WO2022166281A1 - 血流动力学指标数据的处理方法和系统 - Google Patents

血流动力学指标数据的处理方法和系统 Download PDF

Info

Publication number
WO2022166281A1
WO2022166281A1 PCT/CN2021/128039 CN2021128039W WO2022166281A1 WO 2022166281 A1 WO2022166281 A1 WO 2022166281A1 CN 2021128039 W CN2021128039 W CN 2021128039W WO 2022166281 A1 WO2022166281 A1 WO 2022166281A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
processing
interest
blood vessel
point
Prior art date
Application number
PCT/CN2021/128039
Other languages
English (en)
French (fr)
Inventor
崔巍
许永松
吴健
秦川
唐航
Original Assignee
北京泰杰伟业科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 北京泰杰伟业科技有限公司 filed Critical 北京泰杰伟业科技有限公司
Publication of WO2022166281A1 publication Critical patent/WO2022166281A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30204—Marker

Definitions

  • the invention relates to the technical field of data processing, in particular to a method and system for processing hemodynamic index data.
  • geometric data of blood vessels of interest such as human cerebral aneurysms
  • comparing the geometric data differences between the ruptured cerebral aneurysm data and the unruptured cerebral aneurysm data can carry out further risk analysis, but it is impossible to analyze and process the reasons for the obtained risk.
  • the present invention provides a method and system for processing hemodynamic index data, which simulates the hemodynamics of blood vessels of interest by using fluid mechanics (CFD) according to medical image data and clinical measurement data.
  • the hemodynamic index data can be processed according to the distribution of the biological index, so that the hemodynamic index data can be processed non-invasively, with fast processing speed and high visualization accuracy.
  • a first aspect of the embodiments of the present invention provides a method for processing hemodynamic index data, the method comprising:
  • Step 1 collecting medical image data and clinical measurement data;
  • the medical image data includes one or more of cerebral artery computed tomography data, nuclear magnetic resonance image data, digital subtraction angiography data 3D-DSA and ultrasound image data.
  • the clinical measurement data includes heart rate data and blood pressure data;
  • Step 2 performing image segmentation processing according to the medical image data and clinical measurement data, and using a centerline extraction algorithm to generate specific anatomical model data of the blood vessel of interest;
  • Step 3 Perform grid division processing on the specific anatomical model data, and perform hydrodynamic simulation processing using boundary conditions, so as to obtain the distribution status data of the hemodynamic index;
  • the distribution status data includes the wall shear stress. , time-averaged wall shear stress, oscillatory shear index and velocity streamline;
  • step 4 the distribution status data of the hemodynamic index is visualized so as to be displayed on the three-dimensional model of the cerebral aneurysm.
  • the step 2 specifically includes:
  • Step 21 detecting the marker points of the blood vessel of interest, and identifying the starting point and the end point of the blood vessel of interest;
  • Step 22 tracking the blood vessel by identifying the tracking path between the starting point and the end point of the blood vessel of interest to be segmented;
  • Step 23 according to the tracking path, use a random walk algorithm based on the image intensity and gradient along the tracking path to preliminarily segment the blood vessel of interest;
  • Step 24 extracting a centerline from the initially segmented blood vessels of interest
  • Step 25 accurately segment the blood vessel of interest, and extract the cross-sectional contour of the blood vessel of interest at each point of the center line;
  • Step 26 generating a geometric surface model of the blood vessel of interest from the accurately segmented cross-sectional contour by a lofting method to obtain anatomical model data.
  • a trained marker point detector is used to identify the starting point and the ending point of the blood vessel of interest
  • the marker point detector is trained using the training data, the real positions of each starting point and the end point are marked in the training data, and a positive sample is generated at the marked real position, and a positive sample is generated at the real position far away from the real position.
  • Negative samples are generated locally, and positive samples and negative samples are then subjected to feature extraction, where Haar Haar features and steerable features are calculated for each sample; the features extracted from the samples are passed to a statistical classifier, which distinguishes positive and negative samples. , thereby evaluating the probability of processing the received data to determine the starting point or ending point of a positive sample.
  • a shortest path algorithm based on Dijkstra's algorithm is used to identify the path between the starting point and the ending point.
  • performing grid division processing on the specific anatomical model data specifically includes:
  • Step 31 Calculate the distance from each point on the vessel wall of the vessel of interest to the centerline;
  • the boundary layer is divided into triangular prism meshes.
  • the thickness of the boundary layer is a quarter of the distance from each point on the pipe wall to the center line.
  • the boundary layer grid is thickened layer by layer from the outside to the inside according to a uniform proportional value, and is divided into 5 layers;
  • d is the distance from each point on the pipe wall to the center line, and a is the uniform proportional value
  • Step 33 Use tetrahedral mesh to divide the area in the boundary layer, and the mesh size is one tenth of the distance from each point on the pipe wall to the center line;
  • Step 34 Assemble the obtained boundary layer mesh and volume mesh to complete the division of the mesh.
  • the method further includes: setting material properties and boundary conditions of the blood of the blood vessel of interest.
  • the material properties are that the blood in the blood vessel of interest is set as a Newtonian fluid, the blood density is 1060 kg/m 3 , and the hemodynamic viscosity is 0.004 Pa ⁇ S.
  • the boundary condition is that the wall surface of the blood vessel of interest adopts a rigid wall boundary condition; the pressure at each point of the inlet cross-section at the same time is consistent, and the pressure value is obtained from the blood pressure data and heart rate measured by the arm to obtain the blood pressure-time Variation map changes; outlet boundary conditions are lumped parameter models simulating the resistance of vessels and microvessels downstream of the outlet.
  • a second aspect of the embodiments of the present invention provides a system for processing hemodynamic index data, the system comprising:
  • a data acquisition module for acquiring medical image data and clinical measurement data
  • the medical image data includes one or more of cerebral artery computed tomography data, nuclear magnetic resonance image data, 3D-DSA and ultrasound image data
  • the Clinical measurement data includes heart rate data and blood pressure data
  • an anatomical modeling system module for performing image segmentation processing according to the medical image data and clinical measurement data, and generating specific anatomical model data of the blood vessel of interest by using a centerline extraction algorithm;
  • the fluid mechanics simulation module is used to perform grid division processing on the specific anatomical model data, and perform fluid mechanics simulation processing by using boundary conditions, so as to obtain the distribution status data of the hemodynamic indexes;
  • the distribution status data includes: Wall shear stress, time-averaged wall shear stress, oscillatory shear index and velocity streamlines;
  • the visualization processing module is used to visualize the distribution status data of the hemodynamic index, so as to be displayed on the three-dimensional model of the cerebral aneurysm.
  • a third aspect of the embodiments of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a computer, causes the computer to execute the first step.
  • the method and system for processing hemodynamic index data provided by the embodiments of the present invention extract blood vessels of interest, such as specific anatomical model data of cerebral aneurysm, from medical image data and clinical measurement data of the brain.
  • Specific boundary conditions for the CFD model were calculated based on the anatomical model and non-invasive clinical measurements.
  • the specific anatomical model data and specific boundary conditions are used to simulate the distribution of the hemodynamic indexes in the blood vessel of interest (eg, cerebral aneurysm) and visualize, process and display, with fast processing speed and high visualization accuracy.
  • FIG. 1 is a flowchart of a method for processing hemodynamic index data provided by an embodiment of the present invention
