformation on the second lung surface mesh model to obtain a first lung surface mesh model, wherein the first lung surface mesh model has no overlapping part with the first ventricle surface mesh model, and the target surface mesh model comprises the first thoracic surface mesh model, the first ventricle surface mesh model and the first lung surface mesh model.
CN202110358329.5A 2021-04-01 2021-04-01 Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy Active CN113288167B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110358329.5A CN113288167B (en) 2021-04-01 2021-04-01 Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110358329.5A CN113288167B (en) 2021-04-01 2021-04-01 Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy

Publications (2)

Publication Number Publication Date
CN113288167A CN113288167A (en) 2021-08-24
CN113288167B true CN113288167B (en) 2022-12-23

Family

ID=77319371

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110358329.5A Active CN113288167B (en) 2021-04-01 2021-04-01 Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy

Country Status (1)

Country Link
CN (1) CN113288167B (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
PT103448A (en) * 2006-03-10 2006-07-31 Inst Superior Tecnico NEW MODEL OF TEMPORAL REPRESENTATION OF THE SIGN OF AN ELECTROCARDIOGRAM TRACING 6 GENERALIZED GAUSSIANS

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2058554C1 (en) * 1992-07-17 1996-04-20 Украинский научно-исследовательский институт кардиологии им.акад.Н.П.Страженко Method for diagnostication of cardiac insufficiency in patients ill with hypertrophic cardiomyopathy
EP2785280A2 (en) * 2011-12-02 2014-10-08 Rainer Zotz A device for performing diagnostics and/or therapy
JP6338055B2 (en) * 2014-06-25 2018-06-06 富士通株式会社 Visualization device, visualization method, and visualization program
CN110706803B (en) * 2019-08-20 2023-06-27 南京医基云医疗数据研究院有限公司 Method and device for determining myocardial fibrosis, readable medium and electronic equipment

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
PT103448A (en) * 2006-03-10 2006-07-31 Inst Superior Tecnico NEW MODEL OF TEMPORAL REPRESENTATION OF THE SIGN OF AN ELECTROCARDIOGRAM TRACING 6 GENERALIZED GAUSSIANS

Also Published As

Publication number Publication date
CN113288167A (en) 2021-08-24

Similar Documents

Publication Publication Date Title
US10970926B2 (en) System and method for lung-volume-gated x-ray imaging
RU2409313C2 (en) Method for noninvasive electrophysiological examination of heart
RU2435518C2 (en) Method of non-invasive electrophysiological examination of heart
Sermesant et al. Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation
US20190051419A1 (en) System and Method for Characterization of Electrical Properties of the Heart from Medical Images and Body Surface Potentials
US10299692B2 (en) Systems, components, devices and methods for cardiac mapping using numerical reconstruction of cardiac action potentials
WO2020136569A1 (en) Method and system to characterize disease using parametric features of a volumetric object and machine learning
EP2189945A1 (en) Method of and arrangement for linking image coordinates to coordinates of reference model
CN108324263B (en) Noninvasive cardiac electrophysiology inversion method based on low-rank sparse constraint
Prakosa et al. Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology
Sermesant et al. Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging
US11950940B2 (en) System and method for determining radiation parameters
Vassilevski et al. Patient-specific anatomical models in human physiology
JP3660781B2 (en) Diagnostic device for intracardiac electrical phenomena
US20040249314A1 (en) Tempero-spatial physiological signal detection method and apparatus
Zacur et al. MRI-based heart and torso personalization for computer modeling and simulation of cardiac electrophysiology
CN110393522B (en) Non-invasive heart electrophysiological inversion method based on total variation constraint of graph
Kania et al. Prediction of the exit site of ventricular tachycardia based on different ECG lead systems
CN113288167B (en) Auxiliary diagnosis equipment, device and computer readable storage medium for cardiomyopathy
Sermesant et al. Personalized computational models of the heart for cardiac resynchronization therapy
Miri et al. Applicability of body surface potential map in computerized optimization of biventricular pacing
CA3214686A1 (en) Tracking segmental movement of the heart using tensors
Amelon Development and characterization of a finite element model of lung motion
Rasoolzadeh SOLVING THE INVERSE PROBLEM OF ELECTROCARDIOGRAPHY FOR SPONTANEOUS PVC LOCALIZATION: ANALYSIS OF CLINICAL ELECTROCARDIOGRAPHIC DATA
Crabb et al. Biventricular Statistical Shape Atlas of Unloaded Reference Geometries: A Novel Method to Control for Hemodynamic Variations in End-Diastolic Pressure

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant