CN112150454B - Aortic dissection assessment method, device, equipment and storage medium - Google Patents
Aortic dissection assessment method, device, equipment and storage medium Download PDFInfo
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- 208000002251 Dissecting Aneurysm Diseases 0.000 title claims abstract description 84
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Abstract
The embodiment of the invention discloses an aortic dissection evaluation method, device, equipment and storage medium. Acquiring an image to be processed and clinical information of a current object, and constructing a geometric model comprising a target segmentation part according to the image to be processed; performing grid division on the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and clinical information, and performing hemodynamic simulation based on the boundary conditions; verifying the hemodynamic simulation result according to clinical information; when the hemodynamic simulation result fails verification, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition; and taking the blood dynamics simulation result passing the verification as an evaluation result of aortic dissection. The aortic dissection is evaluated from the aspect of hemodynamics, and the accuracy of an aortic dissection evaluation result is improved.
Description
Technical Field
The embodiment of the invention relates to an image processing technology, in particular to an aortic dissection evaluation method, device, equipment and storage medium.
Background
Aortic dissection (Aortic dissection, AD for short) generally refers to a case where blood in the aortic lumen enters the middle membranous layer or middle membranous junction of the aorta through an intima tearing port on the aortic wall, so that the aortic wall is torn into two layers to form a true lumen and a false lumen, and the aortic dissection extends along the longitudinal axis of the aorta, which can lead to death of a patient due to aortic rupture in a short period, or stenosis or even occlusion due to the fact that the dissection true lumen is pressed by the false lumen, and ischemic changes occur in important viscera blood supply of the true lumen, so that serious complications are caused.
In the prior art, blood vessel analysis software is generally adopted to perform blood vessel analysis, such as accurate extraction, complete bone removal, rapid automatic measurement and the like on fine four-level blood vessels. Medical image analysis software systems typically employ image segmentation techniques in analysis of blood vessels, and image display techniques to morphologically three-dimensional simulated reconstruction of blood vessels of a respective patient. The doctor analyzes and processes the lesion degree by aiming at the indexes (such as the vascular stenosis degree, the false cavity expansion degree, the tearing port site size and the like) on the vascular morphology, but the available information is single, and the nature of vascular lesions cannot be intuitively reflected.
Disclosure of Invention
The embodiment of the invention provides an aortic dissection evaluation method, device, equipment and storage medium, which are used for evaluating aortic dissection from the aspect of blood flow dynamics and improving the accuracy and reliability of an aortic dissection evaluation result.
In a first aspect, an embodiment of the present invention provides an aortic dissection evaluation method, including:
acquiring an image to be processed and clinical information of a current object;
constructing a geometric model comprising a target segmentation part according to the image to be processed, wherein the target segmentation part comprises an ascending aorta and a descending aorta;
meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions;
verifying the hemodynamic simulation result according to the clinical information;
when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions;
And taking the blood flow dynamics simulation result passing the verification as an aortic dissection evaluation result.
In a second aspect, an embodiment of the present invention further provides an aortic dissection evaluation device, including:
the data acquisition module is used for acquiring the image to be processed and clinical information of the current object;
a geometric model construction module for constructing a geometric model comprising a target segmentation part according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches;
the boundary condition determining module is used for meshing the geometric model, determining an inlet and at least one outlet of the geometric model, and determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information;
a hemodynamic simulation module for performing hemodynamic simulation based on the boundary condition;
the verification module is used for verifying the hemodynamic simulation result according to the clinical information;
the boundary condition adjustment module is used for adjusting the boundary condition of the geometric model when the hemodynamic simulation result fails to pass verification, and carrying out hemodynamic simulation on the geometric model based on the adjusted boundary condition;
And the evaluation result determining module is used for taking the verified hemodynamic simulation result as an aortic dissection evaluation result.
In a third aspect, an embodiment of the present invention further provides an aortic dissection evaluation device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the aortic dissection evaluation method according to any one of the first aspects when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, implement the aortic dissection evaluation method according to any of the first aspects.
According to the technical scheme, an image to be processed and clinical information of a current object are obtained, and a geometric model comprising a target segmentation part is constructed according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches; meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions; verifying the hemodynamic simulation result according to the clinical information; when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions; and taking the blood flow dynamics simulation result passing the verification as an aortic dissection evaluation result. Solves the problem that the prior art can only obtain the index on the morphology of the blood vessel and can not intuitively reflect the essence of the vascular lesion. The purpose of evaluating the aortic dissection from the aspect of blood flow dynamics is achieved, and accuracy and reliability of an aortic dissection evaluation result are improved.
Drawings
FIG. 1 is a flowchart of an aortic dissection evaluation method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an aortic dissection evaluation method according to an embodiment of the invention;
fig. 3 is a flowchart of an aortic dissection evaluation method according to a second embodiment of the invention;
fig. 4 is a schematic structural diagram of an aortic dissection evaluation device according to a third embodiment of the invention;
fig. 5 is a schematic structural diagram of an aortic dissection evaluation device according to a fourth embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a schematic flow chart of an aortic dissection evaluation method according to an embodiment of the present invention, which is applicable to a case of performing hemodynamic simulation on a geometric model constructed according to an image to be processed and determining an evaluation result of aortic dissection according to the hemodynamic simulation result, the method may be performed by an aortic dissection evaluation device, wherein the system may be implemented by software and/or hardware and is generally integrated in an aortic dissection evaluation apparatus. Referring specifically to fig. 1, the method may include the steps of:
S110, acquiring a to-be-processed image and clinical information of the current object.
The current object may be a subject or a scanned portion of the subject, for example, a chest or head portion of the subject. The image to be processed may be a magnetic resonance image, a digitized X-ray subtraction image, an ultrasound image, etc. of the current object. In this embodiment, the image to be processed is an aortic image, and the clinical information may be clinical data corresponding to the current object, and may include information such as aortic flow rate at chest and abdomen, blood pressure value, aortic and ultrasonic flow rate of each branch. The blood pressure may be calculated from the systolic and diastolic blood pressure.
S120, constructing a geometric model comprising the target segmentation part for the image to be processed.
Wherein the target segmentation site comprises an aorta and at least a portion of an aortic branch. The aorta and at least part of the aortic branches may each have a false lumen, i.e. the ascending and descending aorta of the target segmentation may each have a dissection. In the embodiment, a geometric model is constructed on an image to be processed, and hemodynamic simulation is performed on the geometric model to perform aortic dissection evaluation. The present embodiment can construct a geometric model including the target segmentation site by the model construction section. Optionally, the constructing a geometric model including the target segmentation part according to the image to be processed includes: inputting the image to be processed into a pre-trained aorta extraction model to obtain an aorta model and a target positioning site, wherein the aorta extraction model is obtained by training a sample aorta model and the sample positioning site; and dividing the aorta model at the target positioning site of the aorta model to obtain the geometric model.
The aortic extraction model may include, but is not limited to, a vector machine algorithm (Support Vector Machine, SVM), a Long Short-Term Memory (LSTM), a logistic regression model (Logistics Regression, LR), a full convolution Network (Fully Convolutional Networks, FCN), a cyclic convolution Network (Recurrent Neural Network, RNN), a Residual Network (ResNet), and the like. The present embodiment may segment the aortic model at least one target location site according to a clicking operation obtained from the outside, or the aortic dissection evaluation device automatically segments the aortic model at the target location site of the aortic model according to a feature analysis result by performing feature analysis on the aortic model at each target location site, where the feature analysis result may include information such as a shape of a blood vessel at the target location site and a position of the target location site. In this embodiment, the geometric model may be obtained by segmenting specific sites (such as a tear, a false lumen site, an aortic arch site, etc.) or non-specific sites of the aortic model according to a thresholding method, a region growing algorithm, or the like. The aortic model may be an aortic dissection model. The aorta model can be accurately output through the aorta extraction model, and the target positioning site can be accurately positioned, so that the accurate geometric model can be obtained.
S130, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and clinical information, and performing hemodynamic simulation based on the boundary conditions.
Optionally, the target segmentation site may be partitioned into a structured grid and/or an unstructured grid according to a grid partition parameter, wherein the grid partition parameter comprises a grid partition size, a grid quality index, and an optimization iteration number.
For a false lumen site on a target segmentation part, the vascular state at the false lumen site is complex, a smaller grid division size and a larger grid quality index can be set, and a larger optimization iteration number is set; for non-false cavity sites on the target segmentation part, larger grid division size, smaller grid quality index and smaller optimization iteration times can be set. Optionally, a plurality of vascular boundary layers may be disposed on the vascular wall surface on the unstructured grid in the embodiment, and the inlet/outlet tangential plane and the vascular wall area of the target segmentation part are adjusted by adjusting the grid dividing parameters and the vascular boundary layers.
Optionally, the determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information includes: determining a blood pressure value according to the clinical information, and determining a cardiac output according to the image to be processed and the clinical information; based on the cardiac output and the blood pressure values, and based on a boundary condition model, determining boundary conditions of an inlet and all outlets of the geometric model, wherein the boundary condition model is determined according to a three-dimensional geometry of the geometric model, and the outlets comprise at least one of a thoracic-abdominal descending aorta outlet, a innominate artery outlet, a subclavian vein outlet, and an outlet of a common carotid artery of the geometric model.
As described in the previous steps, the clinical information may include information such as the aortic flow rate at the chest and abdomen, blood pressure values, the aortic and branch ultrasound flow rates. The blood pressure value may be calculated from the systolic and diastolic blood pressure. And carrying out feature analysis on the clinical information and the image to be processed, and determining the cardiac output and the information such as the heart displacement, the heart rate, the myocardial quality and the like of each beat. Specifically, taking cardiac output as an inlet flow of a geometric model, and determining all outlet flows based on a boundary condition model and the inlet flow; determining an inlet velocity based on the cross-sectional area or cross-sectional radius of the aorta in combination with the inlet flow; determining all outlet velocities based on the branch size of the aortic branch in combination with all outlet flows; and determining boundary conditions of an inlet of the geometric model according to the inlet flow and/or the inlet speed and the blood pressure value, and determining boundary conditions of an outlet of the geometric model according to the outlet flow and/or the outlet speed and the blood pressure value.
Optionally, the present embodiment may further determine boundary conditions of the inlet and all outlets of the geometric model based on the cardiac output, the blood pressure value and the flow distribution ratio. The flow distribution ratio may be a fixed distribution ratio. Specifically, taking cardiac output as an inlet flow of a geometric model, and determining all outlet flows based on a flow distribution proportion and the inlet flow; determining an inlet velocity based on the cross-sectional area or cross-sectional radius of the aorta in combination with the inlet flow; determining all outlet velocities based on the branch size of the aortic branch in combination with all outlet flows; and determining boundary conditions of an inlet of the geometric model according to the inlet flow and/or the inlet speed and the blood pressure value, and determining boundary conditions of an outlet of the geometric model according to the outlet flow and/or the outlet speed and the blood pressure value.
Optionally, the method of hemodynamic simulation is: and carrying out hemodynamic simulation on the target segmentation part based on the set boundary conditions and combining with a conservation law to obtain a hemodynamic simulation result, wherein the hemodynamic simulation result comprises a blood pressure difference, a flow velocity field and wall shear stress. Alternatively, the conservation law includes energy conservation law, mass conservation law, and the like. In this embodiment, a low-order coupling model of a blood vessel may be obtained, and the low-order coupling model is used as a boundary condition of the geometric model, and the hemodynamic simulation is performed on the target segmentation part by using the combination conservation law, so as to obtain a hemodynamic simulation result.
And S140, verifying the hemodynamic simulation result according to the clinical information.
And S150, when the hemodynamic simulation result fails to pass the verification, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition.
Optionally, a hemodynamic simulation threshold may be preset, the hemodynamic simulation result is compared with clinical information, and if the difference between the hemodynamic simulation result and the clinical information is smaller than the hemodynamic simulation threshold, it is determined that the hemodynamic simulation result passes verification; if the hemodynamic simulation result exceeds the hemodynamic simulation threshold, determining that the hemodynamic simulation result fails to verify, readjusting the boundary conditions of the geometric model, and carrying out hemodynamic simulation on the geometric model again according to the adjusted boundary conditions and by combining the conservation law until the difference between the redetermined hemodynamic simulation result and clinical information is smaller than the hemodynamic simulation threshold, determining that the hemodynamic simulation result passes the verification, and carrying out hemodynamic simulation on the target segmentation part by combining the conservation law to obtain the hemodynamic simulation result.
And S160, taking the blood dynamics simulation result passing the verification as an evaluation result of aortic dissection.
In combination with the logic diagram of aortic dissection evaluation shown in fig. 2, the evaluation result of aortic dissection can be determined according to the acquired site or three-dimensional coordinate of the region of interest. The specific method comprises the following steps: acquiring a three-dimensional coordinate point or a spatial locus of a target segmentation part aiming at the geometric model; and determining the interesting sites and the hemodynamic simulation results of the interesting sites on the geometric model according to the three-dimensional coordinate points or the spatial sites, and taking the hemodynamic simulation results of all interesting sites as the evaluation results of the aortic dissection. The three-dimensional coordinate point can be a central point of the grid in the previous step, the space locus can be any pixel point of the target segmentation part, and the corresponding interesting locus and the blood flow pressure difference, the flow velocity field, the wall shear stress and other blood flow dynamics simulation results of the interesting locus are determined on the combined geometric model according to the three-dimensional coordinate point and the space locus.
According to the technical scheme, an image to be processed and clinical information of a current object are obtained, and a geometric model comprising a target segmentation part is constructed according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches; meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions; verifying the hemodynamic simulation result according to the clinical information; when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions; and taking the blood flow dynamics simulation result passing the verification as an aortic dissection evaluation result. Solves the problem that the prior art can only obtain the index on the morphology of the blood vessel and can not intuitively reflect the essence of the vascular lesion. The purpose of evaluating the aortic dissection from the aspect of blood flow dynamics is achieved, and accuracy and reliability of an aortic dissection evaluation result are improved.
Example two
Fig. 3 is a flowchart of an aortic dissection evaluation method according to a second embodiment of the invention. The technical solution of this embodiment is refined on the basis of the foregoing embodiment, and optionally, when the hemodynamic simulation result fails to be verified, the adjusting the boundary condition of the geometric model includes: comparing the hemodynamic simulation result with the clinical information to determine a result difference value between the hemodynamic simulation result and the clinical information; and adjusting boundary conditions of the geometric model based on a corresponding relation between a predetermined result difference value and an adjustment value of the geometric model, wherein the corresponding relation is obtained by training an original parameter adjustment model according to a sample simulation result, sample clinical information and a sample adjustment value. For parts which are not described in detail in this method embodiment, reference is made to the above-described embodiments. Referring specifically to fig. 3, the method may include the steps of:
s210, acquiring a to-be-processed image and clinical information of the current object.
S220, constructing a geometric model comprising the target segmentation part according to the image to be processed.
Wherein the target segmentation site comprises an active vascular lumen, an aorta and an aortic branch.
S230, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and clinical information, and performing hemodynamic simulation based on the boundary conditions.
S240, verifying the hemodynamic simulation result according to the clinical information.
S250, comparing the hemodynamic simulation result with clinical information when the hemodynamic simulation result fails to be verified, and determining a result difference value between the hemodynamic simulation result and the clinical information.
S260, adjusting the boundary condition of the geometric model based on the corresponding relation between the preset result difference value and the adjustment value of the geometric model, and carrying out hemodynamic simulation on the geometric model based on the adjusted boundary condition.
The corresponding relation is obtained by training an original parameter adjustment model according to a sample simulation result, sample clinical information and a sample adjustment value.
In this embodiment, a correspondence relationship between the result difference value and the adjustment value of the boundary body condition of the geometric model may be established in advance. For example, a correspondence between each boundary condition adjustment value and a result difference interval is determined, after the result difference is obtained, a corresponding boundary condition adjustment value is determined according to the result difference interval to which the result difference belongs, the boundary condition of the geometric model is adjusted according to the boundary condition adjustment value, and then the hemodynamic simulation is performed on the geometric model again according to the adjusted boundary condition of the geometric model. Optionally, the raw parameter adjustment model may include, but is not limited to, one-dimensional convolutional neural network, one-dimensional residual neural network, deep neural network (Deep Neural Networks, DNN), full convolutional network, distributed gradient lifting framework based on decision tree algorithm (Light Gradient Boosting Machine, lightGBM), adaptive iterative algorithm (Adaptive Boosting, adaboost), iterative algorithm based on SMOTE (Synthetic Minority Oversampling Technique, minority-class oversampling technique) (SMOTEboost), etc. Through the corresponding relation, the boundary parameter adjustment value can be automatically determined, the boundary parameter of the target segmentation part can be accurately adjusted according to the boundary parameter adjustment value, and further the reliability of the hemodynamic simulation result is improved, and a reliable evaluation result is obtained.
And S270, taking the blood dynamics simulation result passing the verification as an evaluation result of aortic dissection.
As in the previous embodiments, by acquiring a three-dimensional coordinate point or a spatial locus for a target segmentation site of the geometric model; and determining the interesting sites and the hemodynamic simulation results of the interesting sites on the geometric model according to the three-dimensional coordinate points or the spatial sites, and taking the hemodynamic simulation results of all interesting sites as the evaluation results of the aortic dissection.
After determining the hemodynamic simulation results of the interested site, the hemodynamic simulation results of the interested site can be drawn into a hemodynamic parameter change curve according to time sequence, and the hemodynamic parameter change curve is output and displayed, namely, the hemodynamic simulation results are made at different time points based on a geometric model, so that the hemodynamic parameter change curve is obtained. The technician is facilitated to evaluate the target segmentation site from the perspective of the vascular intrinsic lesion, such as evaluating whether a false lumen exists, based on the display information. Wherein the hemodynamic parameter variation curve includes a maximum, minimum, or average value of hemodynamic simulation results. Optionally, the hemodynamic parameter variation curve can be drawn according to any section, curve, voxel and other information input from the outside.
According to the technical scheme provided by the embodiment, the hemodynamic simulation result is compared with the clinical information, so that a result difference value between the hemodynamic simulation result and the clinical information is determined; and adjusting the boundary condition of the geometric model based on the corresponding relation between the preset result difference value and the adjustment value of the geometric model. The method can automatically determine the boundary condition adjustment value, accurately adjust the boundary condition of the geometric model according to the boundary condition adjustment value, further improve the reliability of the hemodynamic simulation result and obtain a reliable evaluation result.
Example III
Fig. 4 is a schematic structural diagram of an aortic dissection evaluation device according to a third embodiment of the invention. Referring to fig. 4, the apparatus includes: a data acquisition module 310, a geometric model construction module 320, a boundary condition determination module 330, a hemodynamic simulation module 340, a verification module 350, a boundary condition adjustment module 360, and an evaluation result determination module 370.
The data acquisition module 310 is configured to acquire an image to be processed and clinical information of a current object;
a geometric model construction module 320, configured to construct a geometric model including a target segmentation site according to the image to be processed, where the target segmentation site includes an aorta and at least a part of an aortic branch;
A boundary condition determining module 330, configured to grid-divide the geometric model, determine an inlet and at least one outlet of the geometric model, and determine boundary conditions of the inlet and at least one outlet of the geometric model according to the image to be processed and the clinical information;
a hemodynamic simulation module 340 for performing hemodynamic simulation based on the boundary conditions;
a verification module 350, configured to verify a hemodynamic simulation result according to the clinical information;
a boundary condition adjustment module 360, configured to adjust a boundary condition of the geometric model when the hemodynamic simulation result fails verification, and perform hemodynamic simulation on the geometric model based on the adjusted boundary condition;
the evaluation result determination module 370 is configured to take the validated hemodynamic simulation result as an aortic dissection evaluation result.
Based on the above aspects, the boundary condition adjustment module 360 is further configured to compare the hemodynamic simulation result with the clinical information, and determine a result difference value between the hemodynamic simulation result and the clinical information;
and adjusting the boundary condition of the geometric model based on the corresponding relation between the preset result difference value and the adjustment value of the geometric model.
On the basis of the above technical solutions, the geometric model construction module 320 is further configured to input the image to be processed into a pre-trained aortic extraction model, to obtain an aortic model and a target positioning site, where the aortic extraction model is obtained by training a sample aortic model and a sample positioning site;
and dividing the aorta model at the target positioning site of the aorta model to obtain the geometric model.
Based on the above aspects, the boundary condition determining module 330 is further configured to divide the target segmentation location into a structured grid and/or an unstructured grid according to a grid division parameter.
Based on the above aspects, the boundary condition determining module 330 is further configured to determine a blood pressure value according to the clinical information, and determine a cardiac output according to the image to be processed and the clinical information;
based on the cardiac output and the blood pressure values, and based on a boundary condition model, boundary conditions of the inlet and all outlets of the geometric model are determined.
Based on the above technical solutions, the evaluation result determining module 370 is further configured to obtain a three-dimensional coordinate point or a spatial locus of the target segmentation part for the geometric model;
And determining the sites of interest and the hemodynamic simulation results of the sites of interest on the geometric model according to the three-dimensional coordinate points or the spatial sites, and taking the hemodynamic simulation results of all the sites of interest as the aortic dissection evaluation results.
On the basis of the technical schemes, the device further comprises: the hemodynamic parameter change curve drawing and displaying module; the system comprises a hemodynamic parameter change curve drawing and displaying module, a hemodynamic parameter change curve analyzing module and a hemodynamic parameter change curve analyzing module, wherein the hemodynamic parameter change curve drawing and displaying module is used for drawing hemodynamic parameter change curves of the interested sites according to time sequence and outputting and displaying the hemodynamic parameter change curves.
According to the technical scheme, an image to be processed and clinical information of a current object are obtained, and a geometric model comprising a target segmentation part is constructed according to the image to be processed, wherein the target segmentation part comprises an ascending aorta and a descending aorta; meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions; verifying the hemodynamic simulation result according to the clinical information; when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions; and taking the blood flow dynamics simulation result passing the verification as an aortic dissection evaluation result. Solves the problem that the prior art can only obtain the index on the morphology of the blood vessel and can not intuitively reflect the essence of the vascular lesion. The purpose of evaluating the aortic dissection from the aspect of blood flow dynamics is achieved, and accuracy and reliability of an aortic dissection evaluation result are improved.
Example IV
Fig. 5 is a schematic structural diagram of an aortic dissection evaluation device according to a fourth embodiment of the invention. Fig. 5 shows a block diagram of an exemplary aortic dissection evaluation device 12 suitable for use in implementing embodiments of the present invention. The aortic dissection evaluation device 12 shown in fig. 5 is only an example and should not be construed as limiting the function and scope of use of the embodiment of the present invention.
As shown in fig. 5, aortic dissection assessment device 12 is in the form of a general purpose computing device. Components of aortic dissection evaluation device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Aortic dissection assessment device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by aortic dissection evaluation device 12, including volatile and non-volatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Aortic dissection evaluation device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The memory 28 may include at least one program product having a set of program modules (e.g., a data acquisition module 310, a geometric model construction module 320, a boundary condition determination module 330, a hemodynamic simulation module 340, a verification module 350, a boundary condition adjustment module 360, and an assessment result determination module 360) configured to perform the functions of the various embodiments of the present invention.
The program/utility 44 having a set of program modules 46 (e.g., data acquisition module 310, geometric model construction module 320, boundary condition determination module 330, hemodynamic simulation module 340, verification module 350, boundary condition adjustment module 360, and evaluation result determination module 360) may be stored in, for example, memory 28, such program modules 46 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 46 generally perform the functions and/or methods of the embodiments described herein.
Aortic dissection assessment device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the aortic dissection assessment device 12, and/or with any device (e.g., network card, modem, etc.) that enables the aortic dissection assessment device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, aortic dissection evaluation device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through network adapter 20. As shown, the network adapter 20 communicates with other modules of the aortic dissection evaluation device 12 via the bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with aortic dissection evaluation device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement an aortic dissection evaluation method provided by an embodiment of the present invention, the method comprising:
acquiring an image to be processed and clinical information of a current object;
constructing a geometric model comprising a target segmentation part according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches;
meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions;
verifying the hemodynamic simulation result according to the clinical information;
when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions;
and taking the blood flow dynamics simulation result passing the verification as an aortic dissection evaluation result.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement an aortic dissection evaluation method provided by an embodiment of the present invention.
Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the aortic dissection assessment method according to any embodiment of the present invention.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an aortic dissection evaluation method as provided by the embodiment of the present invention, the method comprising:
acquiring an image to be processed and clinical information of a current object;
constructing a geometric model comprising a target segmentation part according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches;
meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions;
Verifying the hemodynamic simulation result according to the clinical information;
when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions;
and taking the blood flow dynamics simulation result passing the verification as an aortic dissection evaluation result.
Of course, the computer-readable storage medium provided by the embodiments of the present invention, on which the computer program stored, is not limited to the above-described method operations, but may also perform the relevant operations in an aortic dissection evaluation method provided by any of the embodiments of the present invention.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device.
The computer readable signal medium may include clinical information, boundary conditions, hemodynamic simulation results, and the like, in which computer readable program code is embodied. Such propagated clinical information, boundary conditions, hemodynamic simulation results, and the like. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that, in the above embodiment of the aortic dissection evaluation device, each included module is only divided according to the functional logic, but not limited to the above division, so long as the corresponding function can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. A method of aortic dissection assessment comprising:
acquiring an image to be processed and clinical information of a current object;
constructing a geometric model comprising a target segmentation part according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches;
Meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information, and performing hemodynamic simulation based on the boundary conditions;
verifying the hemodynamic simulation result according to the clinical information;
when the hemodynamic simulation result fails to pass verification, adjusting boundary conditions of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary conditions;
taking the verified hemodynamic simulation result as an aortic dissection evaluation result;
the constructing a geometric model containing the target segmentation part according to the image to be processed comprises the following steps:
inputting the image to be processed into a pre-trained aorta extraction model to obtain an aorta model and a target positioning site, wherein the aorta extraction model is obtained by training a sample aorta model and the sample positioning site; the aortic model is an aortic dissection model;
performing feature analysis on the aortic model at each target positioning site to obtain a feature analysis result, and dividing the aortic model at the target positioning site of the aortic model according to the feature analysis result to obtain the geometric model;
Said determining boundary conditions of an inlet and at least one outlet of said geometric model from said image to be processed and said clinical information comprises:
determining a blood pressure value according to the clinical information, and determining a cardiac output according to the image to be processed and the clinical information;
determining boundary conditions of an inlet and at least one outlet of the geometric model based on the cardiac output and the blood pressure values and based on a flow distribution ratio;
said determining boundary conditions of an inlet and at least one outlet of said geometric model from said cardiac output and said blood pressure values and based on flow distribution proportions, comprising:
determining at least one outlet flow based on the flow distribution ratio and the inlet flow using the cardiac output as an inlet flow of the geometric model;
determining an inlet velocity based on a cross-sectional area or a cross-sectional radius of the aorta in combination with the inlet flow;
determining at least one outlet velocity based on a branch size of the aortic branch in combination with the at least one outlet flow;
determining boundary conditions of an inlet of the geometric model according to the inlet flow and/or the inlet speed and the blood pressure value;
Determining a boundary condition of at least one outlet of the geometric model from the at least one outlet flow and/or the at least one outlet velocity and the blood pressure value.