  • FIG. 2 is a schematic diagram of boundary conditions of a method for processing hemodynamic index data provided by an embodiment of the present invention
  • 3A is a schematic diagram of a TAWSS processing method for hemodynamic index data provided by an embodiment of the present invention.
  • 3B is a schematic diagram of FFR of a processing method for hemodynamic index data provided by an embodiment of the present invention.
  • 3C is a schematic diagram of the pipelines of a processing method of hemodynamic index data provided by an embodiment of the present invention.
  • FIG. 4 is a schematic block diagram of a system for processing hemodynamic index data according to an embodiment of the present invention.
  • the method and system for processing hemodynamic index data relate to a method for non-invasive evaluation of hemodynamic index in a blood vessel of interest (such as a cerebral aneurysm) using medical image data and CFD simulation and system.
  • a blood vessel of interest such as a cerebral aneurysm
  • Embodiments of the present invention are described to give a visual understanding of the distribution of hemodynamic indices for simulating a brain aneurysm.
  • Identification and manipulation objects are often used here to describe digital representations of objects.
  • Such operations are virtual operations implemented in computer system memory or other circuits/hardware. Accordingly, it will be appreciated that embodiments of the present invention may be implemented within a computer system using data stored in a computer system.
  • FIG. 1 is a flowchart of a method for processing hemodynamic index data provided by an embodiment of the present invention. As shown in the figure, this embodiment specifically includes the following steps:
  • Step 101 collecting medical image data and clinical measurement data;
  • the medical image data includes one or more of cerebral artery computed tomography data, nuclear magnetic resonance image data, digital subtraction angiography data (3D-DSA) and ultrasound image data.
  • the clinical measurement data includes heart rate data and blood pressure data;
  • obtain 3D or 4D medical image data and other non-invasive clinical measurement data of a patient can receive medical image data from one or more imaging modalities, such as computed tomography (CTA), magnetic resonance imaging (MRA), 3D-DSA, ultrasound images or any other type of medical imaging modality.
  • CTA computed tomography
  • MRA magnetic resonance imaging
  • 3D-DSA ultrasound images or any other type of medical imaging modality.
  • the medical image data may be received directly from one or more image acquisition devices of a CT scanner, an MR scanner, an angiography scanner or an ultrasound device, or via pre-stored medical image data.
  • Step 102 performing image segmentation processing according to the medical image data and clinical measurement data, and using a centerline extraction algorithm to generate specific anatomical model data of the blood vessel of interest;
  • step 102 includes the following steps:
  • Step 201 detecting the marker points of the blood vessel of interest, and identifying the starting point and the end point of the blood vessel of interest;
  • the trained landmark detector is used to identify the starting point and end point of the blood vessel of interest; in addition, before this step, the landmark detector is also trained using the training data, and the real position of each starting point and end point is calculated in the training data.
  • Annotate generate positive samples at the marked real positions, and generate negative samples at places far from the real positions, and the positive samples and negative samples are then subjected to feature extraction, in which Haar features and steerable features are calculated for each sample;
  • the features extracted from the samples are passed to a statistical classifier, which distinguishes between positive and negative samples, thereby evaluating the probability of processing the received data to determine the starting point or ending point of a positive sample.
  • a trained landmark detector was used to identify the origin and endpoint of vessels of interest.
  • the landmark detector is trained using training data in which the true location of each landmark (i.e. start and end point) is annotated.
  • the learning system can generate positive samples at the labeled ground-truth locations and negative samples at locations far from the ground-truth locations.
  • Positive samples and negative samples are then subjected to feature extraction, where Haar features and steerable features are calculated for each sample.
  • the features extracted from the samples are passed to a statistical classifier, such as a Probabilistic Boosting Tree (PBT), which automatically learns to distinguish positive and negative samples in the best way.
  • PBT Probabilistic Boosting Tree
  • the trained classifier evaluates the volume data of the received 3D medical image and determines the probability that it is a positive sample (ie, starting point or ending point).
  • Step 202 tracking the blood vessel by identifying the tracking path between the start point and the end point of the blood vessel of interest to be segmented;
  • the marker point detection in this step can identify the starting point and the ending point of the blood vessel of interest to be segmented. Then, a shortest path algorithm based on Dijkstra's algorithm is used to identify the path between the starting point and the ending point.
  • Step 203 uses a random walk algorithm based on the image intensity and gradient along the tracking path to preliminarily segment the blood vessels of interest;
  • the blood vessels of interest are preliminarily segmented according to the tracking path.
  • the identified shortest path is not necessarily the centerline of the vessel.
  • the blood vessels are segmented in the 3D image.
  • a random walks algorithm based on image intensities and gradients along the tracked path is used for preliminary segmentation of the vessels of interest.
  • Step 204 extracting a centerline from the preliminary segmented blood vessels of interest
  • the centerline is extracted from the preliminarily segmented blood vessels of interest.
  • Centerlines can be extracted using any centerline extraction method.
  • Step 205 accurately segment the blood vessel of interest, and extract the cross-sectional contour line of the blood vessel of interest at each point of the center line;
  • the vessel segmentation in the previous step provides a preliminary segmentation of the vessel of interest, but is not precise enough. Therefore, this step can utilize a machine-learning-based approach to accurately segment vessels using boundary classifiers learned from the annotated training data. That is, after extracting the centerline, extract the cross-sectional contour of the blood vessel of interest at each point of the centerline.
  • Step 206 generating a geometric surface model of the blood vessel of interest from the accurately segmented cross-sectional contour line by a lofting method to obtain anatomical model data.
  • anatomical modeling task can be performed in a fully automatic manner.
  • a specific 3D anatomical model is automatically generated according to medical image data.
  • Step 103 Perform grid division processing on the specific anatomical model data, and perform hydrodynamic simulation processing using boundary conditions, so as to obtain the distribution status data of the hemodynamic index;
  • the distribution status data includes wall shear stress, time-averaged wall surface Shear Stress, Oscillating Shear Index and Velocity Streamlines;
  • the meshing processing of specific anatomical model data includes:
  • Step 301 Calculate the distance from each point on the vessel wall of the vessel of interest to the center line;
  • a hybrid mesh type (hybrid mesh) is used, that is, a tetrahedral-triangular prism hybrid mesh, which is close to the boundary of the blood vessel wall.
  • the layer uses a triangular prism mesh with a tetrahedral mesh inside. Use the extracted centerline and calculate the distance d from each point on the vessel wall of interest to the centerline.
  • Step 302 divide the boundary layer into a triangular prism mesh, the thickness of the boundary layer is one-fourth of the distance from each point on the pipe wall to the center line, and the mesh size of the outermost layer close to the pipe wall is 0.02mm,
  • this step is to mesh the boundary layer
  • the mesh type is a triangular prism mesh
  • the thickness of the boundary layer is 1/4 times the distance d.
  • the mesh size is uniformly defined as 0.02 mm
  • the boundary layer meshes are thickened layer by layer in proportion to a from the outside to the inside, and are divided into 5 layers in total.
  • Step 303 Use a tetrahedral mesh to divide the area in the boundary layer, and the mesh size is one tenth of the distance from each point on the pipe wall to the center line; specifically, use a tetrahedral mesh for the area in the boundary layer
  • the grid is divided, and the grid size is 1/10 times the distance d.
  • Step 304 Assemble the obtained boundary layer mesh and volume mesh to complete the division of the mesh.
  • the material properties and boundary conditions of the blood also need to be set.
  • the material properties are as follows: since the diameter of the current-carrying artery adjacent to the vessel of interest (eg, an aneurysm) is relatively large and the flow velocity is relatively fast, the non-Newtonian nature of the blood can be ignored.
  • blood is assumed to be a Newtonian fluid, and the blood density ⁇ is 1060 kg/m3.
  • the hemodynamic viscosity is 0.004Pa ⁇ S.
  • the accuracy of the CFD simulation of the blood vessel of interest is not only related to the accurate specific anatomical model, but also related to the boundary conditions of the model.
  • the boundary conditions of this embodiment are as follows:
  • the wall adopts rigid wall boundary conditions, ignoring the elasticity of blood vessels and ignoring the influence of blood vessel deformation on blood flow during vasodilation and contraction.
  • the inlet boundary condition sets the pressure condition.
  • the pressure at each point of the inlet cross-section at the same time is the same, and the pressure value changes with the blood pressure-time change graph obtained from the blood pressure data measured by the patient's arm and the heart rate.
  • a lumped parameter model (here a simple RCR model) is used to simulate the resistance of vessels and microvessels downstream of the outlet.
  • the method for obtaining the vascular resistance of each outlet is as follows:
  • patient-specific brain volumes were calculated from cerebral arterial medical imaging data, thereby using population-based relationships to calculate cerebral arterial flow from brain volume data, and based on cerebral arterial flow and non-invasive clinical measurements (such as the patient's upper arm blood pressure, heart rate, etc.) to calculate the total cerebral arterial resistance, and then according to the parameters such as the diameter of each outlet in the anatomical model, using the population-based vascular resistance relationship, the total cerebral arterial resistance is allocated to each outlet. Blood vessel.
  • FIG. 2 is a schematic diagram of boundary conditions of a method for processing hemodynamic index data provided by an embodiment of the present invention.
  • an or Multiple capacitances C suscitating blood vessel elasticity
  • inductance L suscating blood flow inertia
  • the model is simulated and solved by CFD, that is, a series of partial differential equations, such as Navier-Stokes equations, are solved.
  • CFD a series of partial differential equations, such as Navier-Stokes equations
  • step 104 the distribution status data of the hemodynamic index is visualized so as to be displayed on the three-dimensional model of the cerebral aneurysm.
  • this step is to visualize each hemodynamic index in the blood vessel of interest (eg, a cerebral aneurysm).
  • the processing method of the hemodynamic index data shows the schematic diagram of time-averaged wall shear stress (TAWSS), the schematic diagram of fractional flow reserve (FFR) and the velocity streamline (streamlines) Schematic.
  • the system may be a terminal device or a server that implements the method of the embodiment of the present invention, or may be connected to the above-mentioned terminal device or server.
  • the system of the method in the embodiment of the present invention for example, the system may be an apparatus or a chip system of the above-mentioned terminal device or server. As shown in Figure 3, the system includes:
  • the data acquisition module 41 is used to acquire medical image data and clinical measurement data;
  • the medical image data includes one or more of cerebral artery computed tomography data, nuclear magnetic resonance image data, 3D-DSA and ultrasound image data; clinical measurement data including heart rate data and blood pressure data;
  • the anatomical modeling system module 42 is used to perform image segmentation processing according to the medical image data and clinical measurement data, and use the centerline extraction algorithm to generate specific anatomical model data of the blood vessel of interest;
  • the fluid mechanics simulation module 43 is used to perform grid division processing on the specific anatomical model data, and perform fluid mechanics simulation processing by using boundary conditions, so as to obtain the distribution status data of the hemodynamic indexes;
  • the distribution status data includes wall shearing Stress, time-averaged wall shear stress, oscillatory shear index, and velocity streamlines;
  • the visualization processing module 44 is configured to perform visualization processing on the distribution status data of the hemodynamic index, so as to be displayed on the three-dimensional model of the cerebral aneurysm.
  • the system for processing hemodynamic index data provided by the embodiments of the present invention can execute the method steps in the above method embodiments, and the implementation principles and technical effects thereof are similar, and are not repeated here.
  • each module of the above apparatus is only a division of logical functions, and may be fully or partially integrated into a physical entity in actual implementation, or may be physically separated.
  • these modules can all be implemented in the form of software calling through processing elements; they can also all be implemented in hardware; some modules can also be implemented in the form of calling software through processing elements, and some modules can be implemented in hardware.
  • the flow pool card status maintenance module can be a separately established processing element, or can be integrated into a certain chip of the above-mentioned device.
  • it can also be stored in the memory of the above-mentioned device in the form of program code.
  • a certain processing element invokes and executes the functions of the above-identified modules.
  • each step of the above-mentioned method or each of the above-mentioned modules can be completed by an integrated logic circuit of hardware in the processor element or an instruction in the form of software.
  • the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuit (ASIC), or one or more Digital Signal Processors (Digital Signal Processor) Signal Processor, DSP), or one or more Field Programmable Gate Array (Field Programmable Gate Array, FPGA), etc.
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Signal Processor
  • FPGA Field Programmable Gate Array
  • the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processors that can call program codes.
  • these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).
  • SOC system-on-a-chip
  • the above-mentioned embodiments it may be implemented in whole or in part by software, hardware, firmware or any combination thereof.
  • software it can be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions.
  • the computer program instructions When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the embodiments of the present invention are generated in whole or in part.
  • the aforementioned computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • the above-mentioned computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the above-mentioned computer instructions may be transmitted from a website site, computer, server or data center via wired communication. (eg coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (eg infrared, wireless, bluetooth, microwave, etc.) to another website site, computer, server or data center.
  • the above-mentioned computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server, data center, etc. that includes one or more available media integrated.
  • the above-mentioned usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVD), or semiconductor media (eg, solid state disk (SSD)), and the like.
  • embodiments of the present invention also provide a computer-readable storage medium, where instructions are stored in the readable storage medium, and when the readable storage medium runs on a computer, the computer can execute the methods and processing procedures provided in the above-mentioned embodiments. .
  • the method and system for processing hemodynamic index data provided by the embodiments of the present invention extract blood vessels of interest, such as specific anatomical model data of cerebral aneurysm, from medical image data and clinical measurement data of the brain. Based on the anatomical model (such as cerebral vascular volume, total flow, vascular outlet diameter, etc.) and non-invasive clinical measurements (such as blood pressure and heart rate, etc.), the specific boundary conditions of the CFD model are calculated. The specific anatomical model data and specific boundary conditions are used to simulate the distribution of the hemodynamic indexes in the blood vessel of interest (eg, cerebral aneurysm) and visualize, process and display, with fast processing speed and high visualization accuracy.
  • blood vessels of interest such as specific anatomical model data of cerebral aneurysm

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Cardiology (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Biomedical Technology (AREA)
  • Pathology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Public Health (AREA)
  • Biophysics (AREA)
  • Veterinary Medicine (AREA)
  • Physiology (AREA)
  • Mathematical Physics (AREA)
  • Hematology (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Fluid Mechanics (AREA)
  • Computing Systems (AREA)
  • Algebra (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Vascular Medicine (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

一种血流动力学指标数据的处理方法和系统，方法包括：采集医学图像数据和临床测量数据（101）；根据医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据（102）；对特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到血流动力学指标的分布状况数据（103）；分布状况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上（104）。所述血流动力学指标数据的处理方法和系统可以模拟感兴趣血管内的血流动力学指标的分布状况并可视化处理和显示，处理速度快，可视化精度高。

Description

血流动力学指标数据的处理方法和系统
本申请要求于2021年02月08日提交中国专利局、申请号为202110170627.1、发明名称为“血流动力学指标数据的处理方法和系统”的中国专利申请的优先权。
技术领域
本发明涉及数据处理技术领域，尤其涉及一种血流动力学指标数据的处理方法和系统。
背景技术
由于医学影像技术的不断发展，使得感兴趣血管例如人脑动脉瘤的几何形态数据可以被获取到。同时比较破裂脑动脉瘤数据和未破裂脑动脉瘤数据之间的几何形态数据差异可以进行进一步的风险分析，但是还无法分析处理得到风险的原因。
发明内容
本发明针对现有技术的缺陷，提供了一种血流动力学指标数据的处理方法和系统，通过根据医学影像数据和临床测量数据，利用流体力学(CFD)仿真模拟感兴趣血管的血流动力学指标的分布状况来处理血流动力学指标数据，从而可以无创的对血流动力学指标数据进行处理，处理速度快，可视化精度高。
为实现上述目的，本发明实施例第一方面提供了一种血流动力学指标数据的处理方法，所述方法包括：
步骤1，采集医学图像数据和临床测量数据；所述医学图像数据包括脑动脉计算机断层摄影数据、核磁共振影像数据、数字减影血管造影术数据3D-DSA和超声影像数据中的一种或多种；所述临床测量数据包括心率数据和血压数据；
步骤2，根据所述医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据；
步骤3，对所述特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到得血流动力学指标的分布状况数据；所述分布状况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；
步骤4，将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上。
优选的，所述步骤2具体包括：
步骤21，检测所述感兴趣血管的标志点，识别所述感兴趣血管的起点和终点；
步骤22，通过识别待分割的所述感兴趣血管的起点和终点之间的跟踪路径来跟踪血管；
步骤23，根据所述跟踪路径，利用基于沿跟踪路径的图像强度和梯度的随机游走算法对所述感兴趣血管进行初步分割；
步骤24，从初步分割的所述感兴趣血管中提取中心线；
步骤25，对感兴趣血管进行精确分割，在所述中心线的每个点出提取所述感兴趣血管的横截面轮廓线；
步骤26，通过放样的方法将精确分割的所述横截面轮廓线生成所述感兴趣血管的几何表面模型，得到解剖学模型数据。
优选的，所述步骤21中，使用经过训练的标志点探测器来识别所述感兴趣血管的起点和终点；
所述步骤21之前还包括，标志点探测器使用训练数据进行训练，在训练数据中对每个起点和终点的真实位置进行标注，在已标注的真实位置处生成正样本，在远离真实位置的地方生成负样本，正样本和负样本再经过特征提取，其中为每个样本计算哈尔Haar特征和steerable特征；将从样本中提取的特征传递给统计分类器，所述分类器区分正负样本，从而评估处理接收到的数据确定正样本起始点或终点的概率。
优选的，所述步骤22中，使用基于迪克斯特拉Dijkstra算法的最短路径算法来识别所述起点和终点之间的路径。
优选的，所述步骤3中对所述特异性解剖学模型数据进行网格划分处理具体包括：
步骤31，计算所述感兴趣血管的管壁上各点到中心线的距离；
步骤32，对边界层进行三棱柱网格划分，边界层厚度为管壁上各点到中心线的距离的四分之一，最外层靠近管壁的网格，网格大小为0.02mm，边界层网格从外往内按统一比例值逐层增厚，划分5层；
根据公式0.02(1+a+a 2+a 3+a 4)＝(1/4)*d计算得到统一比例值；
其中，d为管壁上各点到中心线的距离，a为统一比例值；
步骤33，对边界层内的区域采用四面体网格进行划分，网格大小为管壁上各点到中心线的距离的十分之一；
步骤34，将得到的边界层网格和体网格进行装配，完成网格的划分。
优选的，所述步骤3之前还包括：设置所述感兴趣血管的血液的材料属性和边界条件。
优选的，所述材料属性为将所述感兴趣血管中的血液设定为牛顿流体，血液密度为1060kg/m 3，血液动力粘度为0.004Pa·S。
优选的，所述边界条件为所述感兴趣血管的管壁面采用刚性壁边界条件；同一时刻入口横截面各点处的压强大小一致，其压强值随手臂测量的血压数据和心率得到血压-时间变化图进行变化；出口边界条件为集中参数模型模拟 出口下游血管和微血管的阻力。
为实现上述目的，本发明实施例第二方面提供了一种血流动力学指标数据的处理系统，所述系统包括：
数据采集模块，用于采集医学图像数据和临床测量数据；所述医学图像数据包括脑动脉计算机断层摄影数据、核磁共振影像数据、3D-DSA和超声影像数据中的一种或多种；所述临床测量数据包括心率数据和血压数据；
解剖学建模系统模块，用于根据所述医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据；
流体力学模拟模块，用于对所述特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到得血流动力学指标的分布状况数据；所述分布状况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；
可视化处理模块，用于将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上。
为实现上述目的，本发明实施例第三方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机指令，当所述计算机指令被计算机执行时，使得所述计算机执行第一方面所述的方法。
本发明实施例提供的血流动力学指标数据的处理方法和系统，从脑部的医学图像数据和临床测量数据中提取感兴趣血管，例如脑动脉瘤的特异性解剖学模型数据。基于该解剖学模型和非侵入性临床测量来计算得到CFD模型的特异性边界条件。使用所述特异性解剖学模型数据和特异性边界条件来模拟感兴趣血管(如脑动脉瘤)内的血流动力学指标的分布状况并可视化处理和显示，处理速度快，可视化精度高。
附图说明
图1为本发明实施例提供的血流动力学指标数据的处理方法的流程图；
图2为本发明实施例提供的血流动力学指标数据的处理方法的边界条件的示意图；
图3A为本发明实施例提供的血流动力学指标数据的处理方法TAWSS示意图；
图3B为本发明实施例提供的血流动力学指标数据的处理方法FFR示意图；
图3C为本发明实施例提供的血流动力学指标数据的处理方法streamlines示意图；
图4为本发明实施例提供的血流动力学指标数据的处理系统的模块示意图。
下面通过附图和实施例，对本发明的技术方案做进一步的详细描述。
具体实施方式
本发明实施例提供的血流动力学指标数据的处理方法和系统涉及使用医学图像数据和CFD模拟的用于感兴趣血管(如脑动脉瘤)内血流动力学指标的非侵入性评估的方法和系统。对本发明的实施例进行描述以给出用于仿真模拟脑动脉瘤的血流动力学指标分布状况的可视化理解。此处通常用标识和操作对象来描述对象的数字表示形式。此类操作是在计算机系统测存储器或其他电路/硬件中实现的虚拟操作。因此，可以理解的是，可以在计算机系统内使用计算机系统中存储的数据来执行本发明的实施例。
图1为本发明实施例提供的血流动力学指标数据的处理方法的流程图，如图所示，本实施例具体包括如下步骤：
步骤101，采集医学图像数据和临床测量数据；医学图像数据包括脑动脉计算机断层摄影数据、核磁共振影像数据、数字减影血管造影术数据(3D-DSA)和超声影像数据中的一种或多种；临床测量数据包括心率数据和血压数据；
具体的，获取患者的3D或4D医学图像数据和其他非侵入性临床测量数据；可以接收来自一个或多个成像模式的医学图像数据，如计算机断层摄影(CTA)、核磁共振影像(MRA)、3D-DSA、超声影像或任何其他类型的医学成像模式。例如可以直接从CT扫描机、MR扫描机、血管造影扫描机或超声设备等的一个或多个图像采集设备接收医学图像数据，也可以通过预先存储的医学图像数据来接收。
步骤102，根据医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据；
具体的，步骤102包括如下步骤：
步骤201，检测感兴趣血管的标志点，识别感兴趣血管的起点和终点；
本步骤中使用经过训练的标志点探测器来识别感兴趣血管的起点和终点；另外本步骤之前还包括标志点探测器使用训练数据进行训练，在训练数据中对每个起点和终点的真实位置进行标注，在已标注的真实位置处生成正样本，在远离真实位置的地方生成负样本，正样本和负样本再经过特征提取，其中为每个样本计算哈尔(Haar)特征和steerable特征；将从样本中提取的特征传递给统计分类器，分类器区分正负样本，从而评估处理接收到的数据确定正样本起始点或终点的概率。
具体的，为了对脑动脉瘤进行本地化，使用经过训练的标志点探测器来识别感兴趣血管的起点和终点。标志点探测器使用训练数据进行训练，在训练数据中对每个标志点(即起点和终点)的真实位置进行标注。这样学习系统能够在已标注的真实位置处生成正样本，在远离真实位置的地方生成负样本。正样本和负样本再经过特征提取，其中为每个样本计算Haar特征和steerable特征。然后，将从样本中提取的特征传递给统计分类器，如概率提升树(Probabilistic Boosting Tree,PBT)，该分类器可自动学习用最佳方式区分正负样本。经过训练的分类器可评估接收到的3D医学图像的体数据，并确定其为正样本(即起始点或终点)的概率。
步骤202，通过识别待分割的感兴趣血管的起点和终点之间的跟踪路径来跟踪血管；
具体的，本步骤的标志点检测可识别待分割感兴趣血管的起点和终点。然后，使用基于迪克斯特拉(Dijkstra)算法的最短路径算法来识别起点和终点之间的路径。
步骤203，根据跟踪路径，利用基于沿跟踪路径的图像强度和梯度的随机游走算法对感兴趣血管进行初步分割；
具体的，本步骤根据跟踪路径对感兴趣血管进行初步分割。标识的最短路径不一定是血管的中心线。为了获得更精确的中心线，在3D图像中对血管进行分割。在本发明的实施例中，利用基于沿跟踪路径的图像强度和梯度的随机游走算法(random walks algorithm)进行感兴趣血管初步分割。
步骤204，从初步分割的感兴趣血管中提取中心线；
具体的，本步骤中从初步分割的感兴趣血管中提取中心线。可以使用任意中心线提取方法提取中心线。
步骤205，对感兴趣血管进行精确分割，在中心线的每个点出提取感兴趣血管的横截面轮廓线；
具体的，在上一步骤中血管分割提供了对感兴趣血管的初步分割，但不够精确。因此，本步骤可以利用一种基于机器学习的方法，利用从已标注的训练数据中学到的边界分类器来准确分割血管。即在提取中心线后，在中心线的每个点出提取感兴趣血管的横截面轮廓线。
步骤206，通过放样的方法将精确分割的横截面轮廓线生成感兴趣血管的几何表面模型，得到解剖学模型数据。
上述解剖学建模任务可以采用全自动地方法执行，本发明的实施例中特异性3D解剖学模型根据医学图像数据自动生成。
步骤103，对特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到得血流动力学指标的分布状况数据；分布状 况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；
对特异性解剖学模型数据进行网格划分处理具体包括：
步骤301，计算感兴趣血管的管壁上各点到中心线的距离；
具体的，网格划分时最重要的是确定网格类型和网格大小，在本实施例中采用混合网格类型(hybrid mesh)，即四面体一三棱柱混合网格，靠近血管壁的边界层使用三棱柱网格，内部为四面体网格。利用提取的中心线，并计算感兴趣血管壁上各点到中心线的距离d。
步骤302，对边界层进行三棱柱网格划分，边界层厚度为管壁上各点到中心线的距离的四分之一，最外层靠近管壁的网格，网格大小为0.02mm，边界层网格从外往内按统一比例值逐层增厚，划分5层；根据公式0.02(1+a+a 2+a 3+a 4)＝(1/4)*d计算得到统一比例值；其中，d为管壁上各点到中心线的距离，a为统一比例值；
具体的，本步骤是对边界层进行网格划分，其网格类型为三棱柱网格，边界层厚度为1/4倍距离d。最外层靠近血管壁的网格，网格大小统一定义为0.02mm，边界层网格从外往内按比例a逐层增厚，共划分5层。
步骤303，对边界层内的区域采用四面体网格进行划分，网格大小为管壁上各点到中心线的距离的十分之一；具体的，对边界层内的区域采用四面体网格进行划分，网格大小为1/10倍距离d。
步骤304，将得到的边界层网格和体网格进行装配，完成网格的划分。
进一步的，在步骤301中进行脑动脉瘤的CFD模拟前，还需要对血液的材料属性、边界条件设置。
具体的，材料属性如下：由于与感兴趣血管(如动脉瘤)相邻的载流动脉直径较大，且流速相对较快，因此血液的非牛顿性可以忽略。在本实施例中，血液假设为牛顿流体，血液密度ρ为1060kg/m3。，血液动力粘度为0.004Pa·S。
感兴趣血管的CFD模拟的精确与否，除与准确的特异性解剖学模型有关外，还与模型的边界条件有关，本实施例的边界条件如下：
1)壁面采用刚性壁边界条件，忽略血管的弹性，不考虑血管舒张和收缩过程中血管形变对血流的影响。
2)入口边界条件设置压强条件，同一时刻入口横截面各点处的压强大小一致，其压强值随患者手臂测量的血压数据和心率得到血压-时间变化图进行变化。
3)出口边界条件使用集中参数模型(此处为简单的RCR模型)模拟出口下游血管和微血管的阻力。
其中，各出口的血管阻力的获取方法如下：
首先，从脑动脉医学影像数据中计算得到患者特异性的脑部容量，从而使用基于人群的关系从脑部容量数据计算得到脑动脉血管的流量，并根据脑动脉血管流量和非侵入性临床测量(如患者的上臂血压、心率等)计算得到总的脑动脉阻力，然后依据解剖学模型中各出口的直径等参数，使用基于人群的血管阻力关系，将总的脑动脉阻力分配到给各出口血管。
图2为本发明实施例提供的血流动力学指标数据的处理方法的边界条件的示意图，如图所示，为模拟开口下游血管的弹性、血流惯性等，可在电路模型中添加一个或多个电容C(模拟血管弹性)、电感L(模拟血流惯性)等。元件越多需要确定的参数越多，但模型越能反映真实的人体生理状况。
设置好材料属性和边界条件后，对模型进行CFD模拟求解，即求解一系列的偏微分方程组，如Navier-Stokes方程等。在本实施例中，因模型为瞬态问题，模拟了4个心动周期下脑动脉瘤内的血液流动情况，每个心动周期分为800个时间步。以第四个心动周期的计算结果来分析脑动脉瘤内的血流动力学指标的分布状况。
步骤104，将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上。
具体的，本步骤就是对感兴趣血管(如脑动脉瘤)内的各血流动力学指标进行可视化处理。在本实施例中，如图3A、图3B和图3C所示的血流动力学指标数据的处理方法时间平均壁面剪切应力(TAWSS)示意图，血流储备分数(FFR)示意图和速度流线(streamlines)示意图。
图4为本发明实施例提供的血流动力学指标数据的处理系统的模块示意图，该系统可以为实现本发明实施例方法的终端设备或者服务器，也可以为与上述终端设备或者服务器连接的实现本发明实施例方法的系统，例如该系统可以是上述终端设备或者服务器的装置或芯片系统。如图3所示，该系统包括：
数据采集模块41，用于采集医学图像数据和临床测量数据；医学图像数据包括脑动脉计算机断层摄影数据、核磁共振影像数据、3D-DSA和超声影像数据中的一种或多种；临床测量数据包括心率数据和血压数据；
解剖学建模系统模块42，用于根据医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据；
流体力学模拟模块43，用于对特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到得血流动力学指标的分布状况数据；分布状况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；
可视化处理模块44，用于将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上。
本发明实施例提供的血流动力学指标数据的处理系统，可以执行上述方法实施例中的方法步骤，其实现原理和技术效果类似，在此不再赘述。
需要说明的是，应理解以上装置的各个模块的划分仅仅是一种逻辑功能的划分，实际实现时可以全部或部分集成到一个物理实体上，也可以物理上分开。且这些模块可以全部以软件通过处理元件调用的形式实现；也可以全部以硬件的形式实现；还可以部分模块通过处理元件调用软件的形 式实现，部分模块通过硬件的形式实现。例如，流量池卡状态维护模块可以为单独设立的处理元件，也可以集成在上述装置的某一个芯片中实现，此外，也可以以程序代码的形式存储于上述装置的存储器中，由上述装置的某一个处理元件调用并执行以上确定模块的功能。其它模块的实现与之类似。此外这些模块全部或部分可以集成在一起，也可以独立实现。这里所描述的处理元件可以是一种集成电路，具有信号的处理能力。在实现过程中，上述方法的各步骤或以上各个模块可以通过处理器元件中的硬件的集成逻辑电路或者软件形式的指令完成。
例如，以上这些模块可以是被配置成实施以上方法的一个或多个集成电路，例如：一个或多个特定集成电路(Application Specific Integrated Circuit，ASIC)，或一个或多个数字信号处理器(Digital Signal Processor，DSP)，或一个或者多个现场可编程门阵列(Field Programmable Gate Array，FPGA)等。再如，当以上某个模块通过处理元件调度程序代码的形式实现时，该处理元件可以是通用处理器，例如中央处理器(Central Processing Unit，CPU)或其它可以调用程序代码的处理器。再如，这些模块可以集成在一起，以片上系统(System-on-a-chip，SOC)的形式实现。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。该计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行该计算机程序指令时，全部或部分地产生按照本发明实施例所描述的流程或功能。上述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。上述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，上述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线路(Digital Subscriber Line，DSL))或无线(例如红外、无线、蓝牙、微波等)方式向另一个网站站 点、计算机、服务器或数据中心进行传输。上述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。上述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘(solid state disk，SSD))等。
需要说明的是，本发明实施例还提供一种计算机可读存储介质，该可读存储介质中存储有指令，当其在计算机上运行时，使得计算机执行上述实施例中提供的方法和处理过程。
本发明实施例提供的血流动力学指标数据的处理方法和系统，从脑部的医学图像数据和临床测量数据中提取感兴趣血管，例如脑动脉瘤的特异性解剖学模型数据。基于该解剖学模型(如脑血管容量、总流量、血管出口直径等)和非侵入性临床测量(如血压和心率等)来计算得到CFD模型的特异性边界条件。使用所述特异性解剖学模型数据和特异性边界条件来模拟感兴趣血管(如脑动脉瘤)内的血流动力学指标的分布状况并可视化处理和显示，处理速度快，可视化精度高。
以上所述的具体实施方式，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具体实施方式而已，并不用于限定本发明的保护范围，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种血流动力学指标数据的处理方法，其特征在于，所述方法包括：
    步骤1，采集医学图像数据和临床测量数据；所述医学图像数据包括脑动脉计算机断层摄影数据、核磁共振影像数据、数字减影血管造影术数据和超声影像数据中的一种或多种；所述临床测量数据包括心率数据和血压数据；
    步骤2，根据所述医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据；
    步骤3，对所述特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到得血流动力学指标的分布状况数据；所述分布状况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；
    步骤4，将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上。
  2. 根据权利要求1所述的血流动力学指标数据的处理方法，其特征在于，所述步骤2具体包括：
    步骤21，检测所述感兴趣血管的标志点，识别所述感兴趣血管的起点和终点；
    步骤22，通过识别待分割的所述感兴趣血管的起点和终点之间的跟踪路径来跟踪血管；
    步骤23，根据所述跟踪路径，利用基于沿跟踪路径的图像强度和梯度的随机游走算法对所述感兴趣血管进行初步分割；
    步骤24，从初步分割的所述感兴趣血管中提取中心线；
    步骤25，对感兴趣血管进行精确分割，在所述中心线的每个点出提取所述感兴趣血管的横截面轮廓线；
    步骤26，通过放样的方法将精确分割的所述横截面轮廓线生成所述感兴趣血管的几何表面模型，得到解剖学模型数据。
  3. 根据权利要求2所述的血流动力学指标数据的处理方法，其特征在于，所述步骤21中，使用经过训练的标志点探测器来识别所述感兴趣血管的起点和终点；
    所述步骤21之前还包括，标志点探测器使用训练数据进行训练，在训练数据中对每个起点和终点的真实位置进行标注，在已标注的真实位置处生成正样本，在远离真实位置的地方生成负样本，正样本和负样本再经过特征提取，其中为每个样本计算哈尔特征和steerable特征；将从样本中提取的特征传递给统计分类器，所述分类器区分正负样本，从而评估处理接收到的数据确定正样本起始点或终点的概率。
  4. 根据权利要求3所述的血流动力学指标数据的处理方法，其特征在于，所述步骤22中，使用基于迪克斯特拉算法的最短路径算法来识别所述起点和终点之间的路径。
  5. 根据权利要求1所述的血流动力学指标数据的处理方法，其特征在于，所述步骤3中对所述特异性解剖学模型数据进行网格划分处理具体包括：
    步骤31，计算所述感兴趣血管的管壁上各点到中心线的距离；
    步骤32，对边界层进行三棱柱网格划分，边界层厚度为管壁上各点到中心线的距离的四分之一，最外层靠近管壁的网格，网格大小为0.02mm，边界层网格从外往内按统一比例值逐层增厚，划分5层；
    根据公式0.02(1+a+a 2+a 3+a 4)＝(1/4)*d计算得到统一比例值；
    其中，d为管壁上各点到中心线的距离，a为统一比例值；
    步骤33，对边界层内的区域采用四面体网格进行划分，网格大小为管壁上各点到中心线的距离的十分之一；
    步骤34，将得到的边界层网格和体网格进行装配，完成网格的划分。
  6. 根据权利要求5所述的血流动力学指标数据的处理方法，其特征在于，所述步骤3之前还包括：设置所述感兴趣血管的血液的材料属性和边界条件。
  7. 根据权利要求6所述的血流动力学指标数据的处理方法，其特征在于，所述材料属性为将所述感兴趣血管中的血液设定为牛顿流体，血液密度为1060kg/m 3，血液动力粘度为0.004Pa·S。
  8. 根据权利要求6所述的血流动力学指标数据的处理方法，其特征在于，所述边界条件为所述感兴趣血管的管壁面采用刚性壁边界条件；同一时刻入口横截面各点处的压强大小一致，其压强值随手臂测量的血压数据和心率得到血压-时间变化图进行变化；出口边界条件为集中参数模型模拟出口下游血管和微血管的阻力。
  9. 一种血流动力学指标数据的处理系统，其特征在于，所述系统包括：
    数据采集模块，用于采集医学图像数据和临床测量数据；所述医学图像数据包括脑动脉计算机断层摄影数据、核磁共振影像数据、3DSA和超声影像数据中的一种或多种；所述临床测量数据包括心率数据和血压数据；
    解剖学建模系统模块，用于根据所述医学图像数据和临床测量数据进行图像分割处理，利用中心线提取算法生成感兴趣血管的特异性解剖学模型数据；
    流体力学模拟模块，用于对所述特异性解剖学模型数据进行网格划分处理，利用边界条件进行流体力学模拟处理，从而得到得血流动力学指标的分布状况数据；所述分布状况数据包括壁面剪切应力、时间平均壁面剪切应力、震荡剪切指数和速度流线；
    可视化处理模块，用于将血流动力学指标的分布状况数据进行可视化处理，从而显示在脑动脉瘤的三维模型上。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机指令，当所述计算机指令被计算机执行时，使得所述计算机执行权利要求1-8任一项所述的方法。
PCT/CN2021/128039 2021-02-08 2021-11-02 血流动力学指标数据的处理方法和系统 WO2022166281A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110170627.1 2021-02-08
CN202110170627.1A CN112749521A (zh) 2021-02-08 2021-02-08 血流动力学指标数据的处理方法和系统

