CN112150454A - Aortic dissection assessment method, device, equipment and storage medium - Google Patents

Aortic dissection assessment method, device, equipment and storage medium Download PDF

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CN112150454A
CN112150454A CN202011062771.5A CN202011062771A CN112150454A CN 112150454 A CN112150454 A CN 112150454A CN 202011062771 A CN202011062771 A CN 202011062771A CN 112150454 A CN112150454 A CN 112150454A
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geometric model
aortic dissection
hemodynamic simulation
clinical information
image
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CN112150454B (en
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郭健
吴柯
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Shanghai United Imaging Healthcare Co Ltd
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Abstract

The embodiment of the invention discloses an aortic dissection assessment method, device, equipment and storage medium. Acquiring an image to be processed and clinical information of a current object, and constructing a geometric model including a target segmentation part according to the image to be processed; performing mesh 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 is not verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition; the verified hemodynamic simulation results were used as the aortic dissection assessment results. The aortic dissection evaluation method has the advantages that the aortic dissection is evaluated from the aspect of hemodynamics, and the accuracy of the aortic dissection evaluation result is improved.

Description

Aortic dissection assessment method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to an image processing technology, in particular to an aortic dissection assessment method, device, equipment and storage medium.
Background
Aortic dissection (Aortic dissection, abbreviated as AD) generally refers to a condition in which blood in an Aortic lumen enters the interface of the middle-outer membrane or the middle-outer membrane of the aorta through an intimal laceration port on the Aortic wall, so that the Aortic wall is lacerated into two layers to form a true lumen and a false lumen, and extends along the longitudinal axis of the aorta, which can lead to death of a patient due to Aortic rupture in a short time, or leads to stenosis or even occlusion due to compression of the true lumen of the dissection by the false lumen, and leads to serious complications due to ischemic change of important organs supplied with blood from the true lumen.
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 generally employ image segmentation techniques and image display techniques for morphological three-dimensional simulation reconstruction of blood vessels of a corresponding patient in blood vessel-specific analysis. Doctors analyze and process the pathological changes according to the indexes (the stenosis degree of the blood vessel, the expansion degree of the false cavity, the size of the tearing opening site and the like) in the blood vessel morphology, but the available information is single, and the essence of the pathological changes of the blood vessel cannot be reflected visually.
Disclosure of Invention
The embodiment of the invention provides an aortic dissection assessment method, device, equipment and storage medium, which are used for assessing aortic dissection from the aspect of hemodynamics and improving the accuracy and reliability of an aortic dissection assessment result.
In a first aspect, an embodiment of the present invention provides an aortic dissection assessment 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition;
the verified hemodynamic simulation results were used as aortic dissection assessment results.
In a second aspect, embodiments of the present invention further provide an aortic dissection evaluation apparatus, including:
the data acquisition module is used for acquiring the image to be processed and the clinical information of the current object;
the geometric model construction module is used 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 aorta branches;
the boundary condition determining module is used for carrying out meshing on 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;
the hemodynamics simulation module is used for performing hemodynamics 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 adjusting module is used for adjusting the boundary condition of the geometric model when the hemodynamic simulation result is not verified, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition;
and the evaluation result determination 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 assessment apparatus, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the aortic dissection assessment method according to any one of the first aspect when executing the computer program.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, implement the aortic dissection assessment method according to any one of the first aspect.
According to the technical scheme of the embodiment of the invention, the image to be processed and the clinical information of the current object are obtained, and the geometric model comprising the 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 aorta 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition; the verified hemodynamic simulation results were used as aortic dissection assessment results. Solves the problem that the essence of the vascular lesion cannot be reflected intuitively because only the indexes on the vascular morphology can be obtained in the prior art. The purpose of evaluating the aortic dissection from the aspect of hemodynamics is achieved, and the accuracy and the reliability of the aortic dissection evaluation result are improved.
Drawings
Fig. 1 is a schematic flow chart of an aortic dissection assessment method according to an embodiment of the present invention;
FIG. 2 is a logic diagram of an aortic dissection assessment method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of an aortic dissection evaluation method according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an aortic dissection evaluation apparatus according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an aortic dissection evaluation apparatus according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow chart of an aortic dissection assessment method according to an embodiment of the present invention, which is applicable to performing hemodynamic simulation on a geometric model constructed from an image to be processed, and determining an assessment result of an aortic dissection according to the hemodynamic simulation result, where the method may be performed by an aortic dissection assessment apparatus, where the system may be implemented by software and/or hardware, and is generally integrated in an aortic dissection assessment device. Referring specifically to fig. 1, the method may include the steps of:
and S110, acquiring the to-be-processed image and clinical information of the current object.
The current object may be a subject or a scanning part of the subject, for example, a chest or a head 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 subject. In this embodiment, the image to be processed is an aorta image, and the clinical information may be clinical data corresponding to the current object, and may include information such as an aorta flow rate, a blood pressure value, an aorta and ultrasound flow rates of branches at the thoracoabdominal region. The blood pressure may be calculated from the systolic and diastolic blood pressure.
And S120, constructing a geometric model including the target segmentation part for the image to be processed.
Wherein the target segmentation site comprises an aorta and at least part of an aortic branch. False lumens may be present in both the aorta and at least a portion of the aortic branches, i.e., dissections may be present in both the ascending aorta and the descending aorta of the target segment. In this embodiment, a geometric model is constructed for an image to be processed, and hemodynamic simulation is performed on the geometric model to perform aortic dissection assessment. The present embodiment may construct a geometric model including the target segmentation site by the model construction means. Optionally, the constructing a geometric model including a 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 according to a sample aorta model and a sample positioning site; and segmenting the aorta model at the target positioning position of the aorta model to obtain the geometric model.
The aorta extraction model may include, but is not limited to, a Vector Machine algorithm (SVM), a Long Short-Term Memory Network (LSTM), a logistic Regression model (LR), a Full Convolution Network (FCN), a cyclic convolution Network (RNN), a Residual Network (ResNet), and the like. In this embodiment, the aorta model at least one target location point may be segmented according to a pointing operation obtained from the outside, or the aortic dissection evaluation device automatically segments the aorta model at the target location point of the aorta model according to a result of feature analysis by performing feature analysis on the aorta model at each target location point, where the result of feature analysis may include information such as a shape of a blood vessel at the target location point and a position of the target location point. In this embodiment, the geometric model may be obtained by segmenting a specific region (for example, a tear, a false lumen, an aortic arch, or other sites) or a non-specific region of the aorta model according to a threshold segmentation method, a region growing algorithm, or the like. The aortic model may be an aortic dissection model. The aorta extraction model can accurately output the aorta model and accurately position the target positioning site, and is favorable for obtaining an accurate geometric model.
S130, grid division is carried out on the geometric model, an inlet and at least one outlet of the geometric model are determined, boundary conditions of the inlet and the at least one outlet of the geometric model are determined according to the image to be processed and the clinical information, and haemodynamics simulation is carried out based on the boundary conditions.
Optionally, the target segmentation site may be divided into structured meshes and/or unstructured meshes according to meshing parameters, wherein the meshing parameters include a meshing size, a mesh quality index, and an optimization iteration number.
For the false cavity sites on the target segmentation part, the blood vessel state at the false cavity sites is complex, a smaller mesh division size, a larger mesh quality index and a larger optimization iteration number can be set; for non-false cavity sites on the target segmentation part, a larger mesh division size, a smaller mesh quality index and a smaller optimization iteration number can be set. Optionally, the vessel wall surface on the unstructured grid in this embodiment may be provided with a plurality of vessel boundary layers, and the entrance and exit tangent planes and the vessel wall area of the target segmentation portion are adjusted by adjusting the grid division parameters and the vessel boundary layers.
Optionally, the determining boundary conditions of an entrance and at least one exit 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 cardiac output according to the image to be processed and the clinical information; determining boundary conditions for an inlet and all outlets of the geometric model based on the cardiac output and the blood pressure values and based on a boundary condition model, wherein the boundary condition model is determined according to a three-dimensional geometry of the geometric model, and the outlets include at least one of a thoraco-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 of aortic flow rate, blood pressure value, aortic and branch ultrasound flow rate, etc. at the thoraco-abdominal region. The blood pressure value may be calculated from the systolic and diastolic blood pressure. And performing characteristic analysis on the clinical information and the image to be processed, determining the cardiac output and determining the information such as the cardiac output, the heart rate, the myocardial quality and the like. Specifically, the cardiac output is taken as the inlet flow of the geometric model, and all outlet flows are determined based on the 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; further, boundary conditions of the inlet of the geometric model are determined according to the inlet flow and/or the inlet speed and the blood pressure value, and boundary conditions of the outlet of the geometric model are determined 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 allocation ratio. The flow distribution ratio may be a fixed distribution ratio. Specifically, the cardiac output is taken as the inlet flow of the geometric model, and all outlet flows are determined based on the 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; further, boundary conditions of the inlet of the geometric model are determined according to the inlet flow and/or the inlet speed and the blood pressure value, and boundary conditions of the outlet of the geometric model are determined according to the outlet flow and/or the outlet speed and the blood pressure value.
Optionally, the method for performing hemodynamic simulation comprises: and performing hemodynamic simulation on the target segmentation part based on the set boundary conditions and in combination with the conservation law to obtain a hemodynamic simulation result, wherein the hemodynamic simulation result comprises a blood flow pressure difference, a flow velocity field and a wall shear stress. Alternatively, the conservation law includes an energy conservation law, a mass conservation law, and the like. In this embodiment, a low-order coupling model of the blood vessel may also be obtained, the low-order coupling model is used as a boundary condition of the geometric model, and hemodynamic simulation is performed on the target segmentation part according to the conservation law, so as to obtain a hemodynamic simulation result.
And S140, verifying the hemodynamic simulation result according to the clinical information.
S150, when the hemodynamic simulation result is not verified, 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 emulation threshold may be preset, the hemodynamic emulation result is compared with the clinical information, and if a difference between the hemodynamic emulation result and the clinical information is smaller than the hemodynamic emulation threshold, it is determined that the hemodynamic emulation result is verified; and if the hemodynamic simulation result exceeds the hemodynamic simulation threshold, determining that the hemodynamic simulation result does not pass verification, readjusting the boundary conditions of the geometric model, performing hemodynamic simulation on the geometric model again according to the adjusted boundary conditions and in combination with the conservation law until the difference value between the redetermined hemodynamic simulation result and the clinical information is smaller than the hemodynamic simulation threshold, determining that the hemodynamic simulation result passes verification, and performing hemodynamic simulation on the target segmentation part in combination with the conservation law to obtain the hemodynamic simulation result.
And S160, taking the verified hemodynamic simulation result as the evaluation result of the aortic dissection.
In conjunction with the logic diagram of aortic dissection assessment shown in fig. 2, the results of aortic dissection assessment can be determined from the acquired region of interest or three-dimensional coordinates. The specific method comprises the following steps: acquiring a three-dimensional coordinate point or a space 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 space sites, and taking the hemodynamic simulation results of all the interesting sites as the evaluation result of the aortic dissection. The three-dimensional coordinate point can be the central point of the grid in the previous step, the spatial point can be any pixel point of the target segmentation part, and the blood flow dynamics simulation results such as the blood flow pressure difference, the flow velocity field, the wall shear stress and the like of the corresponding interested point and the interested point are determined on the combined geometric model according to the three-dimensional coordinate point and the spatial point.
According to the technical scheme of the embodiment of the invention, the image to be processed and the clinical information of the current object are obtained, and the geometric model comprising the 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 aorta 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition; the verified hemodynamic simulation results were used as aortic dissection assessment results. Solves the problem that the essence of the vascular lesion cannot be reflected intuitively because only the indexes on the vascular morphology can be obtained in the prior art. The purpose of evaluating the aortic dissection from the aspect of hemodynamics is achieved, and the accuracy and the reliability of the aortic dissection evaluation result are improved.
Example two
Fig. 3 is a schematic flow chart of an aortic dissection evaluation method according to a second embodiment of the present invention. The technical solution of this embodiment is refined on the basis of the foregoing embodiment, and optionally, when the result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model includes: comparing the hemodynamic simulation result with the clinical information to determine a result difference 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 predetermined result difference and the adjustment value of the geometric model, wherein the corresponding relation is obtained by training the original parameter adjustment model according to the sample simulation result, the sample clinical information and the sample adjustment value. In the method, reference is made to the above-described embodiments for those parts which are not described in detail. Referring specifically to fig. 3, the method may include the steps of:
s210, acquiring the 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 aortic lumen, an aorta, and aortic branches.
And S230, 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 the clinical information, and performing hemodynamic simulation based on the boundary conditions.
And S240, verifying the hemodynamic simulation result according to the clinical information.
And S250, when the hemodynamic simulation result is not verified, 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 S260, adjusting the boundary condition of the geometric model based on the corresponding relation between the predetermined result difference and the adjustment value of the geometric model, and performing the hemodynamic simulation on the geometric model based on the adjusted boundary condition.
And 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 between the result difference and the adjustment value of the boundary condition of the geometric model may be established in advance. For example, the corresponding relationship between each boundary condition adjustment value and the result difference interval is determined, after the result difference is obtained, the 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 original parameter adjustment model may include, but is not limited to, a one-dimensional convolutional Neural network, a one-dimensional residual Neural network, a Deep Neural Network (DNN), a full convolutional network, a decision tree algorithm-based distributed Gradient Boosting Machine (LightGBM), an Adaptive iterative algorithm (Adaptive Boosting), an iterative algorithm based on SMOTE (Synthetic minimum over sampling Technique, small class over sampling Technique) (SMOTEboost), and the like. By the corresponding relation, the boundary parameter adjustment value can be automatically determined, the boundary parameter of the target segmentation part is accurately adjusted according to the boundary parameter adjustment value, and therefore the reliability of the hemodynamic simulation result is improved, and a reliable evaluation result is obtained.