2. The method of claim 1, wherein adjusting the boundary condition of the geometric model when the hemodynamic simulation results are not validated comprises:
comparing the hemodynamic simulation result with the clinical information to determine a result difference value between the hemodynamic simulation result and the clinical information;
and adjusting the boundary condition of the geometric model based on the corresponding relation between the preset result difference value and the adjustment value of the geometric model.
3. The method of claim 1, wherein meshing the geometric model comprises:
dividing the target segmentation part into a structured grid and/or an unstructured grid according to grid division parameters.
4. The method of claim 1, wherein the using the validated hemodynamic simulation results as aortic dissection assessment results comprises:
acquiring a three-dimensional coordinate point or a spatial locus of a target segmentation part aiming at the geometric model;
And determining the sites of interest and the hemodynamic simulation results of the sites of interest on the geometric model according to the three-dimensional coordinate points or the spatial sites, and taking the hemodynamic simulation results of all the sites of interest as the aortic dissection evaluation results.
5. The method as recited in claim 4, further comprising:
and drawing a hemodynamic parameter change curve according to the hemodynamic simulation result of the interested site in time sequence, and outputting and displaying the hemodynamic parameter change curve.
6. An aortic dissection evaluation device, comprising:
the data acquisition module is used for acquiring the image to be processed and clinical information of the current object;
a geometric model construction module for constructing a geometric model comprising a target segmentation part according to the image to be processed, wherein the target segmentation part comprises an aorta and at least part of aortic branches;
the boundary condition determining module is used for meshing the geometric model, determining an inlet and at least one outlet of the geometric model, and determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be processed and the clinical information;
A hemodynamic simulation module for performing hemodynamic simulation based on the boundary condition;
the verification module is used for verifying the hemodynamic simulation result according to the clinical information;
the boundary condition adjustment module is used for adjusting the boundary condition of the geometric model when the hemodynamic simulation result fails to pass verification, and carrying out hemodynamic simulation on the geometric model based on the adjusted boundary condition;
the evaluation result determining module is used for taking the verified hemodynamic simulation result as an aortic dissection evaluation result;
the geometric model construction module is further used for inputting the image to be processed into a pre-trained aorta extraction model to obtain an aorta model and target positioning sites, performing feature analysis on the aorta model at each target positioning site to obtain feature analysis results, and dividing the aorta model at the target positioning site of the aorta model according to the feature analysis results to obtain the geometric model; the aorta extraction model is obtained through training according to a sample aorta model and a sample positioning site; the aortic model is an aortic dissection model;
The boundary condition determining module is further used for determining a blood pressure value according to the clinical information and determining a cardiac output according to the image to be processed and the clinical information;
determining boundary conditions of an inlet and at least one outlet of the geometric model based on the cardiac output and the blood pressure values and based on a flow distribution ratio;
said determining boundary conditions of an inlet and at least one outlet of said geometric model from said cardiac output and said blood pressure values and based on flow distribution proportions, comprising:
determining at least one outlet flow based on the flow distribution ratio and the inlet flow using the cardiac output as an inlet flow of the geometric model;
determining an inlet velocity based on a cross-sectional area or a cross-sectional radius of the aorta in combination with the inlet flow;
determining at least one outlet velocity based on a branch size of the aortic branch in combination with the at least one outlet flow;
determining boundary conditions of an inlet of the geometric model according to the inlet flow and/or the inlet speed and the blood pressure value;
determining a boundary condition of at least one outlet of the geometric model from the at least one outlet flow and/or the at least one outlet velocity and the blood pressure value.
7. Aortic dissection assessment device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the aortic dissection assessment method according to any of claims 1-5 when executing the computer program.
8. A storage medium containing computer executable instructions which, when executed by a computer processor, implement the aortic dissection assessment method of any one of claims 1-5.
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| CN113763337A (en) * | 2021-08-24 | 2021-12-07 | 慧影医疗科技（北京）有限公司 | Method and system for detecting blood supply of aortic dissection false cavity |
| CN113712528A (en) * | 2021-09-06 | 2021-11-30 | 复旦大学附属中山医院 | Method for evaluating blood flow state of artificial cavity after aortic dissection intraluminal repair |
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