Publications (1)

Publication Number Publication Date
WO2022166281A1 true WO2022166281A1 (zh) 2022-08-11

Family

ID=75651890

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/128039 WO2022166281A1 (zh) 2021-02-08 2021-11-02 血流动力学指标数据的处理方法和系统

Country Status (2)

Country Link
CN (1) CN112749521A (zh)
WO (1) WO2022166281A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115587971A (zh) * 2022-09-21 2023-01-10 四川大学华西医院 基于心脏超声节段活动的机体反应及血流动力学监测方法及系统
CN115944389A (zh) * 2023-03-14 2023-04-11 杭州脉流科技有限公司 弹簧圈模拟植入的方法和计算机设备

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112749521A (zh) * 2021-02-08 2021-05-04 北京泰杰伟业科技有限公司 血流动力学指标数据的处理方法和系统
CN113836832A (zh) * 2021-08-13 2021-12-24 中国人民解放军东部战区总医院 一种确定血管状态参数的方法和装置
CN114462329A (zh) * 2022-01-10 2022-05-10 中山大学孙逸仙纪念医院 一种升主动脉流体力学参数的测算方法和装置
CN114066888B (zh) * 2022-01-11 2022-04-19 浙江大学 一种血流动力学指标确定方法、装置、设备及存储介质
CN114663362B (zh) * 2022-03-04 2024-03-29 强联智创（北京）科技有限公司 一种融合方法、装置以及设备

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120203530A1 (en) * 2011-02-07 2012-08-09 Siemens Corporation Method and System for Patient-Specific Computational Modeling and Simulation for Coupled Hemodynamic Analysis of Cerebral Vessels
CN105096388A (zh) * 2014-04-23 2015-11-25 北京冠生云医疗技术有限公司 基于计算流体力学的冠状动脉血流仿真系统和方法
CN109961850A (zh) * 2019-03-19 2019-07-02 肖仁德 一种评估颅内动脉瘤破裂风险的方法、装置、计算机设备
CN110866914A (zh) * 2019-11-21 2020-03-06 北京冠生云医疗技术有限公司 脑动脉瘤血流动力学指标的评估方法、系统、设备及介质
CN112749521A (zh) * 2021-02-08 2021-05-04 北京泰杰伟业科技有限公司 血流动力学指标数据的处理方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120203530A1 (en) * 2011-02-07 2012-08-09 Siemens Corporation Method and System for Patient-Specific Computational Modeling and Simulation for Coupled Hemodynamic Analysis of Cerebral Vessels
CN105096388A (zh) * 2014-04-23 2015-11-25 北京冠生云医疗技术有限公司 基于计算流体力学的冠状动脉血流仿真系统和方法
CN109961850A (zh) * 2019-03-19 2019-07-02 肖仁德 一种评估颅内动脉瘤破裂风险的方法、装置、计算机设备
CN110866914A (zh) * 2019-11-21 2020-03-06 北京冠生云医疗技术有限公司 脑动脉瘤血流动力学指标的评估方法、系统、设备及介质
CN112749521A (zh) * 2021-02-08 2021-05-04 北京泰杰伟业科技有限公司 血流动力学指标数据的处理方法和系统

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115587971A (zh) * 2022-09-21 2023-01-10 四川大学华西医院 基于心脏超声节段活动的机体反应及血流动力学监测方法及系统
CN115587971B (zh) * 2022-09-21 2023-10-24 四川大学华西医院 基于心脏超声节段活动的机体反应及血流动力学监测方法及系统
CN115944389A (zh) * 2023-03-14 2023-04-11 杭州脉流科技有限公司 弹簧圈模拟植入的方法和计算机设备
CN115944389B (zh) * 2023-03-14 2023-05-23 杭州脉流科技有限公司 弹簧圈模拟植入的方法和计算机设备

Also Published As

Publication number Publication date
CN112749521A (zh) 2021-05-04

Similar Documents

Publication Publication Date Title
WO2022166281A1 (zh) 血流动力学指标数据的处理方法和系统
CN110866914B (zh) 脑动脉瘤血流动力学指标的评估方法、系统、设备及介质
JP6409114B2 (ja) 患者固有の幾何学的形状モデルを変更することによって治療を決定する方法及びシステム
CN107730497B (zh) 一种基于深度迁移学习的血管内斑块属性分析方法
Cetin et al. A higher-order tensor vessel tractography for segmentation of vascular structures
US7773791B2 (en) Analyzing lesions in a medical digital image
US10803995B2 (en) Method and system for non-invasive functional assessment of coronary artery stenosis using flow computations in diseased and hypothetical normal anatomical models
US20190117094A1 (en) Method of Determining Virtual Hepatic Venous Pressure Gradient
CN108550388A (zh) 基于血管造影的计算血管血流参数的装置和系统
CN107615335A (zh) 医学图象处理方法和系统
CN104768465A (zh) 血流储备分数(ffr)指标
JP6362853B2 (ja) 血管解析装置、および血管解析装置の作動方法
JP6713690B2 (ja) 血管形状構築装置、その方法及びコンピュータソフトウエアプログラム
Oeltze‐Jafra et al. Generation and visual exploration of medical flow data: Survey, research trends and future challenges
WO2021174992A1 (en) Systems and methods for determining hemodynamic parameters
Rajabzadeh-Oghaz et al. Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches
CN103976720A (zh) 血管内压力的无创测量方法及其装置
JP2018529446A (ja) モバイルｆｆｒシミュレーション
Ralovich et al. Noninvasive hemodynamic assessment, treatment outcome prediction and follow‐up of aortic coarctation from MR imaging
CN113077435A (zh) 基于血流动力学的动脉瘤分析方法及装置
CN116486211A (zh) 一种模型训练方法、血流储备分数计算方法、装置及设备
JP7300392B2 (ja) 標準化された冠動脈疾患メトリック
Yevtushenko et al. Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine
Pepe et al. Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Huang et al. A Dual-Functional System for the Classification and Diameter Measurement of Aortic Dissections Using CTA Volumes via Deep Learning

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21924277

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21924277

Country of ref document: EP

Kind code of ref document: A1