And S270, taking the verified hemodynamic simulation result as the evaluation result of the aortic dissection.
As described in the previous embodiments, by obtaining three-dimensional coordinate points or spatial loci 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 space sites, and taking the hemodynamic simulation results of all the interesting sites as the evaluation result of the aortic dissection.
After determining the hemodynamic simulation result of the site of interest, the hemodynamic simulation result of the site of interest may be further plotted according to a time sequence to output and display a hemodynamic parameter variation curve, that is, a hemodynamic simulation result is performed at different time points based on a geometric model to obtain a hemodynamic parameter variation curve. The technician can conveniently evaluate the target segmentation part from the perspective of the vascular intrinsic lesion according to the display information, such as evaluating whether a false cavity exists. Wherein the hemodynamic parameter variation curve comprises a maximum value, a minimum value or an average value of the hemodynamic simulation result. Alternatively, the hemodynamic parameter variation curve can be drawn according to information such as an arbitrary section, a curve, and a voxel, which is externally input.
According to the technical scheme provided by the embodiment, the hemodynamic simulation result is compared with the clinical information, and 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 predetermined 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 apparatus according to a third embodiment of the present 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 a to-be-processed image and clinical information of a current subject;
a geometric model construction module 320, configured to construct a geometric model including a target segmentation site according to the image to be processed, wherein the target segmentation site includes an aorta and at least a part of aortic branches;
a boundary condition determining module 330, configured to perform mesh division on the geometric model, determine an entrance and at least one exit of the geometric model, and determine boundary conditions of the entrance and the at least one exit of the geometric model according to the image to be processed and the clinical information;
a hemodynamic simulation module 340 configured to perform hemodynamic simulation based on the boundary condition;
a verification module 350, configured to verify the hemodynamic simulation result according to the clinical information;
a boundary condition adjusting module 360, configured to adjust a boundary condition of the geometric model when the hemodynamic simulation result fails to be verified, and perform hemodynamic simulation on the geometric model based on the adjusted boundary condition;
and an evaluation result determination module 370 for taking the verified hemodynamic simulation result as an aortic dissection evaluation result.
On the basis of the above technical solutions, the boundary condition adjusting module 360 is further configured to compare the hemodynamic simulation result with the clinical information, and determine a result difference 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 predetermined 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 to a pre-trained aorta extraction model to obtain an aorta model and a target location point, where the aorta extraction model is obtained by training according to a sample aorta model and a sample location point;
and segmenting the aorta model at the target positioning position of the aorta model to obtain the geometric model.
On the basis of the foregoing technical solutions, the boundary condition determining module 330 is further configured to divide the target segmentation portion into a structured grid and/or an unstructured grid according to a grid division parameter.
On the basis of the above technical solutions, 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 on a boundary condition model, boundary conditions of an inlet and all outlets of the geometric model are determined.
On the basis of 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 a target segmentation part for 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 space sites, and taking the hemodynamic simulation results of all the interesting sites as the aortic dissection assessment result.
On the basis of the above technical solutions, the apparatus further includes: the hemodynamics parameter change curve drawing and displaying module is used for drawing and displaying a hemodynamics parameter change curve; and the hemodynamics parameter change curve drawing and displaying module is used for drawing a hemodynamics parameter change curve according to the hemodynamics simulation result of the interested site in time sequence, and outputting and displaying the hemodynamics parameter change curve.
According to the technical scheme of the embodiment of the invention, the image to be processed and the clinical information of the current object are obtained, and the geometric model comprising the 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition; the verified hemodynamic simulation results were used as aortic dissection assessment results. Solves the problem that the essence of the vascular lesion cannot be reflected intuitively because only the indexes on the vascular morphology can be obtained in the prior art. The purpose of evaluating the aortic dissection from the aspect of hemodynamics is achieved, and the accuracy and the reliability of the aortic dissection evaluation result are improved.
Example four
Fig. 5 is a schematic structural diagram of an aortic dissection evaluation apparatus according to a fourth embodiment of the present 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 apparatus 12 shown in fig. 5 is merely an example and should not impose any limitation on the function and scope of use of embodiments of the present invention.
As shown in fig. 5, the aortic dissection evaluation device 12 is in the form of a general purpose computing device. The components of the aortic dissection evaluation device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, 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 evaluation device 12 typically includes a variety of computer system readable media. These media may be any available media that can be accessed by the aortic dissection evaluation device 12 and include both 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 32. Aortic dissection evaluation device 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory 28 may include at least one program product having a set of program modules (e.g., the data acquisition module 310, the geometric model construction module 320, the boundary condition determination module 330, the hemodynamic simulation module 340, the verification module 350, the boundary condition adjustment module 360, and the assessment result determination module 360 of the aortic dissection apparatus) configured to perform the functions of the various embodiments of the invention.
A 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 assessment result determination module 360 of the aortic dissection apparatus) may be stored, for example, in 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 of which or some combination of which may comprise an implementation of a network environment. Program modules 46 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The aortic dissection evaluation device 12 may 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 evaluation device 12, and/or with any device (e.g., network card, modem, etc.) that enables the aortic dissection evaluation device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the 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 the network adapter 20. As shown, the network adapter 20 communicates with the other modules of the aortic dissection evaluation device 12 over the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the aortic dissection assessment apparatus 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing an aortic dissection assessment method provided by an embodiment of the present invention, the 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 aorta and at least part of aorta 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition;
the verified hemodynamic simulation results were used as aortic dissection assessment results.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement an aortic dissection assessment method provided by the embodiment of the present invention.
Of course, those skilled in the art will appreciate that the processor may also implement the solution of the aortic dissection assessment method provided in any embodiment of the present invention.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an aortic dissection assessment method according to an embodiment of the present invention, where the method includes:
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 aorta 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition;
the verified hemodynamic simulation results were used as aortic dissection assessment results.
Of course, the computer-readable storage medium provided by the embodiments of the present invention, on which the computer program is stored, is not limited to the above method operations, and may also perform related operations in an aortic dissection assessment method provided by any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ 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. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination 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 the context of 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.
A computer readable signal medium may be embodied in clinical information, boundary conditions, hemodynamic simulation results, or the like, and may carry computer readable program code embodied therein. Such disseminated 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 for aspects 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 + +, or the like, as well as 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that, in the embodiment of the aortic dissection assessment apparatus, the included modules are merely divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. 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, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. An aortic dissection assessment 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 aorta 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 result of the hemodynamic simulation fails to be verified, adjusting the boundary condition of the geometric model, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition;
the verified hemodynamic simulation results were used as aortic dissection assessment results.
2. The method of claim 1, wherein when the hemodynamic simulation result is not validated, adjusting boundary conditions of the geometric model comprises:
comparing the hemodynamic simulation result with the clinical information to determine a result difference 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 predetermined result difference value and the adjustment value of the geometric model.
3. The method according to claim 1, wherein the constructing a geometric model containing a target segmentation part according to the image to be processed comprises:
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 according to a sample aorta model and a sample positioning site;
and segmenting the aorta model at the target positioning position of the aorta model to obtain the geometric model.
4. The method of claim 1, wherein said meshing said geometric model comprises:
and dividing the target segmentation part into structured grids and/or unstructured grids according to the grid division parameters.
5. The method of claim 1, wherein determining boundary conditions for an entrance and all exits of the geometric model from the image to be processed and the clinical information comprises:
determining a blood pressure value according to the clinical information, and determining cardiac output according to the image to be processed and the clinical information;
based on the cardiac output and the blood pressure values and on a boundary condition model, boundary conditions of an inlet and all outlets of the geometric model are determined.
6. 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 space 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 space sites, and taking the hemodynamic simulation results of all the interesting sites as the aortic dissection assessment result.
7. The method of claim 6, further comprising:
and drawing a hemodynamic parameter change curve according to the hemodynamic simulation result of the interesting site according to a time sequence, and outputting and displaying the hemodynamic parameter change curve.
8. An aortic dissection evaluation device, comprising:
the data acquisition module is used for acquiring the image to be processed and the clinical information of the current object;
the geometric model construction module is used 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 aorta branches;
the boundary condition determining module is used for carrying out meshing on 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;
the hemodynamics simulation module is used for performing hemodynamics 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 adjusting module is used for adjusting the boundary condition of the geometric model when the hemodynamic simulation result is not verified, and performing hemodynamic simulation on the geometric model based on the adjusted boundary condition;
and the evaluation result determination module is used for taking the verified hemodynamic simulation result as an aortic dissection evaluation result.
9. Aortic dissection assessment device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aortic dissection assessment method according to any one of claims 1-7.
10. 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 1-7.
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